Tue Dec  3 14:25:13 UTC 2024  I: starting to build octave-statistics/trixie/i386 on jenkins on '2024-12-03 14:24'
Tue Dec  3 14:25:13 UTC 2024  I: The jenkins build log is/was available at https://jenkins.debian.net/userContent/reproducible/debian/build_service/i386_16/50738/console.log
Tue Dec  3 14:25:13 UTC 2024  I: Downloading source for trixie/octave-statistics=1.7.0-2
--2024-12-03 14:25:14--  http://deb.debian.org/debian/pool/main/o/octave-statistics/octave-statistics_1.7.0-2.dsc
Connecting to 46.16.76.132:3128... connected.
Proxy request sent, awaiting response... 200 OK
Length: 2285 (2.2K) [text/prs.lines.tag]
Saving to: ‘octave-statistics_1.7.0-2.dsc’

     0K ..                                                    100%  316M=0s

2024-12-03 14:25:14 (316 MB/s) - ‘octave-statistics_1.7.0-2.dsc’ saved [2285/2285]

Tue Dec  3 14:25:14 UTC 2024  I: octave-statistics_1.7.0-2.dsc
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Hash: SHA512

Format: 3.0 (quilt)
Source: octave-statistics
Binary: octave-statistics, octave-statistics-common
Architecture: any all
Version: 1.7.0-2
Maintainer: Debian Octave Group <team+pkg-octave-team@tracker.debian.org>
Uploaders: Sébastien Villemot <sebastien@debian.org>, Rafael Laboissière <rafael@debian.org>
Homepage: https://gnu-octave.github.io/packages/statistics/
Standards-Version: 4.7.0
Vcs-Browser: https://salsa.debian.org/pkg-octave-team/octave-statistics
Vcs-Git: https://salsa.debian.org/pkg-octave-team/octave-statistics.git
Testsuite: autopkgtest-pkg-octave
Build-Depends: debhelper-compat (= 13), dh-octave (>= 1.2.3), dh-sequence-octave, octave, octave-io
Build-Conflicts: octave-nan
Package-List:
 octave-statistics deb math optional arch=any
 octave-statistics-common deb math optional arch=all
Checksums-Sha1:
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 3086681c41c0311c3e1d666dc1af9271e413965f 9464 octave-statistics_1.7.0-2.debian.tar.xz
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Files:
 12c3524034d747d85b9072675a74c659 1293187 octave-statistics_1.7.0.orig.tar.gz
 917104f2e6584f9206f318543ddd79a1 9464 octave-statistics_1.7.0-2.debian.tar.xz

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Tue Dec  3 14:25:14 UTC 2024  I: Checking whether the package is not for us
Tue Dec  3 14:25:14 UTC 2024  I: Starting 1st build on remote node infom08-i386.debian.net.
Tue Dec  3 14:25:14 UTC 2024  I: Preparing to do remote build '1' on infom08-i386.debian.net.
Tue Dec  3 14:37:57 UTC 2024  I: Deleting $TMPDIR on infom08-i386.debian.net.
I: pbuilder: network access will be disabled during build
I: Current time: Mon Jan  5 08:48:15 -12 2026
I: pbuilder-time-stamp: 1767646095
I: Building the build Environment
I: extracting base tarball [/var/cache/pbuilder/trixie-reproducible-base.tgz]
I: copying local configuration
W: --override-config is not set; not updating apt.conf Read the manpage for details.
I: mounting /proc filesystem
I: mounting /sys filesystem
I: creating /{dev,run}/shm
I: mounting /dev/pts filesystem
I: redirecting /dev/ptmx to /dev/pts/ptmx
I: policy-rc.d already exists
I: using eatmydata during job
I: Copying source file
I: copying [octave-statistics_1.7.0-2.dsc]
I: copying [./octave-statistics_1.7.0.orig.tar.gz]
I: copying [./octave-statistics_1.7.0-2.debian.tar.xz]
I: Extracting source
gpgv: Signature made Mon Sep 16 17:05:21 2024 
gpgv:                using RSA key 53951D95272E0C5B82BE8C4A2CECE9350ECEBE4A
gpgv: Can't check signature: No public key
dpkg-source: warning: cannot verify inline signature for ./octave-statistics_1.7.0-2.dsc: no acceptable signature found
dpkg-source: info: extracting octave-statistics in octave-statistics-1.7.0
dpkg-source: info: unpacking octave-statistics_1.7.0.orig.tar.gz
dpkg-source: info: unpacking octave-statistics_1.7.0-2.debian.tar.xz
dpkg-source: info: using patch list from debian/patches/series
dpkg-source: info: applying test-ClassificationPartitionedModel.patch
I: Not using root during the build.
I: Installing the build-deps
I: user script /srv/workspace/pbuilder/29294/tmp/hooks/D02_print_environment starting
I: set
  BUILDDIR='/build/reproducible-path'
  BUILDUSERGECOS='first user,first room,first work-phone,first home-phone,first other'
  BUILDUSERNAME='pbuilder1'
  BUILD_ARCH='i386'
  DEBIAN_FRONTEND='noninteractive'
  DEB_BUILD_OPTIONS='buildinfo=+all reproducible=+all parallel=6 '
  DISTRIBUTION='trixie'
  HOME='/root'
  HOST_ARCH='i386'
  IFS=' 	
  '
  INVOCATION_ID='42caa1ffa7cf40b39c3103996fd60241'
  LANG='C'
  LANGUAGE='en_US:en'
  LC_ALL='C'
  LD_LIBRARY_PATH='/usr/lib/libeatmydata'
  LD_PRELOAD='libeatmydata.so'
  MAIL='/var/mail/root'
  OPTIND='1'
  PATH='/usr/sbin:/usr/bin:/sbin:/bin:/usr/games'
  PBCURRENTCOMMANDLINEOPERATION='build'
  PBUILDER_OPERATION='build'
  PBUILDER_PKGDATADIR='/usr/share/pbuilder'
  PBUILDER_PKGLIBDIR='/usr/lib/pbuilder'
  PBUILDER_SYSCONFDIR='/etc'
  PPID='29294'
  PS1='# '
  PS2='> '
  PS4='+ '
  PWD='/'
  SHELL='/bin/bash'
  SHLVL='2'
  SUDO_COMMAND='/usr/bin/timeout -k 18.1h 18h /usr/bin/ionice -c 3 /usr/bin/nice /usr/sbin/pbuilder --build --configfile /srv/reproducible-results/rbuild-debian/r-b-build.1LVxXKuH/pbuilderrc_jZTx --distribution trixie --hookdir /etc/pbuilder/first-build-hooks --debbuildopts -b --basetgz /var/cache/pbuilder/trixie-reproducible-base.tgz --buildresult /srv/reproducible-results/rbuild-debian/r-b-build.1LVxXKuH/b1 --logfile b1/build.log octave-statistics_1.7.0-2.dsc'
  SUDO_GID='111'
  SUDO_UID='104'
  SUDO_USER='jenkins'
  TERM='unknown'
  TZ='/usr/share/zoneinfo/Etc/GMT+12'
  USER='root'
  _='/usr/bin/systemd-run'
I: uname -a
  Linux infom08-i386 6.11.5+bpo-amd64 #1 SMP PREEMPT_DYNAMIC Debian 6.11.5-1~bpo12+1 (2024-11-11) x86_64 GNU/Linux
I: ls -l /bin
  lrwxrwxrwx 1 root root 7 Nov 22  2024 /bin -> usr/bin
I: user script /srv/workspace/pbuilder/29294/tmp/hooks/D02_print_environment finished
 -> Attempting to satisfy build-dependencies
 -> Creating pbuilder-satisfydepends-dummy package
Package: pbuilder-satisfydepends-dummy
Version: 0.invalid.0
Architecture: i386
Maintainer: Debian Pbuilder Team <pbuilder-maint@lists.alioth.debian.org>
Description: Dummy package to satisfy dependencies with aptitude - created by pbuilder
 This package was created automatically by pbuilder to satisfy the
 build-dependencies of the package being currently built.
Depends: debhelper-compat (= 13), dh-octave (>= 1.2.3), dh-sequence-octave, octave, octave-io
Conflicts: octave-nan
dpkg-deb: building package 'pbuilder-satisfydepends-dummy' in '/tmp/satisfydepends-aptitude/pbuilder-satisfydepends-dummy.deb'.
Selecting previously unselected package pbuilder-satisfydepends-dummy.
(Reading database ... 19950 files and directories currently installed.)
Preparing to unpack .../pbuilder-satisfydepends-dummy.deb ...
Unpacking pbuilder-satisfydepends-dummy (0.invalid.0) ...
dpkg: pbuilder-satisfydepends-dummy: dependency problems, but configuring anyway as you requested:
 pbuilder-satisfydepends-dummy depends on debhelper-compat (= 13); however:
  Package debhelper-compat is not installed.
 pbuilder-satisfydepends-dummy depends on dh-octave (>= 1.2.3); however:
  Package dh-octave is not installed.
 pbuilder-satisfydepends-dummy depends on dh-sequence-octave; however:
  Package dh-sequence-octave is not installed.
 pbuilder-satisfydepends-dummy depends on octave; however:
  Package octave is not installed.
 pbuilder-satisfydepends-dummy depends on octave-io; however:
  Package octave-io is not installed.

Setting up pbuilder-satisfydepends-dummy (0.invalid.0) ...
Reading package lists...
Building dependency tree...
Reading state information...
Initializing package states...
Writing extended state information...
Building tag database...
pbuilder-satisfydepends-dummy is already installed at the requested version (0.invalid.0)
pbuilder-satisfydepends-dummy is already installed at the requested version (0.invalid.0)
The following NEW packages will be installed:
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libregexp-pattern-license-perl{a} libregexp-pattern-perl{a} libregexp-wildcards-perl{a} librole-tiny-perl{a} librtmp-dev{a} librtmp1{a} libsamplerate0{a} libsasl2-2{a} libsasl2-modules-db{a} libsensors-config{a} libsensors5{a} libsereal-decoder-perl{a} libsereal-encoder-perl{a} libset-intspan-perl{a} libsharpyuv0{a} libsm6{a} libsndfile1{a} libsoftware-copyright-perl{a} libsoftware-license-perl{a} libsoftware-licensemoreutils-perl{a} libsort-versions-perl{a} libspqr4{a} libssh2-1-dev{a} libssh2-1t64{a} libssl-dev{a} libstemmer0d{a} libstrictures-perl{a} libstring-copyright-perl{a} libstring-escape-perl{a} libstring-license-perl{a} libstring-rewriteprefix-perl{a} libsub-exporter-perl{a} libsub-exporter-progressive-perl{a} libsub-identify-perl{a} libsub-install-perl{a} libsub-name-perl{a} libsub-quote-perl{a} libsub-uplevel-perl{a} libsuitesparseconfig7{a} libsvtav1enc2{a} libsyntax-keyword-try-perl{a} libsz2{a} libtasn1-6-dev{a} libterm-readkey-perl{a} libtest-exception-perl{a} libtext-autoformat-perl{a} libtext-glob-perl{a} libtext-levenshtein-damerau-perl{a} libtext-levenshteinxs-perl{a} libtext-markdown-discount-perl{a} libtext-reform-perl{a} libtext-template-perl{a} libtext-unidecode-perl{a} libtext-xslate-perl{a} libthai-data{a} libthai0{a} libtiff6{a} libtime-duration-perl{a} libtime-moment-perl{a} libtimedate-perl{a} libtoml-tiny-perl{a} libtool{a} libtry-tiny-perl{a} libts0t64{a} libuchardet0{a} libumfpack6{a} libunbound8{a} libunicode-utf8-perl{a} liburi-perl{a} libvariable-magic-perl{a} libvorbis0a{a} libvorbisenc2{a} libvulkan1{a} libwacom-common{a} libwacom9{a} libwayland-client0{a} libwayland-server0{a} libwebp7{a} libwebpmux3{a} libwmflite-0.2-7{a} libwww-mechanize-perl{a} libwww-perl{a} libwww-robotrules-perl{a} libx11-6{a} libx11-data{a} libx11-dev{a} libx11-xcb1{a} libxau-dev{a} libxau6{a} libxcb-cursor0{a} libxcb-dri2-0{a} libxcb-dri3-0{a} libxcb-glx0{a} libxcb-icccm4{a} libxcb-image0{a} libxcb-keysyms1{a} libxcb-present0{a} libxcb-randr0{a} libxcb-render-util0{a} libxcb-render0{a} libxcb-shape0{a} libxcb-shm0{a} libxcb-sync1{a} libxcb-util1{a} libxcb-xfixes0{a} libxcb-xinput0{a} libxcb-xkb1{a} libxcb1{a} libxcb1-dev{a} libxcursor1{a} libxdmcp-dev{a} libxdmcp6{a} libxext6{a} libxfixes3{a} libxft2{a} libxinerama1{a} libxkbcommon-x11-0{a} libxkbcommon0{a} libxml-libxml-perl{a} libxml-namespacesupport-perl{a} libxml-sax-base-perl{a} libxml-sax-perl{a} libxml2{a} libxmlb2{a} libxpm4{a} libxrender1{a} libxs-parse-keyword-perl{a} libxs-parse-sublike-perl{a} libxshmfence1{a} libxxf86vm1{a} libyaml-0-2{a} libyaml-libyaml-perl{a} libyaml-pp-perl{a} libyaml-tiny-perl{a} libyuv0{a} libz3-4{a} libzstd-dev{a} licensecheck{a} lintian{a} lzop{a} m4{a} man-db{a} mesa-libgallium{a} netbase{a} nettle-dev{a} octave{a} octave-common{a} octave-dev{a} octave-io{a} openssl{a} patchutils{a} perl-openssl-defaults{a} pkgconf{a} pkgconf-bin{a} plzip{a} po-debconf{a} readline-common{a} sensible-utils{a} shared-mime-info{a} t1utils{a} tex-common{a} texinfo{a} texinfo-lib{a} ucf{a} unzip{a} x11-common{a} x11proto-dev{a} xkb-data{a} xorg-sgml-doctools{a} xtrans-dev{a} zlib1g-dev{a} 
The following packages are RECOMMENDED but will NOT be installed:
  alsa-topology-conf alsa-ucm-conf bash-completion curl dbus default-jre-headless epstool fonts-liberation fonts-urw-base35 ghostscript gnupg groff krb5-locales libapache-poi-java libarchive-cpio-perl libauthen-sasl-perl libblis4 libcgi-fast-perl libclass-c3-xs-perl libconfig-model-approx-perl libconfig-model-lcdproc-perl libconfig-model-openssh-perl libconfig-model-systemd-perl libconfig-model-tkui-perl libdata-dump-perl libfreezethaw-perl libfuse-perl libglib2.0-data libgpm2 libheif-plugin-aomenc libheif-plugin-x265 libhtml-format-perl libhttp-daemon-perl libio-compress-brotli-perl libipc-shareable-perl libjexcelapi-java libjopendocument-java libjson-xs-perl libldap-common liblist-someutils-xs-perl liblog-dispatch-perl libltdl-dev libmail-sendmail-perl libmath-base85-perl libopenblas0 libpackage-stash-xs-perl libqt6sql6-ibase libqt6sql6-mysql libqt6sql6-odbc libqt6sql6-psql libqt6sql6-sqlite libre-engine-re2-perl libreoffice-core libreoffice-java-common libsasl2-modules libsocket6-perl libtasn1-doc libtie-ixhash-perl libtypes-serialiser-perl libxerces2-java libxml-sax-expat-perl libxstring-perl lynx mesa-vulkan-drivers octave-doc pstoedit publicsuffix qt6-gtk-platformtheme qt6-qpa-plugins qt6-translations-l10n qt6-wayland wget xdg-user-dirs 
0 packages upgraded, 524 newly installed, 0 to remove and 0 not upgraded.
Need to get 211 MB of archives. After unpacking 802 MB will be used.
Writing extended state information...
Get: 1 http://deb.debian.org/debian trixie/main i386 netbase all 6.4 [12.8 kB]
Get: 2 http://deb.debian.org/debian trixie/main i386 readline-common all 8.2-5 [69.3 kB]
Get: 3 http://deb.debian.org/debian trixie/main i386 sensible-utils all 0.0.24 [24.8 kB]
Get: 4 http://deb.debian.org/debian trixie/main i386 openssl i386 3.3.2-2 [1387 kB]
Get: 5 http://deb.debian.org/debian trixie/main i386 ca-certificates all 20240203 [158 kB]
Get: 6 http://deb.debian.org/debian trixie/main i386 libmagic-mgc i386 1:5.45-3+b1 [314 kB]
Get: 7 http://deb.debian.org/debian trixie/main i386 libmagic1t64 i386 1:5.45-3+b1 [115 kB]
Get: 8 http://deb.debian.org/debian trixie/main i386 file i386 1:5.45-3+b1 [43.2 kB]
Get: 9 http://deb.debian.org/debian trixie/main i386 gettext-base i386 0.22.5-2 [201 kB]
Get: 10 http://deb.debian.org/debian trixie/main i386 libuchardet0 i386 0.0.8-1+b2 [69.2 kB]
Get: 11 http://deb.debian.org/debian trixie/main i386 groff-base i386 1.23.0-5 [1196 kB]
Get: 12 http://deb.debian.org/debian trixie/main i386 bsdextrautils i386 2.40.2-11 [95.6 kB]
Get: 13 http://deb.debian.org/debian trixie/main i386 libpipeline1 i386 1.5.8-1 [41.2 kB]
Get: 14 http://deb.debian.org/debian trixie/main i386 man-db i386 2.13.0-1 [1428 kB]
Get: 15 http://deb.debian.org/debian trixie/main i386 ucf all 3.0043+nmu1 [55.2 kB]
Get: 16 http://deb.debian.org/debian trixie/main i386 aglfn all 1.7+git20191031.4036a9c-2 [30.5 kB]
Get: 17 http://deb.debian.org/debian trixie/main i386 libglib2.0-0t64 i386 2.82.2-3 [1571 kB]
Get: 18 http://deb.debian.org/debian trixie/main i386 libicu72 i386 72.1-5+b1 [9583 kB]
Get: 19 http://deb.debian.org/debian trixie/main i386 libxml2 i386 2.12.7+dfsg+really2.9.14-0.2+b1 [734 kB]
Get: 20 http://deb.debian.org/debian trixie/main i386 shared-mime-info i386 2.4-5+b1 [761 kB]
Get: 21 http://deb.debian.org/debian trixie/main i386 libbrotli1 i386 1.1.0-2+b6 [308 kB]
Get: 22 http://deb.debian.org/debian trixie/main i386 libkrb5support0 i386 1.21.3-3 [34.9 kB]
Get: 23 http://deb.debian.org/debian trixie/main i386 libcom-err2 i386 1.47.1-1+b1 [23.4 kB]
Get: 24 http://deb.debian.org/debian trixie/main i386 libk5crypto3 i386 1.21.3-3 [83.6 kB]
Get: 25 http://deb.debian.org/debian trixie/main i386 libkeyutils1 i386 1.6.3-4 [9600 B]
Get: 26 http://deb.debian.org/debian trixie/main i386 libkrb5-3 i386 1.21.3-3 [350 kB]
Get: 27 http://deb.debian.org/debian trixie/main i386 libgssapi-krb5-2 i386 1.21.3-3 [146 kB]
Get: 28 http://deb.debian.org/debian trixie/main i386 libsasl2-modules-db i386 2.1.28+dfsg1-8 [20.6 kB]
Get: 29 http://deb.debian.org/debian trixie/main i386 libsasl2-2 i386 2.1.28+dfsg1-8 [61.0 kB]
Get: 30 http://deb.debian.org/debian trixie/main i386 libldap-2.5-0 i386 2.5.18+dfsg-3+b1 [200 kB]
Get: 31 http://deb.debian.org/debian trixie/main i386 libnghttp2-14 i386 1.64.0-1 [82.4 kB]
Get: 32 http://deb.debian.org/debian trixie/main i386 libnghttp3-9 i386 1.6.0-2 [75.9 kB]
Get: 33 http://deb.debian.org/debian trixie/main i386 libngtcp2-16 i386 1.9.1-1 [151 kB]
Get: 34 http://deb.debian.org/debian trixie/main i386 libngtcp2-crypto-gnutls8 i386 1.9.1-1 [19.1 kB]
Get: 35 http://deb.debian.org/debian trixie/main i386 libpsl5t64 i386 0.21.2-1.1+b1 [57.7 kB]
Get: 36 http://deb.debian.org/debian trixie/main i386 librtmp1 i386 2.4+20151223.gitfa8646d.1-2+b5 [62.4 kB]
Get: 37 http://deb.debian.org/debian trixie/main i386 libssh2-1t64 i386 1.11.1-1 [256 kB]
Get: 38 http://deb.debian.org/debian trixie/main i386 libcurl3t64-gnutls i386 8.11.0-1 [403 kB]
Get: 39 http://deb.debian.org/debian trixie/main i386 libstemmer0d i386 2.2.0-4+b2 [113 kB]
Get: 40 http://deb.debian.org/debian trixie/main i386 libxmlb2 i386 0.3.21-1 [68.9 kB]
Get: 41 http://deb.debian.org/debian trixie/main i386 libyaml-0-2 i386 0.2.5-1+b2 [56.0 kB]
Get: 42 http://deb.debian.org/debian trixie/main i386 libappstream5 i386 1.0.3-1+b1 [241 kB]
Get: 43 http://deb.debian.org/debian trixie/main i386 appstream i386 1.0.3-1+b1 [470 kB]
Get: 44 http://deb.debian.org/debian trixie/main i386 m4 i386 1.4.19-4 [293 kB]
Get: 45 http://deb.debian.org/debian trixie/main i386 autoconf all 2.72-3 [493 kB]
Get: 46 http://deb.debian.org/debian trixie/main i386 autotools-dev all 20220109.1 [51.6 kB]
Get: 47 http://deb.debian.org/debian trixie/main i386 automake all 1:1.16.5-1.3 [823 kB]
Get: 48 http://deb.debian.org/debian trixie/main i386 autopoint all 0.22.5-2 [723 kB]
Get: 49 http://deb.debian.org/debian trixie/main i386 libcapture-tiny-perl all 0.48-2 [24.6 kB]
Get: 50 http://deb.debian.org/debian trixie/main i386 libparams-util-perl i386 1.102-3+b1 [24.7 kB]
Get: 51 http://deb.debian.org/debian trixie/main i386 libsub-install-perl all 0.929-1 [10.5 kB]
Get: 52 http://deb.debian.org/debian trixie/main i386 libdata-optlist-perl all 0.114-1 [10.6 kB]
Get: 53 http://deb.debian.org/debian trixie/main i386 libb-hooks-op-check-perl i386 0.22-3+b2 [10.7 kB]
Get: 54 http://deb.debian.org/debian trixie/main i386 libdynaloader-functions-perl all 0.004-1 [12.1 kB]
Get: 55 http://deb.debian.org/debian trixie/main i386 libdevel-callchecker-perl i386 0.009-1+b1 [16.2 kB]
Get: 56 http://deb.debian.org/debian trixie/main i386 libparams-classify-perl i386 0.015-2+b4 [23.1 kB]
Get: 57 http://deb.debian.org/debian trixie/main i386 libmodule-runtime-perl all 0.016-2 [19.6 kB]
Get: 58 http://deb.debian.org/debian trixie/main i386 libtry-tiny-perl all 0.32-1 [22.9 kB]
Get: 59 http://deb.debian.org/debian trixie/main i386 libmodule-implementation-perl all 0.09-2 [12.6 kB]
Get: 60 http://deb.debian.org/debian trixie/main i386 libpackage-stash-perl all 0.40-1 [22.0 kB]
Get: 61 http://deb.debian.org/debian trixie/main i386 libclass-load-perl all 0.25-2 [15.3 kB]
Get: 62 http://deb.debian.org/debian trixie/main i386 libio-stringy-perl all 2.113-2 [48.3 kB]
Get: 63 http://deb.debian.org/debian trixie/main i386 libparams-validate-perl i386 1.31-2+b3 [64.9 kB]
Get: 64 http://deb.debian.org/debian trixie/main i386 libsub-exporter-perl all 0.990-1 [50.6 kB]
Get: 65 http://deb.debian.org/debian trixie/main i386 libgetopt-long-descriptive-perl all 0.115-1 [27.6 kB]
Get: 66 http://deb.debian.org/debian trixie/main i386 libio-tiecombine-perl all 1.005-3 [10.8 kB]
Get: 67 http://deb.debian.org/debian trixie/main i386 libmodule-pluggable-perl all 5.2-5 [23.0 kB]
Get: 68 http://deb.debian.org/debian trixie/main i386 libstring-rewriteprefix-perl all 0.009-1 [7140 B]
Get: 69 http://deb.debian.org/debian trixie/main i386 libapp-cmd-perl all 0.336-1 [65.8 kB]
Get: 70 http://deb.debian.org/debian trixie/main i386 libboolean-perl all 0.46-3 [9924 B]
Get: 71 http://deb.debian.org/debian trixie/main i386 libsub-uplevel-perl all 0.2800-3 [14.0 kB]
Get: 72 http://deb.debian.org/debian trixie/main i386 libtest-exception-perl all 0.43-3 [16.9 kB]
Get: 73 http://deb.debian.org/debian trixie/main i386 libcarp-assert-more-perl all 2.5.0-1 [20.5 kB]
Get: 74 http://deb.debian.org/debian trixie/main i386 libfile-which-perl all 1.27-2 [15.1 kB]
Get: 75 http://deb.debian.org/debian trixie/main i386 libfile-homedir-perl all 1.006-2 [42.4 kB]
Get: 76 http://deb.debian.org/debian trixie/main i386 libclone-choose-perl all 0.010-2 [8676 B]
Get: 77 http://deb.debian.org/debian trixie/main i386 libhash-merge-perl all 0.302-1 [14.7 kB]
Get: 78 http://deb.debian.org/debian trixie/main i386 libjson-perl all 4.10000-1 [87.5 kB]
Get: 79 http://deb.debian.org/debian trixie/main i386 libexporter-tiny-perl all 1.006002-1 [38.7 kB]
Get: 80 http://deb.debian.org/debian trixie/main i386 liblist-moreutils-xs-perl i386 0.430-4+b2 [45.1 kB]
Get: 81 http://deb.debian.org/debian trixie/main i386 liblist-moreutils-perl all 0.430-2 [46.9 kB]
Get: 82 http://deb.debian.org/debian trixie/main i386 liblog-log4perl-perl all 1.57-1 [367 kB]
Get: 83 http://deb.debian.org/debian trixie/main i386 libmouse-perl i386 2.5.11-1+b1 [146 kB]
Get: 84 http://deb.debian.org/debian trixie/main i386 libmousex-nativetraits-perl all 1.09-3 [53.5 kB]
Get: 85 http://deb.debian.org/debian trixie/main i386 libmousex-strictconstructor-perl all 0.02-3 [5304 B]
Get: 86 http://deb.debian.org/debian trixie/main i386 libparse-recdescent-perl all 1.967015+dfsg-4 [147 kB]
Get: 87 http://deb.debian.org/debian trixie/main i386 libpath-tiny-perl all 0.146-1 [56.2 kB]
Get: 88 http://deb.debian.org/debian trixie/main i386 libpod-pom-perl all 2.01-4 [65.0 kB]
Get: 89 http://deb.debian.org/debian trixie/main i386 libregexp-common-perl all 2024080801-1 [167 kB]
Get: 90 http://deb.debian.org/debian trixie/main i386 libyaml-tiny-perl all 1.74-1 [30.7 kB]
Get: 91 http://deb.debian.org/debian trixie/main i386 libconfig-model-perl all 2.155-1 [398 kB]
Get: 92 http://deb.debian.org/debian trixie/main i386 libyaml-pp-perl all 0.38.0-1 [109 kB]
Get: 93 http://deb.debian.org/debian trixie/main i386 cme all 1.040-1 [68.8 kB]
Get: 94 http://deb.debian.org/debian trixie/main i386 comerr-dev i386 2.1-1.47.1-1+b1 [55.3 kB]
Get: 95 http://deb.debian.org/debian trixie/main i386 libdebhelper-perl all 13.20 [89.7 kB]
Get: 96 http://deb.debian.org/debian trixie/main i386 libtool all 2.4.7-8 [517 kB]
Get: 97 http://deb.debian.org/debian trixie/main i386 dh-autoreconf all 20 [17.1 kB]
Get: 98 http://deb.debian.org/debian trixie/main i386 libarchive-zip-perl all 1.68-1 [104 kB]
Get: 99 http://deb.debian.org/debian trixie/main i386 libfile-stripnondeterminism-perl all 1.14.0-1 [19.5 kB]
Get: 100 http://deb.debian.org/debian trixie/main i386 dh-strip-nondeterminism all 1.14.0-1 [8448 B]
Get: 101 http://deb.debian.org/debian trixie/main i386 libelf1t64 i386 0.192-4 [195 kB]
Get: 102 http://deb.debian.org/debian trixie/main i386 dwz i386 0.15-1+b1 [116 kB]
Get: 103 http://deb.debian.org/debian trixie/main i386 gettext i386 0.22.5-2 [1631 kB]
Get: 104 http://deb.debian.org/debian trixie/main i386 intltool-debian all 0.35.0+20060710.6 [22.9 kB]
Get: 105 http://deb.debian.org/debian trixie/main i386 po-debconf all 1.0.21+nmu1 [248 kB]
Get: 106 http://deb.debian.org/debian trixie/main i386 debhelper all 13.20 [915 kB]
Get: 107 http://deb.debian.org/debian trixie/main i386 gnuplot-data all 6.0.0+dfsg1-3 [72.3 kB]
Get: 108 http://deb.debian.org/debian trixie/main i386 libexpat1 i386 2.6.4-1 [107 kB]
Get: 109 http://deb.debian.org/debian trixie/main i386 libpng16-16t64 i386 1.6.44-2 [288 kB]
Get: 110 http://deb.debian.org/debian trixie/main i386 libfreetype6 i386 2.13.3+dfsg-1 [464 kB]
Get: 111 http://deb.debian.org/debian trixie/main i386 fonts-dejavu-mono all 2.37-8 [489 kB]
Get: 112 http://deb.debian.org/debian trixie/main i386 fonts-dejavu-core all 2.37-8 [840 kB]
Get: 113 http://deb.debian.org/debian trixie/main i386 fonts-freefont-otf all 20211204+svn4273-2 [4328 kB]
Get: 114 http://deb.debian.org/debian trixie/main i386 fontconfig-config i386 2.15.0-1.1+b1 [318 kB]
Get: 115 http://deb.debian.org/debian trixie/main i386 libfontconfig1 i386 2.15.0-1.1+b1 [402 kB]
Get: 116 http://deb.debian.org/debian trixie/main i386 libpixman-1-0 i386 0.44.0-3 [246 kB]
Get: 117 http://deb.debian.org/debian trixie/main i386 libxau6 i386 1:1.0.11-1 [20.7 kB]
Get: 118 http://deb.debian.org/debian trixie/main i386 libxdmcp6 i386 1:1.1.5-1 [28.2 kB]
Get: 119 http://deb.debian.org/debian trixie/main i386 libxcb1 i386 1.17.0-2+b1 [148 kB]
Get: 120 http://deb.debian.org/debian trixie/main i386 libx11-data all 2:1.8.10-2 [337 kB]
Get: 121 http://deb.debian.org/debian trixie/main i386 libx11-6 i386 2:1.8.10-2 [834 kB]
Get: 122 http://deb.debian.org/debian trixie/main i386 libxcb-render0 i386 1.17.0-2+b1 [116 kB]
Get: 123 http://deb.debian.org/debian trixie/main i386 libxcb-shm0 i386 1.17.0-2+b1 [105 kB]
Get: 124 http://deb.debian.org/debian trixie/main i386 libxext6 i386 2:1.3.4-1+b2 [52.6 kB]
Get: 125 http://deb.debian.org/debian trixie/main i386 libxrender1 i386 1:0.9.10-1.1+b2 [28.9 kB]
Get: 126 http://deb.debian.org/debian trixie/main i386 libcairo2 i386 1.18.2-2 [591 kB]
Get: 127 http://deb.debian.org/debian trixie/main i386 libedit2 i386 3.1-20240808-1 [98.0 kB]
Get: 128 http://deb.debian.org/debian trixie/main i386 libaom3 i386 3.11.0-1 [1915 kB]
Get: 129 http://deb.debian.org/debian trixie/main i386 libdav1d7 i386 1.5.0-1+b1 [327 kB]
Get: 130 http://deb.debian.org/debian trixie/main i386 libabsl20230802 i386 20230802.1-4 [521 kB]
Get: 131 http://deb.debian.org/debian trixie/main i386 libgav1-1 i386 0.19.0-2+b2 [326 kB]
Get: 132 http://deb.debian.org/debian trixie/main i386 librav1e0.7 i386 0.7.1-7+b2 [667 kB]
Get: 133 http://deb.debian.org/debian trixie/main i386 libsvtav1enc2 i386 2.2.1+dfsg-2 [1040 kB]
Get: 134 http://deb.debian.org/debian trixie/main i386 libjpeg62-turbo i386 1:2.1.5-3+b1 [171 kB]
Get: 135 http://deb.debian.org/debian trixie/main i386 libyuv0 i386 0.0.1898.20241125-1 [111 kB]
Get: 136 http://deb.debian.org/debian trixie/main i386 libavif16 i386 1.1.1-1 [124 kB]
Get: 137 http://deb.debian.org/debian trixie/main i386 libsharpyuv0 i386 1.4.0-0.1+b1 [113 kB]
Get: 138 http://deb.debian.org/debian trixie/main i386 libheif-plugin-dav1d i386 1.19.3-1 [11.5 kB]
Get: 139 http://deb.debian.org/debian trixie/main i386 libde265-0 i386 1.0.15-1+b2 [199 kB]
Get: 140 http://deb.debian.org/debian trixie/main i386 libheif-plugin-libde265 i386 1.19.3-1 [15.6 kB]
Get: 141 http://deb.debian.org/debian trixie/main i386 libheif1 i386 1.19.3-1 [515 kB]
Get: 142 http://deb.debian.org/debian trixie/main i386 libimagequant0 i386 2.18.0-1+b2 [36.0 kB]
Get: 143 http://deb.debian.org/debian trixie/main i386 libfribidi0 i386 1.0.15-1 [72.4 kB]
Get: 144 http://deb.debian.org/debian trixie/main i386 libgraphite2-3 i386 1.3.14-2+b1 [77.8 kB]
Get: 145 http://deb.debian.org/debian trixie/main i386 libharfbuzz0b i386 10.0.1-1 [503 kB]
Get: 146 http://deb.debian.org/debian trixie/main i386 libraqm0 i386 0.10.1-1+b2 [14.8 kB]
Get: 147 http://deb.debian.org/debian trixie/main i386 libdeflate0 i386 1.22-1 [48.1 kB]
Get: 148 http://deb.debian.org/debian trixie/main i386 libjbig0 i386 2.1-6.1+b2 [32.2 kB]
Get: 149 http://deb.debian.org/debian trixie/main i386 liblerc4 i386 4.0.0+ds-5 [191 kB]
Get: 150 http://deb.debian.org/debian trixie/main i386 libwebp7 i386 1.4.0-0.1+b1 [322 kB]
Get: 151 http://deb.debian.org/debian trixie/main i386 libtiff6 i386 4.5.1+git230720-5 [339 kB]
Get: 152 http://deb.debian.org/debian trixie/main i386 libxpm4 i386 1:3.5.17-1+b2 [58.3 kB]
Get: 153 http://deb.debian.org/debian trixie/main i386 libgd3 i386 2.3.3-12+b1 [131 kB]
Get: 154 http://deb.debian.org/debian trixie/main i386 liblua5.4-0 i386 5.4.7-1+b1 [170 kB]
Get: 155 http://deb.debian.org/debian trixie/main i386 fontconfig i386 2.15.0-1.1+b1 [463 kB]
Get: 156 http://deb.debian.org/debian trixie/main i386 libthai-data all 0.1.29-2 [168 kB]
Get: 157 http://deb.debian.org/debian trixie/main i386 libdatrie1 i386 0.2.13-3+b1 [39.9 kB]
Get: 158 http://deb.debian.org/debian trixie/main i386 libthai0 i386 0.1.29-2+b1 [50.3 kB]
Get: 159 http://deb.debian.org/debian trixie/main i386 libpango-1.0-0 i386 1.54.0+ds-3 [230 kB]
Get: 160 http://deb.debian.org/debian trixie/main i386 libpangoft2-1.0-0 i386 1.54.0+ds-3 [58.1 kB]
Get: 161 http://deb.debian.org/debian trixie/main i386 libpangocairo-1.0-0 i386 1.54.0+ds-3 [36.1 kB]
Get: 162 http://deb.debian.org/debian trixie/main i386 libwebpmux3 i386 1.4.0-0.1+b1 [125 kB]
Get: 163 http://deb.debian.org/debian trixie/main i386 gnuplot-nox i386 6.0.0+dfsg1-3 [925 kB]
Get: 164 http://deb.debian.org/debian trixie/main i386 dh-octave-autopkgtest all 1.8.0 [10.0 kB]
Get: 165 http://deb.debian.org/debian trixie/main i386 libapt-pkg-perl i386 0.1.40+b6 [69.5 kB]
Get: 166 http://deb.debian.org/debian trixie/main i386 libarray-intspan-perl all 2.004-2 [25.7 kB]
Get: 167 http://deb.debian.org/debian trixie/main i386 libyaml-libyaml-perl i386 0.902.0+ds-2+b1 [36.6 kB]
Get: 168 http://deb.debian.org/debian trixie/main i386 libconfig-model-backend-yaml-perl all 2.134-2 [10.8 kB]
Get: 169 http://deb.debian.org/debian trixie/main i386 libexporter-lite-perl all 0.09-2 [10.7 kB]
Get: 170 http://deb.debian.org/debian trixie/main i386 libencode-locale-perl all 1.05-3 [12.9 kB]
Get: 171 http://deb.debian.org/debian trixie/main i386 libtimedate-perl all 2.3300-2 [39.3 kB]
Get: 172 http://deb.debian.org/debian trixie/main i386 libhttp-date-perl all 6.06-1 [10.7 kB]
Get: 173 http://deb.debian.org/debian trixie/main i386 libfile-listing-perl all 6.16-1 [12.4 kB]
Get: 174 http://deb.debian.org/debian trixie/main i386 libhtml-tagset-perl all 3.24-1 [14.7 kB]
Get: 175 http://deb.debian.org/debian trixie/main i386 liburi-perl all 5.30-1 [105 kB]
Get: 176 http://deb.debian.org/debian trixie/main i386 libhtml-parser-perl i386 3.83-1+b1 [101 kB]
Get: 177 http://deb.debian.org/debian trixie/main i386 libhtml-tree-perl all 5.07-3 [211 kB]
Get: 178 http://deb.debian.org/debian trixie/main i386 libclone-perl i386 0.47-1+b1 [14.0 kB]
Get: 179 http://deb.debian.org/debian trixie/main i386 libio-html-perl all 1.004-3 [16.2 kB]
Get: 180 http://deb.debian.org/debian trixie/main i386 liblwp-mediatypes-perl all 6.04-2 [20.2 kB]
Get: 181 http://deb.debian.org/debian trixie/main i386 libhttp-message-perl all 7.00-2 [79.8 kB]
Get: 182 http://deb.debian.org/debian trixie/main i386 libhttp-cookies-perl all 6.11-1 [19.1 kB]
Get: 183 http://deb.debian.org/debian trixie/main i386 libhttp-negotiate-perl all 6.01-2 [13.1 kB]
Get: 184 http://deb.debian.org/debian trixie/main i386 perl-openssl-defaults i386 7+b2 [6720 B]
Get: 185 http://deb.debian.org/debian trixie/main i386 libnet-ssleay-perl i386 1.94-2 [340 kB]
Get: 186 http://deb.debian.org/debian trixie/main i386 libio-socket-ssl-perl all 2.089-1 [223 kB]
Get: 187 http://deb.debian.org/debian trixie/main i386 libnet-http-perl all 6.23-1 [23.9 kB]
Get: 188 http://deb.debian.org/debian trixie/main i386 liblwp-protocol-https-perl all 6.14-1 [10.8 kB]
Get: 189 http://deb.debian.org/debian trixie/main i386 libwww-robotrules-perl all 6.02-1 [12.9 kB]
Get: 190 http://deb.debian.org/debian trixie/main i386 libwww-perl all 6.77-1 [183 kB]
Get: 191 http://deb.debian.org/debian trixie/main i386 liberror-perl all 0.17029-2 [29.0 kB]
Get: 192 http://deb.debian.org/debian trixie/main i386 libparse-debcontrol-perl all 2.005-6 [21.6 kB]
Get: 193 http://deb.debian.org/debian trixie/main i386 libsoftware-copyright-perl all 0.012-2 [18.0 kB]
Get: 194 http://deb.debian.org/debian trixie/main i386 libalgorithm-c3-perl all 0.11-2 [10.8 kB]
Get: 195 http://deb.debian.org/debian trixie/main i386 libclass-c3-perl all 0.35-2 [21.0 kB]
Get: 196 http://deb.debian.org/debian trixie/main i386 libmro-compat-perl all 0.15-2 [11.8 kB]
Get: 197 http://deb.debian.org/debian trixie/main i386 libdata-section-perl all 0.200008-1 [13.1 kB]
Get: 198 http://deb.debian.org/debian trixie/main i386 libtext-template-perl all 1.61-1 [54.4 kB]
Get: 199 http://deb.debian.org/debian trixie/main i386 libsoftware-license-perl all 0.104006-1 [116 kB]
Get: 200 http://deb.debian.org/debian trixie/main i386 libsoftware-licensemoreutils-perl all 1.009-1 [22.0 kB]
Get: 201 http://deb.debian.org/debian trixie/main i386 libsort-versions-perl all 1.62-3 [8928 B]
Get: 202 http://deb.debian.org/debian trixie/main i386 libtext-reform-perl all 1.20-5 [36.0 kB]
Get: 203 http://deb.debian.org/debian trixie/main i386 libtext-autoformat-perl all 1.750000-2 [35.2 kB]
Get: 204 http://deb.debian.org/debian trixie/main i386 libtext-levenshtein-damerau-perl all 0.41-3 [12.3 kB]
Get: 205 http://deb.debian.org/debian trixie/main i386 libtoml-tiny-perl all 0.18-1 [23.0 kB]
Get: 206 http://deb.debian.org/debian trixie/main i386 libclass-inspector-perl all 1.36-3 [17.5 kB]
Get: 207 http://deb.debian.org/debian trixie/main i386 libfile-sharedir-perl all 1.118-3 [16.0 kB]
Get: 208 http://deb.debian.org/debian trixie/main i386 libindirect-perl i386 0.39-2+b4 [28.1 kB]
Get: 209 http://deb.debian.org/debian trixie/main i386 libxs-parse-keyword-perl i386 0.46-1+b1 [66.8 kB]
Get: 210 http://deb.debian.org/debian trixie/main i386 libxs-parse-sublike-perl i386 0.30-1 [45.3 kB]
Get: 211 http://deb.debian.org/debian trixie/main i386 libobject-pad-perl i386 0.815-1 [128 kB]
Get: 212 http://deb.debian.org/debian trixie/main i386 libfeature-compat-class-perl all 0.07-1 [11.5 kB]
Get: 213 http://deb.debian.org/debian trixie/main i386 libsyntax-keyword-try-perl i386 0.30-1+b1 [27.7 kB]
Get: 214 http://deb.debian.org/debian trixie/main i386 libfeature-compat-try-perl all 0.05-1 [10.4 kB]
Get: 215 http://deb.debian.org/debian trixie/main i386 libio-interactive-perl all 1.025-1 [11.4 kB]
Get: 216 http://deb.debian.org/debian trixie/main i386 liblog-any-perl all 1.717-1 [78.9 kB]
Get: 217 http://deb.debian.org/debian trixie/main i386 liblog-any-adapter-screen-perl all 0.141-1 [14.0 kB]
Get: 218 http://deb.debian.org/debian trixie/main i386 libsub-exporter-progressive-perl all 0.001013-3 [7496 B]
Get: 219 http://deb.debian.org/debian trixie/main i386 libvariable-magic-perl i386 0.64-1+b1 [45.8 kB]
Get: 220 http://deb.debian.org/debian trixie/main i386 libb-hooks-endofscope-perl all 0.28-1 [17.5 kB]
Get: 221 http://deb.debian.org/debian trixie/main i386 libsub-identify-perl i386 0.14-3+b3 [11.2 kB]
Get: 222 http://deb.debian.org/debian trixie/main i386 libsub-name-perl i386 0.27-1+b3 [12.6 kB]
Get: 223 http://deb.debian.org/debian trixie/main i386 libnamespace-clean-perl all 0.27-2 [17.8 kB]
Get: 224 http://deb.debian.org/debian trixie/main i386 libnumber-compare-perl all 0.03-3 [6332 B]
Get: 225 http://deb.debian.org/debian trixie/main i386 libtext-glob-perl all 0.11-3 [7676 B]
Get: 226 http://deb.debian.org/debian trixie/main i386 libpath-iterator-rule-perl all 1.015-2 [41.7 kB]
Get: 227 http://deb.debian.org/debian trixie/main i386 libpod-parser-perl all 1.67-1 [94.1 kB]
Get: 228 http://deb.debian.org/debian trixie/main i386 libpod-constants-perl all 0.19-2 [17.3 kB]
Get: 229 http://deb.debian.org/debian trixie/main i386 libset-intspan-perl all 1.19-3 [25.3 kB]
Get: 230 http://deb.debian.org/debian trixie/main i386 libstring-copyright-perl all 0.003014-1 [23.4 kB]
Get: 231 http://deb.debian.org/debian trixie/main i386 libstring-escape-perl all 2010.002-3 [18.7 kB]
Get: 232 http://deb.debian.org/debian trixie/main i386 libregexp-pattern-license-perl all 3.11.2-1 [94.6 kB]
Get: 233 http://deb.debian.org/debian trixie/main i386 libregexp-pattern-perl all 0.2.14-2 [18.7 kB]
Get: 234 http://deb.debian.org/debian trixie/main i386 libstring-license-perl all 0.0.11-1 [34.7 kB]
Get: 235 http://deb.debian.org/debian trixie/main i386 licensecheck all 3.3.9-1 [50.1 kB]
Get: 236 http://deb.debian.org/debian trixie/main i386 diffstat i386 1.67-1 [36.1 kB]
Get: 237 http://deb.debian.org/debian trixie/main i386 libassuan9 i386 3.0.1-2 [62.4 kB]
Get: 238 http://deb.debian.org/debian trixie/main i386 libreadline8t64 i386 8.2-5 [173 kB]
Get: 239 http://deb.debian.org/debian trixie/main i386 gpgconf i386 2.2.45-2 [127 kB]
Get: 240 http://deb.debian.org/debian trixie/main i386 gpg i386 2.2.45-2 [573 kB]
Get: 241 http://deb.debian.org/debian trixie/main i386 iso-codes all 4.17.0-1 [3055 kB]
Get: 242 http://deb.debian.org/debian trixie/main i386 libberkeleydb-perl i386 0.66-1 [126 kB]
Get: 243 http://deb.debian.org/debian trixie/main i386 libclass-xsaccessor-perl i386 1.19-4+b4 [37.5 kB]
Get: 244 http://deb.debian.org/debian trixie/main i386 libconfig-tiny-perl all 2.30-1 [18.9 kB]
Get: 245 http://deb.debian.org/debian trixie/main i386 libconst-fast-perl all 0.014-2 [8792 B]
Get: 246 http://deb.debian.org/debian trixie/main i386 libcpanel-json-xs-perl i386 4.38-1+b1 [134 kB]
Get: 247 http://deb.debian.org/debian trixie/main i386 libaliased-perl all 0.34-3 [13.5 kB]
Get: 248 http://deb.debian.org/debian trixie/main i386 libclass-data-inheritable-perl all 0.10-1 [8632 B]
Get: 249 http://deb.debian.org/debian trixie/main i386 libdevel-stacktrace-perl all 2.0500-1 [26.4 kB]
Get: 250 http://deb.debian.org/debian trixie/main i386 libexception-class-perl all 1.45-1 [34.6 kB]
Get: 251 http://deb.debian.org/debian trixie/main i386 libiterator-perl all 0.03+ds1-2 [18.8 kB]
Get: 252 http://deb.debian.org/debian trixie/main i386 libiterator-util-perl all 0.02+ds1-2 [14.0 kB]
Get: 253 http://deb.debian.org/debian trixie/main i386 libdata-dpath-perl all 0.60-1 [41.8 kB]
Get: 254 http://deb.debian.org/debian trixie/main i386 libnet-domain-tld-perl all 1.75-4 [31.5 kB]
Get: 255 http://deb.debian.org/debian trixie/main i386 libdata-validate-domain-perl all 0.15-1 [11.9 kB]
Get: 256 http://deb.debian.org/debian trixie/main i386 libnet-ipv6addr-perl all 1.02-1 [21.7 kB]
Get: 257 http://deb.debian.org/debian trixie/main i386 libnet-netmask-perl all 2.0002-2 [28.6 kB]
Get: 258 http://deb.debian.org/debian trixie/main i386 libnetaddr-ip-perl i386 4.079+dfsg-2+b4 [98.8 kB]
Get: 259 http://deb.debian.org/debian trixie/main i386 libdata-validate-ip-perl all 0.31-1 [20.6 kB]
Get: 260 http://deb.debian.org/debian trixie/main i386 libdata-validate-uri-perl all 0.07-3 [11.0 kB]
Get: 261 http://deb.debian.org/debian trixie/main i386 libdevel-size-perl i386 0.84-1+b1 [24.6 kB]
Get: 262 http://deb.debian.org/debian trixie/main i386 libemail-address-xs-perl i386 1.05-1+b4 [30.3 kB]
Get: 263 http://deb.debian.org/debian trixie/main i386 libipc-system-simple-perl all 1.30-2 [26.8 kB]
Get: 264 http://deb.debian.org/debian trixie/main i386 libfile-basedir-perl all 0.09-2 [15.1 kB]
Get: 265 http://deb.debian.org/debian trixie/main i386 libfile-find-rule-perl all 0.34-3 [26.6 kB]
Get: 266 http://deb.debian.org/debian trixie/main i386 libio-string-perl all 1.08-4 [12.1 kB]
Get: 267 http://deb.debian.org/debian trixie/main i386 libfont-ttf-perl all 1.06-2 [318 kB]
Get: 268 http://deb.debian.org/debian trixie/main i386 libhtml-html5-entities-perl all 0.004-3 [21.0 kB]
Get: 269 http://deb.debian.org/debian trixie/main i386 libhtml-tokeparser-simple-perl all 3.16-4 [39.1 kB]
Get: 270 http://deb.debian.org/debian trixie/main i386 libipc-run3-perl all 0.049-1 [31.5 kB]
Get: 271 http://deb.debian.org/debian trixie/main i386 libjson-maybexs-perl all 1.004008-1 [12.9 kB]
Get: 272 http://deb.debian.org/debian trixie/main i386 liblist-compare-perl all 0.55-2 [65.7 kB]
Get: 273 http://deb.debian.org/debian trixie/main i386 liblist-someutils-perl all 0.59-1 [37.1 kB]
Get: 274 http://deb.debian.org/debian trixie/main i386 liblist-utilsby-perl all 0.12-2 [15.5 kB]
Get: 275 http://deb.debian.org/debian trixie/main i386 libmldbm-perl all 2.05-4 [16.8 kB]
Get: 276 http://deb.debian.org/debian trixie/main i386 libclass-method-modifiers-perl all 2.15-1 [18.0 kB]
Get: 277 http://deb.debian.org/debian trixie/main i386 libimport-into-perl all 1.002005-2 [11.3 kB]
Get: 278 http://deb.debian.org/debian trixie/main i386 librole-tiny-perl all 2.002004-1 [21.4 kB]
Get: 279 http://deb.debian.org/debian trixie/main i386 libsub-quote-perl all 2.006008-1 [21.8 kB]
Get: 280 http://deb.debian.org/debian trixie/main i386 libmoo-perl all 2.005005-1 [58.0 kB]
Get: 281 http://deb.debian.org/debian trixie/main i386 libstrictures-perl all 2.000006-1 [18.6 kB]
Get: 282 http://deb.debian.org/debian trixie/main i386 libmoox-aliases-perl all 0.001006-2 [7156 B]
Get: 283 http://deb.debian.org/debian trixie/main i386 libperlio-gzip-perl i386 0.20-1+b4 [17.9 kB]
Get: 284 http://deb.debian.org/debian trixie/main i386 libperlio-utf8-strict-perl i386 0.010-1+b3 [11.6 kB]
Get: 285 http://deb.debian.org/debian trixie/main i386 libproc-processtable-perl i386 0.636-1+b3 [42.4 kB]
Get: 286 http://deb.debian.org/debian trixie/main i386 libregexp-wildcards-perl all 1.05-3 [14.1 kB]
Get: 287 http://deb.debian.org/debian trixie/main i386 libsereal-decoder-perl i386 5.004+ds-1+b3 [107 kB]
Get: 288 http://deb.debian.org/debian trixie/main i386 libsereal-encoder-perl i386 5.004+ds-1+b3 [111 kB]
Get: 289 http://deb.debian.org/debian trixie/main i386 libterm-readkey-perl i386 2.38-2+b4 [25.4 kB]
Get: 290 http://deb.debian.org/debian trixie/main i386 libtext-levenshteinxs-perl i386 0.03-5+b4 [8636 B]
Get: 291 http://deb.debian.org/debian trixie/main i386 libmarkdown2 i386 2.2.7-2.1 [37.9 kB]
Get: 292 http://deb.debian.org/debian trixie/main i386 libtext-markdown-discount-perl i386 0.16-1+b3 [13.2 kB]
Get: 293 http://deb.debian.org/debian trixie/main i386 libdata-messagepack-perl i386 1.02-1+b4 [33.2 kB]
Get: 294 http://deb.debian.org/debian trixie/main i386 libtext-xslate-perl i386 3.5.9-2+b1 [177 kB]
Get: 295 http://deb.debian.org/debian trixie/main i386 libtime-duration-perl all 1.21-2 [13.1 kB]
Get: 296 http://deb.debian.org/debian trixie/main i386 libtime-moment-perl i386 0.44-2+b4 [79.8 kB]
Get: 297 http://deb.debian.org/debian trixie/main i386 libunicode-utf8-perl i386 0.62-2+b3 [21.1 kB]
Get: 298 http://deb.debian.org/debian trixie/main i386 libcgi-pm-perl all 4.66-1 [217 kB]
Get: 299 http://deb.debian.org/debian trixie/main i386 libhtml-form-perl all 6.12-1 [32.3 kB]
Get: 300 http://deb.debian.org/debian trixie/main i386 libwww-mechanize-perl all 2.19-1 [114 kB]
Get: 301 http://deb.debian.org/debian trixie/main i386 libxml-namespacesupport-perl all 1.12-2 [15.1 kB]
Get: 302 http://deb.debian.org/debian trixie/main i386 libxml-sax-base-perl all 1.09-3 [20.6 kB]
Get: 303 http://deb.debian.org/debian trixie/main i386 libxml-sax-perl all 1.02+dfsg-4 [53.4 kB]
Get: 304 http://deb.debian.org/debian trixie/main i386 libxml-libxml-perl i386 2.0207+dfsg+really+2.0134-5+b1 [324 kB]
Get: 305 http://deb.debian.org/debian trixie/main i386 liblz1 i386 1.15~rc1-1 [39.2 kB]
Get: 306 http://deb.debian.org/debian trixie/main i386 plzip i386 1.11-2 [67.8 kB]
Get: 307 http://deb.debian.org/debian trixie/main i386 liblzo2-2 i386 2.10-3+b1 [59.5 kB]
Get: 308 http://deb.debian.org/debian trixie/main i386 lzop i386 1.04-2 [87.8 kB]
Get: 309 http://deb.debian.org/debian trixie/main i386 patchutils i386 0.4.2-1 [79.6 kB]
Get: 310 http://deb.debian.org/debian trixie/main i386 t1utils i386 1.41-4 [62.3 kB]
Get: 311 http://deb.debian.org/debian trixie/main i386 unzip i386 6.0-28 [166 kB]
Get: 312 http://deb.debian.org/debian trixie/main i386 lintian all 2.120.0 [1058 kB]
Get: 313 http://deb.debian.org/debian trixie/main i386 libconfig-model-dpkg-perl all 3.005 [173 kB]
Get: 314 http://deb.debian.org/debian trixie/main i386 libconvert-binhex-perl all 1.125-3 [27.4 kB]
Get: 315 http://deb.debian.org/debian trixie/main i386 libnet-smtp-ssl-perl all 1.04-2 [6548 B]
Get: 316 http://deb.debian.org/debian trixie/main i386 libmailtools-perl all 2.22-1 [88.8 kB]
Get: 317 http://deb.debian.org/debian trixie/main i386 libmime-tools-perl all 5.515-1 [203 kB]
Get: 318 http://deb.debian.org/debian trixie/main i386 libsuitesparseconfig7 i386 1:7.8.3+dfsg-2 [24.0 kB]
Get: 319 http://deb.debian.org/debian trixie/main i386 libamd3 i386 1:7.8.3+dfsg-2 [43.5 kB]
Get: 320 http://deb.debian.org/debian trixie/main i386 libblas3 i386 3.12.0-4 [143 kB]
Get: 321 http://deb.debian.org/debian trixie/main i386 libgfortran5 i386 14.2.0-8 [735 kB]
Get: 322 http://deb.debian.org/debian trixie/main i386 liblapack3 i386 3.12.0-4 [2194 kB]
Get: 323 http://deb.debian.org/debian trixie/main i386 libarpack2t64 i386 3.9.1-1.1+b1 [101 kB]
Get: 324 http://deb.debian.org/debian trixie/main i386 libccolamd3 i386 1:7.8.3+dfsg-2 [43.2 kB]
Get: 325 http://deb.debian.org/debian trixie/main i386 libcamd3 i386 1:7.8.3+dfsg-2 [41.1 kB]
Get: 326 http://deb.debian.org/debian trixie/main i386 libcolamd3 i386 1:7.8.3+dfsg-2 [35.6 kB]
Get: 327 http://deb.debian.org/debian trixie/main i386 libcholmod5 i386 1:7.8.3+dfsg-2 [733 kB]
Get: 328 http://deb.debian.org/debian trixie/main i386 libcxsparse4 i386 1:7.8.3+dfsg-2 [102 kB]
Get: 329 http://deb.debian.org/debian trixie/main i386 libfftw3-double3 i386 3.3.10-1+b3 [635 kB]
Get: 330 http://deb.debian.org/debian trixie/main i386 libfftw3-single3 i386 3.3.10-1+b3 [643 kB]
Get: 331 http://deb.debian.org/debian trixie/main i386 libxfixes3 i386 1:6.0.0-2+b2 [20.7 kB]
Get: 332 http://deb.debian.org/debian trixie/main i386 libxcursor1 i386 1:1.2.2-1+b1 [38.9 kB]
Get: 333 http://deb.debian.org/debian trixie/main i386 libxft2 i386 2.3.6-1+b2 [55.3 kB]
Get: 334 http://deb.debian.org/debian trixie/main i386 libxinerama1 i386 2:1.1.4-3+b2 [16.3 kB]
Get: 335 http://deb.debian.org/debian trixie/main i386 libfltk1.3t64 i386 1.3.8-6.1+b1 [582 kB]
Get: 336 http://deb.debian.org/debian trixie/main i386 libglvnd0 i386 1.7.0-1+b2 [44.1 kB]
Get: 337 http://deb.debian.org/debian trixie/main i386 libdrm-common all 2.4.123-1 [8084 B]
Get: 338 http://deb.debian.org/debian trixie/main i386 libdrm2 i386 2.4.123-1 [41.8 kB]
Get: 339 http://deb.debian.org/debian trixie/main i386 libglapi-mesa i386 24.2.8-1 [37.8 kB]
Get: 340 http://deb.debian.org/debian trixie/main i386 libx11-xcb1 i386 2:1.8.10-2 [241 kB]
Get: 341 http://deb.debian.org/debian trixie/main i386 libxcb-dri2-0 i386 1.17.0-2+b1 [107 kB]
Get: 342 http://deb.debian.org/debian trixie/main i386 libxcb-dri3-0 i386 1.17.0-2+b1 [107 kB]
Get: 343 http://deb.debian.org/debian trixie/main i386 libxcb-glx0 i386 1.17.0-2+b1 [124 kB]
Get: 344 http://deb.debian.org/debian trixie/main i386 libxcb-present0 i386 1.17.0-2+b1 [106 kB]
Get: 345 http://deb.debian.org/debian trixie/main i386 libxcb-randr0 i386 1.17.0-2+b1 [118 kB]
Get: 346 http://deb.debian.org/debian trixie/main i386 libxcb-sync1 i386 1.17.0-2+b1 [109 kB]
Get: 347 http://deb.debian.org/debian trixie/main i386 libxcb-xfixes0 i386 1.17.0-2+b1 [110 kB]
Get: 348 http://deb.debian.org/debian trixie/main i386 libxshmfence1 i386 1.3-1+b2 [8992 B]
Get: 349 http://deb.debian.org/debian trixie/main i386 libxxf86vm1 i386 1:1.1.4-1+b3 [20.1 kB]
Get: 350 http://deb.debian.org/debian trixie/main i386 libdrm-amdgpu1 i386 2.4.123-1 [25.0 kB]
Get: 351 http://deb.debian.org/debian trixie/main i386 libpciaccess0 i386 0.17-3+b2 [53.8 kB]
Get: 352 http://deb.debian.org/debian trixie/main i386 libdrm-intel1 i386 2.4.123-1 [66.8 kB]
Get: 353 http://deb.debian.org/debian trixie/main i386 libdrm-radeon1 i386 2.4.123-1 [23.0 kB]
Get: 354 http://deb.debian.org/debian trixie/main i386 libz3-4 i386 4.13.3-1 [9211 kB]
Get: 355 http://deb.debian.org/debian trixie/main i386 libllvm19 i386 1:19.1.4-1 [30.4 MB]
Get: 356 http://deb.debian.org/debian trixie/main i386 libsensors-config all 1:3.6.0-10 [14.6 kB]
Get: 357 http://deb.debian.org/debian trixie/main i386 libsensors5 i386 1:3.6.0-10+b1 [35.8 kB]
Get: 358 http://deb.debian.org/debian trixie/main i386 mesa-libgallium i386 24.2.8-1 [9409 kB]
Get: 359 http://deb.debian.org/debian trixie/main i386 libvulkan1 i386 1.3.296.0-1 [136 kB]
Get: 360 http://deb.debian.org/debian trixie/main i386 libwayland-server0 i386 1.23.0-1+b1 [35.9 kB]
Get: 361 http://deb.debian.org/debian trixie/main i386 libgbm1 i386 24.2.8-1 [43.9 kB]
Get: 362 http://deb.debian.org/debian trixie/main i386 libgl1-mesa-dri i386 24.2.8-1 [42.5 kB]
Get: 363 http://deb.debian.org/debian trixie/main i386 libglx-mesa0 i386 24.2.8-1 [160 kB]
Get: 364 http://deb.debian.org/debian trixie/main i386 libglx0 i386 1.7.0-1+b2 [36.8 kB]
Get: 365 http://deb.debian.org/debian trixie/main i386 libgl1 i386 1.7.0-1+b2 [82.7 kB]
Get: 366 http://deb.debian.org/debian trixie/main i386 libfltk-gl1.3t64 i386 1.3.8-6.1+b1 [63.1 kB]
Get: 367 http://deb.debian.org/debian trixie/main i386 libgl2ps1.4 i386 1.4.2+dfsg1-2 [42.0 kB]
Get: 368 http://deb.debian.org/debian trixie/main i386 libltdl7 i386 2.4.7-8 [395 kB]
Get: 369 http://deb.debian.org/debian trixie/main i386 libglpk40 i386 5.0-1+b2 [422 kB]
Get: 370 http://deb.debian.org/debian trixie/main i386 libopengl0 i386 1.7.0-1+b2 [29.3 kB]
Get: 371 http://deb.debian.org/debian trixie/main i386 libglu1-mesa i386 9.0.2-1.1+b2 [190 kB]
Get: 372 http://deb.debian.org/debian trixie/main i386 libgif7 i386 5.2.2-1+b1 [45.6 kB]
Get: 373 http://deb.debian.org/debian trixie/main i386 libhwy1t64 i386 1.2.0-2+b1 [856 kB]
Get: 374 http://deb.debian.org/debian trixie/main i386 liblcms2-2 i386 2.16-2 [171 kB]
Get: 375 http://deb.debian.org/debian trixie/main i386 libimath-3-1-29t64 i386 3.1.12-1+b1 [37.0 kB]
Get: 376 http://deb.debian.org/debian trixie/main i386 libopenexr-3-1-30 i386 3.1.5-5.1+b3 [975 kB]
Get: 377 http://deb.debian.org/debian trixie/main i386 libjxl0.9 i386 0.9.2-10+b1 [1172 kB]
Get: 378 http://deb.debian.org/debian trixie/main i386 libwmflite-0.2-7 i386 0.2.13-1.1+b3 [78.6 kB]
Get: 379 http://deb.debian.org/debian trixie/main i386 libgraphicsmagick-q16-3t64 i386 1.4+really1.3.45-1+b1 [1283 kB]
Get: 380 http://deb.debian.org/debian trixie/main i386 libgraphicsmagick++-q16-12t64 i386 1.4+really1.3.45-1+b1 [136 kB]
Get: 381 http://deb.debian.org/debian trixie/main i386 libcurl4t64 i386 8.11.0-1 [392 kB]
Get: 382 http://deb.debian.org/debian trixie/main i386 libaec0 i386 1.1.3-1+b1 [25.0 kB]
Get: 383 http://deb.debian.org/debian trixie/main i386 libsz2 i386 1.1.3-1+b1 [8128 B]
Get: 384 http://deb.debian.org/debian trixie/main i386 libhdf5-103-1t64 i386 1.10.10+repack-4 [1186 kB]
Get: 385 http://deb.debian.org/debian trixie/main i386 libasound2-data all 1.2.12-1 [21.0 kB]
Get: 386 http://deb.debian.org/debian trixie/main i386 libasound2t64 i386 1.2.12-1+b1 [397 kB]
Get: 387 http://deb.debian.org/debian trixie/main i386 libopus0 i386 1.5.2-2 [2843 kB]
Get: 388 http://deb.debian.org/debian trixie/main i386 libsamplerate0 i386 0.2.2-4+b2 [953 kB]
Get: 389 http://deb.debian.org/debian trixie/main i386 libjack-jackd2-0 i386 1.9.22~dfsg-3 [317 kB]
Get: 390 http://deb.debian.org/debian trixie/main i386 libportaudio2 i386 19.6.0-1.2+b3 [66.9 kB]
Get: 391 http://deb.debian.org/debian trixie/main i386 libqhull-r8.0 i386 2020.2-6+b2 [259 kB]
Get: 392 http://deb.debian.org/debian trixie/main i386 libqrupdate1 i386 1.1.5-1 [39.7 kB]
Get: 393 http://deb.debian.org/debian trixie/main i386 libqscintilla2-qt6-l10n all 2.14.1+dfsg-1 [105 kB]
Get: 394 http://deb.debian.org/debian trixie/main i386 libb2-1 i386 0.98.1-1.1+b2 [46.1 kB]
Get: 395 http://deb.debian.org/debian trixie/main i386 libdouble-conversion3 i386 3.3.0-1+b2 [45.7 kB]
Get: 396 http://deb.debian.org/debian trixie/main i386 libpcre2-16-0 i386 10.44-4 [258 kB]
Get: 397 http://deb.debian.org/debian trixie/main i386 libqt6core6t64 i386 6.7.2+dfsg-4 [1899 kB]
Get: 398 http://deb.debian.org/debian trixie/main i386 libwayland-client0 i386 1.23.0-1+b1 [27.2 kB]
Get: 399 http://deb.debian.org/debian trixie/main i386 libegl-mesa0 i386 24.2.8-1 [140 kB]
Get: 400 http://deb.debian.org/debian trixie/main i386 libegl1 i386 1.7.0-1+b2 [36.4 kB]
Get: 401 http://deb.debian.org/debian trixie/main i386 x11-common all 1:7.7+23.1 [216 kB]
Get: 402 http://deb.debian.org/debian trixie/main i386 libice6 i386 2:1.1.1-1 [67.8 kB]
Get: 403 http://deb.debian.org/debian trixie/main i386 libevdev2 i386 1.13.3+dfsg-1 [30.4 kB]
Get: 404 http://deb.debian.org/debian trixie/main i386 libmtdev1t64 i386 1.1.6-1.2+b1 [23.0 kB]
Get: 405 http://deb.debian.org/debian trixie/main i386 libgudev-1.0-0 i386 238-5+b1 [15.2 kB]
Get: 406 http://deb.debian.org/debian trixie/main i386 libwacom-common all 2.13.0-1 [98.0 kB]
Get: 407 http://deb.debian.org/debian trixie/main i386 libwacom9 i386 2.13.0-1 [25.8 kB]
Get: 408 http://deb.debian.org/debian trixie/main i386 libinput-bin i386 1.26.2-1 [25.6 kB]
Get: 409 http://deb.debian.org/debian trixie/main i386 libinput10 i386 1.26.2-1 [146 kB]
Get: 410 http://deb.debian.org/debian trixie/main i386 libmd4c0 i386 0.5.2-2+b1 [49.2 kB]
Get: 411 http://deb.debian.org/debian trixie/main i386 libdbus-1-3 i386 1.14.10-6 [217 kB]
Get: 412 http://deb.debian.org/debian trixie/main i386 libqt6dbus6 i386 6.7.2+dfsg-4 [284 kB]
Get: 413 http://deb.debian.org/debian trixie/main i386 libsm6 i386 2:1.2.4-1 [35.9 kB]
Get: 414 http://deb.debian.org/debian trixie/main i386 libts0t64 i386 1.22-1.1+b1 [63.9 kB]
Get: 415 http://deb.debian.org/debian trixie/main i386 libxcb-util1 i386 0.4.0-1+b2 [22.8 kB]
Get: 416 http://deb.debian.org/debian trixie/main i386 libxcb-image0 i386 0.4.0-2+b2 [22.7 kB]
Get: 417 http://deb.debian.org/debian trixie/main i386 libxcb-render-util0 i386 0.3.9-1+b2 [17.8 kB]
Get: 418 http://deb.debian.org/debian trixie/main i386 libxcb-cursor0 i386 0.1.4-1+b2 [17.4 kB]
Get: 419 http://deb.debian.org/debian trixie/main i386 libxcb-icccm4 i386 0.4.2-1 [28.2 kB]
Get: 420 http://deb.debian.org/debian trixie/main i386 libxcb-keysyms1 i386 0.4.0-1+b3 [15.9 kB]
Get: 421 http://deb.debian.org/debian trixie/main i386 libxcb-shape0 i386 1.17.0-2+b1 [106 kB]
Get: 422 http://deb.debian.org/debian trixie/main i386 libxcb-xinput0 i386 1.17.0-2+b1 [133 kB]
Get: 423 http://deb.debian.org/debian trixie/main i386 libxcb-xkb1 i386 1.17.0-2+b1 [131 kB]
Get: 424 http://deb.debian.org/debian trixie/main i386 xkb-data all 2.42-1 [790 kB]
Get: 425 http://deb.debian.org/debian trixie/main i386 libxkbcommon0 i386 1.6.0-1+b2 [116 kB]
Get: 426 http://deb.debian.org/debian trixie/main i386 libxkbcommon-x11-0 i386 1.6.0-1+b2 [17.1 kB]
Get: 427 http://deb.debian.org/debian trixie/main i386 libqt6gui6 i386 6.7.2+dfsg-4 [3285 kB]
Get: 428 http://deb.debian.org/debian trixie/main i386 libavahi-common-data i386 0.8-13+b3 [112 kB]
Get: 429 http://deb.debian.org/debian trixie/main i386 libavahi-common3 i386 0.8-13+b3 [45.2 kB]
Get: 430 http://deb.debian.org/debian trixie/main i386 libavahi-client3 i386 0.8-13+b3 [49.6 kB]
Get: 431 http://deb.debian.org/debian trixie/main i386 libcups2t64 i386 2.4.10-2 [267 kB]
Get: 432 http://deb.debian.org/debian trixie/main i386 libqt6widgets6 i386 6.7.2+dfsg-4 [2861 kB]
Get: 433 http://deb.debian.org/debian trixie/main i386 libqt6printsupport6 i386 6.7.2+dfsg-4 [233 kB]
Get: 434 http://deb.debian.org/debian trixie/main i386 libqscintilla2-qt6-15 i386 2.14.1+dfsg-1+b4 [1253 kB]
Get: 435 http://deb.debian.org/debian trixie/main i386 libqt6core5compat6 i386 6.7.2-3 [145 kB]
Get: 436 http://deb.debian.org/debian trixie/main i386 libqt6sql6 i386 6.7.2+dfsg-4 [150 kB]
Get: 437 http://deb.debian.org/debian trixie/main i386 libqt6help6 i386 6.7.2-6 [197 kB]
Get: 438 http://deb.debian.org/debian trixie/main i386 libduktape207 i386 2.7.0-2+b2 [138 kB]
Get: 439 http://deb.debian.org/debian trixie/main i386 libproxy1v5 i386 0.5.9-1 [27.7 kB]
Get: 440 http://deb.debian.org/debian trixie/main i386 libqt6network6 i386 6.7.2+dfsg-4 [846 kB]
Get: 441 http://deb.debian.org/debian trixie/main i386 libqt6opengl6 i386 6.7.2+dfsg-4 [440 kB]
Get: 442 http://deb.debian.org/debian trixie/main i386 libqt6openglwidgets6 i386 6.7.2+dfsg-4 [51.3 kB]
Get: 443 http://deb.debian.org/debian trixie/main i386 libqt6xml6 i386 6.7.2+dfsg-4 [90.1 kB]
Get: 444 http://deb.debian.org/debian trixie/main i386 libogg0 i386 1.3.5-3+b2 [24.6 kB]
Get: 445 http://deb.debian.org/debian trixie/main i386 libflac12t64 i386 1.4.3+ds-2.1+b1 [206 kB]
Get: 446 http://deb.debian.org/debian trixie/main i386 libmp3lame0 i386 3.100-6+b3 [362 kB]
Get: 447 http://deb.debian.org/debian trixie/main i386 libmpg123-0t64 i386 1.32.9-1 [152 kB]
Get: 448 http://deb.debian.org/debian trixie/main i386 libvorbis0a i386 1.3.7-2+b1 [89.6 kB]
Get: 449 http://deb.debian.org/debian trixie/main i386 libvorbisenc2 i386 1.3.7-2+b1 [69.9 kB]
Get: 450 http://deb.debian.org/debian trixie/main i386 libsndfile1 i386 1.2.2-1+b3 [221 kB]
Get: 451 http://deb.debian.org/debian trixie/main i386 libspqr4 i386 1:7.8.3+dfsg-2 [167 kB]
Get: 452 http://deb.debian.org/debian trixie/main i386 libumfpack6 i386 1:7.8.3+dfsg-2 [323 kB]
Get: 453 http://deb.debian.org/debian trixie/main i386 libtext-unidecode-perl all 1.30-3 [101 kB]
Get: 454 http://deb.debian.org/debian trixie/main i386 texinfo-lib i386 7.1.1-1+b1 [236 kB]
Get: 455 http://deb.debian.org/debian trixie/main i386 tex-common all 6.18 [32.5 kB]
Get: 456 http://deb.debian.org/debian trixie/main i386 texinfo all 7.1.1-1 [1753 kB]
Get: 457 http://deb.debian.org/debian trixie/main i386 octave-common all 9.2.0-3 [6591 kB]
Get: 458 http://deb.debian.org/debian trixie/main i386 octave i386 9.2.0-3+b1 [10.2 MB]
Get: 459 http://deb.debian.org/debian trixie/main i386 libncurses6 i386 6.5-2+b1 [112 kB]
Get: 460 http://deb.debian.org/debian trixie/main i386 libncurses-dev i386 6.5-2+b1 [505 kB]
Get: 461 http://deb.debian.org/debian trixie/main i386 libreadline-dev i386 8.2-5 [166 kB]
Get: 462 http://deb.debian.org/debian trixie/main i386 libhdf5-fortran-102t64 i386 1.10.10+repack-4 [98.1 kB]
Get: 463 http://deb.debian.org/debian trixie/main i386 libhdf5-hl-100t64 i386 1.10.10+repack-4 [76.0 kB]
Get: 464 http://deb.debian.org/debian trixie/main i386 libhdf5-hl-fortran-100t64 i386 1.10.10+repack-4 [41.9 kB]
Get: 465 http://deb.debian.org/debian trixie/main i386 libhdf5-cpp-103-1t64 i386 1.10.10+repack-4 [137 kB]
Get: 466 http://deb.debian.org/debian trixie/main i386 libhdf5-hl-cpp-100t64 i386 1.10.10+repack-4 [21.9 kB]
Get: 467 http://deb.debian.org/debian trixie/main i386 zlib1g-dev i386 1:1.3.dfsg+really1.3.1-1+b1 [916 kB]
Get: 468 http://deb.debian.org/debian trixie/main i386 libjpeg62-turbo-dev i386 1:2.1.5-3+b1 [305 kB]
Get: 469 http://deb.debian.org/debian trixie/main i386 libjpeg-dev i386 1:2.1.5-3+b1 [72.2 kB]
Get: 470 http://deb.debian.org/debian trixie/main i386 libaec-dev i386 1.1.3-1+b1 [23.0 kB]
Get: 471 http://deb.debian.org/debian trixie/main i386 libbrotli-dev i386 1.1.0-2+b6 [313 kB]
Get: 472 http://deb.debian.org/debian trixie/main i386 libidn2-dev i386 2.3.7-2+b1 [126 kB]
Get: 473 http://deb.debian.org/debian trixie/main i386 libgssrpc4t64 i386 1.21.3-3 [63.1 kB]
Get: 474 http://deb.debian.org/debian trixie/main i386 libkadm5clnt-mit12 i386 1.21.3-3 [43.2 kB]
Get: 475 http://deb.debian.org/debian trixie/main i386 libkdb5-10t64 i386 1.21.3-3 [45.1 kB]
Get: 476 http://deb.debian.org/debian trixie/main i386 libkadm5srv-mit12 i386 1.21.3-3 [57.0 kB]
Get: 477 http://deb.debian.org/debian trixie/main i386 krb5-multidev i386 1.21.3-3 [126 kB]
Get: 478 http://deb.debian.org/debian trixie/main i386 libkrb5-dev i386 1.21.3-3 [15.7 kB]
Get: 479 http://deb.debian.org/debian trixie/main i386 libldap-dev i386 2.5.18+dfsg-3+b1 [324 kB]
Get: 480 http://deb.debian.org/debian trixie/main i386 libpkgconf3 i386 1.8.1-4 [38.4 kB]
Get: 481 http://deb.debian.org/debian trixie/main i386 pkgconf-bin i386 1.8.1-4 [30.6 kB]
Get: 482 http://deb.debian.org/debian trixie/main i386 pkgconf i386 1.8.1-4 [26.2 kB]
Get: 483 http://deb.debian.org/debian trixie/main i386 libnghttp2-dev i386 1.64.0-1 [123 kB]
Get: 484 http://deb.debian.org/debian trixie/main i386 libpsl-dev i386 0.21.2-1.1+b1 [78.4 kB]
Get: 485 http://deb.debian.org/debian trixie/main i386 libgmpxx4ldbl i386 2:6.3.0+dfsg-2+b2 [330 kB]
Get: 486 http://deb.debian.org/debian trixie/main i386 libgmp-dev i386 2:6.3.0+dfsg-2+b2 [661 kB]
Get: 487 http://deb.debian.org/debian trixie/main i386 libevent-2.1-7t64 i386 2.1.12-stable-10+b1 [195 kB]
Get: 488 http://deb.debian.org/debian trixie/main i386 libunbound8 i386 1.22.0-1 [632 kB]
Get: 489 http://deb.debian.org/debian trixie/main i386 libgnutls-dane0t64 i386 3.8.8-2 [447 kB]
Get: 490 http://deb.debian.org/debian trixie/main i386 libgnutls-openssl27t64 i386 3.8.8-2 [447 kB]
Get: 491 http://deb.debian.org/debian trixie/main i386 libp11-kit-dev i386 0.25.5-2+b1 [208 kB]
Get: 492 http://deb.debian.org/debian trixie/main i386 libtasn1-6-dev i386 4.19.0-3+b3 [102 kB]
Get: 493 http://deb.debian.org/debian trixie/main i386 nettle-dev i386 3.10-1+b1 [1334 kB]
Get: 494 http://deb.debian.org/debian trixie/main i386 libgnutls28-dev i386 3.8.8-2 [1453 kB]
Get: 495 http://deb.debian.org/debian trixie/main i386 librtmp-dev i386 2.4+20151223.gitfa8646d.1-2+b5 [72.3 kB]
Get: 496 http://deb.debian.org/debian trixie/main i386 libssl-dev i386 3.3.2-2 [2790 kB]
Get: 497 http://deb.debian.org/debian trixie/main i386 libssh2-1-dev i386 1.11.1-1 [407 kB]
Get: 498 http://deb.debian.org/debian trixie/main i386 libzstd-dev i386 1.5.6+dfsg-1+b1 [355 kB]
Get: 499 http://deb.debian.org/debian trixie/main i386 libcurl4-openssl-dev i386 8.11.0-1 [520 kB]
Get: 500 http://deb.debian.org/debian trixie/main i386 hdf5-helpers i386 1.10.10+repack-4 [23.5 kB]
Get: 501 http://deb.debian.org/debian trixie/main i386 libhdf5-dev i386 1.10.10+repack-4 [2783 kB]
Get: 502 http://deb.debian.org/debian trixie/main i386 xorg-sgml-doctools all 1:1.11-1.1 [22.1 kB]
Get: 503 http://deb.debian.org/debian trixie/main i386 x11proto-dev all 2024.1-1 [603 kB]
Get: 504 http://deb.debian.org/debian trixie/main i386 libxau-dev i386 1:1.0.11-1 [24.0 kB]
Get: 505 http://deb.debian.org/debian trixie/main i386 libxdmcp-dev i386 1:1.1.5-1 [45.0 kB]
Get: 506 http://deb.debian.org/debian trixie/main i386 xtrans-dev all 1.4.0-1 [98.7 kB]
Get: 507 http://deb.debian.org/debian trixie/main i386 libxcb1-dev i386 1.17.0-2+b1 [186 kB]
Get: 508 http://deb.debian.org/debian trixie/main i386 libx11-dev i386 2:1.8.10-2 [917 kB]
Get: 509 http://deb.debian.org/debian trixie/main i386 libglx-dev i386 1.7.0-1+b2 [15.8 kB]
Get: 510 http://deb.debian.org/debian trixie/main i386 libgl-dev i386 1.7.0-1+b2 [101 kB]
Get: 511 http://deb.debian.org/debian trixie/main i386 libblas-dev i386 3.12.0-4 [155 kB]
Get: 512 http://deb.debian.org/debian trixie/main i386 liblapack-dev i386 3.12.0-4 [4452 kB]
Get: 513 http://deb.debian.org/debian trixie/main i386 libfftw3-long3 i386 3.3.10-1+b3 [340 kB]
Get: 514 http://deb.debian.org/debian trixie/main i386 libfftw3-quad3 i386 3.3.10-1+b3 [1798 kB]
Get: 515 http://deb.debian.org/debian trixie/main i386 libfftw3-bin i386 3.3.10-1+b3 [49.8 kB]
Get: 516 http://deb.debian.org/debian trixie/main i386 libfftw3-dev i386 3.3.10-1+b3 [3199 kB]
Get: 517 http://deb.debian.org/debian trixie/main i386 libgfortran-14-dev i386 14.2.0-8 [800 kB]
Get: 518 http://deb.debian.org/debian trixie/main i386 gfortran-14-i686-linux-gnu i386 14.2.0-8 [12.5 MB]
Get: 519 http://deb.debian.org/debian trixie/main i386 gfortran-14 i386 14.2.0-8 [12.2 kB]
Get: 520 http://deb.debian.org/debian trixie/main i386 gfortran-i686-linux-gnu i386 4:14.2.0-1 [1280 B]
Get: 521 http://deb.debian.org/debian trixie/main i386 gfortran i386 4:14.2.0-1 [1432 B]
Get: 522 http://deb.debian.org/debian trixie/main i386 octave-dev i386 9.2.0-3+b1 [1004 kB]
Get: 523 http://deb.debian.org/debian trixie/main i386 dh-octave all 1.8.0 [22.7 kB]
Get: 524 http://deb.debian.org/debian trixie/main i386 octave-io i386 2.6.4-3+b2 [204 kB]
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Adding 'diversion of /usr/bin/podselect to /usr/bin/podselect.bundled by libpod-parser-perl'
Adding 'diversion of /usr/share/man/man1/podselect.1.gz to /usr/share/man/man1/podselect.bundled.1.gz by libpod-parser-perl'
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Adding 'diversion of /lib/i386-linux-gnu/libhistory.so.8 to /lib/i386-linux-gnu/libhistory.so.8.usr-is-merged by libreadline8t64'
Adding 'diversion of /lib/i386-linux-gnu/libhistory.so.8.2 to /lib/i386-linux-gnu/libhistory.so.8.2.usr-is-merged by libreadline8t64'
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update-alternatives: using /usr/bin/lzip.plzip to provide /usr/bin/lzip (lzip) in auto mode
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invoke-rc.d: could not determine current runlevel
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Setting up libpcre2-16-0:i386 (10.44-4) ...
Setting up libaec0:i386 (1.1.3-1+b1) ...
Setting up libnet-netmask-perl (2.0002-2) ...
Setting up libopengl0:i386 (1.7.0-1+b2) ...
Setting up libsub-install-perl (0.929-1) ...
Setting up libelf1t64:i386 (0.192-4) ...
Setting up libxcb-util1:i386 (0.4.0-1+b2) ...
Setting up libpsl-dev:i386 (0.21.2-1.1+b1) ...
Setting up libindirect-perl (0.39-2+b4) ...
Setting up libxcb-xkb1:i386 (1.17.0-2+b1) ...
Setting up libxcb-image0:i386 (0.4.0-2+b2) ...
Setting up libkrb5support0:i386 (1.21.3-3) ...
Setting up libnumber-compare-perl (0.03-3) ...
Setting up libsasl2-modules-db:i386 (2.1.28+dfsg1-8) ...
Setting up libxcb-present0:i386 (1.17.0-2+b1) ...
Setting up liberror-perl (0.17029-2) ...
Setting up libasound2-data (1.2.12-1) ...
Setting up libjson-maybexs-perl (1.004008-1) ...
Setting up libxml-sax-base-perl (1.09-3) ...
Setting up libio-string-perl (1.08-4) ...
Setting up libboolean-perl (0.46-3) ...
Setting up libnetaddr-ip-perl (4.079+dfsg-2+b4) ...
Setting up xtrans-dev (1.4.0-1) ...
Setting up autotools-dev (20220109.1) ...
Setting up libz3-4:i386 (4.13.3-1) ...
Setting up libglib2.0-0t64:i386 (2.82.2-3) ...
No schema files found: doing nothing.
Setting up libblas3:i386 (3.12.0-4) ...
update-alternatives: using /usr/lib/i386-linux-gnu/blas/libblas.so.3 to provide /usr/lib/i386-linux-gnu/libblas.so.3 (libblas.so.3-i386-linux-gnu) in auto mode
Setting up libclass-data-inheritable-perl (0.10-1) ...
Setting up libunbound8:i386 (1.22.0-1) ...
Setting up libpkgconf3:i386 (1.8.1-4) ...
Setting up libgmpxx4ldbl:i386 (2:6.3.0+dfsg-2+b2) ...
Setting up libalgorithm-c3-perl (0.11-2) ...
Setting up libasound2t64:i386 (1.2.12-1+b1) ...
Setting up liblog-log4perl-perl (1.57-1) ...
Setting up libtext-reform-perl (1.20-5) ...
Setting up libjpeg62-turbo:i386 (1:2.1.5-3+b1) ...
Setting up libgnutls-dane0t64:i386 (3.8.8-2) ...
Setting up libx11-data (2:1.8.10-2) ...
Setting up libsvtav1enc2:i386 (2.2.1+dfsg-2) ...
Setting up libjpeg62-turbo-dev:i386 (1:2.1.5-3+b1) ...
Setting up libfile-find-rule-perl (0.34-3) ...
Setting up libxcb-sync1:i386 (1.17.0-2+b1) ...
Setting up librtmp1:i386 (2.4+20151223.gitfa8646d.1-2+b5) ...
Setting up libipc-system-simple-perl (1.30-2) ...
Setting up libio-tiecombine-perl (1.005-3) ...
Setting up libnet-domain-tld-perl (1.75-4) ...
Setting up libperlio-utf8-strict-perl (0.010-1+b3) ...
Setting up aglfn (1.7+git20191031.4036a9c-2) ...
Setting up libxcb-cursor0:i386 (0.1.4-1+b2) ...
Setting up libavahi-common-data:i386 (0.8-13+b3) ...
Setting up libncurses6:i386 (6.5-2+b1) ...
Setting up libdbus-1-3:i386 (1.14.10-6) ...
Setting up libfftw3-quad3:i386 (3.3.10-1+b3) ...
Setting up libfribidi0:i386 (1.0.15-1) ...
Setting up libopus0:i386 (1.5.2-2) ...
Setting up t1utils (1.41-4) ...
Setting up diffstat (1.67-1) ...
Setting up libimagequant0:i386 (2.18.0-1+b2) ...
Setting up comerr-dev:i386 (2.1-1.47.1-1+b1) ...
Setting up fonts-dejavu-mono (2.37-8) ...
Setting up libssl-dev:i386 (3.3.2-2) ...
Setting up libpng16-16t64:i386 (1.6.44-2) ...
Setting up libimath-3-1-29t64:i386 (3.1.12-1+b1) ...
Setting up libvorbis0a:i386 (1.3.7-2+b1) ...
Setting up libvariable-magic-perl (0.64-1+b1) ...
Setting up libio-html-perl (1.004-3) ...
Setting up libtext-template-perl (1.61-1) ...
Setting up libpod-parser-perl (1.67-1) ...
Setting up autopoint (0.22.5-2) ...
Setting up libb-hooks-op-check-perl:i386 (0.22-3+b2) ...
Setting up fonts-dejavu-core (2.37-8) ...
Setting up liblist-moreutils-xs-perl (0.430-4+b2) ...
Setting up pkgconf-bin (1.8.1-4) ...
Setting up libsensors5:i386 (1:3.6.0-10+b1) ...
Setting up libk5crypto3:i386 (1.21.3-3) ...
Setting up libqscintilla2-qt6-l10n (2.14.1+dfsg-1) ...
Setting up libltdl7:i386 (2.4.7-8) ...
Setting up libidn2-dev:i386 (2.3.7-2+b1) ...
Setting up libfftw3-double3:i386 (3.3.10-1+b3) ...
Setting up libglapi-mesa:i386 (24.2.8-1) ...
Setting up libparams-util-perl (1.102-3+b1) ...
Setting up libsasl2-2:i386 (2.1.28+dfsg1-8) ...
Setting up libgfortran5:i386 (14.2.0-8) ...
Setting up libvulkan1:i386 (1.3.296.0-1) ...
Setting up libtime-duration-perl (1.21-2) ...
Setting up autoconf (2.72-3) ...
Setting up libtext-xslate-perl:i386 (3.5.9-2+b1) ...
Setting up libnghttp3-9:i386 (1.6.0-2) ...
Setting up libsub-exporter-progressive-perl (0.001013-3) ...
Setting up libwebp7:i386 (1.4.0-0.1+b1) ...
Setting up libarray-intspan-perl (2.004-2) ...
Setting up libcapture-tiny-perl (0.48-2) ...
Setting up libtimedate-perl (2.3300-2) ...
Setting up libexporter-lite-perl (0.09-2) ...
Setting up libxcb-dri2-0:i386 (1.17.0-2+b1) ...
Setting up libsub-name-perl:i386 (0.27-1+b3) ...
Setting up libgif7:i386 (5.2.2-1+b1) ...
Setting up zlib1g-dev:i386 (1:1.3.dfsg+really1.3.1-1+b1) ...
Setting up dwz (0.15-1+b1) ...
Setting up libdata-validate-domain-perl (0.15-1) ...
Setting up libproc-processtable-perl:i386 (0.636-1+b3) ...
Setting up libparse-recdescent-perl (1.967015+dfsg-4) ...
Setting up libdav1d7:i386 (1.5.0-1+b1) ...
Setting up libmtdev1t64:i386 (1.1.6-1.2+b1) ...
Setting up sensible-utils (0.0.24) ...
Setting up libduktape207:i386 (2.7.0-2+b2) ...
Setting up libxshmfence1:i386 (1.3-1+b2) ...
Setting up libtiff6:i386 (4.5.1+git230720-5) ...
Setting up libxcb-randr0:i386 (1.17.0-2+b1) ...
Setting up librav1e0.7:i386 (0.7.1-7+b2) ...
Setting up libpath-tiny-perl (0.146-1) ...
Setting up libuchardet0:i386 (0.0.8-1+b2) ...
Setting up lzop (1.04-2) ...
Setting up libassuan9:i386 (3.0.1-2) ...
Setting up libjson-perl (4.10000-1) ...
Setting up liblog-any-adapter-screen-perl (0.141-1) ...
Setting up librole-tiny-perl (2.002004-1) ...
Setting up libipc-run3-perl (0.049-1) ...
Setting up libmd4c0:i386 (0.5.2-2+b1) ...
Setting up libregexp-wildcards-perl (1.05-3) ...
Setting up libmousex-strictconstructor-perl (0.02-3) ...
Setting up libfile-sharedir-perl (1.118-3) ...
Setting up libsub-uplevel-perl (0.2800-3) ...
Setting up libsuitesparseconfig7:i386 (1:7.8.3+dfsg-2) ...
Setting up liblua5.4-0:i386 (5.4.7-1+b1) ...
Setting up libx11-6:i386 (2:1.8.10-2) ...
Setting up libaliased-perl (0.34-3) ...
Setting up libthai-data (0.1.29-2) ...
Setting up xorg-sgml-doctools (1:1.11-1.1) ...
Setting up netbase (6.4) ...
Setting up libabsl20230802:i386 (20230802.1-4) ...
Setting up libngtcp2-16:i386 (1.9.1-1) ...
Setting up libstrictures-perl (2.000006-1) ...
Setting up libsub-quote-perl (2.006008-1) ...
Setting up libdevel-stacktrace-perl (2.0500-1) ...
Setting up libclass-xsaccessor-perl (1.19-4+b4) ...
Setting up libtext-autoformat-perl (1.750000-2) ...
Setting up libkrb5-3:i386 (1.21.3-3) ...
Setting up libglu1-mesa:i386 (9.0.2-1.1+b2) ...
Setting up libflac12t64:i386 (1.4.3+ds-2.1+b1) ...
Setting up libtoml-tiny-perl (0.18-1) ...
Setting up libstemmer0d:i386 (2.2.0-4+b2) ...
Setting up libsort-versions-perl (1.62-3) ...
Setting up libssh2-1t64:i386 (1.11.1-1) ...
Setting up libexporter-tiny-perl (1.006002-1) ...
Setting up libterm-readkey-perl (2.38-2+b4) ...
Setting up libtext-unidecode-perl (1.30-3) ...
Setting up libde265-0:i386 (1.0.15-1+b2) ...
Setting up libfont-ttf-perl (1.06-2) ...
Setting up libfile-homedir-perl (1.006-2) ...
Setting up libsamplerate0:i386 (0.2.2-4+b2) ...
Setting up libtasn1-6-dev:i386 (4.19.0-3+b3) ...
Setting up openssl (3.3.2-2) ...
Setting up libwebpmux3:i386 (1.4.0-0.1+b1) ...
Setting up libtext-levenshteinxs-perl (0.03-5+b4) ...
Setting up libperlio-gzip-perl (0.20-1+b4) ...
Setting up libdrm-common (2.4.123-1) ...
Setting up libyuv0:i386 (0.0.1898.20241125-1) ...
Setting up libevdev2:i386 (1.13.3+dfsg-1) ...
Setting up readline-common (8.2-5) ...
Setting up libxml2:i386 (2.12.7+dfsg+really2.9.14-0.2+b1) ...
Setting up libhtml-html5-entities-perl (0.004-3) ...
Setting up libtext-levenshtein-damerau-perl (0.41-3) ...
Setting up libsereal-decoder-perl (5.004+ds-1+b3) ...
Setting up libmarkdown2:i386 (2.2.7-2.1) ...
Setting up liburi-perl (5.30-1) ...
Setting up iso-codes (4.17.0-1) ...
Setting up libnet-ipv6addr-perl (1.02-1) ...
Setting up libbrotli-dev:i386 (1.1.0-2+b6) ...
Setting up libgudev-1.0-0:i386 (238-5+b1) ...
Setting up libngtcp2-crypto-gnutls8:i386 (1.9.1-1) ...
Setting up libp11-kit-dev:i386 (0.25.5-2+b1) ...
Setting up libmp3lame0:i386 (3.100-6+b3) ...
Setting up libblas-dev:i386 (3.12.0-4) ...
update-alternatives: using /usr/lib/i386-linux-gnu/blas/libblas.so to provide /usr/lib/i386-linux-gnu/libblas.so (libblas.so-i386-linux-gnu) in auto mode
Setting up libsz2:i386 (1.1.3-1+b1) ...
Setting up libvorbisenc2:i386 (1.3.7-2+b1) ...
Setting up libdata-validate-ip-perl (0.31-1) ...
Setting up libwacom-common (2.13.0-1) ...
Setting up libmousex-nativetraits-perl (1.09-3) ...
Setting up libemail-address-xs-perl (1.05-1+b4) ...
Setting up libxkbcommon0:i386 (1.6.0-1+b2) ...
Setting up libwayland-client0:i386 (1.23.0-1+b1) ...
Setting up libnet-ssleay-perl:i386 (1.94-2) ...
Setting up automake (1:1.16.5-1.3) ...
update-alternatives: using /usr/bin/automake-1.16 to provide /usr/bin/automake (automake) in auto mode
Setting up libgfortran-14-dev:i386 (14.2.0-8) ...
Setting up x11proto-dev (2024.1-1) ...
Setting up libfile-stripnondeterminism-perl (1.14.0-1) ...
Setting up libxcb-dri3-0:i386 (1.17.0-2+b1) ...
Setting up gnuplot-data (6.0.0+dfsg1-3) ...
Setting up libllvm19:i386 (1:19.1.4-1) ...
Setting up libx11-xcb1:i386 (2:1.8.10-2) ...
Setting up libice6:i386 (2:1.1.1-1) ...
Setting up libhttp-date-perl (6.06-1) ...
Setting up liblapack3:i386 (3.12.0-4) ...
update-alternatives: using /usr/lib/i386-linux-gnu/lapack/liblapack.so.3 to provide /usr/lib/i386-linux-gnu/liblapack.so.3 (liblapack.so.3-i386-linux-gnu) in auto mode
Setting up libncurses-dev:i386 (6.5-2+b1) ...
Setting up libfile-basedir-perl (0.09-2) ...
Setting up gettext (0.22.5-2) ...
Setting up libarpack2t64:i386 (3.9.1-1.1+b1) ...
Setting up libgmp-dev:i386 (2:6.3.0+dfsg-2+b2) ...
Setting up libamd3:i386 (1:7.8.3+dfsg-2) ...
Setting up libfile-listing-perl (6.16-1) ...
Setting up libxau-dev:i386 (1:1.0.11-1) ...
Setting up libxpm4:i386 (1:3.5.17-1+b2) ...
Setting up nettle-dev:i386 (3.10-1+b1) ...
Setting up libxrender1:i386 (1:0.9.10-1.1+b2) ...
Setting up libtool (2.4.7-8) ...
Setting up libcolamd3:i386 (1:7.8.3+dfsg-2) ...
Setting up libfftw3-bin (3.3.10-1+b3) ...
Setting up libwacom9:i386 (2.13.0-1) ...
Setting up fontconfig-config (2.15.0-1.1+b1) ...
Setting up liblist-moreutils-perl (0.430-2) ...
Setting up libxmlb2:i386 (0.3.21-1) ...
Setting up libpod-constants-perl (0.19-2) ...
Setting up libhash-merge-perl (0.302-1) ...
Setting up libsoftware-copyright-perl (0.012-2) ...
Setting up libaec-dev:i386 (1.1.3-1+b1) ...
Setting up libavahi-common3:i386 (0.8-13+b3) ...
Setting up libcxsparse4:i386 (1:7.8.3+dfsg-2) ...
Setting up libjpeg-dev:i386 (1:2.1.5-3+b1) ...
Setting up libxext6:i386 (2:1.3.4-1+b2) ...
Setting up libnet-http-perl (6.23-1) ...
Setting up libpath-iterator-rule-perl (1.015-2) ...
Setting up libtext-markdown-discount-perl (0.16-1+b3) ...
Setting up libexception-class-perl (1.45-1) ...
Setting up libclass-c3-perl (0.35-2) ...
Setting up libqrupdate1:i386 (1.1.5-1) ...
Setting up libdevel-callchecker-perl:i386 (0.009-1+b1) ...
Setting up libldap-2.5-0:i386 (2.5.18+dfsg-3+b1) ...
Setting up libcamd3:i386 (1:7.8.3+dfsg-2) ...
Setting up pkgconf:i386 (1.8.1-4) ...
Setting up libxxf86vm1:i386 (1:1.1.4-1+b3) ...
Setting up libinput-bin (1.26.2-1) ...
Setting up libxs-parse-sublike-perl:i386 (0.30-1) ...
Setting up intltool-debian (0.35.0+20060710.6) ...
Setting up dh-autoreconf (20) ...
Setting up patchutils (0.4.2-1) ...
Setting up libthai0:i386 (0.1.29-2+b1) ...
Setting up ca-certificates (20240203) ...
Updating certificates in /etc/ssl/certs...
146 added, 0 removed; done.
Setting up libxdmcp-dev:i386 (1:1.1.5-1) ...
Setting up libdata-validate-uri-perl (0.07-3) ...
Setting up libxs-parse-keyword-perl (0.46-1+b1) ...
Setting up libtest-exception-perl (0.43-3) ...
Setting up libfreetype6:i386 (2.13.3+dfsg-1) ...
Setting up libglpk40:i386 (5.0-1+b2) ...
Setting up libxfixes3:i386 (1:6.0.0-2+b2) ...
Setting up libstring-copyright-perl (0.003014-1) ...
Setting up libldap-dev:i386 (2.5.18+dfsg-3+b1) ...
Setting up libopenexr-3-1-30:i386 (3.1.5-5.1+b3) ...
Setting up shared-mime-info (2.4-5+b1) ...
Setting up libxinerama1:i386 (2:1.1.4-3+b2) ...
Setting up libxkbcommon-x11-0:i386 (1.6.0-1+b2) ...
Setting up liblapack-dev:i386 (3.12.0-4) ...
update-alternatives: using /usr/lib/i386-linux-gnu/lapack/liblapack.so to provide /usr/lib/i386-linux-gnu/liblapack.so (liblapack.so-i386-linux-gnu) in auto mode
Setting up gfortran-14-i686-linux-gnu (14.2.0-8) ...
Setting up libgssapi-krb5-2:i386 (1.21.3-3) ...
Setting up libdata-optlist-perl (0.114-1) ...
Setting up libgav1-1:i386 (0.19.0-2+b2) ...
Setting up ucf (3.0043+nmu1) ...
Setting up libssh2-1-dev:i386 (1.11.1-1) ...
Setting up libccolamd3:i386 (1:7.8.3+dfsg-2) ...
Setting up libreadline8t64:i386 (8.2-5) ...
Setting up dh-strip-nondeterminism (1.14.0-1) ...
Setting up libwww-robotrules-perl (6.02-1) ...
Setting up libsyntax-keyword-try-perl (0.30-1+b1) ...
Setting up libjack-jackd2-0:i386 (1.9.22~dfsg-3) ...
Setting up libdrm2:i386 (2.4.123-1) ...
Setting up groff-base (1.23.0-5) ...
Setting up libhtml-parser-perl:i386 (3.83-1+b1) ...
Setting up gpgconf (2.2.45-2) ...
Setting up libharfbuzz0b:i386 (10.0.1-1) ...
Setting up libfontconfig1:i386 (2.15.0-1.1+b1) ...
Setting up libsndfile1:i386 (1.2.2-1+b3) ...
Setting up libmro-compat-perl (0.15-2) ...
Setting up gfortran-14 (14.2.0-8) ...
Setting up libsm6:i386 (2:1.2.4-1) ...
Setting up libfftw3-dev:i386 (3.3.10-1+b3) ...
Setting up libavahi-client3:i386 (0.8-13+b3) ...
Setting up libio-socket-ssl-perl (2.089-1) ...
Setting up gpg (2.2.45-2) ...
Setting up libsub-exporter-perl (0.990-1) ...
Setting up libhttp-message-perl (7.00-2) ...
Setting up libdrm-amdgpu1:i386 (2.4.123-1) ...
Setting up libhtml-form-perl (6.12-1) ...
Setting up libiterator-perl (0.03+ds1-2) ...
Setting up libgnutls28-dev:i386 (3.8.8-2) ...
Setting up libinput10:i386 (1.26.2-1) ...
Setting up libnghttp2-dev:i386 (1.64.0-1) ...
Setting up libportaudio2:i386 (19.6.0-1.2+b3) ...
Setting up libqt6core6t64:i386 (6.7.2+dfsg-4) ...
Setting up libhttp-negotiate-perl (6.01-2) ...
Setting up fontconfig (2.15.0-1.1+b1) ...
Regenerating fonts cache... done.
Setting up libavif16:i386 (1.1.1-1) ...
Setting up libcarp-assert-more-perl (2.5.0-1) ...
Setting up libcholmod5:i386 (1:7.8.3+dfsg-2) ...
Setting up libxft2:i386 (2.3.6-1+b2) ...
Setting up libfeature-compat-try-perl (0.05-1) ...
Setting up libxcb1-dev:i386 (1.17.0-2+b1) ...
Setting up libiterator-util-perl (0.02+ds1-2) ...
Setting up libcurl4t64:i386 (8.11.0-1) ...
Setting up libhttp-cookies-perl (6.11-1) ...
Setting up libspqr4:i386 (1:7.8.3+dfsg-2) ...
Setting up libdrm-radeon1:i386 (2.4.123-1) ...
Setting up po-debconf (1.0.21+nmu1) ...
Setting up libhtml-tree-perl (5.07-3) ...
Setting up libxcursor1:i386 (1:1.2.2-1+b1) ...
Setting up libparams-classify-perl:i386 (0.015-2+b4) ...
Setting up libpango-1.0-0:i386 (1.54.0+ds-3) ...
Setting up libdrm-intel1:i386 (2.4.123-1) ...
Setting up libcgi-pm-perl (4.66-1) ...
Setting up libx11-dev:i386 (2:1.8.10-2) ...
Setting up libcurl3t64-gnutls:i386 (8.11.0-1) ...
Setting up libjxl0.9:i386 (0.9.2-10+b1) ...
Setting up libappstream5:i386 (1.0.3-1+b1) ...
Setting up libreadline-dev:i386 (8.2-5) ...
Setting up man-db (2.13.0-1) ...
Not building database; man-db/auto-update is not 'true'.
Setting up libxml-sax-perl (1.02+dfsg-4) ...
update-perl-sax-parsers: Registering Perl SAX parser XML::SAX::PurePerl with priority 10...
update-perl-sax-parsers: Updating overall Perl SAX parser modules info file...

Creating config file /etc/perl/XML/SAX/ParserDetails.ini with new version
Setting up libcairo2:i386 (1.18.2-2) ...
Setting up libobject-pad-perl (0.815-1) ...
Setting up tex-common (6.18) ...
update-language: texlive-base not installed and configured, doing nothing!
Setting up appstream (1.0.3-1+b1) ...
? Metadata cache was updated successfully.
Setting up libnet-smtp-ssl-perl (1.04-2) ...
Setting up libqt6xml6:i386 (6.7.2+dfsg-4) ...
Setting up gfortran-i686-linux-gnu (4:14.2.0-1) ...
Setting up libqt6sql6:i386 (6.7.2+dfsg-4) ...
Setting up libmodule-runtime-perl (0.016-2) ...
Setting up libmailtools-perl (2.22-1) ...
Setting up libgssrpc4t64:i386 (1.21.3-3) ...
Setting up libraqm0:i386 (0.10.1-1+b2) ...
Setting up libconfig-model-perl (2.155-1) ...
Setting up librtmp-dev:i386 (2.4+20151223.gitfa8646d.1-2+b5) ...
Setting up libxml-libxml-perl (2.0207+dfsg+really+2.0134-5+b1) ...
update-perl-sax-parsers: Registering Perl SAX parser XML::LibXML::SAX::Parser with priority 50...
update-perl-sax-parsers: Registering Perl SAX parser XML::LibXML::SAX with priority 50...
update-perl-sax-parsers: Updating overall Perl SAX parser modules info file...
Replacing config file /etc/perl/XML/SAX/ParserDetails.ini with new version
Setting up libumfpack6:i386 (1:7.8.3+dfsg-2) ...
Setting up libconst-fast-perl (0.014-2) ...
Setting up libdata-section-perl (0.200008-1) ...
Setting up libqt6core5compat6:i386 (6.7.2-3) ...
Setting up libpangoft2-1.0-0:i386 (1.54.0+ds-3) ...
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update-alternatives: using /usr/bin/gfortran to provide /usr/bin/f95 (f95) in auto mode
update-alternatives: using /usr/bin/gfortran to provide /usr/bin/f77 (f77) in auto mode
Setting up libsoftware-license-perl (0.104006-1) ...
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Setting up texinfo (7.1.1-1) ...
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Setting up krb5-multidev:i386 (1.21.3-3) ...
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Setting up libcurl4-openssl-dev:i386 (8.11.0-1) ...
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update-alternatives: using /usr/lib/i386-linux-gnu/pkgconfig/hdf5-serial.pc to provide /usr/lib/i386-linux-gnu/pkgconfig/hdf5.pc (hdf5.pc) in auto mode
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update-alternatives: using /usr/bin/gnuplot-nox to provide /usr/bin/gnuplot (gnuplot) in auto mode
Setting up libgraphicsmagick-q16-3t64 (1.4+really1.3.45-1+b1) ...
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Setting up octave (9.2.0-3+b1) ...
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Setting up dh-octave (1.8.0) ...
Processing triggers for libc-bin (2.40-3) ...
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Updating certificates in /etc/ssl/certs...
0 added, 0 removed; done.
Running hooks in /etc/ca-certificates/update.d...
done.
Reading package lists...
Building dependency tree...
Reading state information...
Reading extended state information...
Initializing package states...
Writing extended state information...
Building tag database...
 -> Finished parsing the build-deps
I: Building the package
I: Running cd /build/reproducible-path/octave-statistics-1.7.0/ && env PATH="/usr/sbin:/usr/bin:/sbin:/bin:/usr/games" HOME="/nonexistent/first-build" dpkg-buildpackage -us -uc -b && env PATH="/usr/sbin:/usr/bin:/sbin:/bin:/usr/games" HOME="/nonexistent/first-build" dpkg-genchanges -S  > ../octave-statistics_1.7.0-2_source.changes
dpkg-buildpackage: info: source package octave-statistics
dpkg-buildpackage: info: source version 1.7.0-2
dpkg-buildpackage: info: source distribution unstable
dpkg-buildpackage: info: source changed by Sébastien Villemot <sebastien@debian.org>
 dpkg-source --before-build .
dpkg-buildpackage: info: host architecture i386
 debian/rules clean
dh clean --buildsystem=octave
   dh_auto_clean -O--buildsystem=octave
	dh_octave_clean
make[1]: Entering directory '/build/reproducible-path/octave-statistics-1.7.0'
make[1]: *** No rule to make target 'clean'.
make[1]: *** No rule to make target 'distclean'.
make[1]: Leaving directory '/build/reproducible-path/octave-statistics-1.7.0'
make[1]: Entering directory '/build/reproducible-path/octave-statistics-1.7.0/src'
make[1]: *** No rule to make target 'clean'.
make[1]: *** No rule to make target 'distclean'.
make[1]: Leaving directory '/build/reproducible-path/octave-statistics-1.7.0/src'
   dh_autoreconf_clean -O--buildsystem=octave
   dh_clean -O--buildsystem=octave
 debian/rules binary
dh binary --buildsystem=octave
   dh_update_autotools_config -O--buildsystem=octave
   dh_autoreconf -O--buildsystem=octave
   dh_octave_version -O--buildsystem=octave
Checking the Octave version... ok
   dh_auto_configure -O--buildsystem=octave
   dh_auto_build -O--buildsystem=octave
   dh_auto_test -O--buildsystem=octave
   create-stamp debian/debhelper-build-stamp
   dh_testroot -O--buildsystem=octave
   dh_prep -O--buildsystem=octave
   dh_auto_install -O--buildsystem=octave
	octave --no-gui --no-history --silent --no-init-file --no-window-system /usr/share/dh-octave/install-pkg.m /build/reproducible-path/octave-statistics-1.7.0/debian/tmp/usr/share/octave/packages /build/reproducible-path/octave-statistics-1.7.0/debian/tmp/usr/lib/i386-linux-gnu/octave/packages
make[1]: Entering directory '/build/reproducible-path/octave-statistics-1.7.0/src'
/usr/bin/mkoctfile --verbose       editDistance.cc
g++ -c -Wdate-time -D_FORTIFY_SOURCE=2 -fPIC -I/usr/include/octave-9.2.0/octave/.. -I/usr/include/octave-9.2.0/octave  -pthread -fopenmp -mieee-fp -g -O2 -ffile-prefix-map=/build/reproducible-path/octave-statistics-1.7.0=. -fstack-protector-strong -Wformat -Werror=format-security    editDistance.cc -o /tmp/oct-rILVic.o
g++ -I/usr/include/octave-9.2.0/octave/.. -I/usr/include/octave-9.2.0/octave  -pthread -fopenmp -mieee-fp -g -O2 -ffile-prefix-map=/build/reproducible-path/octave-statistics-1.7.0=. -fstack-protector-strong -Wformat -Werror=format-security  -o editDistance.oct  /tmp/oct-rILVic.o   -shared -Wl,-Bsymbolic -Wl,-z,relro  -flto=auto -ffat-lto-objects -Wl,-z,relro 
/usr/bin/mkoctfile --verbose       libsvmread.cc
g++ -c -Wdate-time -D_FORTIFY_SOURCE=2 -fPIC -I/usr/include/octave-9.2.0/octave/.. -I/usr/include/octave-9.2.0/octave  -pthread -fopenmp -mieee-fp -g -O2 -ffile-prefix-map=/build/reproducible-path/octave-statistics-1.7.0=. -fstack-protector-strong -Wformat -Werror=format-security    libsvmread.cc -o /tmp/oct-LtZqmy.o
g++ -I/usr/include/octave-9.2.0/octave/.. -I/usr/include/octave-9.2.0/octave  -pthread -fopenmp -mieee-fp -g -O2 -ffile-prefix-map=/build/reproducible-path/octave-statistics-1.7.0=. -fstack-protector-strong -Wformat -Werror=format-security  -o libsvmread.oct  /tmp/oct-LtZqmy.o   -shared -Wl,-Bsymbolic -Wl,-z,relro  -flto=auto -ffat-lto-objects -Wl,-z,relro 
/usr/bin/mkoctfile --verbose       libsvmwrite.cc
g++ -c -Wdate-time -D_FORTIFY_SOURCE=2 -fPIC -I/usr/include/octave-9.2.0/octave/.. -I/usr/include/octave-9.2.0/octave  -pthread -fopenmp -mieee-fp -g -O2 -ffile-prefix-map=/build/reproducible-path/octave-statistics-1.7.0=. -fstack-protector-strong -Wformat -Werror=format-security    libsvmwrite.cc -o /tmp/oct-bWe5As.o
libsvmwrite.cc: In function 'void write(std::string, ColumnVector, SparseMatrix)':
libsvmwrite.cc:75:25: warning: format '%lu' expects argument of type 'long unsigned int', but argument 3 has type 'size_t' {aka 'unsigned int'} [-Wformat=]
   75 |         fprintf(fp ," %lu:%g", (size_t)ir[k]+1, samples[k]);
      |                       ~~^      ~~~~~~~~~~~~~~~
      |                         |                   |
      |                         long unsigned int   size_t {aka unsigned int}
      |                       %u
g++ -I/usr/include/octave-9.2.0/octave/.. -I/usr/include/octave-9.2.0/octave  -pthread -fopenmp -mieee-fp -g -O2 -ffile-prefix-map=/build/reproducible-path/octave-statistics-1.7.0=. -fstack-protector-strong -Wformat -Werror=format-security  -o libsvmwrite.oct  /tmp/oct-bWe5As.o   -shared -Wl,-Bsymbolic -Wl,-z,relro  -flto=auto -ffat-lto-objects -Wl,-z,relro 
/usr/bin/mkoctfile --verbose       svmpredict.cc svm.cpp svm_model_octave.cc
g++ -c -Wdate-time -D_FORTIFY_SOURCE=2 -fPIC -I/usr/include/octave-9.2.0/octave/.. -I/usr/include/octave-9.2.0/octave  -pthread -fopenmp -mieee-fp -g -O2 -ffile-prefix-map=/build/reproducible-path/octave-statistics-1.7.0=. -fstack-protector-strong -Wformat -Werror=format-security    svmpredict.cc -o /tmp/oct-XGiFXP.o
g++ -c -Wdate-time -D_FORTIFY_SOURCE=2 -fPIC -I/usr/include/octave-9.2.0/octave/.. -I/usr/include/octave-9.2.0/octave  -pthread -fopenmp -mieee-fp -g -O2 -ffile-prefix-map=/build/reproducible-path/octave-statistics-1.7.0=. -fstack-protector-strong -Wformat -Werror=format-security    svm.cpp -o /tmp/oct-tSTdFZ.o
g++ -c -Wdate-time -D_FORTIFY_SOURCE=2 -fPIC -I/usr/include/octave-9.2.0/octave/.. -I/usr/include/octave-9.2.0/octave  -pthread -fopenmp -mieee-fp -g -O2 -ffile-prefix-map=/build/reproducible-path/octave-statistics-1.7.0=. -fstack-protector-strong -Wformat -Werror=format-security    svm_model_octave.cc -o /tmp/oct-maUU0s.o
g++ -I/usr/include/octave-9.2.0/octave/.. -I/usr/include/octave-9.2.0/octave  -pthread -fopenmp -mieee-fp -g -O2 -ffile-prefix-map=/build/reproducible-path/octave-statistics-1.7.0=. -fstack-protector-strong -Wformat -Werror=format-security  -o svmpredict.oct  /tmp/oct-XGiFXP.o /tmp/oct-tSTdFZ.o /tmp/oct-maUU0s.o   -shared -Wl,-Bsymbolic -Wl,-z,relro  -flto=auto -ffat-lto-objects -Wl,-z,relro 
/usr/bin/mkoctfile --verbose       svmtrain.cc svm.cpp svm_model_octave.cc
g++ -c -Wdate-time -D_FORTIFY_SOURCE=2 -fPIC -I/usr/include/octave-9.2.0/octave/.. -I/usr/include/octave-9.2.0/octave  -pthread -fopenmp -mieee-fp -g -O2 -ffile-prefix-map=/build/reproducible-path/octave-statistics-1.7.0=. -fstack-protector-strong -Wformat -Werror=format-security    svmtrain.cc -o /tmp/oct-E6UEZp.o
g++ -c -Wdate-time -D_FORTIFY_SOURCE=2 -fPIC -I/usr/include/octave-9.2.0/octave/.. -I/usr/include/octave-9.2.0/octave  -pthread -fopenmp -mieee-fp -g -O2 -ffile-prefix-map=/build/reproducible-path/octave-statistics-1.7.0=. -fstack-protector-strong -Wformat -Werror=format-security    svm.cpp -o /tmp/oct-NxMoDU.o
g++ -c -Wdate-time -D_FORTIFY_SOURCE=2 -fPIC -I/usr/include/octave-9.2.0/octave/.. -I/usr/include/octave-9.2.0/octave  -pthread -fopenmp -mieee-fp -g -O2 -ffile-prefix-map=/build/reproducible-path/octave-statistics-1.7.0=. -fstack-protector-strong -Wformat -Werror=format-security    svm_model_octave.cc -o /tmp/oct-yv6KMI.o
g++ -I/usr/include/octave-9.2.0/octave/.. -I/usr/include/octave-9.2.0/octave  -pthread -fopenmp -mieee-fp -g -O2 -ffile-prefix-map=/build/reproducible-path/octave-statistics-1.7.0=. -fstack-protector-strong -Wformat -Werror=format-security  -o svmtrain.oct  /tmp/oct-E6UEZp.o /tmp/oct-NxMoDU.o /tmp/oct-yv6KMI.o   -shared -Wl,-Bsymbolic -Wl,-z,relro  -flto=auto -ffat-lto-objects -Wl,-z,relro 
/usr/bin/mkoctfile --verbose       fcnntrain.cc
g++ -c -Wdate-time -D_FORTIFY_SOURCE=2 -fPIC -I/usr/include/octave-9.2.0/octave/.. -I/usr/include/octave-9.2.0/octave  -pthread -fopenmp -mieee-fp -g -O2 -ffile-prefix-map=/build/reproducible-path/octave-statistics-1.7.0=. -fstack-protector-strong -Wformat -Werror=format-security    fcnntrain.cc -o /tmp/oct-ynrPyI.o
g++ -I/usr/include/octave-9.2.0/octave/.. -I/usr/include/octave-9.2.0/octave  -pthread -fopenmp -mieee-fp -g -O2 -ffile-prefix-map=/build/reproducible-path/octave-statistics-1.7.0=. -fstack-protector-strong -Wformat -Werror=format-security  -o fcnntrain.oct  /tmp/oct-ynrPyI.o   -shared -Wl,-Bsymbolic -Wl,-z,relro  -flto=auto -ffat-lto-objects -Wl,-z,relro 
/usr/bin/mkoctfile --verbose       fcnnpredict.cc
g++ -c -Wdate-time -D_FORTIFY_SOURCE=2 -fPIC -I/usr/include/octave-9.2.0/octave/.. -I/usr/include/octave-9.2.0/octave  -pthread -fopenmp -mieee-fp -g -O2 -ffile-prefix-map=/build/reproducible-path/octave-statistics-1.7.0=. -fstack-protector-strong -Wformat -Werror=format-security    fcnnpredict.cc -o /tmp/oct-lCXzAR.o
g++ -I/usr/include/octave-9.2.0/octave/.. -I/usr/include/octave-9.2.0/octave  -pthread -fopenmp -mieee-fp -g -O2 -ffile-prefix-map=/build/reproducible-path/octave-statistics-1.7.0=. -fstack-protector-strong -Wformat -Werror=format-security  -o fcnnpredict.oct  /tmp/oct-lCXzAR.o   -shared -Wl,-Bsymbolic -Wl,-z,relro  -flto=auto -ffat-lto-objects -Wl,-z,relro 
make[1]: Leaving directory '/build/reproducible-path/octave-statistics-1.7.0/src'
copyfile /build/reproducible-path/octave-statistics-1.7.0/./src/editDistance.oct /build/reproducible-path/octave-statistics-1.7.0/./src/fcnnpredict.oct /build/reproducible-path/octave-statistics-1.7.0/./src/fcnntrain.oct /build/reproducible-path/octave-statistics-1.7.0/./src/libsvmread.oct /build/reproducible-path/octave-statistics-1.7.0/./src/libsvmwrite.oct /build/reproducible-path/octave-statistics-1.7.0/./src/svmpredict.oct /build/reproducible-path/octave-statistics-1.7.0/./src/svmtrain.oct /build/reproducible-path/octave-statistics-1.7.0/./src/editDistance.cc-tst /build/reproducible-path/octave-statistics-1.7.0/./src/fcnnpredict.cc-tst /build/reproducible-path/octave-statistics-1.7.0/./src/fcnntrain.cc-tst /build/reproducible-path/octave-statistics-1.7.0/./src/libsvmread.cc-tst /build/reproducible-path/octave-statistics-1.7.0/./src/libsvmwrite.cc-tst /build/reproducible-path/octave-statistics-1.7.0/./src/svmpredict.cc-tst /build/reproducible-path/octave-statistics-1.7.0/./src/svmtrain.cc-tst /build/reproducible-path/octave-statistics-1.7.0/./inst/i686-pc-linux-gnu-api-v59
warning: function /build/reproducible-path/octave-statistics-1.7.0/debian/tmp/usr/share/octave/packages/statistics-1.7.0/shadow9/median.m shadows a core library function
warning: called from
    doc_cache_create>gen_doc_cache_in_dir at line 146 column 5
    doc_cache_create at line 62 column 12
    install>generate_lookfor_cache at line 838 column 5
    install at line 243 column 7
    pkg at line 619 column 9
    /usr/share/dh-octave/install-pkg.m at line 38 column 1

warning: function /build/reproducible-path/octave-statistics-1.7.0/debian/tmp/usr/share/octave/packages/statistics-1.7.0/shadow9/std.m shadows a core library function
warning: called from
    doc_cache_create>gen_doc_cache_in_dir at line 146 column 5
    doc_cache_create at line 62 column 12
    install>generate_lookfor_cache at line 838 column 5
    install at line 243 column 7
    pkg at line 619 column 9
    /usr/share/dh-octave/install-pkg.m at line 38 column 1

warning: function /build/reproducible-path/octave-statistics-1.7.0/debian/tmp/usr/share/octave/packages/statistics-1.7.0/shadow9/mad.m shadows a core library function
warning: called from
    doc_cache_create>gen_doc_cache_in_dir at line 146 column 5
    doc_cache_create at line 62 column 12
    install>generate_lookfor_cache at line 838 column 5
    install at line 243 column 7
    pkg at line 619 column 9
    /usr/share/dh-octave/install-pkg.m at line 38 column 1

warning: function /build/reproducible-path/octave-statistics-1.7.0/debian/tmp/usr/share/octave/packages/statistics-1.7.0/shadow9/var.m shadows a core library function
warning: called from
    doc_cache_create>gen_doc_cache_in_dir at line 146 column 5
    doc_cache_create at line 62 column 12
    install>generate_lookfor_cache at line 838 column 5
    install at line 243 column 7
    pkg at line 619 column 9
    /usr/share/dh-octave/install-pkg.m at line 38 column 1

warning: function /build/reproducible-path/octave-statistics-1.7.0/debian/tmp/usr/share/octave/packages/statistics-1.7.0/shadow9/mean.m shadows a core library function
warning: called from
    doc_cache_create>gen_doc_cache_in_dir at line 146 column 5
    doc_cache_create at line 62 column 12
    install>generate_lookfor_cache at line 838 column 5
    install at line 243 column 7
    pkg at line 619 column 9
    /usr/share/dh-octave/install-pkg.m at line 38 column 1

For information about changes from previous versions of the statistics package, run 'news statistics'.
rm: cannot remove '/build/reproducible-path/octave-statistics-1.7.0/debian/tmp/usr/share/octave/packages/statistics-1.7.0/doc': Is a directory
   dh_octave_check -O--buildsystem=octave
Checking package...
Run the unit tests...
Checking m files ...
warning: function /build/reproducible-path/octave-statistics-1.7.0/debian/tmp/usr/share/octave/packages/statistics-1.7.0/shadow9/median.m shadows a core library function
warning: called from
    /tmp/tmp.knTBKLi3ba at line 12 column 1

warning: function /build/reproducible-path/octave-statistics-1.7.0/debian/tmp/usr/share/octave/packages/statistics-1.7.0/shadow9/std.m shadows a core library function
warning: called from
    /tmp/tmp.knTBKLi3ba at line 12 column 1

warning: function /build/reproducible-path/octave-statistics-1.7.0/debian/tmp/usr/share/octave/packages/statistics-1.7.0/shadow9/mad.m shadows a core library function
warning: called from
    /tmp/tmp.knTBKLi3ba at line 12 column 1

warning: function /build/reproducible-path/octave-statistics-1.7.0/debian/tmp/usr/share/octave/packages/statistics-1.7.0/shadow9/var.m shadows a core library function
warning: called from
    /tmp/tmp.knTBKLi3ba at line 12 column 1

warning: function /build/reproducible-path/octave-statistics-1.7.0/debian/tmp/usr/share/octave/packages/statistics-1.7.0/shadow9/mean.m shadows a core library function
warning: called from
    /tmp/tmp.knTBKLi3ba at line 12 column 1

[inst/logit.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/logit.m
***** test
 p = [0.01:0.01:0.99];
 assert (logit (p), log (p ./ (1-p)), 25*eps);
***** assert (logit ([-1, 0, 0.5, 1, 2]), [NaN, -Inf, 0, +Inf, NaN])
***** error logit ()
***** error logit (1, 2)
4 tests, 4 passed, 0 known failure, 0 skipped
[inst/harmmean.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/harmmean.m
***** test
 x = [0:10];
 y = [x;x+5;x+10];
 assert (harmmean (x), 0);
 m = [0 8.907635160795225 14.30854471766802];
 assert (harmmean (y, 2), m', 4e-14);
 assert (harmmean (y, "all"), 0);
 y(2,4) = NaN;
 m(2) = 9.009855936313949;
 assert (harmmean (y, 2), [0 NaN m(3)]', 4e-14);
 assert (harmmean (y', "omitnan"), m, 4e-14);
 z = y + 20;
 assert (harmmean (z, "all"), NaN);
 assert (harmmean (z, "all", "includenan"), NaN);
 assert (harmmean (z, "all", "omitnan"), 29.1108719858295, 4e-14);
 m = [24.59488458841874 NaN 34.71244385944397];
 assert (harmmean (z'), m, 4e-14);
 assert (harmmean (z', "includenan"), m, 4e-14);
 m(2) = 29.84104075528277;
 assert (harmmean (z', "omitnan"), m, 4e-14);
 assert (harmmean (z, 2, "omitnan"), m', 4e-14);
***** test
 x = repmat ([1:20;6:25], [5 2 6 3]);
 assert (size (harmmean (x, [3 2])), [10 1 1 3]);
 assert (size (harmmean (x, [1 2])), [1 1 6 3]);
 assert (size (harmmean (x, [1 2 4])), [1 1 6]);
 assert (size (harmmean (x, [1 4 3])), [1 40]);
 assert (size (harmmean (x, [1 2 3 4])), [1 1]);
***** test
 x = repmat ([1:20;6:25], [5 2 6 3]);
 m = repmat ([5.559045930488016;13.04950789021461], [5 1 1 3]);
 assert (harmmean (x, [3 2]), m, 4e-14);
 x(2,5,6,3) = NaN;
 m(2,3) = NaN;
 assert (harmmean (x, [3 2]), m, 4e-14);
 m(2,3) = 13.06617961315406;
 assert (harmmean (x, [3 2], "omitnan"), m, 4e-14);
***** error <harmmean: X must contain real nonnegative values.> harmmean ("char")
***** error <harmmean: X must contain real nonnegative values.> harmmean ([1 -1 3])
***** error <harmmean: DIM must be a positive integer scalar or vector.> ...
 harmmean (repmat ([1:20;6:25], [5 2 6 3 5]), -1)
***** error <harmmean: DIM must be a positive integer scalar or vector.> ...
 harmmean (repmat ([1:20;6:25], [5 2 6 3 5]), 0)
***** error <harmmean: VECDIM must contain non-repeating positive integers.> ...
 harmmean (repmat ([1:20;6:25], [5 2 6 3 5]), [1 1])
8 tests, 8 passed, 0 known failure, 0 skipped
[inst/dendrogram.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dendrogram.m
***** demo
 ## simple dendrogram
 y = [4, 5; 2, 6; 3, 7; 8, 9; 1, 10];
 y(:,3) = 1:5;
 dendrogram (y);
 title ("simple dendrogram");
***** demo
 ## another simple dendrogram
 v = 2 * rand (30, 1) - 1;
 d = abs (bsxfun (@minus, v(:, 1), v(:, 1)'));
 y = linkage (squareform (d, "tovector"));
 dendrogram (y);
 title ("another simple dendrogram");
***** demo
 ## collapsed tree, find all the leaves of node 5
 X = randn (60, 2);
 D = pdist (X);
 y = linkage (D, "average");
 subplot (2, 1, 1);
 title ("original tree");
 dendrogram (y, 0);
 subplot (2, 1, 2);
 title ("collapsed tree");
 [~, t] = dendrogram (y, 20);
 find(t == 5)
***** demo
 ## optimal leaf order
 X = randn (30, 2);
 D = pdist (X);
 y = linkage (D, "average");
 order = optimalleaforder (y, D);
 subplot (2, 1, 1);
 title ("original leaf order");
 dendrogram (y);
 subplot (2, 1, 2);
 title ("optimal leaf order");
 dendrogram (y, "Reorder", order);
***** demo
 ## horizontal orientation and labels
 X = randn (8, 2);
 D = pdist (X);
 L = ["Snow White"; "Doc"; "Grumpy"; "Happy"; "Sleepy"; "Bashful"; ...
      "Sneezy"; "Dopey"];
 y = linkage (D, "average");
 dendrogram (y, "Orientation", "left", "Labels", L);
 title ("horizontal orientation and labels");
***** shared visibility_setting
 visibility_setting = get (0, "DefaultFigureVisible");
***** test
 hf = figure ("visible", "off");
 unwind_protect
   y = [4, 5; 2, 6; 3, 7; 8, 9; 1, 10];
   y(:,3) = 1:5;
   dendrogram (y);
 unwind_protect_cleanup
   close (hf);
 end_unwind_protect
warning: using the gnuplot graphics toolkit is discouraged

The gnuplot graphics toolkit is not actively maintained and has a number
of limitations that are unlikely to be fixed.  Communication with gnuplot
uses a one-directional pipe and limited information is passed back to the
Octave interpreter so most changes made interactively in the plot window
will not be reflected in the graphics properties managed by Octave.  For
example, if the plot window is closed with a mouse click, Octave will not
be notified and will not update its internal list of open figure windows.
The qt toolkit is recommended instead.
***** test
 hf = figure ("visible", "off");
 unwind_protect
   y = [4, 5; 2, 6; 3, 7; 8, 9; 1, 10];
   y(:,3) = 1:5;
   dendrogram (y);
 unwind_protect_cleanup
   close (hf);
 end_unwind_protect
***** test
 hf = figure ("visible", "off");
 unwind_protect
   v = 2 * rand (30, 1) - 1;
   d = abs (bsxfun (@minus, v(:, 1), v(:, 1)'));
   y = linkage (squareform (d, "tovector"));
   dendrogram (y);
 unwind_protect_cleanup
   close (hf);
 end_unwind_protect
***** test
 hf = figure ("visible", "off");
 unwind_protect
   X = randn (30, 2);
   D = pdist (X);
   y = linkage (D, "average");
   order = optimalleaforder (y, D);
   subplot (2, 1, 1);
   title ("original leaf order");
   dendrogram (y);
   subplot (2, 1, 2);
   title ("optimal leaf order");
   dendrogram (y, "Reorder", order);
 unwind_protect_cleanup
   close (hf);
 end_unwind_protect
***** error dendrogram ();
***** error <tree must be .*> dendrogram (ones (2, 2), 1);
***** error <unknown property .*> dendrogram ([1 2 1], 1, "xxx", "xxx");
***** error <reorder.*> dendrogram ([1 2 1], "Reorder", "xxx");
***** error <reorder.*> dendrogram ([1 2 1], "Reorder", [1 2 3 4]);
 fail ('dendrogram ([1 2 1], "Orientation", "north")', "invalid orientation .*")
9 tests, 9 passed, 0 known failure, 0 skipped
[inst/sigma_pts.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/sigma_pts.m
***** demo
 K      = [1 0.5; 0.5 1]; # covaraince matrix
 # calculate and build associated ellipse
 [R,S,~] = svd (K);
 theta   = atan2 (R(2,1), R(1,1));
 v       = sqrt (diag (S));
 v       = v .* [cos(theta) sin(theta); -sin(theta) cos(theta)];
 t       = linspace (0, 2*pi, 100).';
 xe      = v(1,1) * cos (t) + v(2,1) * sin (t);
 ye      = v(1,2) * cos (t) + v(2,2) * sin (t);

 figure(1); clf; hold on
 # Plot ellipse and axes
 line ([0 0; v(:,1).'],[0 0; v(:,2).'])
 plot (xe,ye,'-r');

 col = 'rgb';
 l     = [-1.8 -1 1.5];
 for li = 1:3
  p     = sigma_pts (2, [], K, l(li));
  tmp   = plot (p(2:end,1), p(2:end,2), ['x' col(li)], ...
               p(1,1), p(1,2), ['o' col(li)]);
  h(li) = tmp(1);
 endfor
 hold off
 axis image
 legend (h, arrayfun (@(x) sprintf ("l:%.2g", x), l, "unif", 0));
***** test
 p = sigma_pts (5);
 assert (mean (p), zeros(1,5), sqrt(eps));
 assert (cov (p), eye(5), sqrt(eps));
***** test
 m = randn(1, 5);
 p = sigma_pts (5, m);
 assert (mean (p), m, sqrt(eps));
 assert (cov (p), eye(5), sqrt(eps));
***** test
 x = linspace (0,1,5);
 K = exp (- (x.' - x).^2/ 0.5);
 p = sigma_pts (5, [], K);
 assert (mean (p), zeros(1,5), sqrt(eps));
 assert (cov (p), K, sqrt(eps));
***** error sigma_pts(2,1);
***** error sigma_pts(2,[],1);
***** error sigma_pts(2,1,1);
***** error sigma_pts(2,[0.5 0.5],[-1 0; 0 0]);
7 tests, 7 passed, 0 known failure, 0 skipped
[inst/dist_obj/InverseGaussianDistribution.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_obj/InverseGaussianDistribution.m
***** shared pd, t
 pd = InverseGaussianDistribution (1, 1);
 t = truncate (pd, 2, 4);
***** assert (cdf (pd, [0:5]), [0, 0.6681, 0.8855, 0.9532, 0.9791, 0.9901], 1e-4);
***** assert (cdf (t, [0:5]), [0, 0, 0, 0.7234, 1, 1], 1e-4);
***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.8108, 0.8855, 0.9532, 0.9791], 1e-4);
***** assert (cdf (t, [1.5, 2, 3, 4]), [0, 0, 0.7234, 1], 1e-4);
***** assert (icdf (pd, [0:0.2:1]), [0, 0.3320, 0.5411, 0.8483, 1.4479, Inf], 1e-4);
***** assert (icdf (t, [0:0.2:1]), [2, 2.1889, 2.4264, 2.7417, 3.1993, 4], 1e-4);
***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 0.5411, 0.8483, 1.4479, Inf, NaN], 1e-4);
***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.4264, 2.7417, 3.1993, 4, NaN], 1e-4);
***** assert (iqr (pd), 0.8643, 1e-4);
***** assert (iqr (t), 0.8222, 1e-4);
***** assert (mean (pd), 1);
***** assert (mean (t), 2.6953, 1e-4);
***** assert (median (pd), 0.6758, 1e-4);
***** assert (median (t), 2.5716, 1e-4);
***** assert (pdf (pd, [0:5]), [0, 0.3989, 0.1098, 0.0394, 0.0162, 0.0072], 1e-4);
***** assert (pdf (t, [0:5]), [0, 0, 1.1736, 0.4211, 0.1730, 0], 1e-4);
***** assert (pdf (pd, [-1, 1:4, NaN]), [0, 0.3989, 0.1098, 0.0394, 0.0162, NaN], 1e-4);
***** assert (pdf (t, [-1, 1:4, NaN]), [0, 0, 1.1736, 0.4211, 0.1730, NaN], 1e-4);
***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
***** assert (any (random (t, 1000, 1) < 2), false);
***** assert (any (random (t, 1000, 1) > 4), false);
***** assert (std (pd), 1);
***** assert (std (t), 0.5332, 1e-4);
***** assert (var (pd), 1);
***** assert (var (t), 0.2843, 1e-4);
***** error <InverseGaussianDistribution: MU must be a positive real scalar.> ...
 InverseGaussianDistribution(0, 1)
***** error <InverseGaussianDistribution: MU must be a positive real scalar.> ...
 InverseGaussianDistribution(Inf, 1)
***** error <InverseGaussianDistribution: MU must be a positive real scalar.> ...
 InverseGaussianDistribution(i, 1)
***** error <InverseGaussianDistribution: MU must be a positive real scalar.> ...
 InverseGaussianDistribution("a", 1)
***** error <InverseGaussianDistribution: MU must be a positive real scalar.> ...
 InverseGaussianDistribution([1, 2], 1)
***** error <InverseGaussianDistribution: MU must be a positive real scalar.> ...
 InverseGaussianDistribution(NaN, 1)
***** error <InverseGaussianDistribution: LAMBDA must be a positive real scalar.> ...
 InverseGaussianDistribution(1, 0)
***** error <InverseGaussianDistribution: LAMBDA must be a positive real scalar.> ...
 InverseGaussianDistribution(1, -1)
***** error <InverseGaussianDistribution: LAMBDA must be a positive real scalar.> ...
 InverseGaussianDistribution(1, Inf)
***** error <InverseGaussianDistribution: LAMBDA must be a positive real scalar.> ...
 InverseGaussianDistribution(1, i)
***** error <InverseGaussianDistribution: LAMBDA must be a positive real scalar.> ...
 InverseGaussianDistribution(1, "a")
***** error <InverseGaussianDistribution: LAMBDA must be a positive real scalar.> ...
 InverseGaussianDistribution(1, [1, 2])
***** error <InverseGaussianDistribution: LAMBDA must be a positive real scalar.> ...
 InverseGaussianDistribution(1, NaN)
***** error <cdf: invalid argument for upper tail.> ...
 cdf (InverseGaussianDistribution, 2, "uper")
***** error <cdf: invalid argument for upper tail.> ...
 cdf (InverseGaussianDistribution, 2, 3)
***** shared x
 x = invgrnd (1, 1, [1, 100]);
***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
 paramci (InverseGaussianDistribution.fit (x), "alpha")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (InverseGaussianDistribution.fit (x), "alpha", 0)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (InverseGaussianDistribution.fit (x), "alpha", 1)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (InverseGaussianDistribution.fit (x), "alpha", [0.5 2])
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (InverseGaussianDistribution.fit (x), "alpha", "")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (InverseGaussianDistribution.fit (x), "alpha", {0.05})
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (InverseGaussianDistribution.fit (x), "parameter", "mu", ...
          "alpha", {0.05})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (InverseGaussianDistribution.fit (x), ...
          "parameter", {"mu", "lambda", "param"})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (InverseGaussianDistribution.fit (x), "alpha", 0.01, ...
          "parameter", {"mu", "lambda", "param"})
***** error <paramci: unknown distribution parameter.> ...
 paramci (InverseGaussianDistribution.fit (x), "parameter", "param")
***** error <paramci: unknown distribution parameter.> ...
 paramci (InverseGaussianDistribution.fit (x), "alpha", 0.01, ...
          "parameter", "param")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (InverseGaussianDistribution.fit (x), "NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (InverseGaussianDistribution.fit (x), "alpha", 0.01, "NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (InverseGaussianDistribution.fit (x), "alpha", 0.01, ...
          "parameter", "mu", "NAME", "value")
***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
 plot (InverseGaussianDistribution, "Parent")
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (InverseGaussianDistribution, "PlotType", 12)
***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
 plot (InverseGaussianDistribution, "PlotType", {"pdf", "cdf"})
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (InverseGaussianDistribution, "PlotType", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (InverseGaussianDistribution, "Discrete", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (InverseGaussianDistribution, "Discrete", [1, 0])
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (InverseGaussianDistribution, "Discrete", {true})
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (InverseGaussianDistribution, "Parent", 12)
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (InverseGaussianDistribution, "Parent", "hax")
***** error <plot: invalid NAME for optional argument.> ...
 plot (InverseGaussianDistribution, "invalidNAME", "pdf")
***** error <plot: no fitted DATA to plot a probability plot.> ...
 plot (InverseGaussianDistribution, "PlotType", "probability")
***** error <proflik: no fitted data available.> ...
 proflik (InverseGaussianDistribution, 2)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (InverseGaussianDistribution.fit (x), 3)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (InverseGaussianDistribution.fit (x), [1, 2])
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (InverseGaussianDistribution.fit (x), {1})
***** error <proflik: SETPARAM must be a numeric vector.> ...
 proflik (InverseGaussianDistribution.fit (x), 1, ones (2))
***** error <proflik: missing VALUE for 'Display' argument.> ...
 proflik (InverseGaussianDistribution.fit (x), 1, "Display")
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (InverseGaussianDistribution.fit (x), 1, "Display", 1)
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (InverseGaussianDistribution.fit (x), 1, "Display", {1})
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (InverseGaussianDistribution.fit (x), 1, "Display", {"on"})
***** error <proflik: invalid VALUE size for 'Display' argument.> ...
 proflik (InverseGaussianDistribution.fit (x), 1, "Display", ["on"; "on"])
***** error <proflik: invalid VALUE for 'Display' argument.> ...
 proflik (InverseGaussianDistribution.fit (x), 1, "Display", "onnn")
***** error <proflik: invalid NAME for optional arguments.> ...
 proflik (InverseGaussianDistribution.fit (x), 1, "NAME", "on")
***** error <proflik: invalid optional argument.> ...
 proflik (InverseGaussianDistribution.fit (x), 1, {"NAME"}, "on")
***** error <proflik: invalid optional argument.> ...
 proflik (InverseGaussianDistribution.fit (x), 1, {[1 2 3]}, "Display", "on")
***** error <truncate: missing input argument.> ...
 truncate (InverseGaussianDistribution)
***** error <truncate: missing input argument.> ...
 truncate (InverseGaussianDistribution, 2)
***** error <truncate: invalid lower upper limits.> ...
 truncate (InverseGaussianDistribution, 4, 2)
***** shared pd
 pd = InverseGaussianDistribution(1, 1);
 pd(2) = InverseGaussianDistribution(1, 3);
***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
***** error <iqr: requires a scalar probability distribution.> iqr (pd)
***** error <mean: requires a scalar probability distribution.> mean (pd)
***** error <median: requires a scalar probability distribution.> median (pd)
***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
***** error <paramci: requires a scalar probability distribution.> paramci (pd)
***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
***** error <plot: requires a scalar probability distribution.> plot (pd)
***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
***** error <random: requires a scalar probability distribution.> random (pd)
***** error <std: requires a scalar probability distribution.> std (pd)
***** error <truncate: requires a scalar probability distribution.> ...
 truncate (pd, 2, 4)
***** error <var: requires a scalar probability distribution.> var (pd)
96 tests, 96 passed, 0 known failure, 0 skipped
[inst/dist_obj/MultinomialDistribution.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_obj/MultinomialDistribution.m
***** shared pd, t
 pd = MultinomialDistribution ([0.1, 0.2, 0.3, 0.2, 0.1, 0.1]);
 t = truncate (pd, 2, 4);
***** assert (cdf (pd, [2, 3, 4]), [0.3, 0.6, 0.8], eps);
***** assert (cdf (t, [2, 3, 4]), [0.2857, 0.7143, 1], 1e-4);
***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.1, 0.3, 0.6, 0.8], eps);
***** assert (cdf (pd, [1.5, 2-eps, 3, 4]), [0.1, 0.1, 0.6, 0.8], eps);
***** assert (cdf (t, [1.5, 2, 3, 4]), [0, 0.2857, 0.7143, 1], 1e-4);
***** assert (cdf (t, [1.5, 2-eps, 3, 4]), [0, 0, 0.7143, 1], 1e-4);
***** assert (cdf (pd, [1, 2.5, 4, 6]), [0.1, 0.3, 0.8, 1], eps);
***** assert (icdf (pd, [0, 0.2857, 0.7143, 1]), [1, 2, 4, 6]);
***** assert (icdf (t, [0, 0.2857, 0.7143, 1]), [2, 2, 4, 4]);
***** assert (icdf (t, [0, 0.35, 0.7143, 1]), [2, 3, 4, 4]);
***** assert (icdf (t, [0, 0.35, 0.7143, 1, NaN]), [2, 3, 4, 4, NaN]);
***** assert (icdf (t, [-0.5, 0, 0.35, 0.7143, 1, NaN]), [NaN, 2, 3, 4, 4, NaN]);
***** assert (icdf (pd, [-0.5, 0, 0.35, 0.7143, 1, NaN]), [NaN, 1, 3, 4, 6, NaN]);
***** assert (iqr (pd), 2);
***** assert (iqr (t), 2);
***** assert (mean (pd), 3.3, 1e-14);
***** assert (mean (t), 3, eps);
***** assert (median (pd), 3);
***** assert (median (t), 3);
***** assert (pdf (pd, [-5, 1, 2.5, 4, 6, NaN, 9]), [0, 0.1, 0, 0.2, 0.1, NaN, 0]);
***** assert (pdf (pd, [-5, 1, 2, 3, 4, 6, NaN, 9]), ...
 [0, 0.1, 0.2, 0.3, 0.2, 0.1, NaN, 0]);
***** assert (pdf (t, [-5, 1, 2, 3, 4, 6, NaN, 0]), ...
 [0, 0, 0.2857, 0.4286, 0.2857, 0, NaN, 0], 1e-4);
***** assert (pdf (t, [-5, 1, 2, 4, 6, NaN, 0]), ...
 [0, 0, 0.2857, 0.2857, 0, NaN, 0], 1e-4);
***** assert (unique (random (pd, 1000, 5)), [1, 2, 3, 4, 5, 6]');
***** assert (unique (random (t, 1000, 5)), [2, 3, 4]');
***** assert (std (pd), 1.4177, 1e-4);
***** assert (std (t), 0.7559, 1e-4);
***** assert (var (pd), 2.0100, 1e-4);
***** assert (var (t), 0.5714, 1e-4);
***** error <MultinomialDistribution: PROBABILITIES must be a vector of positive real scalars that sum up to 1.> ...
 MultinomialDistribution(0)
***** error <MultinomialDistribution: PROBABILITIES must be a vector of positive real scalars that sum up to 1.> ...
 MultinomialDistribution(-1)
***** error <MultinomialDistribution: PROBABILITIES must be a vector of positive real scalars that sum up to 1.> ...
 MultinomialDistribution(Inf)
***** error <MultinomialDistribution: PROBABILITIES must be a vector of positive real scalars that sum up to 1.> ...
 MultinomialDistribution(i)
***** error <MultinomialDistribution: PROBABILITIES must be a vector of positive real scalars that sum up to 1.> ...
 MultinomialDistribution("a")
***** error <MultinomialDistribution: PROBABILITIES must be a vector of positive real scalars that sum up to 1.> ...
 MultinomialDistribution([1, 2])
***** error <MultinomialDistribution: PROBABILITIES must be a vector of positive real scalars that sum up to 1.> ...
 MultinomialDistribution(NaN)
***** error <cdf: invalid argument for upper tail.> ...
 cdf (MultinomialDistribution, 2, "uper")
***** error <cdf: invalid argument for upper tail.> ...
 cdf (MultinomialDistribution, 2, 3)
***** error <cdf: X must be real.> ...
 cdf (MultinomialDistribution, i)
***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
 plot (MultinomialDistribution, "Parent")
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (MultinomialDistribution, "PlotType", 12)
***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
 plot (MultinomialDistribution, "PlotType", {"pdf", "cdf"})
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (MultinomialDistribution, "PlotType", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (MultinomialDistribution, "Discrete", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (MultinomialDistribution, "Discrete", [1, 0])
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (MultinomialDistribution, "Discrete", {true})
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (MultinomialDistribution, "Parent", 12)
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (MultinomialDistribution, "Parent", "hax")
***** error <plot: invalid NAME for optional argument.> ...
 plot (MultinomialDistribution, "invalidNAME", "pdf")
***** error <plot: 'probability' PlotType is not supported for 'MultinomialDistribution'.> ...
 plot (MultinomialDistribution, "PlotType", "probability")
***** error <truncate: is_nan input argument.> ...
 truncate (MultinomialDistribution)
***** error <truncate: is_nan input argument.> ...
 truncate (MultinomialDistribution, 2)
***** error <truncate: invalid lower upper limits.> ...
 truncate (MultinomialDistribution, 4, 2)
***** shared pd
 pd = MultinomialDistribution([0.1, 0.2, 0.3, 0.4]);
 pd(2) = MultinomialDistribution([0.1, 0.2, 0.3, 0.4]);
***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
***** error <iqr: requires a scalar probability distribution.> iqr (pd)
***** error <mean: requires a scalar probability distribution.> mean (pd)
***** error <median: requires a scalar probability distribution.> median (pd)
***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
***** error <plot: requires a scalar probability distribution.> plot (pd)
***** error <random: requires a scalar probability distribution.> random (pd)
***** error <std: requires a scalar probability distribution.> std (pd)
***** error <truncate: requires a scalar probability distribution.> ...
 truncate (pd, 2, 4)
***** error <var: requires a scalar probability distribution.> var (pd)
64 tests, 64 passed, 0 known failure, 0 skipped
[inst/dist_obj/UniformDistribution.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_obj/UniformDistribution.m
***** shared pd, t
 pd = UniformDistribution (0, 5);
 t = truncate (pd, 2, 4);
***** assert (cdf (pd, [0:5]), [0, 0.2, 0.4, 0.6, 0.8, 1], 1e-4);
***** assert (cdf (t, [0:5]), [0, 0, 0, 0.5, 1, 1], 1e-4);
***** assert (cdf (pd, [1.5, 2, 3, 4, NaN]), [0.3, 0.4, 0.6, 0.8, NaN], 1e-4);
***** assert (cdf (t, [1.5, 2, 3, 4, NaN]), [0, 0, 0.5, 1, NaN], 1e-4);
***** assert (icdf (pd, [0:0.2:1]), [0, 1, 2, 3, 4, 5], 1e-4);
***** assert (icdf (t, [0:0.2:1]), [2, 2.4, 2.8, 3.2, 3.6, 4], 1e-4);
***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 2, 3, 4, 5, NaN], 1e-4);
***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.8, 3.2, 3.6, 4, NaN], 1e-4);
***** assert (iqr (pd), 2.5, 1e-14);
***** assert (iqr (t), 1, 1e-14);
***** assert (mean (pd), 2.5, 1e-14);
***** assert (mean (t), 3, 1e-14);
***** assert (median (pd), 2.5, 1e-14);
***** assert (median (t), 3, 1e-14);
***** assert (pdf (pd, [0:5]), [0.2, 0.2, 0.2, 0.2, 0.2, 0.2], 1e-4);
***** assert (pdf (t, [0:5]), [0, 0, 0.5, 0.5, 0.5, 0], 1e-4);
***** assert (pdf (pd, [-1, 1.5, NaN]), [0, 0.2, NaN], 1e-4);
***** assert (pdf (t, [-1, 1.5, NaN]), [0, 0, NaN], 1e-4);
***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
***** assert (any (random (t, 1000, 1) < 2), false);
***** assert (any (random (t, 1000, 1) > 4), false);
***** assert (std (pd), 1.4434, 1e-4);
***** assert (std (t), 0.5774, 1e-4);
***** assert (var (pd), 2.0833, 1e-4);
***** assert (var (t), 0.3333, 1e-4);
***** error <UniformDistribution: LOWER must be a real scalar.> ...
 UniformDistribution (i, 1)
***** error <UniformDistribution: LOWER must be a real scalar.> ...
 UniformDistribution (Inf, 1)
***** error <UniformDistribution: LOWER must be a real scalar.> ...
 UniformDistribution ([1, 2], 1)
***** error <UniformDistribution: LOWER must be a real scalar.> ...
 UniformDistribution ("a", 1)
***** error <UniformDistribution: LOWER must be a real scalar.> ...
 UniformDistribution (NaN, 1)
***** error <UniformDistribution: UPPER must be a real scalar.> ...
 UniformDistribution (1, i)
***** error <UniformDistribution: UPPER must be a real scalar.> ...
 UniformDistribution (1, Inf)
***** error <UniformDistribution: UPPER must be a real scalar.> ...
 UniformDistribution (1, [1, 2])
***** error <UniformDistribution: UPPER must be a real scalar.> ...
 UniformDistribution (1, "a")
***** error <UniformDistribution: UPPER must be a real scalar.> ...
 UniformDistribution (1, NaN)
***** error <UniformDistribution: LOWER must be less than UPPER.> ...
 UniformDistribution (2, 1)
***** error <cdf: invalid argument for upper tail.> ...
 cdf (UniformDistribution, 2, "uper")
***** error <cdf: invalid argument for upper tail.> ...
 cdf (UniformDistribution, 2, 3)
***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
 plot (UniformDistribution, "Parent")
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (UniformDistribution, "PlotType", 12)
***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
 plot (UniformDistribution, "PlotType", {"pdf", "cdf"})
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (UniformDistribution, "PlotType", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (UniformDistribution, "Discrete", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (UniformDistribution, "Discrete", [1, 0])
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (UniformDistribution, "Discrete", {true})
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (UniformDistribution, "Parent", 12)
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (UniformDistribution, "Parent", "hax")
***** error <plot: invalid NAME for optional argument.> ...
 plot (UniformDistribution, "invalidNAME", "pdf")
***** error <plot: 'probability' PlotType is not supported for 'UniformDistribution'.> ...
 plot (UniformDistribution, "PlotType", "probability")
***** error <truncate: missing input argument.> ...
 truncate (UniformDistribution)
***** error <truncate: missing input argument.> ...
 truncate (UniformDistribution, 2)
***** error <truncate: invalid lower upper limits.> ...
 truncate (UniformDistribution, 4, 2)
***** shared pd
 pd = UniformDistribution (0, 1);
 pd(2) = UniformDistribution (0, 2);
***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
***** error <iqr: requires a scalar probability distribution.> iqr (pd)
***** error <mean: requires a scalar probability distribution.> mean (pd)
***** error <median: requires a scalar probability distribution.> median (pd)
***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
***** error <plot: requires a scalar probability distribution.> plot (pd)
***** error <random: requires a scalar probability distribution.> random (pd)
***** error <std: requires a scalar probability distribution.> std (pd)
***** error <truncate: requires a scalar probability distribution.> ...
 truncate (pd, 2, 4)
***** error <var: requires a scalar probability distribution.> var (pd)
63 tests, 63 passed, 0 known failure, 0 skipped
[inst/dist_obj/WeibullDistribution.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_obj/WeibullDistribution.m
***** shared pd, t
 pd = WeibullDistribution;
 t = truncate (pd, 2, 4);
***** assert (cdf (pd, [0:5]), [0, 0.6321, 0.8647, 0.9502, 0.9817, 0.9933], 1e-4);
***** assert (cdf (t, [0:5]), [0, 0, 0, 0.7311, 1, 1], 1e-4);
***** assert (cdf (pd, [1.5, 2, 3, 4, NaN]), [0.7769, 0.8647, 0.9502, 0.9817, NaN], 1e-4);
***** assert (cdf (t, [1.5, 2, 3, 4, NaN]), [0, 0, 0.7311, 1, NaN], 1e-4);
***** assert (icdf (pd, [0:0.2:1]), [0, 0.2231, 0.5108, 0.9163, 1.6094, Inf], 1e-4);
***** assert (icdf (t, [0:0.2:1]), [2, 2.1899, 2.4244, 2.7315, 3.1768, 4], 1e-4);
***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 0.5108, 0.9163, 1.6094, Inf, NaN], 1e-4);
***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.4244, 2.7315, 3.1768, 4, NaN], 1e-4);
***** assert (iqr (pd), 1.0986, 1e-4);
***** assert (iqr (t), 0.8020, 1e-4);
***** assert (mean (pd), 1, 1e-14);
***** assert (mean (t), 2.6870, 1e-4);
***** assert (median (pd), 0.6931, 1e-4);
***** assert (median (t), 2.5662, 1e-4);
***** assert (pdf (pd, [0:5]), [1, 0.3679, 0.1353, 0.0498, 0.0183, 0.0067], 1e-4);
***** assert (pdf (t, [0:5]), [0, 0, 1.1565, 0.4255, 0.1565, 0], 1e-4);
***** assert (pdf (pd, [-1, 1.5, NaN]), [0, 0.2231, NaN], 1e-4);
***** assert (pdf (t, [-1, 1.5, NaN]), [0, 0, NaN], 1e-4);
***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
***** assert (any (random (t, 1000, 1) < 2), false);
***** assert (any (random (t, 1000, 1) > 4), false);
***** assert (std (pd), 1, 1e-14);
***** assert (std (t), 0.5253, 1e-4);
***** assert (var (pd), 1, 1e-14);
***** assert (var (t), 0.2759, 1e-4);
***** error <WeibullDistribution: LAMBDA must be a positive real scalar.> ...
 WeibullDistribution(0, 1)
***** error <WeibullDistribution: LAMBDA must be a positive real scalar.> ...
 WeibullDistribution(-1, 1)
***** error <WeibullDistribution: LAMBDA must be a positive real scalar.> ...
 WeibullDistribution(Inf, 1)
***** error <WeibullDistribution: LAMBDA must be a positive real scalar.> ...
 WeibullDistribution(i, 1)
***** error <WeibullDistribution: LAMBDA must be a positive real scalar.> ...
 WeibullDistribution("a", 1)
***** error <WeibullDistribution: LAMBDA must be a positive real scalar.> ...
 WeibullDistribution([1, 2], 1)
***** error <WeibullDistribution: LAMBDA must be a positive real scalar.> ...
 WeibullDistribution(NaN, 1)
***** error <WeibullDistribution: K must be a positive real scalar.> ...
 WeibullDistribution(1, 0)
***** error <WeibullDistribution: K must be a positive real scalar.> ...
 WeibullDistribution(1, -1)
***** error <WeibullDistribution: K must be a positive real scalar.> ...
 WeibullDistribution(1, Inf)
***** error <WeibullDistribution: K must be a positive real scalar.> ...
 WeibullDistribution(1, i)
***** error <WeibullDistribution: K must be a positive real scalar.> ...
 WeibullDistribution(1, "a")
***** error <WeibullDistribution: K must be a positive real scalar.> ...
 WeibullDistribution(1, [1, 2])
***** error <WeibullDistribution: K must be a positive real scalar.> ...
 WeibullDistribution(1, NaN)
***** error <cdf: invalid argument for upper tail.> ...
 cdf (WeibullDistribution, 2, "uper")
***** error <cdf: invalid argument for upper tail.> ...
 cdf (WeibullDistribution, 2, 3)
***** shared x
 x = wblrnd (1, 1, [1, 100]);
***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
 paramci (WeibullDistribution.fit (x), "alpha")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (WeibullDistribution.fit (x), "alpha", 0)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (WeibullDistribution.fit (x), "alpha", 1)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (WeibullDistribution.fit (x), "alpha", [0.5 2])
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (WeibullDistribution.fit (x), "alpha", "")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (WeibullDistribution.fit (x), "alpha", {0.05})
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (WeibullDistribution.fit (x), "parameter", "k", "alpha", {0.05})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (WeibullDistribution.fit (x), "parameter", {"lambda", "k", "param"})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (WeibullDistribution.fit (x), "alpha", 0.01, ...
          "parameter", {"lambda", "k", "param"})
***** error <paramci: unknown distribution parameter.> ...
 paramci (WeibullDistribution.fit (x), "parameter", "param")
***** error <paramci: unknown distribution parameter.> ...
 paramci (WeibullDistribution.fit (x), "alpha", 0.01, "parameter", "param")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (WeibullDistribution.fit (x), "NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (WeibullDistribution.fit (x), "alpha", 0.01, "NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (WeibullDistribution.fit (x), "alpha", 0.01, "parameter", "k", ...
          "NAME", "value")
***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
 plot (WeibullDistribution, "Parent")
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (WeibullDistribution, "PlotType", 12)
***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
 plot (WeibullDistribution, "PlotType", {"pdf", "cdf"})
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (WeibullDistribution, "PlotType", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (WeibullDistribution, "Discrete", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (WeibullDistribution, "Discrete", [1, 0])
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (WeibullDistribution, "Discrete", {true})
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (WeibullDistribution, "Parent", 12)
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (WeibullDistribution, "Parent", "hax")
***** error <plot: invalid NAME for optional argument.> ...
 plot (WeibullDistribution, "invalidNAME", "pdf")
***** error <plot: no fitted DATA to plot a probability plot.> ...
 plot (WeibullDistribution, "PlotType", "probability")
***** error <proflik: no fitted data available.> ...
 proflik (WeibullDistribution, 2)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (WeibullDistribution.fit (x), 3)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (WeibullDistribution.fit (x), [1, 2])
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (WeibullDistribution.fit (x), {1})
***** error <proflik: SETPARAM must be a numeric vector.> ...
 proflik (WeibullDistribution.fit (x), 1, ones (2))
***** error <proflik: missing VALUE for 'Display' argument.> ...
 proflik (WeibullDistribution.fit (x), 1, "Display")
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (WeibullDistribution.fit (x), 1, "Display", 1)
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (WeibullDistribution.fit (x), 1, "Display", {1})
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (WeibullDistribution.fit (x), 1, "Display", {"on"})
***** error <proflik: invalid VALUE size for 'Display' argument.> ...
 proflik (WeibullDistribution.fit (x), 1, "Display", ["on"; "on"])
***** error <proflik: invalid VALUE for 'Display' argument.> ...
 proflik (WeibullDistribution.fit (x), 1, "Display", "onnn")
***** error <proflik: invalid NAME for optional arguments.> ...
 proflik (WeibullDistribution.fit (x), 1, "NAME", "on")
***** error <proflik: invalid optional argument.> ...
 proflik (WeibullDistribution.fit (x), 1, {"NAME"}, "on")
***** error <proflik: invalid optional argument.> ...
 proflik (WeibullDistribution.fit (x), 1, {[1 2 3 4]}, "Display", "on")
***** error <truncate: missing input argument.> ...
 truncate (WeibullDistribution)
***** error <truncate: missing input argument.> ...
 truncate (WeibullDistribution, 2)
***** error <truncate: invalid lower upper limits.> ...
 truncate (WeibullDistribution, 4, 2)
***** shared pd
 pd = WeibullDistribution(1, 1);
 pd(2) = WeibullDistribution(1, 3);
***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
***** error <iqr: requires a scalar probability distribution.> iqr (pd)
***** error <mean: requires a scalar probability distribution.> mean (pd)
***** error <median: requires a scalar probability distribution.> median (pd)
***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
***** error <paramci: requires a scalar probability distribution.> paramci (pd)
***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
***** error <plot: requires a scalar probability distribution.> plot (pd)
***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
***** error <random: requires a scalar probability distribution.> random (pd)
***** error <std: requires a scalar probability distribution.> std (pd)
***** error <truncate: requires a scalar probability distribution.> ...
 truncate (pd, 2, 4)
***** error <var: requires a scalar probability distribution.> var (pd)
97 tests, 97 passed, 0 known failure, 0 skipped
[inst/dist_obj/GeneralizedParetoDistribution.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_obj/GeneralizedParetoDistribution.m
***** shared pd, t
 pd = GeneralizedParetoDistribution (1, 1, 1);
 t = truncate (pd, 2, 4);
***** assert (cdf (pd, [0:5]), [0, 0, 0.5, 0.6667, 0.75, 0.8], 1e-4);
***** assert (cdf (t, [0:5]), [0, 0, 0, 0.6667, 1, 1], 1e-4);
***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.3333, 0.5, 0.6667, 0.75], 1e-4);
***** assert (cdf (t, [1.5, 2, 3, 4]), [0, 0, 0.6667, 1], 1e-4);
***** assert (icdf (pd, [0:0.2:1]), [1, 1.25, 1.6667, 2.5, 5, Inf], 1e-4);
***** assert (icdf (t, [0:0.2:1]), [2, 2.2222, 2.5, 2.8571, 3.3333, 4], 1e-4);
***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 1.6667, 2.5, 5, Inf, NaN], 1e-4);
***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.5, 2.8571, 3.3333, 4, NaN], 1e-4);
***** assert (iqr (pd), 2.6667, 1e-4);
***** assert (iqr (t), 0.9143, 1e-4);
***** assert (mean (pd), Inf);
***** assert (mean (t), 2.7726, 1e-4);
***** assert (median (pd), 2);
***** assert (median (t), 2.6667, 1e-4);
***** assert (pdf (pd, [0:5]), [0, 1, 0.25, 0.1111, 0.0625, 0.04], 1e-4);
***** assert (pdf (t, [0:5]), [0, 0, 1, 0.4444, 0.25, 0], 1e-4);
***** assert (pdf (pd, [-1, 1:4, NaN]), [0, 1, 0.25, 0.1111, 0.0625, NaN], 1e-4);
***** assert (pdf (t, [-1, 1:4, NaN]), [0, 0, 1, 0.4444, 0.25, NaN], 1e-4);
***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
***** assert (any (random (t, 1000, 1) < 2), false);
***** assert (any (random (t, 1000, 1) > 4), false);
***** assert (std (pd), Inf);
***** assert (std (t), 0.5592, 1e-4);
***** assert (var (pd), Inf);
***** assert (var (t), 0.3128, 1e-4);
***** error <GeneralizedParetoDistribution: MU must be a real scalar.> ...
 GeneralizedParetoDistribution(Inf, 1, 1)
***** error <GeneralizedParetoDistribution: MU must be a real scalar.> ...
 GeneralizedParetoDistribution(i, 1, 1)
***** error <GeneralizedParetoDistribution: MU must be a real scalar.> ...
 GeneralizedParetoDistribution("a", 1, 1)
***** error <GeneralizedParetoDistribution: MU must be a real scalar.> ...
 GeneralizedParetoDistribution([1, 2], 1, 1)
***** error <GeneralizedParetoDistribution: MU must be a real scalar.> ...
 GeneralizedParetoDistribution(NaN, 1, 1)
***** error <GeneralizedParetoDistribution: SIGMA must be a positive real scalar.> ...
 GeneralizedParetoDistribution(1, 0, 1)
***** error <GeneralizedParetoDistribution: SIGMA must be a positive real scalar.> ...
 GeneralizedParetoDistribution(1, -1, 1)
***** error <GeneralizedParetoDistribution: SIGMA must be a positive real scalar.> ...
 GeneralizedParetoDistribution(1, Inf, 1)
***** error <GeneralizedParetoDistribution: SIGMA must be a positive real scalar.> ...
 GeneralizedParetoDistribution(1, i, 1)
***** error <GeneralizedParetoDistribution: SIGMA must be a positive real scalar.> ...
 GeneralizedParetoDistribution(1, "a", 1)
***** error <GeneralizedParetoDistribution: SIGMA must be a positive real scalar.> ...
 GeneralizedParetoDistribution(1, [1, 2], 1)
***** error <GeneralizedParetoDistribution: SIGMA must be a positive real scalar.> ...
 GeneralizedParetoDistribution(1, NaN, 1)
***** error <GeneralizedParetoDistribution: THETA must be a real scalar.> ...
 GeneralizedParetoDistribution(1, 1, Inf)
***** error <GeneralizedParetoDistribution: THETA must be a real scalar.> ...
 GeneralizedParetoDistribution(1, 1, i)
***** error <GeneralizedParetoDistribution: THETA must be a real scalar.> ...
 GeneralizedParetoDistribution(1, 1, "a")
***** error <GeneralizedParetoDistribution: THETA must be a real scalar.> ...
 GeneralizedParetoDistribution(1, 1, [1, 2])
***** error <GeneralizedParetoDistribution: THETA must be a real scalar.> ...
 GeneralizedParetoDistribution(1, 1, NaN)
***** error <cdf: invalid argument for upper tail.> ...
 cdf (GeneralizedParetoDistribution, 2, "uper")
***** error <cdf: invalid argument for upper tail.> ...
 cdf (GeneralizedParetoDistribution, 2, 3)
***** shared x
 x = gprnd (1, 1, 1, [1, 100]);
***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
 paramci (GeneralizedParetoDistribution.fit (x, 1), "alpha")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (GeneralizedParetoDistribution.fit (x, 1), "alpha", 0)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (GeneralizedParetoDistribution.fit (x, 1), "alpha", 1)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (GeneralizedParetoDistribution.fit (x, 1), "alpha", [0.5 2])
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (GeneralizedParetoDistribution.fit (x, 1), "alpha", "")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (GeneralizedParetoDistribution.fit (x, 1), "alpha", {0.05})
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (GeneralizedParetoDistribution.fit (x, 1), ...
          "parameter", "sigma", "alpha", {0.05})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (GeneralizedParetoDistribution.fit (x, 1), ...
          "parameter", {"k", "sigma", "param"})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (GeneralizedParetoDistribution.fit (x, 1), "alpha", 0.01, ...
          "parameter", {"k", "sigma", "param"})
***** error <paramci: unknown distribution parameter.> ...
 paramci (GeneralizedParetoDistribution.fit (x, 1), "parameter", "param")
***** error <paramci: unknown distribution parameter.> ...
 paramci (GeneralizedParetoDistribution.fit (x, 1), "alpha", 0.01, ...
          "parameter", "param")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (GeneralizedParetoDistribution.fit (x, 1), "NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (GeneralizedParetoDistribution.fit (x, 1), "alpha", 0.01, ...
          "NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (GeneralizedParetoDistribution.fit (x, 1), "alpha", 0.01, ...
          "parameter", "sigma", "NAME", "value")
***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
 plot (GeneralizedParetoDistribution, "Parent")
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (GeneralizedParetoDistribution, "PlotType", 12)
***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
 plot (GeneralizedParetoDistribution, "PlotType", {"pdf", "cdf"})
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (GeneralizedParetoDistribution, "PlotType", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (GeneralizedParetoDistribution, "Discrete", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (GeneralizedParetoDistribution, "Discrete", [1, 0])
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (GeneralizedParetoDistribution, "Discrete", {true})
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (GeneralizedParetoDistribution, "Parent", 12)
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (GeneralizedParetoDistribution, "Parent", "hax")
***** error <plot: invalid NAME for optional argument.> ...
 plot (GeneralizedParetoDistribution, "invalidNAME", "pdf")
***** error <plot: no fitted DATA to plot a probability plot.> ...
 plot (GeneralizedParetoDistribution, "PlotType", "probability")
***** error <proflik: no fitted data available.> ...
 proflik (GeneralizedParetoDistribution, 2)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (GeneralizedParetoDistribution.fit (x, 1), 3)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (GeneralizedParetoDistribution.fit (x, 1), [1, 2])
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (GeneralizedParetoDistribution.fit (x, 1), {1})
***** error <proflik: SETPARAM must be a numeric vector.> ...
 proflik (GeneralizedParetoDistribution.fit (x, 1), 1, ones (2))
***** error <proflik: missing VALUE for 'Display' argument.> ...
 proflik (GeneralizedParetoDistribution.fit (x, 1), 1, "Display")
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (GeneralizedParetoDistribution.fit (x, 1), 1, "Display", 1)
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (GeneralizedParetoDistribution.fit (x, 1), 1, "Display", {1})
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (GeneralizedParetoDistribution.fit (x, 1), 1, "Display", {"on"})
***** error <proflik: invalid VALUE size for 'Display' argument.> ...
 proflik (GeneralizedParetoDistribution.fit (x, 1), 1, ...
          "Display", ["on"; "on"])
***** error <proflik: invalid VALUE for 'Display' argument.> ...
 proflik (GeneralizedParetoDistribution.fit (x, 1), 1, "Display", "onnn")
***** error <proflik: invalid NAME for optional arguments.> ...
 proflik (GeneralizedParetoDistribution.fit (x, 1), 1, "NAME", "on")
***** error <proflik: invalid optional argument.> ...
 proflik (GeneralizedParetoDistribution.fit (x, 1), 1, {"NAME"}, "on")
***** error <proflik: invalid optional argument.> ...
 proflik (GeneralizedParetoDistribution.fit (x, 1), 1, {[1 2 3 4]}, ...
          "Display", "on")
***** error <truncate: missing input argument.> ...
 truncate (GeneralizedParetoDistribution)
***** error <truncate: missing input argument.> ...
 truncate (GeneralizedParetoDistribution, 2)
***** error <truncate: invalid lower upper limits.> ...
 truncate (GeneralizedParetoDistribution, 4, 2)
***** shared pd
 pd = GeneralizedParetoDistribution(1, 1, 1);
 pd(2) = GeneralizedParetoDistribution(1, 3, 1);
***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
***** error <iqr: requires a scalar probability distribution.> iqr (pd)
***** error <mean: requires a scalar probability distribution.> mean (pd)
***** error <median: requires a scalar probability distribution.> median (pd)
***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
***** error <paramci: requires a scalar probability distribution.> paramci (pd)
***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
***** error <plot: requires a scalar probability distribution.> plot (pd)
***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
***** error <random: requires a scalar probability distribution.> random (pd)
***** error <std: requires a scalar probability distribution.> std (pd)
***** error <truncate: requires a scalar probability distribution.> ...
 truncate (pd, 2, 4)
***** error <var: requires a scalar probability distribution.> var (pd)
100 tests, 100 passed, 0 known failure, 0 skipped
[inst/dist_obj/BirnbaumSaundersDistribution.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_obj/BirnbaumSaundersDistribution.m
***** demo
 ## Generate a data set of 5000 random samples from a Birnbaum-Saunders
 ## distribution with parameters β = 1 and γ = 0.5.  Fit a Birnbaum-Saunders
 ## distribution to this data and plot a PDF of the fitted distribution
 ## superimposed on a histogram of the data

 pd = makedist ("BirnbaumSaunders", "beta", 1, "gamma", 0.5)
 randg ("seed", 21);
 data = random (pd, 5000, 1);
 pd = fitdist (data, "BirnbaumSaunders")
 plot (pd)
 msg = "Fitted Birnbaum-Saunders distribution with a = %0.2f and b = %0.2f";
 title (sprintf (msg, pd.beta, pd.gamma))
***** demo
 ## Plot the PDF of a Birnbaum-Saunders distribution, with parameters beta = 1
 ## and gamma = 0.5, truncated at [0, 2] intervals.  Generate 10000 random
 ## samples from this truncated distribution and superimpose a histogram with
 ## 100 bins scaled accordingly

 pd = makedist ("BirnbaumSaunders", "beta", 1, "gamma", 0.5)
 t = truncate (pd, 0, 2)
 randg ("seed", 21);
 data = random (t, 10000, 1);
 plot (t)
 title ("Birnbaum-Saunders distribution (a = 2, b = 4) truncated at [0.1, 0.8]")
 hold on
 hist (data, 100, 50)
 hold off
***** demo
 ## Generate a data set of 100 random samples from a Birnbaum-Saunders
 ## distribution with parameters β = 1 and γ = 0.5.  Fit a Birnbaum-Saunders
 ## distribution to this data and plot its CDF superimposed over an empirical
 ## CDF of the data

 pd = makedist ("BirnbaumSaunders", "beta", 1, "gamma", 0.5)
 randg ("seed", 21);
 data = random (pd, 100, 1);
 pd = fitdist (data, "BirnbaumSaunders")
 plot (pd, "plottype", "cdf")
 title (sprintf ("Fitted Beta distribution with a = %0.2f and b = %0.2f", ...
                 pd.beta, pd.gamma))
 legend ({"empirical CDF", "fitted CDF"}, "location", "east")
***** shared pd, t
 pd = BirnbaumSaundersDistribution;
 t = truncate (pd, 2, 4);
***** assert (cdf (pd, [0:5]), [0, 0.5, 0.7602, 0.8759, 0.9332, 0.9632], 1e-4);
***** assert (cdf (t, [0:5]), [0, 0, 0, 0.6687, 1, 1], 1e-4);
***** assert (cdf (pd, [1.5, 2, 3, 4, NaN]), [0.6585, 0.7602, 0.8759, 0.9332, NaN], 1e-4);
***** assert (cdf (t, [1.5, 2, 3, 4, NaN]), [0, 0, 0.6687, 1, NaN], 1e-4);
***** assert (icdf (pd, [0:0.2:1]), [0, 0.4411, 0.7767, 1.2875, 2.2673, Inf], 1e-4);
***** assert (icdf (t, [0:0.2:1]), [2, 2.2293, 2.5073, 2.8567, 3.3210, 4], 1e-4);
***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 0.7767, 1.2875, 2.2673, Inf, NaN], 1e-4);
***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.5073, 2.8567, 3.3210, 4, NaN], 1e-4);
***** assert (iqr (pd), 1.4236, 1e-4);
***** assert (iqr (t), 0.8968, 1e-4);
***** assert (mean (pd), 1.5, eps);
***** assert (mean (t), 2.7723, 1e-4);
***** assert (median (pd), 1, 1e-4);
***** assert (median (t), 2.6711, 1e-4);
***** assert (pdf (pd, [0:5]), [0, 0.3989, 0.1648, 0.0788, 0.0405, 0.0216], 1e-4);
***** assert (pdf (t, [0:5]), [0, 0, 0.9528, 0.4559, 0.2340, 0], 1e-4);
***** assert (pdf (pd, [-1, 1.5, NaN]), [0, 0.2497, NaN], 1e-4);
***** assert (pdf (t, [-1, 1.5, NaN]), [0, 0, NaN], 1e-4);
***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
***** assert (any (random (t, 1000, 1) < 2), false);
***** assert (any (random (t, 1000, 1) > 4), false);
***** assert (std (pd), 1.5, eps);
***** assert (std (t), 0.5528, 1e-4);
***** assert (var (pd), 2.25, eps);
***** assert (var (t), 0.3056, 1e-4);
***** error <BirnbaumSaundersDistribution: BETA must be a positive real scalar.> ...
 BirnbaumSaundersDistribution(0, 1)
***** error <BirnbaumSaundersDistribution: BETA must be a positive real scalar.> ...
 BirnbaumSaundersDistribution(Inf, 1)
***** error <BirnbaumSaundersDistribution: BETA must be a positive real scalar.> ...
 BirnbaumSaundersDistribution(i, 1)
***** error <BirnbaumSaundersDistribution: BETA must be a positive real scalar.> ...
 BirnbaumSaundersDistribution("beta", 1)
***** error <BirnbaumSaundersDistribution: BETA must be a positive real scalar.> ...
 BirnbaumSaundersDistribution([1, 2], 1)
***** error <BirnbaumSaundersDistribution: BETA must be a positive real scalar.> ...
 BirnbaumSaundersDistribution(NaN, 1)
***** error <BirnbaumSaundersDistribution: GAMMA must be a positive real scalar.> ...
 BirnbaumSaundersDistribution(1, 0)
***** error <BirnbaumSaundersDistribution: GAMMA must be a positive real scalar.> ...
 BirnbaumSaundersDistribution(1, -1)
***** error <BirnbaumSaundersDistribution: GAMMA must be a positive real scalar.> ...
 BirnbaumSaundersDistribution(1, Inf)
***** error <BirnbaumSaundersDistribution: GAMMA must be a positive real scalar.> ...
 BirnbaumSaundersDistribution(1, i)
***** error <BirnbaumSaundersDistribution: GAMMA must be a positive real scalar.> ...
 BirnbaumSaundersDistribution(1, "beta")
***** error <BirnbaumSaundersDistribution: GAMMA must be a positive real scalar.> ...
 BirnbaumSaundersDistribution(1, [1, 2])
***** error <BirnbaumSaundersDistribution: GAMMA must be a positive real scalar.> ...
 BirnbaumSaundersDistribution(1, NaN)
***** error <cdf: invalid argument for upper tail.> ...
 cdf (BirnbaumSaundersDistribution, 2, "uper")
***** error <cdf: invalid argument for upper tail.> ...
 cdf (BirnbaumSaundersDistribution, 2, 3)
***** shared x
 rand ("seed", 5);
 x = bisarnd (1, 1, [100, 1]);
***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
 paramci (BirnbaumSaundersDistribution.fit (x), "alpha")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (BirnbaumSaundersDistribution.fit (x), "alpha", 0)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (BirnbaumSaundersDistribution.fit (x), "alpha", 1)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (BirnbaumSaundersDistribution.fit (x), "alpha", [0.5 2])
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (BirnbaumSaundersDistribution.fit (x), "alpha", "")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (BirnbaumSaundersDistribution.fit (x), "alpha", {0.05})
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (BirnbaumSaundersDistribution.fit (x), "parameter", ...
          "beta", "alpha", {0.05})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (BirnbaumSaundersDistribution.fit (x), ...
          "parameter", {"beta", "gamma", "param"})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (BirnbaumSaundersDistribution.fit (x), "alpha", 0.01, ...
          "parameter", {"beta", "gamma", "param"})
***** error <paramci: unknown distribution parameter.> ...
 paramci (BirnbaumSaundersDistribution.fit (x), "parameter", "param")
***** error <paramci: unknown distribution parameter.> ...
 paramci (BirnbaumSaundersDistribution.fit (x), "alpha", 0.01, ...
          "parameter", "param")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (BirnbaumSaundersDistribution.fit (x), "NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (BirnbaumSaundersDistribution.fit (x), "alpha", 0.01, ...
          "NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (BirnbaumSaundersDistribution.fit (x), "alpha", 0.01, ...
          "parameter", "beta", "NAME", "value")
***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
 plot (BirnbaumSaundersDistribution, "Parent")
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (BirnbaumSaundersDistribution, "PlotType", 12)
***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
 plot (BirnbaumSaundersDistribution, "PlotType", {"pdf", "cdf"})
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (BirnbaumSaundersDistribution, "PlotType", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (BirnbaumSaundersDistribution, "Discrete", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (BirnbaumSaundersDistribution, "Discrete", [1, 0])
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (BirnbaumSaundersDistribution, "Discrete", {true})
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (BirnbaumSaundersDistribution, "Parent", 12)
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (BirnbaumSaundersDistribution, "Parent", "hax")
***** error <plot: invalid NAME for optional argument.> ...
 plot (BirnbaumSaundersDistribution, "invalidNAME", "pdf")
***** error <plot: no fitted DATA to plot a probability plot.> ...
 plot (BirnbaumSaundersDistribution, "PlotType", "probability")
***** error <proflik: no fitted data available.> ...
 proflik (BirnbaumSaundersDistribution, 2)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (BirnbaumSaundersDistribution.fit (x), 3)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (BirnbaumSaundersDistribution.fit (x), [1, 2])
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (BirnbaumSaundersDistribution.fit (x), {1})
***** error <proflik: SETPARAM must be a numeric vector.> ...
 proflik (BirnbaumSaundersDistribution.fit (x), 1, ones (2))
***** error <proflik: missing VALUE for 'Display' argument.> ...
 proflik (BirnbaumSaundersDistribution.fit (x), 1, "Display")
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (BirnbaumSaundersDistribution.fit (x), 1, "Display", 1)
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (BirnbaumSaundersDistribution.fit (x), 1, "Display", {1})
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (BirnbaumSaundersDistribution.fit (x), 1, "Display", {"on"})
***** error <proflik: invalid VALUE size for 'Display' argument.> ...
 proflik (BirnbaumSaundersDistribution.fit (x), 1, "Display", ["on"; "on"])
***** error <proflik: invalid VALUE for 'Display' argument.> ...
 proflik (BirnbaumSaundersDistribution.fit (x), 1, "Display", "onnn")
***** error <proflik: invalid NAME for optional arguments.> ...
 proflik (BirnbaumSaundersDistribution.fit (x), 1, "NAME", "on")
***** error <proflik: invalid optional argument.> ...
 proflik (BirnbaumSaundersDistribution.fit (x), 1, {"NAME"}, "on")
***** error <proflik: invalid optional argument.> ...
 proflik (BirnbaumSaundersDistribution.fit (x), 1, {[1 2 3 4]}, "Display", "on")
***** error <truncate: missing input argument.> ...
 truncate (BirnbaumSaundersDistribution)
***** error <truncate: missing input argument.> ...
 truncate (BirnbaumSaundersDistribution, 2)
***** error <truncate: invalid lower upper limits.> ...
 truncate (BirnbaumSaundersDistribution, 4, 2)
***** shared pd
 pd = BirnbaumSaundersDistribution(1, 1);
 pd(2) = BirnbaumSaundersDistribution(1, 3);
***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
***** error <iqr: requires a scalar probability distribution.> iqr (pd)
***** error <mean: requires a scalar probability distribution.> mean (pd)
***** error <median: requires a scalar probability distribution.> median (pd)
***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
***** error <paramci: requires a scalar probability distribution.> paramci (pd)
***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
***** error <plot: requires a scalar probability distribution.> plot (pd)
***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
***** error <random: requires a scalar probability distribution.> random (pd)
***** error <std: requires a scalar probability distribution.> std (pd)
***** error <truncate: requires a scalar probability distribution.> ...
 truncate (pd, 2, 4)
***** error <var: requires a scalar probability distribution.> var (pd)
96 tests, 96 passed, 0 known failure, 0 skipped
[inst/dist_obj/PoissonDistribution.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_obj/PoissonDistribution.m
***** shared pd, t, t_inf
 pd = PoissonDistribution;
 t = truncate (pd, 2, 4);
 t_inf = truncate (pd, 2, Inf);
***** assert (cdf (pd, [0:5]), [0.3679, 0.7358, 0.9197, 0.9810, 0.9963, 0.9994], 1e-4);
***** assert (cdf (t, [0:5]), [0, 0, 0.7059, 0.9412, 1, 1], 1e-4);
***** assert (cdf (t_inf, [0:5]), [0, 0, 0.6961, 0.9281, 0.9861, 0.9978], 1e-4);
***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.7358, 0.9197, 0.9810, 0.9963], 1e-4);
***** assert (cdf (t, [1.5, 2, 3, 4]), [0, 0.7059, 0.9412, 1], 1e-4);
***** assert (icdf (pd, [0:0.2:1]), [0, 0, 1, 1, 2, Inf], 1e-4);
***** assert (icdf (t, [0:0.2:1]), [2, 2, 2, 2, 3, 4], 1e-4);
***** assert (icdf (t_inf, [0:0.2:1]), [2, 2, 2, 2, 3, Inf], 1e-4);
***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 1, 1, 2, Inf, NaN], 1e-4);
***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2, 2, 3, 4, NaN], 1e-4);
***** assert (iqr (pd), 2);
***** assert (iqr (t), 1);
***** assert (mean (pd), 1);
***** assert (mean (t), 2.3529, 1e-4);
***** assert (mean (t_inf), 2.3922, 1e-4);
***** assert (median (pd), 1);
***** assert (median (t), 2);
***** assert (median (t_inf), 2);
***** assert (pdf (pd, [0:5]), [0.3679, 0.3679, 0.1839, 0.0613, 0.0153, 0.0031], 1e-4);
***** assert (pdf (t, [0:5]), [0, 0, 0.7059, 0.2353, 0.0588, 0], 1e-4);
***** assert (pdf (t_inf, [0:5]), [0, 0, 0.6961, 0.2320, 0.0580, 0.0116], 1e-4);
***** assert (pdf (pd, [-1, 1:4, NaN]), [0, 0.3679, 0.1839, 0.0613, 0.0153, NaN], 1e-4);
***** assert (pdf (t, [-1, 1:4, NaN]), [0, 0, 0.7059, 0.2353, 0.0588, NaN], 1e-4);
***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
***** assert (any (random (t, 1000, 1) < 2), false);
***** assert (any (random (t, 1000, 1) > 4), false);
***** assert (std (pd), 1);
***** assert (std (t), 0.5882, 1e-4);
***** assert (std (t_inf), 0.6738, 1e-4);
***** assert (var (pd), 1);
***** assert (var (t), 0.3460, 1e-4);
***** assert (var (t_inf), 0.4540, 1e-4);
***** error <PoissonDistribution: LAMBDA must be a positive real scalar.> ...
 PoissonDistribution(0)
***** error <PoissonDistribution: LAMBDA must be a positive real scalar.> ...
 PoissonDistribution(-1)
***** error <PoissonDistribution: LAMBDA must be a positive real scalar.> ...
 PoissonDistribution(Inf)
***** error <PoissonDistribution: LAMBDA must be a positive real scalar.> ...
 PoissonDistribution(i)
***** error <PoissonDistribution: LAMBDA must be a positive real scalar.> ...
 PoissonDistribution("a")
***** error <PoissonDistribution: LAMBDA must be a positive real scalar.> ...
 PoissonDistribution([1, 2])
***** error <PoissonDistribution: LAMBDA must be a positive real scalar.> ...
 PoissonDistribution(NaN)
***** error <cdf: invalid argument for upper tail.> ...
 cdf (PoissonDistribution, 2, "uper")
***** error <cdf: invalid argument for upper tail.> ...
 cdf (PoissonDistribution, 2, 3)
***** shared x
 x = poissrnd (1, [1, 100]);
***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
 paramci (PoissonDistribution.fit (x), "alpha")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (PoissonDistribution.fit (x), "alpha", 0)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (PoissonDistribution.fit (x), "alpha", 1)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (PoissonDistribution.fit (x), "alpha", [0.5 2])
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (PoissonDistribution.fit (x), "alpha", "")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (PoissonDistribution.fit (x), "alpha", {0.05})
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (PoissonDistribution.fit (x), "parameter", "lambda", "alpha", {0.05})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (PoissonDistribution.fit (x), "parameter", {"lambda", "param"})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (PoissonDistribution.fit (x), "alpha", 0.01, ...
          "parameter", {"lambda", "param"})
***** error <paramci: unknown distribution parameter.> ...
 paramci (PoissonDistribution.fit (x), "parameter", "param")
***** error <paramci: unknown distribution parameter.> ...
 paramci (PoissonDistribution.fit (x), "alpha", 0.01, "parameter", "param")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (PoissonDistribution.fit (x), "NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (PoissonDistribution.fit (x), "alpha", 0.01, "NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (PoissonDistribution.fit (x), "alpha", 0.01, ...
          "parameter", "lambda", "NAME", "value")
***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
 plot (PoissonDistribution, "Parent")
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (PoissonDistribution, "PlotType", 12)
***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
 plot (PoissonDistribution, "PlotType", {"pdf", "cdf"})
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (PoissonDistribution, "PlotType", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (PoissonDistribution, "Discrete", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (PoissonDistribution, "Discrete", [1, 0])
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (PoissonDistribution, "Discrete", {true})
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (PoissonDistribution, "Parent", 12)
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (PoissonDistribution, "Parent", "hax")
***** error <plot: invalid NAME for optional argument.> ...
 plot (PoissonDistribution, "invalidNAME", "pdf")
***** error <plot: no fitted DATA to plot a probability plot.> ...
 plot (PoissonDistribution, "PlotType", "probability")
***** error <proflik: no fitted data available.> ...
 proflik (PoissonDistribution, 2)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (PoissonDistribution.fit (x), 3)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (PoissonDistribution.fit (x), [1, 2])
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (PoissonDistribution.fit (x), {1})
***** error <proflik: SETPARAM must be a numeric vector.> ...
 proflik (PoissonDistribution.fit (x), 1, ones (2))
***** error <proflik: missing VALUE for 'Display' argument.> ...
 proflik (PoissonDistribution.fit (x), 1, "Display")
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (PoissonDistribution.fit (x), 1, "Display", 1)
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (PoissonDistribution.fit (x), 1, "Display", {1})
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (PoissonDistribution.fit (x), 1, "Display", {"on"})
***** error <proflik: invalid VALUE size for 'Display' argument.> ...
 proflik (PoissonDistribution.fit (x), 1, "Display", ["on"; "on"])
***** error <proflik: invalid VALUE for 'Display' argument.> ...
 proflik (PoissonDistribution.fit (x), 1, "Display", "onnn")
***** error <proflik: invalid NAME for optional arguments.> ...
 proflik (PoissonDistribution.fit (x), 1, "NAME", "on")
***** error <proflik: invalid optional argument.> ...
 proflik (PoissonDistribution.fit (x), 1, {"NAME"}, "on")
***** error <proflik: invalid optional argument.> ...
 proflik (PoissonDistribution.fit (x), 1, {[1 2 3 4]}, "Display", "on")
***** error <truncate: missing input argument.> ...
 truncate (PoissonDistribution)
***** error <truncate: missing input argument.> ...
 truncate (PoissonDistribution, 2)
***** error <truncate: invalid lower upper limits.> ...
 truncate (PoissonDistribution, 4, 2)
***** shared pd
 pd = PoissonDistribution(1);
 pd(2) = PoissonDistribution(3);
***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
***** error <iqr: requires a scalar probability distribution.> iqr (pd)
***** error <mean: requires a scalar probability distribution.> mean (pd)
***** error <median: requires a scalar probability distribution.> median (pd)
***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
***** error <paramci: requires a scalar probability distribution.> paramci (pd)
***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
***** error <plot: requires a scalar probability distribution.> plot (pd)
***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
***** error <random: requires a scalar probability distribution.> random (pd)
***** error <std: requires a scalar probability distribution.> std (pd)
***** error <truncate: requires a scalar probability distribution.> ...
 truncate (pd, 2, 4)
***** error <var: requires a scalar probability distribution.> var (pd)
97 tests, 97 passed, 0 known failure, 0 skipped
[inst/dist_obj/TriangularDistribution.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_obj/TriangularDistribution.m
***** shared pd, t
 pd = TriangularDistribution (0, 3, 5);
 t = truncate (pd, 2, 4);
***** assert (cdf (pd, [0:5]), [0, 0.0667, 0.2667, 0.6000, 0.9000, 1], 1e-4);
***** assert (cdf (t, [0:5]), [0, 0, 0, 0.5263, 1, 1], 1e-4);
***** assert (cdf (pd, [1.5, 2, 3, 4, NaN]), [0.1500, 0.2667, 0.6, 0.9, NaN], 1e-4);
***** assert (cdf (t, [1.5, 2, 3, 4, NaN]), [0, 0, 0.5263, 1, NaN], 1e-4);
***** assert (icdf (pd, [0:0.2:1]), [0, 1.7321, 2.4495, 3, 3.5858, 5], 1e-4);
***** assert (icdf (t, [0:0.2:1]), [2, 2.4290, 2.7928, 3.1203, 3.4945, 4], 1e-4);
***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 2.4495, 3, 3.5858, 5, NaN], 1e-4);
***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.7928, 3.1203, 3.4945, 4, NaN], 1e-4);
***** assert (iqr (pd), 1.4824, 1e-4);
***** assert (iqr (t), 0.8678, 1e-4);
***** assert (mean (pd), 2.6667, 1e-4);
***** assert (mean (t), 2.9649, 1e-4);
***** assert (median (pd), 2.7386, 1e-4);
***** assert (median (t), 2.9580, 1e-4);
***** assert (pdf (pd, [0:5]), [0, 0.1333, 0.2667, 0.4, 0.2, 0], 1e-4);
***** assert (pdf (t, [0:5]), [0, 0, 0.4211, 0.6316, 0.3158, 0], 1e-4);
***** assert (pdf (pd, [-1, 1.5, NaN]), [0, 0.2, NaN], 1e-4);
***** assert (pdf (t, [-1, 1.5, NaN]), [0, 0, NaN], 1e-4);
***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
***** assert (any (random (t, 1000, 1) < 2), false);
***** assert (any (random (t, 1000, 1) > 4), false);
***** assert (std (pd), 1.0274, 1e-4);
***** assert (std (t), 0.5369, 1e-4);
***** assert (var (pd), 1.0556, 1e-4);
***** assert (var (t), 0.2882, 1e-4);
***** error <TriangularDistribution: lower limit A must be a real scalar.> ...
 TriangularDistribution (i, 1, 2)
***** error <TriangularDistribution: lower limit A must be a real scalar.> ...
 TriangularDistribution (Inf, 1, 2)
***** error <TriangularDistribution: lower limit A must be a real scalar.> ...
 TriangularDistribution ([1, 2], 1, 2)
***** error <TriangularDistribution: lower limit A must be a real scalar.> ...
 TriangularDistribution ("a", 1, 2)
***** error <TriangularDistribution: lower limit A must be a real scalar.> ...
 TriangularDistribution (NaN, 1, 2)
***** error <TriangularDistribution: mode B must be a real scalar.> ...
 TriangularDistribution (1, i, 2)
***** error <TriangularDistribution: mode B must be a real scalar.> ...
 TriangularDistribution (1, Inf, 2)
***** error <TriangularDistribution: mode B must be a real scalar.> ...
 TriangularDistribution (1, [1, 2], 2)
***** error <TriangularDistribution: mode B must be a real scalar.> ...
 TriangularDistribution (1, "a", 2)
***** error <TriangularDistribution: mode B must be a real scalar.> ...
 TriangularDistribution (1, NaN, 2)
***** error <TriangularDistribution: upper limit C must be a real scalar.> ...
 TriangularDistribution (1, 2, i)
***** error <TriangularDistribution: upper limit C must be a real scalar.> ...
 TriangularDistribution (1, 2, Inf)
***** error <TriangularDistribution: upper limit C must be a real scalar.> ...
 TriangularDistribution (1, 2, [1, 2])
***** error <TriangularDistribution: upper limit C must be a real scalar.> ...
 TriangularDistribution (1, 2, "a")
***** error <TriangularDistribution: upper limit C must be a real scalar.> ...
 TriangularDistribution (1, 2, NaN)
***** error <TriangularDistribution: lower limit A must be less than upper limit C.> ...
 TriangularDistribution (1, 1, 1)
***** error <TriangularDistribution: mode B must be within lower limit A and upper limit C.> ...
 TriangularDistribution (1, 0.5, 2)
***** error <cdf: invalid argument for upper tail.> ...
 cdf (TriangularDistribution, 2, "uper")
***** error <cdf: invalid argument for upper tail.> ...
 cdf (TriangularDistribution, 2, 3)
***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
 plot (TriangularDistribution, "Parent")
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (TriangularDistribution, "PlotType", 12)
***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
 plot (TriangularDistribution, "PlotType", {"pdf", "cdf"})
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (TriangularDistribution, "PlotType", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (TriangularDistribution, "Discrete", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (TriangularDistribution, "Discrete", [1, 0])
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (TriangularDistribution, "Discrete", {true})
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (TriangularDistribution, "Parent", 12)
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (TriangularDistribution, "Parent", "hax")
***** error <plot: invalid NAME for optional argument.> ...
 plot (TriangularDistribution, "invalidNAME", "pdf")
***** error <'probability' PlotType is not supported for 'TriangularDistribution'.> ...
 plot (TriangularDistribution, "PlotType", "probability")
***** error <truncate: missing input argument.> ...
 truncate (TriangularDistribution)
***** error <truncate: missing input argument.> ...
 truncate (TriangularDistribution, 2)
***** error <truncate: invalid lower upper limits.> ...
 truncate (TriangularDistribution, 4, 2)
***** shared pd
 pd = TriangularDistribution (0, 1, 2);
 pd(2) = TriangularDistribution (0, 1, 2);
***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
***** error <iqr: requires a scalar probability distribution.> iqr (pd)
***** error <mean: requires a scalar probability distribution.> mean (pd)
***** error <median: requires a scalar probability distribution.> median (pd)
***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
***** error <plot: requires a scalar probability distribution.> plot (pd)
***** error <random: requires a scalar probability distribution.> random (pd)
***** error <std: requires a scalar probability distribution.> std (pd)
***** error <truncate: requires a scalar probability distribution.> ...
 truncate (pd, 2, 4)
***** error <var: requires a scalar probability distribution.> var (pd)
69 tests, 69 passed, 0 known failure, 0 skipped
[inst/dist_obj/NakagamiDistribution.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_obj/NakagamiDistribution.m
***** shared pd, t
 pd = NakagamiDistribution;
 t = truncate (pd, 2, 4);
***** assert (cdf (pd, [0:5]), [0, 0.6321, 0.9817, 0.9999, 1, 1], 1e-4);
***** assert (cdf (t, [0:5]), [0, 0, 0, 0.9933, 1, 1], 1e-4);
***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.8946, 0.9817, 0.9999, 1], 1e-4);
***** assert (cdf (t, [1.5, 2, 3, 4]), [0, 0, 0.9933, 1], 1e-4);
***** assert (icdf (pd, [0:0.2:1]), [0, 0.4724, 0.7147, 0.9572, 1.2686, Inf], 1e-4);
***** assert (icdf (t, [0:0.2:1]), [2, 2.0550, 2.1239, 2.2173, 2.3684, 4], 1e-4);
***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 0.7147, 0.9572, 1.2686, Inf, NaN], 1e-4);
***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.1239, 2.2173, 2.3684, 4, NaN], 1e-4);
***** assert (iqr (pd), 0.6411, 1e-4);
***** assert (iqr (t), 0.2502, 1e-4);
***** assert (mean (pd), 0.8862, 1e-4);
***** assert (mean (t), 2.2263, 1e-4);
***** assert (median (pd), 0.8326, 1e-4);
***** assert (median (t), 2.1664, 1e-4);
***** assert (pdf (pd, [0:5]), [0, 0.7358, 0.0733, 0.0007, 0, 0], 1e-4);
***** assert (pdf (t, [0:5]), [0, 0, 4, 0.0404, 0, 0], 1e-4);
***** assert (pdf (pd, [-1, 1:4, NaN]), [0, 0.7358, 0.0733, 0.0007, 0, NaN], 1e-4);
***** assert (pdf (t, [-1, 1:4, NaN]), [0, 0, 4, 0.0404, 0, NaN], 1e-4);
***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
***** assert (any (random (t, 1000, 1) < 2), false);
***** assert (any (random (t, 1000, 1) > 4), false);
***** assert (std (pd), 0.4633, 1e-4);
***** assert (std (t), 0.2083, 1e-4);
***** assert (var (pd), 0.2146, 1e-4);
***** assert (var (t), 0.0434, 1e-4);
***** error <NakagamiDistribution: MU must be a real scalar of at least 0.5.> ...
 NakagamiDistribution(Inf, 1)
***** error <NakagamiDistribution: MU must be a real scalar of at least 0.5.> ...
 NakagamiDistribution(i, 1)
***** error <NakagamiDistribution: MU must be a real scalar of at least 0.5.> ...
 NakagamiDistribution("a", 1)
***** error <NakagamiDistribution: MU must be a real scalar of at least 0.5.> ...
 NakagamiDistribution([1, 2], 1)
***** error <NakagamiDistribution: MU must be a real scalar of at least 0.5.> ...
 NakagamiDistribution(NaN, 1)
***** error <NakagamiDistribution: OMEGA must be a positive real scalar.> ...
 NakagamiDistribution(1, 0)
***** error <NakagamiDistribution: OMEGA must be a positive real scalar.> ...
 NakagamiDistribution(1, -1)
***** error <NakagamiDistribution: OMEGA must be a positive real scalar.> ...
 NakagamiDistribution(1, Inf)
***** error <NakagamiDistribution: OMEGA must be a positive real scalar.> ...
 NakagamiDistribution(1, i)
***** error <NakagamiDistribution: OMEGA must be a positive real scalar.> ...
 NakagamiDistribution(1, "a")
***** error <NakagamiDistribution: OMEGA must be a positive real scalar.> ...
 NakagamiDistribution(1, [1, 2])
***** error <NakagamiDistribution: OMEGA must be a positive real scalar.> ...
 NakagamiDistribution(1, NaN)
***** error <cdf: invalid argument for upper tail.> ...
 cdf (NakagamiDistribution, 2, "uper")
***** error <cdf: invalid argument for upper tail.> ...
 cdf (NakagamiDistribution, 2, 3)
***** shared x
 x = nakarnd (1, 0.5, [1, 100]);
***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
 paramci (NakagamiDistribution.fit (x), "alpha")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (NakagamiDistribution.fit (x), "alpha", 0)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (NakagamiDistribution.fit (x), "alpha", 1)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (NakagamiDistribution.fit (x), "alpha", [0.5 2])
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (NakagamiDistribution.fit (x), "alpha", "")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (NakagamiDistribution.fit (x), "alpha", {0.05})
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (NakagamiDistribution.fit (x), "parameter", "mu", "alpha", {0.05})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (NakagamiDistribution.fit (x), "parameter", {"mu", "omega", "param"})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (NakagamiDistribution.fit (x), "alpha", 0.01, ...
          "parameter", {"mu", "omega", "param"})
***** error <paramci: unknown distribution parameter.> ...
 paramci (NakagamiDistribution.fit (x), "parameter", "param")
***** error <paramci: unknown distribution parameter.> ...
 paramci (NakagamiDistribution.fit (x), "alpha", 0.01, "parameter", "param")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (NakagamiDistribution.fit (x), "NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (NakagamiDistribution.fit (x), "alpha", 0.01, "NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (NakagamiDistribution.fit (x), "alpha", 0.01, "parameter", "mu", ...
          "NAME", "value")
***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
 plot (NakagamiDistribution, "Parent")
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (NakagamiDistribution, "PlotType", 12)
***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
 plot (NakagamiDistribution, "PlotType", {"pdf", "cdf"})
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (NakagamiDistribution, "PlotType", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (NakagamiDistribution, "Discrete", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (NakagamiDistribution, "Discrete", [1, 0])
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (NakagamiDistribution, "Discrete", {true})
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (NakagamiDistribution, "Parent", 12)
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (NakagamiDistribution, "Parent", "hax")
***** error <plot: invalid NAME for optional argument.> ...
 plot (NakagamiDistribution, "invalidNAME", "pdf")
***** error <plot: no fitted DATA to plot a probability plot.> ...
 plot (NakagamiDistribution, "PlotType", "probability")
***** error <proflik: no fitted data available.> ...
 proflik (NakagamiDistribution, 2)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (NakagamiDistribution.fit (x), 3)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (NakagamiDistribution.fit (x), [1, 2])
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (NakagamiDistribution.fit (x), {1})
***** error <proflik: SETPARAM must be a numeric vector.> ...
 proflik (NakagamiDistribution.fit (x), 1, ones (2))
***** error <proflik: missing VALUE for 'Display' argument.> ...
 proflik (NakagamiDistribution.fit (x), 1, "Display")
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (NakagamiDistribution.fit (x), 1, "Display", 1)
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (NakagamiDistribution.fit (x), 1, "Display", {1})
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (NakagamiDistribution.fit (x), 1, "Display", {"on"})
***** error <proflik: invalid VALUE size for 'Display' argument.> ...
 proflik (NakagamiDistribution.fit (x), 1, "Display", ["on"; "on"])
***** error <proflik: invalid VALUE for 'Display' argument.> ...
 proflik (NakagamiDistribution.fit (x), 1, "Display", "onnn")
***** error <proflik: invalid NAME for optional arguments.> ...
 proflik (NakagamiDistribution.fit (x), 1, "NAME", "on")
***** error <proflik: invalid optional argument.> ...
 proflik (NakagamiDistribution.fit (x), 1, {"NAME"}, "on")
***** error <proflik: invalid optional argument.> ...
 proflik (NakagamiDistribution.fit (x), 1, {[1 2 3 4]}, "Display", "on")
***** error <truncate: missing input argument.> ...
 truncate (NakagamiDistribution)
***** error <truncate: missing input argument.> ...
 truncate (NakagamiDistribution, 2)
***** error <truncate: invalid lower upper limits.> ...
 truncate (NakagamiDistribution, 4, 2)
***** shared pd
 pd = NakagamiDistribution(1, 0.5);
 pd(2) = NakagamiDistribution(1, 0.6);
***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
***** error <iqr: requires a scalar probability distribution.> iqr (pd)
***** error <mean: requires a scalar probability distribution.> mean (pd)
***** error <median: requires a scalar probability distribution.> median (pd)
***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
***** error <paramci: requires a scalar probability distribution.> paramci (pd)
***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
***** error <plot: requires a scalar probability distribution.> plot (pd)
***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
***** error <random: requires a scalar probability distribution.> random (pd)
***** error <std: requires a scalar probability distribution.> std (pd)
***** error <truncate: requires a scalar probability distribution.> ...
 truncate (pd, 2, 4)
***** error <var: requires a scalar probability distribution.> var (pd)
95 tests, 95 passed, 0 known failure, 0 skipped
[inst/dist_obj/PiecewiseLinearDistribution.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_obj/PiecewiseLinearDistribution.m
***** shared pd, t
 load patients
 [f, x] = ecdf (Weight);
 f = f(1:5:end);
 x = x(1:5:end);
 pd = PiecewiseLinearDistribution (x, f);
 t = truncate (pd, 130, 180);
***** assert (cdf (pd, [120, 130, 140, 150, 200]), [0.0767, 0.25, 0.4629, 0.5190, 0.9908], 1e-4);
***** assert (cdf (t, [120, 130, 140, 150, 200]), [0, 0, 0.4274, 0.5403, 1], 1e-4);
***** assert (cdf (pd, [100, 250, NaN]), [0, 1, NaN], 1e-4);
***** assert (cdf (t, [115, 290, NaN]), [0, 1, NaN], 1e-4);
***** assert (icdf (pd, [0:0.2:1]), [111, 127.5, 136.62, 169.67, 182.17, 202], 1e-2);
***** assert (icdf (t, [0:0.2:1]), [130, 134.15, 139.26, 162.5, 173.99, 180], 1e-2);
***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NA, 136.62, 169.67, 182.17, 202, NA], 1e-2);
***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NA, 139.26, 162.5, 173.99, 180, NA], 1e-2);
***** assert (iqr (pd), 50.0833, 1e-4);
***** assert (iqr (t), 36.8077, 1e-4);
***** assert (mean (pd), 153.61, 1e-10);
***** assert (mean (t), 152.311, 1e-3);
***** assert (median (pd), 142, 1e-10);
***** assert (median (t), 141.9462, 1e-4);
***** assert (pdf (pd, [120, 130, 140, 150, 200]), [0.0133, 0.0240, 0.0186, 0.0024, 0.0046], 1e-4);
***** assert (pdf (t, [120, 130, 140, 150, 200]), [0, 0.0482, 0.0373, 0.0048, 0], 1e-4);
***** assert (pdf (pd, [100, 250, NaN]), [0, 0, NaN], 1e-4);
***** assert (pdf (t, [100, 250, NaN]), [0, 0, NaN], 1e-4);
***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
***** assert (any (random (t, 1000, 1) < 130), false);
***** assert (any (random (t, 1000, 1) > 180), false);
***** assert (std (pd), 26.5196, 1e-4);
***** assert (std (t), 18.2941, 1e-4);
***** assert (var (pd), 703.2879, 1e-4);
***** assert (var (t), 334.6757, 1e-4);
***** error <PiecewiseLinearDistribution: X must be a real vector.> ...
 PiecewiseLinearDistribution ([0, i], [0, 1])
***** error <PiecewiseLinearDistribution: X must be a real vector.> ...
 PiecewiseLinearDistribution ([0, Inf], [0, 1])
***** error <PiecewiseLinearDistribution: X must be a real vector.> ...
 PiecewiseLinearDistribution (["a", "c"], [0, 1])
***** error <PiecewiseLinearDistribution: X must be a real vector.> ...
 PiecewiseLinearDistribution ([NaN, 1], [0, 1])
***** error <PiecewiseLinearDistribution: Fx must be a real vector.> ...
 PiecewiseLinearDistribution ([0, 1], [0, i])
***** error <PiecewiseLinearDistribution: Fx must be a real vector.> ...
 PiecewiseLinearDistribution ([0, 1], [0, Inf])
***** error <PiecewiseLinearDistribution: Fx must be a real vector.> ...
 PiecewiseLinearDistribution ([0, 1], ["a", "c"])
***** error <PiecewiseLinearDistribution: Fx must be a real vector.> ...
 PiecewiseLinearDistribution ([0, 1], [NaN, 1])
***** error <PiecewiseLinearDistribution: X and FX must be vectors of equal size.> ...
 PiecewiseLinearDistribution ([0, 1], [0, 0.5, 1])
***** error <PiecewiseLinearDistribution: X and FX must be at least two-elements long.> ...
 PiecewiseLinearDistribution ([0], [1])
***** error <PiecewiseLinearDistribution: FX must be bounded in the range> ...
 PiecewiseLinearDistribution ([0, 0.5, 1], [0, 1, 1.5])
***** error <cdf: invalid argument for upper tail.> ...
 cdf (PiecewiseLinearDistribution, 2, "uper")
***** error <cdf: invalid argument for upper tail.> ...
 cdf (PiecewiseLinearDistribution, 2, 3)
***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
 plot (PiecewiseLinearDistribution, "Parent")
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (PiecewiseLinearDistribution, "PlotType", 12)
***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
 plot (PiecewiseLinearDistribution, "PlotType", {"pdf", "cdf"})
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (PiecewiseLinearDistribution, "PlotType", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (PiecewiseLinearDistribution, "Discrete", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (PiecewiseLinearDistribution, "Discrete", [1, 0])
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (PiecewiseLinearDistribution, "Discrete", {true})
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (PiecewiseLinearDistribution, "Parent", 12)
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (PiecewiseLinearDistribution, "Parent", "hax")
***** error <plot: invalid NAME for optional argument.> ...
 plot (PiecewiseLinearDistribution, "invalidNAME", "pdf")
***** error <plot: 'probability' PlotType is not supported for 'PiecewiseLinearDistribution'.> ...
 plot (PiecewiseLinearDistribution, "PlotType", "probability")
***** error <truncate: missing input argument.> ...
 truncate (PiecewiseLinearDistribution)
***** error <truncate: missing input argument.> ...
 truncate (PiecewiseLinearDistribution, 2)
***** error <truncate: invalid lower upper limits.> ...
 truncate (PiecewiseLinearDistribution, 4, 2)
***** shared pd
 pd = PiecewiseLinearDistribution ();
 pd(2) = PiecewiseLinearDistribution ();
***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
***** error <iqr: requires a scalar probability distribution.> iqr (pd)
***** error <mean: requires a scalar probability distribution.> mean (pd)
***** error <median: requires a scalar probability distribution.> median (pd)
***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
***** error <plot: requires a scalar probability distribution.> plot (pd)
***** error <random: requires a scalar probability distribution.> random (pd)
***** error <std: requires a scalar probability distribution.> std (pd)
***** error <truncate: requires a scalar probability distribution.> ...
 truncate (pd, 2, 4)
***** error <var: requires a scalar probability distribution.> var (pd)
63 tests, 63 passed, 0 known failure, 0 skipped
[inst/dist_obj/LognormalDistribution.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_obj/LognormalDistribution.m
***** shared pd, t
 pd = LognormalDistribution;
 t = truncate (pd, 2, 4);
***** assert (cdf (pd, [0:5]), [0, 0.5, 0.7559, 0.8640, 0.9172, 0.9462], 1e-4);
***** assert (cdf (t, [0:5]), [0, 0, 0, 0.6705, 1, 1], 1e-4);
***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.6574, 0.7559, 0.8640, 0.9172], 1e-4);
***** assert (cdf (t, [1.5, 2, 3, 4]), [0, 0, 0.6705, 1], 1e-4);
***** assert (icdf (pd, [0:0.2:1]), [0, 0.4310, 0.7762, 1.2883, 2.3201, Inf], 1e-4);
***** assert (icdf (t, [0:0.2:1]), [2, 2.2256, 2.5015, 2.8517, 3.3199, 4], 1e-4);
***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 0.7762, 1.2883, 2.3201, Inf, NaN], 1e-4);
***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.5015, 2.8517, 3.3199, 4, NaN], 1e-4);
***** assert (iqr (pd), 1.4536, 1e-4);
***** assert (iqr (t), 0.8989, 1e-4);
***** assert (mean (pd), 1.6487, 1e-4);
***** assert (mean (t), 2.7692, 1e-4);
***** assert (median (pd), 1, 1e-4);
***** assert (median (t), 2.6653, 1e-4);
***** assert (pdf (pd, [0:5]), [0, 0.3989, 0.1569, 0.0727, 0.0382, 0.0219], 1e-4);
***** assert (pdf (t, [0:5]), [0, 0, 0.9727, 0.4509, 0.2366, 0], 1e-4);
***** assert (pdf (pd, [-1, 1:4, NaN]), [0, 0.3989, 0.1569, 0.0727, 0.0382, NaN], 1e-4);
***** assert (pdf (t, [-1, 1:4, NaN]), [0, 0, 0.9727, 0.4509, 0.2366, NaN], 1e-4);
***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
***** assert (any (random (t, 1000, 1) < 2), false);
***** assert (any (random (t, 1000, 1) > 4), false);
***** assert (std (pd), 2.1612, 1e-4);
***** assert (std (t), 0.5540, 1e-4);
***** assert (var (pd), 4.6708, 1e-4);
***** assert (var (t), 0.3069, 1e-4);
***** error <LognormalDistribution: MU must be a real scalar.> ...
 LognormalDistribution(Inf, 1)
***** error <LognormalDistribution: MU must be a real scalar.> ...
 LognormalDistribution(i, 1)
***** error <LognormalDistribution: MU must be a real scalar.> ...
 LognormalDistribution("a", 1)
***** error <LognormalDistribution: MU must be a real scalar.> ...
 LognormalDistribution([1, 2], 1)
***** error <LognormalDistribution: MU must be a real scalar.> ...
 LognormalDistribution(NaN, 1)
***** error <LognormalDistribution: SIGMA must be a positive real scalar.> ...
 LognormalDistribution(1, 0)
***** error <LognormalDistribution: SIGMA must be a positive real scalar.> ...
 LognormalDistribution(1, -1)
***** error <LognormalDistribution: SIGMA must be a positive real scalar.> ...
 LognormalDistribution(1, Inf)
***** error <LognormalDistribution: SIGMA must be a positive real scalar.> ...
 LognormalDistribution(1, i)
***** error <LognormalDistribution: SIGMA must be a positive real scalar.> ...
 LognormalDistribution(1, "a")
***** error <LognormalDistribution: SIGMA must be a positive real scalar.> ...
 LognormalDistribution(1, [1, 2])
***** error <LognormalDistribution: SIGMA must be a positive real scalar.> ...
 LognormalDistribution(1, NaN)
***** error <cdf: invalid argument for upper tail.> ...
 cdf (LognormalDistribution, 2, "uper")
***** error <cdf: invalid argument for upper tail.> ...
 cdf (LognormalDistribution, 2, 3)
***** shared x
 randn ("seed", 1);
 x = lognrnd (1, 1, [1, 100]);
***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
 paramci (LognormalDistribution.fit (x), "alpha")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (LognormalDistribution.fit (x), "alpha", 0)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (LognormalDistribution.fit (x), "alpha", 1)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (LognormalDistribution.fit (x), "alpha", [0.5 2])
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (LognormalDistribution.fit (x), "alpha", "")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (LognormalDistribution.fit (x), "alpha", {0.05})
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (LognormalDistribution.fit (x), "parameter", "mu", "alpha", {0.05})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (LognormalDistribution.fit (x), "parameter", {"mu", "sigma", "parm"})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (LognormalDistribution.fit (x), "alpha", 0.01, ...
          "parameter", {"mu", "sigma", "param"})
***** error <paramci: unknown distribution parameter.> ...
 paramci (LognormalDistribution.fit (x), "parameter", "param")
***** error <paramci: unknown distribution parameter.> ...
 paramci (LognormalDistribution.fit (x), "alpha", 0.01, "parameter", "param")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (LognormalDistribution.fit (x), "NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (LognormalDistribution.fit (x), "alpha", 0.01, "NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (LognormalDistribution.fit (x), "alpha", 0.01, "parameter", "mu", ...
          "NAME", "value")
***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
 plot (LognormalDistribution, "Parent")
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (LognormalDistribution, "PlotType", 12)
***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
 plot (LognormalDistribution, "PlotType", {"pdf", "cdf"})
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (LognormalDistribution, "PlotType", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (LognormalDistribution, "Discrete", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (LognormalDistribution, "Discrete", [1, 0])
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (LognormalDistribution, "Discrete", {true})
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (LognormalDistribution, "Parent", 12)
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (LognormalDistribution, "Parent", "hax")
***** error <plot: invalid NAME for optional argument.> ...
 plot (LognormalDistribution, "invalidNAME", "pdf")
***** error <plot: no fitted DATA to plot a probability plot.> ...
 plot (LognormalDistribution, "PlotType", "probability")
***** error <proflik: no fitted data available.> ...
 proflik (LognormalDistribution, 2)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (LognormalDistribution.fit (x), 3)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (LognormalDistribution.fit (x), [1, 2])
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (LognormalDistribution.fit (x), {1})
***** error <proflik: SETPARAM must be a numeric vector.> ...
 proflik (LognormalDistribution.fit (x), 1, ones (2))
***** error <proflik: missing VALUE for 'Display' argument.> ...
 proflik (LognormalDistribution.fit (x), 1, "Display")
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (LognormalDistribution.fit (x), 1, "Display", 1)
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (LognormalDistribution.fit (x), 1, "Display", {1})
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (LognormalDistribution.fit (x), 1, "Display", {"on"})
***** error <proflik: invalid VALUE size for 'Display' argument.> ...
 proflik (LognormalDistribution.fit (x), 1, "Display", ["on"; "on"])
***** error <proflik: invalid VALUE for 'Display' argument.> ...
 proflik (LognormalDistribution.fit (x), 1, "Display", "onnn")
***** error <proflik: invalid NAME for optional arguments.> ...
 proflik (LognormalDistribution.fit (x), 1, "NAME", "on")
***** error <proflik: invalid optional argument.> ...
 proflik (LognormalDistribution.fit (x), 1, {"NAME"}, "on")
***** error <proflik: invalid optional argument.> ...
 proflik (LognormalDistribution.fit (x), 1, {[1 2 3 4]}, "Display", "on")
***** error <truncate: missing input argument.> ...
 truncate (LognormalDistribution)
***** error <truncate: missing input argument.> ...
 truncate (LognormalDistribution, 2)
***** error <truncate: invalid lower upper limits.> ...
 truncate (LognormalDistribution, 4, 2)
***** shared pd
 pd = LognormalDistribution(1, 1);
 pd(2) = LognormalDistribution(1, 3);
***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
***** error <iqr: requires a scalar probability distribution.> iqr (pd)
***** error <mean: requires a scalar probability distribution.> mean (pd)
***** error <median: requires a scalar probability distribution.> median (pd)
***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
***** error <paramci: requires a scalar probability distribution.> paramci (pd)
***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
***** error <plot: requires a scalar probability distribution.> plot (pd)
***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
***** error <random: requires a scalar probability distribution.> random (pd)
***** error <std: requires a scalar probability distribution.> std (pd)
***** error <truncate: requires a scalar probability distribution.> ...
 truncate (pd, 2, 4)
***** error <var: requires a scalar probability distribution.> var (pd)
95 tests, 95 passed, 0 known failure, 0 skipped
[inst/dist_obj/HalfNormalDistribution.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_obj/HalfNormalDistribution.m
***** shared pd, t
 pd = HalfNormalDistribution (0, 1);
 t = truncate (pd, 2, 4);
***** assert (cdf (pd, [0:5]), [0, 0.6827, 0.9545, 0.9973, 0.9999, 1], 1e-4);
***** assert (cdf (t, [0:5]), [0, 0, 0, 0.9420, 1, 1], 1e-4);
***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.8664, 0.9545, 0.9973, 0.9999], 1e-4);
***** assert (cdf (t, [1.5, 2, 3, 4]), [0, 0, 0.9420, 1], 1e-4);
***** assert (icdf (pd, [0:0.2:1]), [0, 0.2533, 0.5244, 0.8416, 1.2816, Inf], 1e-4);
***** assert (icdf (t, [0:0.2:1]), [2, 2.0923, 2.2068, 2.3607, 2.6064, 4], 1e-4);
***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 0.5244, 0.8416, 1.2816, Inf, NaN], 1e-4);
***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.2068, 2.3607, 2.6064, 4, NaN], 1e-4);
***** assert (iqr (pd), 0.8317, 1e-4);
***** assert (iqr (t), 0.4111, 1e-4);
***** assert (mean (pd), 0.7979, 1e-4);
***** assert (mean (t), 2.3706, 1e-4);
***** assert (median (pd), 0.6745, 1e-4);
***** assert (median (t), 2.2771, 1e-4);
***** assert (pdf (pd, [0:5]), [0.7979, 0.4839, 0.1080, 0.0089, 0.0003, 0], 1e-4);
***** assert (pdf (t, [0:5]), [0, 0, 2.3765, 0.1951, 0.0059, 0], 1e-4);
***** assert (pdf (pd, [-1, 1:4, NaN]), [0, 0.4839, 0.1080, 0.0089, 0.0003, NaN], 1e-4);
***** assert (pdf (t, [-1, 1:4, NaN]), [0, 0, 2.3765, 0.1951, 0.0059, NaN], 1e-4);
***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
***** assert (any (random (t, 1000, 1) < 2), false);
***** assert (any (random (t, 1000, 1) > 4), false);
***** assert (std (pd), 0.6028, 1e-4);
***** assert (std (t), 0.3310, 1e-4);
***** assert (var (pd), 0.3634, 1e-4);
***** assert (var (t), 0.1096, 1e-4);
***** error <HalfNormalDistribution: MU must be a real scalar.> ...
 HalfNormalDistribution(Inf, 1)
***** error <HalfNormalDistribution: MU must be a real scalar.> ...
 HalfNormalDistribution(i, 1)
***** error <HalfNormalDistribution: MU must be a real scalar.> ...
 HalfNormalDistribution("a", 1)
***** error <HalfNormalDistribution: MU must be a real scalar.> ...
 HalfNormalDistribution([1, 2], 1)
***** error <HalfNormalDistribution: MU must be a real scalar.> ...
 HalfNormalDistribution(NaN, 1)
***** error <HalfNormalDistribution: SIGMA must be a positive real scalar.> ...
 HalfNormalDistribution(1, 0)
***** error <HalfNormalDistribution: SIGMA must be a positive real scalar.> ...
 HalfNormalDistribution(1, -1)
***** error <HalfNormalDistribution: SIGMA must be a positive real scalar.> ...
 HalfNormalDistribution(1, Inf)
***** error <HalfNormalDistribution: SIGMA must be a positive real scalar.> ...
 HalfNormalDistribution(1, i)
***** error <HalfNormalDistribution: SIGMA must be a positive real scalar.> ...
 HalfNormalDistribution(1, "a")
***** error <HalfNormalDistribution: SIGMA must be a positive real scalar.> ...
 HalfNormalDistribution(1, [1, 2])
***** error <HalfNormalDistribution: SIGMA must be a positive real scalar.> ...
 HalfNormalDistribution(1, NaN)
***** error <cdf: invalid argument for upper tail.> ...
 cdf (HalfNormalDistribution, 2, "uper")
***** error <cdf: invalid argument for upper tail.> ...
 cdf (HalfNormalDistribution, 2, 3)
***** shared x
 x = hnrnd (1, 1, [1, 100]);
***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
 paramci (HalfNormalDistribution.fit (x, 1), "alpha")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (HalfNormalDistribution.fit (x, 1), "alpha", 0)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (HalfNormalDistribution.fit (x, 1), "alpha", 1)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (HalfNormalDistribution.fit (x, 1), "alpha", [0.5 2])
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (HalfNormalDistribution.fit (x, 1), "alpha", "")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (HalfNormalDistribution.fit (x, 1), "alpha", {0.05})
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (HalfNormalDistribution.fit (x, 1), "parameter", "sigma", ...
          "alpha", {0.05})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (HalfNormalDistribution.fit (x, 1), ...
          "parameter", {"mu", "sigma", "param"})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (HalfNormalDistribution.fit (x, 1), "alpha", 0.01, ...
          "parameter", {"mu", "sigma", "param"})
***** error <paramci: unknown distribution parameter.> ...
 paramci (HalfNormalDistribution.fit (x, 1), "parameter", "param")
***** error <paramci: unknown distribution parameter.> ...
 paramci (HalfNormalDistribution.fit (x, 1), "alpha", 0.01, ...
          "parameter", "param")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (HalfNormalDistribution.fit (x, 1),"NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (HalfNormalDistribution.fit (x, 1), "alpha", 0.01, ...
          "NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (HalfNormalDistribution.fit (x, 1), "alpha", 0.01, ...
          "parameter", "sigma", "NAME", "value")
***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
 plot (HalfNormalDistribution, "Parent")
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (HalfNormalDistribution, "PlotType", 12)
***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
 plot (HalfNormalDistribution, "PlotType", {"pdf", "cdf"})
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (HalfNormalDistribution, "PlotType", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (HalfNormalDistribution, "Discrete", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (HalfNormalDistribution, "Discrete", [1, 0])
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (HalfNormalDistribution, "Discrete", {true})
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (HalfNormalDistribution, "Parent", 12)
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (HalfNormalDistribution, "Parent", "hax")
***** error <plot: invalid NAME for optional argument.> ...
 plot (HalfNormalDistribution, "invalidNAME", "pdf")
***** error <plot: no fitted DATA to plot a probability plot.> ...
 plot (HalfNormalDistribution, "PlotType", "probability")
***** error <proflik: no fitted data available.> ...
 proflik (HalfNormalDistribution, 2)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (HalfNormalDistribution.fit (x, 1), 3)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (HalfNormalDistribution.fit (x, 1), [1, 2])
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (HalfNormalDistribution.fit (x, 1), {1})
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (HalfNormalDistribution.fit (x, 1), 1)
***** error <proflik: SETPARAM must be a numeric vector.> ...
 proflik (HalfNormalDistribution.fit (x, 1), 2, ones (2))
***** error <proflik: missing VALUE for 'Display' argument.> ...
 proflik (HalfNormalDistribution.fit (x, 1), 2, "Display")
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (HalfNormalDistribution.fit (x, 1), 2, "Display", 1)
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (HalfNormalDistribution.fit (x, 1), 2, "Display", {1})
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (HalfNormalDistribution.fit (x, 1), 2, "Display", {"on"})
***** error <proflik: invalid VALUE size for 'Display' argument.> ...
 proflik (HalfNormalDistribution.fit (x, 1), 2, "Display", ["on"; "on"])
***** error <proflik: invalid VALUE for 'Display' argument.> ...
 proflik (HalfNormalDistribution.fit (x, 1), 2, "Display", "onnn")
***** error <proflik: invalid NAME for optional arguments.> ...
 proflik (HalfNormalDistribution.fit (x, 1), 2, "NAME", "on")
***** error <proflik: invalid optional argument.> ...
 proflik (HalfNormalDistribution.fit (x, 1), 2, {"NAME"}, "on")
***** error <proflik: invalid optional argument.> ...
 proflik (HalfNormalDistribution.fit (x, 1), 2, {[1 2 3 4]}, ...
          "Display", "on")
***** error <truncate: missing input argument.> ...
 truncate (HalfNormalDistribution)
***** error <truncate: missing input argument.> ...
 truncate (HalfNormalDistribution, 2)
***** error <truncate: invalid lower upper limits.> ...
 truncate (HalfNormalDistribution, 4, 2)
***** shared pd
 pd = HalfNormalDistribution(1, 1);
 pd(2) = HalfNormalDistribution(1, 3);
***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
***** error <iqr: requires a scalar probability distribution.> iqr (pd)
***** error <mean: requires a scalar probability distribution.> mean (pd)
***** error <median: requires a scalar probability distribution.> median (pd)
***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
***** error <paramci: requires a scalar probability distribution.> paramci (pd)
***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
***** error <plot: requires a scalar probability distribution.> plot (pd)
***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
***** error <random: requires a scalar probability distribution.> random (pd)
***** error <std: requires a scalar probability distribution.> std (pd)
***** error <truncate: requires a scalar probability distribution.> ...
 truncate (pd, 2, 4)
***** error <var: requires a scalar probability distribution.> var (pd)
96 tests, 96 passed, 0 known failure, 0 skipped
[inst/dist_obj/BinomialDistribution.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_obj/BinomialDistribution.m
***** shared pd, t, t_inf
 pd = BinomialDistribution (5, 0.5);
 t = truncate (pd, 2, 4);
 t_inf = truncate (pd, 2, Inf);
***** assert (cdf (pd, [0:5]), [0.0312, 0.1875, 0.5, 0.8125, 0.9688, 1], 1e-4);
***** assert (cdf (t, [0:5]), [0, 0, 0.4, 0.8, 1, 1], 1e-4);
***** assert (cdf (t_inf, [0:5]), [0, 0, 0.3846, 0.7692, 0.9615, 1], 1e-4);
***** assert (cdf (pd, [1.5, 2, 3, 4, NaN]), [0.1875, 0.5, 0.8125, 0.9688, NaN], 1e-4);
***** assert (cdf (t, [1.5, 2, 3, 4, NaN]), [0, 0.4, 0.8, 1, NaN], 1e-4);
***** assert (icdf (pd, [0:0.2:1]), [0, 2, 2, 3, 3, 5], 1e-4);
***** assert (icdf (t, [0:0.2:1]), [2, 2, 2, 3, 3, 4], 1e-4);
***** assert (icdf (t_inf, [0:0.2:1]), [2, 2, 3, 3, 4, 5], 1e-4);
***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 2, 3, 3, 5, NaN], 1e-4);
***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2, 3, 3, 4, NaN], 1e-4);
***** assert (iqr (pd), 1);
***** assert (iqr (t), 1);
***** assert (mean (pd), 2.5, 1e-10);
***** assert (mean (t), 2.8, 1e-10);
***** assert (mean (t_inf), 2.8846, 1e-4);
***** assert (median (pd), 2.5);
***** assert (median (t), 3);
***** assert (pdf (pd, [0:5]), [0.0312, 0.1562, 0.3125, 0.3125, 0.1562, 0.0312], 1e-4);
***** assert (pdf (t, [0:5]), [0, 0, 0.4, 0.4, 0.2, 0], 1e-4);
***** assert (pdf (t_inf, [0:5]), [0, 0, 0.3846, 0.3846, 0.1923, 0.0385], 1e-4);
***** assert (pdf (pd, [-1, 1.5, NaN]), [0, 0, NaN], 1e-4);
***** assert (pdf (t, [-1, 1.5, NaN]), [0, 0, NaN], 1e-4);
***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
***** assert (any (random (t, 1000, 1) < 2), false);
***** assert (any (random (t, 1000, 1) > 4), false);
***** assert (std (pd), 1.1180, 1e-4);
***** assert (std (t), 0.7483, 1e-4);
***** assert (std (t_inf), 0.8470, 1e-4);
***** assert (var (pd), 1.2500, 1e-4);
***** assert (var (t), 0.5600, 1e-4);
***** assert (var (t_inf), 0.7175, 1e-4);
***** error <BinomialDistribution: N must be a positive integer scalar.> ...
 BinomialDistribution(Inf, 0.5)
***** error <BinomialDistribution: N must be a positive integer scalar.> ...
 BinomialDistribution(i, 0.5)
***** error <BinomialDistribution: N must be a positive integer scalar.> ...
 BinomialDistribution("a", 0.5)
***** error <BinomialDistribution: N must be a positive integer scalar.> ...
 BinomialDistribution([1, 2], 0.5)
***** error <BinomialDistribution: N must be a positive integer scalar.> ...
 BinomialDistribution(NaN, 0.5)
***** error <BinomialDistribution: p must be a real scalar bounded in the range> ...
 BinomialDistribution(1, 1.01)
***** error <BinomialDistribution: p must be a real scalar bounded in the range> ...
 BinomialDistribution(1, -0.01)
***** error <BinomialDistribution: p must be a real scalar bounded in the range> ...
 BinomialDistribution(1, Inf)
***** error <BinomialDistribution: p must be a real scalar bounded in the range> ...
 BinomialDistribution(1, i)
***** error <BinomialDistribution: p must be a real scalar bounded in the range> ...
 BinomialDistribution(1, "a")
***** error <BinomialDistribution: p must be a real scalar bounded in the range> ...
 BinomialDistribution(1, [1, 2])
***** error <BinomialDistribution: p must be a real scalar bounded in the range> ...
 BinomialDistribution(1, NaN)
***** error <cdf: invalid argument for upper tail.> ...
 cdf (BinomialDistribution, 2, "uper")
***** error <cdf: invalid argument for upper tail.> ...
 cdf (BinomialDistribution, 2, 3)
***** shared x
 rand ("seed", 2);
 x = binornd (5, 0.5, [1, 100]);
***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
 paramci (BinomialDistribution.fit (x, 6), "alpha")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (BinomialDistribution.fit (x, 6), "alpha", 0)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (BinomialDistribution.fit (x, 6), "alpha", 1)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (BinomialDistribution.fit (x, 6), "alpha", [0.5 2])
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (BinomialDistribution.fit (x, 6), "alpha", "")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (BinomialDistribution.fit (x, 6), "alpha", {0.05})
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (BinomialDistribution.fit (x, 6), "parameter", "p", ...
          "alpha", {0.05})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (BinomialDistribution.fit (x, 6), ...
          "parameter", {"N", "p", "param"})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (BinomialDistribution.fit (x, 6), "alpha", 0.01, ...
          "parameter", {"N", "p", "param"})
***** error <paramci: unknown distribution parameter.> ...
 paramci (BinomialDistribution.fit (x, 6), "parameter", "param")
***** error <paramci: unknown distribution parameter.> ...
 paramci (BinomialDistribution.fit (x, 6), "parameter", "N")
***** error <paramci: unknown distribution parameter.> ...
 paramci (BinomialDistribution.fit (x, 6), "alpha", 0.01, ...
          "parameter", "param")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (BinomialDistribution.fit (x, 6), "NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (BinomialDistribution.fit (x, 6), "alpha", 0.01, ...
          "NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (BinomialDistribution.fit (x, 6), "alpha", 0.01, ...
          "parameter", "p", "NAME", "value")
***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
 plot (BinomialDistribution, "Parent")
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (BinomialDistribution, "PlotType", 12)
***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
 plot (BinomialDistribution, "PlotType", {"pdf", "cdf"})
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (BinomialDistribution, "PlotType", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (BinomialDistribution, "Discrete", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (BinomialDistribution, "Discrete", [1, 0])
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (BinomialDistribution, "Discrete", {true})
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (BinomialDistribution, "Parent", 12)
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (BinomialDistribution, "Parent", "hax")
***** error <plot: invalid NAME for optional argument.> ...
 plot (BinomialDistribution, "invalidNAME", "pdf")
***** error <plot: no fitted DATA to plot a probability plot.> ...
 plot (BinomialDistribution, "PlotType", "probability")
***** error <proflik: no fitted data available.> ...
 proflik (BinomialDistribution, 2)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (BinomialDistribution.fit (x, 6), 3)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (BinomialDistribution.fit (x, 6), [1, 2])
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (BinomialDistribution.fit (x, 6), {1})
***** error <proflik: SETPARAM must be a numeric vector.> ...
 proflik (BinomialDistribution.fit (x, 6), 2, ones (2))
***** error <proflik: missing VALUE for 'Display' argument.> ...
 proflik (BinomialDistribution.fit (x, 6), 2, "Display")
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (BinomialDistribution.fit (x, 6), 2, "Display", 1)
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (BinomialDistribution.fit (x, 6), 2, "Display", {1})
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (BinomialDistribution.fit (x, 6), 2, "Display", {"on"})
***** error <proflik: invalid VALUE size for 'Display' argument.> ...
 proflik (BinomialDistribution.fit (x, 6), 2, "Display", ["on"; "on"])
***** error <proflik: invalid VALUE for 'Display' argument.> ...
 proflik (BinomialDistribution.fit (x, 6), 2, "Display", "onnn")
***** error <proflik: invalid NAME for optional arguments.> ...
 proflik (BinomialDistribution.fit (x, 6), 2, "NAME", "on")
***** error <proflik: invalid optional argument.> ...
 proflik (BinomialDistribution.fit (x, 6), 2, {"NAME"}, "on")
***** error <proflik: invalid optional argument.> ...
 proflik (BinomialDistribution.fit (x, 6), 2, {[1 2 3]}, "Display", "on")
***** error <truncate: missing input argument.> ...
 truncate (BinomialDistribution)
***** error <truncate: missing input argument.> ...
 truncate (BinomialDistribution, 2)
***** error <truncate: invalid lower upper limits.> ...
 truncate (BinomialDistribution, 4, 2)
***** shared pd
 pd = BinomialDistribution(1, 0.5);
 pd(2) = BinomialDistribution(1, 0.6);
***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
***** error <iqr: requires a scalar probability distribution.> iqr (pd)
***** error <mean: requires a scalar probability distribution.> mean (pd)
***** error <median: requires a scalar probability distribution.> median (pd)
***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
***** error <paramci: requires a scalar probability distribution.> paramci (pd)
***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
***** error <plot: requires a scalar probability distribution.> plot (pd)
***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
***** error <random: requires a scalar probability distribution.> random (pd)
***** error <std: requires a scalar probability distribution.> std (pd)
***** error <truncate: requires a scalar probability distribution.> ...
 truncate (pd, 2, 4)
***** error <var: requires a scalar probability distribution.> var (pd)
102 tests, 102 passed, 0 known failure, 0 skipped
[inst/dist_obj/tLocationScaleDistribution.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_obj/tLocationScaleDistribution.m
***** shared pd, t
 pd = tLocationScaleDistribution;
 t = truncate (pd, 2, 4);
***** assert (cdf (pd, [0:5]), [0.5, 0.8184, 0.9490, 0.9850, 0.9948, 0.9979], 1e-4);
***** assert (cdf (t, [0:5]), [0, 0, 0, 0.7841, 1, 1], 1e-4);
***** assert (cdf (pd, [1.5, 2, 3, 4, NaN]), [0.9030, 0.9490, 0.9850, 0.9948, NaN], 1e-4);
***** assert (cdf (t, [1.5, 2, 3, 4, NaN]), [0, 0, 0.7841, 1, NaN], 1e-4);
***** assert (icdf (pd, [0:0.2:1]), [-Inf, -0.9195, -0.2672, 0.2672, 0.9195, Inf], 1e-4);
***** assert (icdf (t, [0:0.2:1]), [2, 2.1559, 2.3533, 2.6223, 3.0432, 4], 1e-4);
***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, -0.2672, 0.2672, 0.9195, Inf, NaN], 1e-4);
***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.3533, 2.6223, 3.0432, 4, NaN], 1e-4);
***** assert (iqr (pd), 1.4534, 1e-4);
***** assert (iqr (t), 0.7139, 1e-4);
***** assert (mean (pd), 0, eps);
***** assert (mean (t), 2.6099, 1e-4);
***** assert (median (pd), 0, eps);
***** assert (median (t), 2.4758, 1e-4);
***** assert (pdf (pd, [0:5]), [0.3796, 0.2197, 0.0651, 0.0173, 0.0051, 0.0018], 1e-4);
***** assert (pdf (t, [0:5]), [0, 0, 1.4209, 0.3775, 0.1119, 0], 1e-4);
***** assert (pdf (pd, [-1, 1.5, NaN]), [0.2197, 0.1245, NaN], 1e-4);
***** assert (pdf (t, [-1, 1.5, NaN]), [0, 0, NaN], 1e-4);
***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
***** assert (any (random (t, 1000, 1) < 2), false);
***** assert (any (random (t, 1000, 1) > 4), false);
***** assert (std (pd), 1.2910, 1e-4);
***** assert (std (t), 0.4989, 1e-4);
***** assert (var (pd), 1.6667, 1e-4);
***** assert (var (t), 0.2489, 1e-4);
***** error <tLocationScaleDistribution: MU must be a real scalar.> ...
 tLocationScaleDistribution(i, 1, 1)
***** error <tLocationScaleDistribution: MU must be a real scalar.> ...
 tLocationScaleDistribution(Inf, 1, 1)
***** error <tLocationScaleDistribution: MU must be a real scalar.> ...
 tLocationScaleDistribution([1, 2], 1, 1)
***** error <tLocationScaleDistribution: MU must be a real scalar.> ...
 tLocationScaleDistribution("a", 1, 1)
***** error <tLocationScaleDistribution: MU must be a real scalar.> ...
 tLocationScaleDistribution(NaN, 1, 1)
***** error <tLocationScaleDistribution: SIGMA must be a positive real scalar.> ...
 tLocationScaleDistribution(0, 0, 1)
***** error <tLocationScaleDistribution: SIGMA must be a positive real scalar.> ...
 tLocationScaleDistribution(0, -1, 1)
***** error <tLocationScaleDistribution: SIGMA must be a positive real scalar.> ...
 tLocationScaleDistribution(0, Inf, 1)
***** error <tLocationScaleDistribution: SIGMA must be a positive real scalar.> ...
 tLocationScaleDistribution(0, i, 1)
***** error <tLocationScaleDistribution: SIGMA must be a positive real scalar.> ...
 tLocationScaleDistribution(0, "a", 1)
***** error <tLocationScaleDistribution: SIGMA must be a positive real scalar.> ...
 tLocationScaleDistribution(0, [1, 2], 1)
***** error <tLocationScaleDistribution: SIGMA must be a positive real scalar.> ...
 tLocationScaleDistribution(0, NaN, 1)
***** error <tLocationScaleDistribution: NU must be a positive real scalar.> ...
 tLocationScaleDistribution(0, 1, 0)
***** error <tLocationScaleDistribution: NU must be a positive real scalar.> ...
 tLocationScaleDistribution(0, 1, -1)
***** error <tLocationScaleDistribution: NU must be a positive real scalar.> ...
 tLocationScaleDistribution(0, 1, Inf)
***** error <tLocationScaleDistribution: NU must be a positive real scalar.> ...
 tLocationScaleDistribution(0, 1, i)
***** error <tLocationScaleDistribution: NU must be a positive real scalar.> ...
 tLocationScaleDistribution(0, 1, "a")
***** error <tLocationScaleDistribution: NU must be a positive real scalar.> ...
 tLocationScaleDistribution(0, 1, [1, 2])
***** error <tLocationScaleDistribution: NU must be a positive real scalar.> ...
 tLocationScaleDistribution(0, 1, NaN)
***** error <cdf: invalid argument for upper tail.> ...
 cdf (tLocationScaleDistribution, 2, "uper")
***** error <cdf: invalid argument for upper tail.> ...
 cdf (tLocationScaleDistribution, 2, 3)
***** shared x
 x = tlsrnd (0, 1, 1, [1, 100]);
***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
 paramci (tLocationScaleDistribution.fit (x), "alpha")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (tLocationScaleDistribution.fit (x), "alpha", 0)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (tLocationScaleDistribution.fit (x), "alpha", 1)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (tLocationScaleDistribution.fit (x), "alpha", [0.5 2])
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (tLocationScaleDistribution.fit (x), "alpha", "")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (tLocationScaleDistribution.fit (x), "alpha", {0.05})
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (tLocationScaleDistribution.fit (x), "parameter", "mu", ...
          "alpha", {0.05})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (tLocationScaleDistribution.fit (x), ...
          "parameter", {"mu", "sigma", "nu", "param"})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (tLocationScaleDistribution.fit (x), "alpha", 0.01, ...
          "parameter", {"mu", "sigma", "nu", "param"})
***** error <paramci: unknown distribution parameter.> ...
 paramci (tLocationScaleDistribution.fit (x), "parameter", "param")
***** error <paramci: unknown distribution parameter.> ...
 paramci (tLocationScaleDistribution.fit (x), "alpha", 0.01, ...
          "parameter", "param")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (tLocationScaleDistribution.fit (x), "NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (tLocationScaleDistribution.fit (x), "alpha", 0.01, "NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (tLocationScaleDistribution.fit (x), "alpha", 0.01, ...
          "parameter", "mu", "NAME", "value")
***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
 plot (tLocationScaleDistribution, "Parent")
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (tLocationScaleDistribution, "PlotType", 12)
***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
 plot (tLocationScaleDistribution, "PlotType", {"pdf", "cdf"})
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (tLocationScaleDistribution, "PlotType", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (tLocationScaleDistribution, "Discrete", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (tLocationScaleDistribution, "Discrete", [1, 0])
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (tLocationScaleDistribution, "Discrete", {true})
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (tLocationScaleDistribution, "Parent", 12)
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (tLocationScaleDistribution, "Parent", "hax")
***** error <plot: invalid NAME for optional argument.> ...
 plot (tLocationScaleDistribution, "invalidNAME", "pdf")
***** error <plot: no fitted DATA to plot a probability plot.> ...
 plot (tLocationScaleDistribution, "PlotType", "probability")
***** error <proflik: no fitted data available.> ...
 proflik (tLocationScaleDistribution, 2)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (tLocationScaleDistribution.fit (x), 4)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (tLocationScaleDistribution.fit (x), [1, 2])
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (tLocationScaleDistribution.fit (x), {1})
***** error <proflik: SETPARAM must be a numeric vector.> ...
 proflik (tLocationScaleDistribution.fit (x), 1, ones (2))
***** error <proflik: missing VALUE for 'Display' argument.> ...
 proflik (tLocationScaleDistribution.fit (x), 1, "Display")
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (tLocationScaleDistribution.fit (x), 1, "Display", 1)
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (tLocationScaleDistribution.fit (x), 1, "Display", {1})
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (tLocationScaleDistribution.fit (x), 1, "Display", {"on"})
***** error <proflik: invalid VALUE size for 'Display' argument.> ...
 proflik (tLocationScaleDistribution.fit (x), 1, "Display", ["on"; "on"])
***** error <proflik: invalid VALUE for 'Display' argument.> ...
 proflik (tLocationScaleDistribution.fit (x), 1, "Display", "onnn")
***** error <proflik: invalid NAME for optional arguments.> ...
 proflik (tLocationScaleDistribution.fit (x), 1, "NAME", "on")
***** error <proflik: invalid optional argument.> ...
 proflik (tLocationScaleDistribution.fit (x), 1, {"NAME"}, "on")
***** error <proflik: invalid optional argument.> ...
 proflik (tLocationScaleDistribution.fit (x), 1, {[1 2 3 4]}, "Display", "on")
***** error <truncate: missing input argument.> ...
 truncate (tLocationScaleDistribution)
***** error <truncate: missing input argument.> ...
 truncate (tLocationScaleDistribution, 2)
***** error <truncate: invalid lower upper limits.> ...
 truncate (tLocationScaleDistribution, 4, 2)
***** shared pd
 pd = tLocationScaleDistribution (0, 1, 1);
 pd(2) = tLocationScaleDistribution (0, 1, 3);
***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
***** error <iqr: requires a scalar probability distribution.> iqr (pd)
***** error <mean: requires a scalar probability distribution.> mean (pd)
***** error <median: requires a scalar probability distribution.> median (pd)
***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
***** error <paramci: requires a scalar probability distribution.> paramci (pd)
***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
***** error <plot: requires a scalar probability distribution.> plot (pd)
***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
***** error <random: requires a scalar probability distribution.> random (pd)
***** error <std: requires a scalar probability distribution.> std (pd)
***** error <truncate: requires a scalar probability distribution.> ...
 truncate (pd, 2, 4)
***** error <var: requires a scalar probability distribution.> var (pd)
102 tests, 102 passed, 0 known failure, 0 skipped
[inst/dist_obj/NegativeBinomialDistribution.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_obj/NegativeBinomialDistribution.m
***** shared pd, t, t_inf
 pd = NegativeBinomialDistribution (5, 0.5);
 t = truncate (pd, 2, 4);
 t_inf = truncate (pd, 2, Inf);
***** assert (cdf (pd, [0:5]), [0.0312, 0.1094, 0.2266, 0.3633, 0.5, 0.6230], 1e-4);
***** assert (cdf (t, [0:5]), [0, 0, 0.3, 0.65, 1, 1], 1e-4);
***** assert (cdf (t_inf, [0:5]), [0, 0, 0.1316, 0.2851, 0.4386, 0.5768], 1e-4);
***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.1094, 0.2266, 0.3633, 0.5000], 1e-4);
***** assert (cdf (t, [1.5, 2, 3, 4]), [0, 0.3, 0.65, 1], 1e-4);
***** assert (icdf (pd, [0:0.2:1]), [0, 2, 4, 5, 7, Inf], 1e-4);
***** assert (icdf (t, [0:0.2:1]), [2, 2, 3, 3, 4, 4], 1e-4);
***** assert (icdf (t_inf, [0:0.2:1]), [2, 3, 4, 6, 8, Inf], 1e-4);
***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 4, 5, 7, Inf, NaN], 1e-4);
***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 3, 3, 4, 4, NaN], 1e-4);
***** assert (iqr (pd), 4);
***** assert (iqr (t), 2);
***** assert (mean (pd), 5);
***** assert (mean (t), 3.0500, 1e-4);
***** assert (mean (t_inf), 5.5263, 1e-4);
***** assert (median (pd), 4);
***** assert (median (t), 3);
***** assert (pdf (pd, [0:5]), [0.0312, 0.0781, 0.1172, 0.1367, 0.1367, 0.1230], 1e-4);
***** assert (pdf (t, [0:5]), [0, 0, 0.3, 0.35, 0.35, 0], 1e-4);
***** assert (pdf (t_inf, [0:5]), [0, 0, 0.1316, 0.1535, 0.1535, 0.1382], 1e-4);
***** assert (pdf (pd, [-1, 1:4, NaN]), [0, 0.0781, 0.1172, 0.1367, 0.1367, NaN], 1e-4);
***** assert (pdf (t, [-1, 1:4, NaN]), [0, 0, 0.3, 0.35, 0.35, NaN], 1e-4);
***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
***** assert (any (random (t, 1000, 1) < 2), false);
***** assert (any (random (t, 1000, 1) > 4), false);
***** assert (std (pd), 3.1623, 1e-4);
***** assert (std (t), 0.8047, 1e-4);
***** assert (std (t_inf), 2.9445, 1e-4);
***** assert (var (pd), 10);
***** assert (var (t), 0.6475, 1e-4);
***** assert (var (t_inf), 8.6704, 1e-4);
***** error <NegativeBinomialDistribution: R must be a positive scalar.> ...
 NegativeBinomialDistribution(Inf, 1)
***** error <NegativeBinomialDistribution: R must be a positive scalar.> ...
 NegativeBinomialDistribution(i, 1)
***** error <NegativeBinomialDistribution: R must be a positive scalar.> ...
 NegativeBinomialDistribution("a", 1)
***** error <NegativeBinomialDistribution: R must be a positive scalar.> ...
 NegativeBinomialDistribution([1, 2], 1)
***** error <NegativeBinomialDistribution: R must be a positive scalar.> ...
 NegativeBinomialDistribution(NaN, 1)
***** error <NegativeBinomialDistribution: P must be a real scalar bounded in the range> ...
 NegativeBinomialDistribution(1, 0)
***** error <NegativeBinomialDistribution: P must be a real scalar bounded in the range> ...
 NegativeBinomialDistribution(1, -1)
***** error <NegativeBinomialDistribution: P must be a real scalar bounded in the range> ...
 NegativeBinomialDistribution(1, Inf)
***** error <NegativeBinomialDistribution: P must be a real scalar bounded in the range> ...
 NegativeBinomialDistribution(1, i)
***** error <NegativeBinomialDistribution: P must be a real scalar bounded in the range> ...
 NegativeBinomialDistribution(1, "a")
***** error <NegativeBinomialDistribution: P must be a real scalar bounded in the range> ...
 NegativeBinomialDistribution(1, [1, 2])
***** error <NegativeBinomialDistribution: P must be a real scalar bounded in the range> ...
 NegativeBinomialDistribution(1, NaN)
***** error <NegativeBinomialDistribution: P must be a real scalar bounded in the range> ...
 NegativeBinomialDistribution(1, 1.2)
***** error <cdf: invalid argument for upper tail.> ...
 cdf (NegativeBinomialDistribution, 2, "uper")
***** error <cdf: invalid argument for upper tail.> ...
 cdf (NegativeBinomialDistribution, 2, 3)
***** shared x
 x = nbinrnd (1, 0.5, [1, 100]);
***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
 paramci (NegativeBinomialDistribution.fit (x), "alpha")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (NegativeBinomialDistribution.fit (x), "alpha", 0)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (NegativeBinomialDistribution.fit (x), "alpha", 1)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (NegativeBinomialDistribution.fit (x), "alpha", [0.5 2])
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (NegativeBinomialDistribution.fit (x), "alpha", "")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (NegativeBinomialDistribution.fit (x), "alpha", {0.05})
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (NegativeBinomialDistribution.fit (x), "parameter", "R", ...
          "alpha", {0.05})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (NegativeBinomialDistribution.fit (x), ...
          "parameter", {"R", "P", "param"})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (NegativeBinomialDistribution.fit (x), "alpha", 0.01, ...
          "parameter", {"R", "P", "param"})
***** error <paramci: unknown distribution parameter.> ...
 paramci (NegativeBinomialDistribution.fit (x), "parameter", "param")
***** error <paramci: unknown distribution parameter.> ...
 paramci (NegativeBinomialDistribution.fit (x), "alpha", 0.01, ...
          "parameter", "param")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (NegativeBinomialDistribution.fit (x), "NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (NegativeBinomialDistribution.fit (x), "alpha", 0.01, ...
          "NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (NegativeBinomialDistribution.fit (x), "alpha", 0.01, ...
          "parameter", "R", "NAME", "value")
***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
 plot (NegativeBinomialDistribution, "Parent")
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (NegativeBinomialDistribution, "PlotType", 12)
***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
 plot (NegativeBinomialDistribution, "PlotType", {"pdf", "cdf"})
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (NegativeBinomialDistribution, "PlotType", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (NegativeBinomialDistribution, "Discrete", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (NegativeBinomialDistribution, "Discrete", [1, 0])
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (NegativeBinomialDistribution, "Discrete", {true})
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (NegativeBinomialDistribution, "Parent", 12)
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (NegativeBinomialDistribution, "Parent", "hax")
***** error <plot: invalid NAME for optional argument.> ...
 plot (NegativeBinomialDistribution, "invalidNAME", "pdf")
***** error <plot: no fitted DATA to plot a probability plot.> ...
 plot (NegativeBinomialDistribution, "PlotType", "probability")
***** error <proflik: no fitted data available.> ...
 proflik (NegativeBinomialDistribution, 2)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (NegativeBinomialDistribution.fit (x), 3)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (NegativeBinomialDistribution.fit (x), [1, 2])
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (NegativeBinomialDistribution.fit (x), {1})
***** error <proflik: SETPARAM must be a numeric vector.> ...
 proflik (NegativeBinomialDistribution.fit (x), 1, ones (2))
***** error <proflik: missing VALUE for 'Display' argument.> ...
 proflik (NegativeBinomialDistribution.fit (x), 1, "Display")
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (NegativeBinomialDistribution.fit (x), 1, "Display", 1)
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (NegativeBinomialDistribution.fit (x), 1, "Display", {1})
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (NegativeBinomialDistribution.fit (x), 1, "Display", {"on"})
***** error <proflik: invalid VALUE size for 'Display' argument.> ...
 proflik (NegativeBinomialDistribution.fit (x), 1, "Display", ["on"; "on"])
***** error <proflik: invalid VALUE for 'Display' argument.> ...
 proflik (NegativeBinomialDistribution.fit (x), 1, "Display", "onnn")
***** error <proflik: invalid NAME for optional arguments.> ...
 proflik (NegativeBinomialDistribution.fit (x), 1, "NAME", "on")
***** error <proflik: invalid optional argument.> ...
 proflik (NegativeBinomialDistribution.fit (x), 1, {"NAME"}, "on")
***** error <proflik: invalid optional argument.> ...
 proflik (NegativeBinomialDistribution.fit (x), 1, {[1 2 3]}, "Display", "on")
***** error <truncate: missing input argument.> ...
 truncate (NegativeBinomialDistribution)
***** error <truncate: missing input argument.> ...
 truncate (NegativeBinomialDistribution, 2)
***** error <truncate: invalid lower upper limits.> ...
 truncate (NegativeBinomialDistribution, 4, 2)
***** shared pd
 pd = NegativeBinomialDistribution(1, 0.5);
 pd(2) = NegativeBinomialDistribution(1, 0.6);
***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
***** error <iqr: requires a scalar probability distribution.> iqr (pd)
***** error <mean: requires a scalar probability distribution.> mean (pd)
***** error <median: requires a scalar probability distribution.> median (pd)
***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
***** error <paramci: requires a scalar probability distribution.> paramci (pd)
***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
***** error <plot: requires a scalar probability distribution.> plot (pd)
***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
***** error <random: requires a scalar probability distribution.> random (pd)
***** error <std: requires a scalar probability distribution.> std (pd)
***** error <truncate: requires a scalar probability distribution.> ...
 truncate (pd, 2, 4)
***** error <var: requires a scalar probability distribution.> var (pd)
102 tests, 102 passed, 0 known failure, 0 skipped
[inst/dist_obj/BurrDistribution.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_obj/BurrDistribution.m
***** demo
 ## Generate a data set of 5000 random samples from a Burr type XII
 ## distribution with parameters alpha = 1, c = 2, and k = 1.  Fit a Burr type
 ## XII distribution to this data and plot a PDF of the fitted distribution
 ## superimposed on a histogram of the data

 pd = makedist ("Burr", "alpha", 1, "c", 2, "k", 1)
 rand ("seed", 21);
 data = random (pd, 5000, 1);
 pd = fitdist (data, "Burr")
 plot (pd)
 msg = strcat (["Fitted Burr type XII distribution with"], ...
                [" alpha = %0.2f, c =  %0.2f, and k = %0.2f"]);
 title (sprintf (msg, pd.alpha, pd.c, pd.k))
***** demo
 ## Plot the PDF of a Burr type XII distribution, with parameters alpha = 1,
 ## c = 2, and k = 1, truncated at [0, 2] intervals. Generate 10000 random
 ## samples from this truncated distribution and superimpose a histogram with
 ## 100 bins scaled accordingly

 pd = makedist ("Burr", "alpha", 1, "c", 2, "k", 1)
 t = truncate (pd, 0.5, 2.5)
 rand ("seed", 21);
 data = random (t, 10000, 1);
 plot (t)
 title ("Burr type XII distribution (alpha = 1, c = 2, k = 1) truncated at [0.5, 2.5]")
 hold on
 hist (data, 100, 50)
 hold off
***** demo
 ## Generate a data set of 100 random samples from a Burr type XII
 ## distribution with parameters alpha = 1, c = 2, and k = 1.  Fit a Burr type
 ## XII  distribution to this data and plot its CDF superimposed over an
 ## empirical CDF of the data

 pd = makedist ("Burr", "alpha", 1, "c", 2, "k", 1)
 rand ("seed", 21);
 data = random (pd, 100, 1);
 pd = fitdist (data, "Burr")
 plot (pd, "plottype", "cdf")
 msg = strcat (["Fitted Burr type XII distribution with"], ...
                [" alpha = %0.2f, c =  %0.2f, and k = %0.2f"]);
 title (sprintf (msg, pd.alpha, pd.c, pd.k))
 legend ({"empirical CDF", "fitted CDF"}, "location", "east")
***** shared pd, t
 pd = BurrDistribution;
 t = truncate (pd, 2, 4);
***** assert (cdf (pd, [0:5]), [0, 0.5, 0.6667, 0.75, 0.8, 0.8333], 1e-4);
***** assert (cdf (t, [0:5]), [0, 0, 0, 0.625, 1, 1], 1e-4);
***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.6, 0.6667, 0.75, 0.8], 1e-4);
***** assert (cdf (t, [1.5, 2, 3, 4]), [0, 0, 0.625, 1], 1e-4);
***** assert (icdf (pd, [0:0.2:1]), [0, 0.25, 0.6667, 1.5, 4, Inf], 1e-4);
***** assert (icdf (t, [0:0.2:1]), [2, 2.2609, 2.5714, 2.9474, 3.4118, 4], 1e-4);
***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 0.6667, 1.5, 4, Inf, NaN], 1e-4);
***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.5714, 2.9474, 3.4118, 4, NaN], 1e-4);
***** assert (iqr (pd), 2.6667, 1e-4);
***** assert (iqr (t), 0.9524, 1e-4);
***** assert (mean (pd), Inf);
***** assert (mean (t), 2.8312, 1e-4);
***** assert (median (pd), 1, 1e-4);
***** assert (median (t), 2.75, 1e-4);
***** assert (pdf (pd, [0:5]), [1, 0.25, 0.1111, 0.0625, 0.04, 0.0278], 1e-4);
***** assert (pdf (t, [0:5]), [0, 0, 0.8333, 0.4687, 0.3, 0], 1e-4);
***** assert (pdf (pd, [-1, 1:4, NaN]), [0, 0.25, 0.1111, 0.0625, 0.04, NaN], 1e-4);
***** assert (pdf (t, [-1, 1:4, NaN]), [0, 0, 0.8333, 0.4687, 0.3, NaN], 1e-4);
***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
***** assert (any (random (t, 1000, 1) < 2), false);
***** assert (any (random (t, 1000, 1) > 4), false);
***** assert (std (pd), Inf);
***** assert (std (t), 0.5674, 1e-4);
***** assert (var (pd), Inf);
***** assert (var (t), 0.3220, 1e-4);
***** error <BurrDistribution: ALPHA must be a positive real scalar.> ...
 BurrDistribution(0, 1, 1)
***** error <BurrDistribution: ALPHA must be a positive real scalar.> ...
 BurrDistribution(-1, 1, 1)
***** error <BurrDistribution: ALPHA must be a positive real scalar.> ...
 BurrDistribution(Inf, 1, 1)
***** error <BurrDistribution: ALPHA must be a positive real scalar.> ...
 BurrDistribution(i, 1, 1)
***** error <BurrDistribution: ALPHA must be a positive real scalar.> ...
 BurrDistribution("a", 1, 1)
***** error <BurrDistribution: ALPHA must be a positive real scalar.> ...
 BurrDistribution([1, 2], 1, 1)
***** error <BurrDistribution: ALPHA must be a positive real scalar.> ...
 BurrDistribution(NaN, 1, 1)
***** error <BurrDistribution: C must be a positive real scalar.> ...
 BurrDistribution(1, 0, 1)
***** error <BurrDistribution: C must be a positive real scalar.> ...
 BurrDistribution(1, -1, 1)
***** error <BurrDistribution: C must be a positive real scalar.> ...
 BurrDistribution(1, Inf, 1)
***** error <BurrDistribution: C must be a positive real scalar.> ...
 BurrDistribution(1, i, 1)
***** error <BurrDistribution: C must be a positive real scalar.> ...
 BurrDistribution(1, "a", 1)
***** error <BurrDistribution: C must be a positive real scalar.> ...
 BurrDistribution(1, [1, 2], 1)
***** error <BurrDistribution: C must be a positive real scalar.> ...
 BurrDistribution(1, NaN, 1)
***** error <BurrDistribution: K must be a positive real scalar.> ...
 BurrDistribution(1, 1, 0)
***** error <BurrDistribution: K must be a positive real scalar.> ...
 BurrDistribution(1, 1, -1)
***** error <BurrDistribution: K must be a positive real scalar.> ...
 BurrDistribution(1, 1, Inf)
***** error <BurrDistribution: K must be a positive real scalar.> ...
 BurrDistribution(1, 1, i)
***** error <BurrDistribution: K must be a positive real scalar.> ...
 BurrDistribution(1, 1, "a")
***** error <BurrDistribution: K must be a positive real scalar.> ...
 BurrDistribution(1, 1, [1, 2])
***** error <BurrDistribution: K must be a positive real scalar.> ...
 BurrDistribution(1, 1, NaN)
***** error <cdf: invalid argument for upper tail.> ...
 cdf (BurrDistribution, 2, "uper")
***** error <cdf: invalid argument for upper tail.> ...
 cdf (BurrDistribution, 2, 3)
***** shared x
 rand ("seed", 4);
 x = burrrnd (1, 1, 1, [1, 100]);
***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
 paramci (BurrDistribution.fit (x), "alpha")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (BurrDistribution.fit (x), "alpha", 0)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (BurrDistribution.fit (x), "alpha", 1)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (BurrDistribution.fit (x), "alpha", [0.5 2])
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (BurrDistribution.fit (x), "alpha", "")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (BurrDistribution.fit (x), "alpha", {0.05})
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (BurrDistribution.fit (x), "parameter", "c", "alpha", {0.05})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (BurrDistribution.fit (x), "parameter", {"alpha", "c", "k", "param"})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (BurrDistribution.fit (x), "alpha", 0.01, ...
          "parameter", {"alpha", "c", "k", "param"})
***** error <paramci: unknown distribution parameter.> ...
 paramci (BurrDistribution.fit (x), "parameter", "param")
***** error <paramci: unknown distribution parameter.> ...
 paramci (BurrDistribution.fit (x), "alpha", 0.01, "parameter", "param")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (BurrDistribution.fit (x), "NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (BurrDistribution.fit (x), "alpha", 0.01, "NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (BurrDistribution.fit (x), "alpha", 0.01, "parameter", "c", ...
          "NAME", "value")
***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
 plot (BurrDistribution, "Parent")
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (BurrDistribution, "PlotType", 12)
***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
 plot (BurrDistribution, "PlotType", {"pdf", "cdf"})
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (BurrDistribution, "PlotType", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (BurrDistribution, "Discrete", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (BurrDistribution, "Discrete", [1, 0])
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (BurrDistribution, "Discrete", {true})
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (BurrDistribution, "Parent", 12)
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (BurrDistribution, "Parent", "hax")
***** error <plot: invalid NAME for optional argument.> ...
 plot (BurrDistribution, "invalidNAME", "pdf")
***** error <plot: no fitted DATA to plot a probability plot.> ...
 plot (BurrDistribution, "PlotType", "probability")
***** error <proflik: no fitted data available.> ...
 proflik (BurrDistribution, 2)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (BurrDistribution.fit (x), 4)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (BurrDistribution.fit (x), [1, 2])
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (BurrDistribution.fit (x), {1})
***** error <proflik: SETPARAM must be a numeric vector.> ...
 proflik (BurrDistribution.fit (x), 1, ones (2))
***** error <proflik: missing VALUE for 'Display' argument.> ...
 proflik (BurrDistribution.fit (x), 1, "Display")
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (BurrDistribution.fit (x), 1, "Display", 1)
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (BurrDistribution.fit (x), 1, "Display", {1})
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (BurrDistribution.fit (x), 1, "Display", {"on"})
***** error <proflik: invalid VALUE size for 'Display' argument.> ...
 proflik (BurrDistribution.fit (x), 1, "Display", ["on"; "on"])
***** error <proflik: invalid VALUE for 'Display' argument.> ...
 proflik (BurrDistribution.fit (x), 1, "Display", "onnn")
***** error <proflik: invalid NAME for optional arguments.> ...
 proflik (BurrDistribution.fit (x), 1, "NAME", "on")
***** error <proflik: invalid optional argument.> ...
 proflik (BurrDistribution.fit (x), 1, {"NAME"}, "on")
***** error <proflik: invalid optional argument.> ...
 proflik (BurrDistribution.fit (x), 1, {[1 2 3 4]}, "Display", "on")
***** error <truncate: missing input argument.> ...
 truncate (BurrDistribution)
***** error <truncate: missing input argument.> ...
 truncate (BurrDistribution, 2)
***** error <truncate: invalid lower upper limits.> ...
 truncate (BurrDistribution, 4, 2)
***** shared pd
 pd = BurrDistribution(1, 1, 1);
 pd(2) = BurrDistribution(1, 3, 1);
***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
***** error <iqr: requires a scalar probability distribution.> iqr (pd)
***** error <mean: requires a scalar probability distribution.> mean (pd)
***** error <median: requires a scalar probability distribution.> median (pd)
***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
***** error <paramci: requires a scalar probability distribution.> paramci (pd)
***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
***** error <plot: requires a scalar probability distribution.> plot (pd)
***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
***** error <random: requires a scalar probability distribution.> random (pd)
***** error <std: requires a scalar probability distribution.> std (pd)
***** error <truncate: requires a scalar probability distribution.> ...
 truncate (pd, 2, 4)
***** error <var: requires a scalar probability distribution.> var (pd)
104 tests, 104 passed, 0 known failure, 0 skipped
[inst/dist_obj/RicianDistribution.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_obj/RicianDistribution.m
***** shared pd, t
 pd = RicianDistribution;
 t = truncate (pd, 2, 4);
***** assert (cdf (pd, [0:5]), [0, 0.2671, 0.7310, 0.9563, 0.9971, 0.9999], 1e-4);
***** assert (cdf (t, [0:5]), [0, 0, 0, 0.8466, 1, 1], 1e-4);
***** assert (cdf (pd, [1.5, 2, 3, 4, NaN]), [0.5120, 0.7310, 0.9563, 0.9971, NaN], 1e-4);
***** assert (cdf (t, [1.5, 2, 3, 4, NaN]), [0, 0, 0.8466, 1, NaN], 1e-4);
***** assert (icdf (pd, [0:0.2:1]), [0, 0.8501, 1.2736, 1.6863, 2.2011, Inf], 1e-4);
***** assert (icdf (t, [0:0.2:1]), [2, 2.1517, 2.3296, 2.5545, 2.8868, 4], 1e-4);
***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 1.2736, 1.6863, 2.2011, Inf, NaN], 1e-4);
***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.3296, 2.5545, 2.8868, 4, NaN], 1e-4);
***** assert (iqr (pd), 1.0890, 1e-4);
***** assert (iqr (t), 0.5928, 1e-4);
***** assert (mean (pd), 1.5486, 1e-4);
***** assert (mean (t), 2.5380, 1e-4);
***** assert (median (pd), 1.4755, 1e-4);
***** assert (median (t), 2.4341, 1e-4);
***** assert (pdf (pd, [0:5]), [0, 0.4658, 0.3742, 0.0987, 0.0092, 0.0003], 1e-4);
***** assert (pdf (t, [0:5]), [0, 0, 1.4063, 0.3707, 0.0346, 0], 1e-4);
***** assert (pdf (pd, [-1, 1.5, NaN]), [0, 0.4864, NaN], 1e-4);
***** assert (pdf (t, [-1, 1.5, NaN]), [0, 0, NaN], 1e-4);
***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
***** assert (any (random (t, 1000, 1) < 2), false);
***** assert (any (random (t, 1000, 1) > 4), false);
***** assert (std (pd), 0.7758, 1e-4);
***** assert (std (t), 0.4294, 1e-4);
***** assert (var (pd), 0.6019, 1e-4);
***** assert (var (t), 0.1844, 1e-4);
***** error <RicianDistribution: NU must be a non-negative real scalar.> ...
 RicianDistribution(-eps, 1)
***** error <RicianDistribution: NU must be a non-negative real scalar.> ...
 RicianDistribution(-1, 1)
***** error <RicianDistribution: NU must be a non-negative real scalar.> ...
 RicianDistribution(Inf, 1)
***** error <RicianDistribution: NU must be a non-negative real scalar.> ...
 RicianDistribution(i, 1)
***** error <RicianDistribution: NU must be a non-negative real scalar.> ...
 RicianDistribution("a", 1)
***** error <RicianDistribution: NU must be a non-negative real scalar.> ...
 RicianDistribution([1, 2], 1)
***** error <RicianDistribution: NU must be a non-negative real scalar.> ...
 RicianDistribution(NaN, 1)
***** error <RicianDistribution: SIGMA must be a positive real scalar.> ...
 RicianDistribution(1, 0)
***** error <RicianDistribution: SIGMA must be a positive real scalar.> ...
 RicianDistribution(1, -1)
***** error <RicianDistribution: SIGMA must be a positive real scalar.> ...
 RicianDistribution(1, Inf)
***** error <RicianDistribution: SIGMA must be a positive real scalar.> ...
 RicianDistribution(1, i)
***** error <RicianDistribution: SIGMA must be a positive real scalar.> ...
 RicianDistribution(1, "a")
***** error <RicianDistribution: SIGMA must be a positive real scalar.> ...
 RicianDistribution(1, [1, 2])
***** error <RicianDistribution: SIGMA must be a positive real scalar.> ...
 RicianDistribution(1, NaN)
***** error <cdf: invalid argument for upper tail.> ...
 cdf (RicianDistribution, 2, "uper")
***** error <cdf: invalid argument for upper tail.> ...
 cdf (RicianDistribution, 2, 3)
***** shared x
 x = gevrnd (1, 1, 1, [1, 100]);
***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
 paramci (RicianDistribution.fit (x), "alpha")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (RicianDistribution.fit (x), "alpha", 0)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (RicianDistribution.fit (x), "alpha", 1)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (RicianDistribution.fit (x), "alpha", [0.5 2])
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (RicianDistribution.fit (x), "alpha", "")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (RicianDistribution.fit (x), "alpha", {0.05})
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (RicianDistribution.fit (x), "parameter", "s", "alpha", {0.05})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (RicianDistribution.fit (x), "parameter", {"s", "sigma", "param"})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (RicianDistribution.fit (x), "alpha", 0.01, ...
          "parameter", {"s", "sigma", "param"})
***** error <paramci: unknown distribution parameter.> ...
 paramci (RicianDistribution.fit (x), "parameter", "param")
***** error <paramci: unknown distribution parameter.> ...
 paramci (RicianDistribution.fit (x), "alpha", 0.01, "parameter", "param")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (RicianDistribution.fit (x), "NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (RicianDistribution.fit (x), "alpha", 0.01, "NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (RicianDistribution.fit (x), "alpha", 0.01, "parameter", "s", ...
          "NAME", "value")
***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
 plot (RicianDistribution, "Parent")
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (RicianDistribution, "PlotType", 12)
***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
 plot (RicianDistribution, "PlotType", {"pdf", "cdf"})
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (RicianDistribution, "PlotType", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (RicianDistribution, "Discrete", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (RicianDistribution, "Discrete", [1, 0])
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (RicianDistribution, "Discrete", {true})
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (RicianDistribution, "Parent", 12)
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (RicianDistribution, "Parent", "hax")
***** error <plot: invalid NAME for optional argument.> ...
 plot (RicianDistribution, "invalidNAME", "pdf")
***** error <plot: no fitted DATA to plot a probability plot.> ...
 plot (RicianDistribution, "PlotType", "probability")
***** error <proflik: no fitted data available.> ...
 proflik (RicianDistribution, 2)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (RicianDistribution.fit (x), 3)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (RicianDistribution.fit (x), [1, 2])
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (RicianDistribution.fit (x), {1})
***** error <proflik: SETPARAM must be a numeric vector.> ...
 proflik (RicianDistribution.fit (x), 1, ones (2))
***** error <proflik: missing VALUE for 'Display' argument.> ...
 proflik (RicianDistribution.fit (x), 1, "Display")
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (RicianDistribution.fit (x), 1, "Display", 1)
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (RicianDistribution.fit (x), 1, "Display", {1})
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (RicianDistribution.fit (x), 1, "Display", {"on"})
***** error <proflik: invalid VALUE size for 'Display' argument.> ...
 proflik (RicianDistribution.fit (x), 1, "Display", ["on"; "on"])
***** error <proflik: invalid VALUE for 'Display' argument.> ...
 proflik (RicianDistribution.fit (x), 1, "Display", "onnn")
***** error <proflik: invalid NAME for optional arguments.> ...
 proflik (RicianDistribution.fit (x), 1, "NAME", "on")
***** error <proflik: invalid optional argument.> ...
 proflik (RicianDistribution.fit (x), 1, {"NAME"}, "on")
***** error <proflik: invalid optional argument.> ...
 proflik (RicianDistribution.fit (x), 1, {[1 2 3 4]}, "Display", "on")
***** error <truncate: missing input argument.> ...
 truncate (RicianDistribution)
***** error <truncate: missing input argument.> ...
 truncate (RicianDistribution, 2)
***** error <truncate: invalid lower upper limits.> ...
 truncate (RicianDistribution, 4, 2)
***** shared pd
 pd = RicianDistribution(1, 1);
 pd(2) = RicianDistribution(1, 3);
***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
***** error <iqr: requires a scalar probability distribution.> iqr (pd)
***** error <mean: requires a scalar probability distribution.> mean (pd)
***** error <median: requires a scalar probability distribution.> median (pd)
***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
***** error <paramci: requires a scalar probability distribution.> paramci (pd)
***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
***** error <plot: requires a scalar probability distribution.> plot (pd)
***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
***** error <random: requires a scalar probability distribution.> random (pd)
***** error <std: requires a scalar probability distribution.> std (pd)
***** error <truncate: requires a scalar probability distribution.> ...
 truncate (pd, 2, 4)
***** error <var: requires a scalar probability distribution.> var (pd)
97 tests, 97 passed, 0 known failure, 0 skipped
[inst/dist_obj/LoglogisticDistribution.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_obj/LoglogisticDistribution.m
***** shared pd, t
 pd = LoglogisticDistribution;
 t = truncate (pd, 2, 4);
***** assert (cdf (pd, [0:5]), [0, 0.5, 0.6667, 0.75, 0.8, 0.8333], 1e-4);
***** assert (cdf (t, [0:5]), [0, 0, 0, 0.625, 1, 1], 1e-4);
***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.6, 0.6667, 0.75, 0.8], 1e-4);
***** assert (cdf (t, [1.5, 2, 3, 4]), [0, 0, 0.625, 1], 1e-4);
***** assert (icdf (pd, [0:0.2:1]), [0, 0.25, 0.6667, 1.5, 4, Inf], 1e-4);
***** assert (icdf (t, [0:0.2:1]), [2, 2.2609, 2.5714, 2.9474, 3.4118, 4], 1e-4);
***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 0.6667, 1.5, 4, Inf, NaN], 1e-4);
***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.5714, 2.9474, 3.4118, 4, NaN], 1e-4);
***** assert (iqr (pd), 2.6667, 1e-4);
***** assert (iqr (t), 0.9524, 1e-4);
***** assert (mean (pd), Inf);
***** assert (mean (t), 2.8312, 1e-4);
***** assert (median (pd), 1, 1e-4);
***** assert (median (t), 2.75, 1e-4);
***** assert (pdf (pd, [0:5]), [0, 0.25, 0.1111, 0.0625, 0.04, 0.0278], 1e-4);
***** assert (pdf (t, [0:5]), [0, 0, 0.8333, 0.4687, 0.3, 0], 1e-4);
***** assert (pdf (pd, [-1, 1:4, NaN]), [0, 0.25, 0.1111, 0.0625, 0.04, NaN], 1e-4);
***** assert (pdf (t, [-1, 1:4, NaN]), [0, 0, 0.8333, 0.4687, 0.3, NaN], 1e-4);
***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
***** assert (any (random (t, 1000, 1) < 2), false);
***** assert (any (random (t, 1000, 1) > 4), false);
***** assert (std (pd), Inf);
***** assert (std (t), 0.5674, 1e-4);
***** assert (var (pd), Inf);
***** assert (var (t), 0.3220, 1e-4);
***** error <LoglogisticDistribution: MU must be a nonnegative real scalar.> ...
 LoglogisticDistribution(Inf, 1)
***** error <LoglogisticDistribution: MU must be a nonnegative real scalar.> ...
 LoglogisticDistribution(i, 1)
***** error <LoglogisticDistribution: MU must be a nonnegative real scalar.> ...
 LoglogisticDistribution("a", 1)
***** error <LoglogisticDistribution: MU must be a nonnegative real scalar.> ...
 LoglogisticDistribution([1, 2], 1)
***** error <LoglogisticDistribution: MU must be a nonnegative real scalar.> ...
 LoglogisticDistribution(NaN, 1)
***** error <LoglogisticDistribution: SIGMA must be a positive real scalar.> ...
 LoglogisticDistribution(1, 0)
***** error <LoglogisticDistribution: SIGMA must be a positive real scalar.> ...
 LoglogisticDistribution(1, -1)
***** error <LoglogisticDistribution: SIGMA must be a positive real scalar.> ...
 LoglogisticDistribution(1, Inf)
***** error <LoglogisticDistribution: SIGMA must be a positive real scalar.> ...
 LoglogisticDistribution(1, i)
***** error <LoglogisticDistribution: SIGMA must be a positive real scalar.> ...
 LoglogisticDistribution(1, "a")
***** error <LoglogisticDistribution: SIGMA must be a positive real scalar.> ...
 LoglogisticDistribution(1, [1, 2])
***** error <LoglogisticDistribution: SIGMA must be a positive real scalar.> ...
 LoglogisticDistribution(1, NaN)
***** error <cdf: invalid argument for upper tail.> ...
 cdf (LoglogisticDistribution, 2, "uper")
***** error <cdf: invalid argument for upper tail.> ...
 cdf (LoglogisticDistribution, 2, 3)
***** shared x
 x = loglrnd (1, 1, [1, 100]);
***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
 paramci (LoglogisticDistribution.fit (x), "alpha")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (LoglogisticDistribution.fit (x), "alpha", 0)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (LoglogisticDistribution.fit (x), "alpha", 1)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (LoglogisticDistribution.fit (x), "alpha", [0.5 2])
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (LoglogisticDistribution.fit (x), "alpha", "")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (LoglogisticDistribution.fit (x), "alpha", {0.05})
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (LoglogisticDistribution.fit (x), "parameter", "mu", "alpha", {0.05})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (LoglogisticDistribution.fit (x), "parameter", {"mu", "sigma", "pa"})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (LoglogisticDistribution.fit (x), "alpha", 0.01, ...
          "parameter", {"mu", "sigma", "param"})
***** error <paramci: unknown distribution parameter.> ...
 paramci (LoglogisticDistribution.fit (x), "parameter", "param")
***** error <paramci: unknown distribution parameter.> ...
 paramci (LoglogisticDistribution.fit (x), "alpha", 0.01, "parameter", "parm")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (LoglogisticDistribution.fit (x), "NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (LoglogisticDistribution.fit (x), "alpha", 0.01, "NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (LoglogisticDistribution.fit (x), "alpha", 0.01, ...
          "parameter", "mu", "NAME", "value")
***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
 plot (LoglogisticDistribution, "Parent")
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (LoglogisticDistribution, "PlotType", 12)
***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
 plot (LoglogisticDistribution, "PlotType", {"pdf", "cdf"})
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (LoglogisticDistribution, "PlotType", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (LoglogisticDistribution, "Discrete", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (LoglogisticDistribution, "Discrete", [1, 0])
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (LoglogisticDistribution, "Discrete", {true})
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (LoglogisticDistribution, "Parent", 12)
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (LoglogisticDistribution, "Parent", "hax")
***** error <plot: invalid NAME for optional argument.> ...
 plot (LoglogisticDistribution, "invalidNAME", "pdf")
***** error <plot: no fitted DATA to plot a probability plot.> ...
 plot (LoglogisticDistribution, "PlotType", "probability")
***** error <proflik: no fitted data available.> ...
 proflik (LoglogisticDistribution, 2)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (LoglogisticDistribution.fit (x), 3)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (LoglogisticDistribution.fit (x), [1, 2])
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (LoglogisticDistribution.fit (x), {1})
***** error <proflik: SETPARAM must be a numeric vector.> ...
 proflik (LoglogisticDistribution.fit (x), 1, ones (2))
***** error <proflik: missing VALUE for 'Display' argument.> ...
 proflik (LoglogisticDistribution.fit (x), 1, "Display")
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (LoglogisticDistribution.fit (x), 1, "Display", 1)
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (LoglogisticDistribution.fit (x), 1, "Display", {1})
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (LoglogisticDistribution.fit (x), 1, "Display", {"on"})
***** error <proflik: invalid VALUE size for 'Display' argument.> ...
 proflik (LoglogisticDistribution.fit (x), 1, "Display", ["on"; "on"])
***** error <proflik: invalid VALUE for 'Display' argument.> ...
 proflik (LoglogisticDistribution.fit (x), 1, "Display", "onnn")
***** error <proflik: invalid NAME for optional arguments.> ...
 proflik (LoglogisticDistribution.fit (x), 1, "NAME", "on")
***** error <proflik: invalid optional argument.> ...
 proflik (LoglogisticDistribution.fit (x), 1, {"NAME"}, "on")
***** error <proflik: invalid optional argument.> ...
 proflik (LoglogisticDistribution.fit (x), 1, {[1 2 3 4]}, "Display", "on")
***** error <truncate: missing input argument.> ...
 truncate (LoglogisticDistribution)
***** error <truncate: missing input argument.> ...
 truncate (LoglogisticDistribution, 2)
***** error <truncate: invalid lower upper limits.> ...
 truncate (LoglogisticDistribution, 4, 2)
***** shared pd
 pd = LoglogisticDistribution(1, 1);
 pd(2) = LoglogisticDistribution(1, 3);
***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
***** error <iqr: requires a scalar probability distribution.> iqr (pd)
***** error <mean: requires a scalar probability distribution.> mean (pd)
***** error <median: requires a scalar probability distribution.> median (pd)
***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
***** error <paramci: requires a scalar probability distribution.> paramci (pd)
***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
***** error <plot: requires a scalar probability distribution.> plot (pd)
***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
***** error <random: requires a scalar probability distribution.> random (pd)
***** error <std: requires a scalar probability distribution.> std (pd)
***** error <truncate: requires a scalar probability distribution.> ...
 truncate (pd, 2, 4)
***** error <var: requires a scalar probability distribution.> var (pd)
95 tests, 95 passed, 0 known failure, 0 skipped
[inst/dist_obj/ExponentialDistribution.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_obj/ExponentialDistribution.m
***** shared pd, t
 pd = ExponentialDistribution (1);
 t = truncate (pd, 2, 4);
***** assert (cdf (pd, [0:5]), [0, 0.6321, 0.8647, 0.9502, 0.9817, 0.9933], 1e-4);
***** assert (cdf (t, [0:5]), [0, 0, 0, 0.7311, 1, 1], 1e-4);
***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.7769, 0.8647, 0.9502, 0.9817], 1e-4);
***** assert (cdf (t, [1.5, 2, 3, 4]), [0, 0, 0.7311, 1], 1e-4);
***** assert (icdf (pd, [0:0.2:1]), [0, 0.2231, 0.5108, 0.9163, 1.6094, Inf], 1e-4);
***** assert (icdf (t, [0:0.2:1]), [2, 2.1899, 2.4244, 2.7315, 3.1768, 4], 1e-4);
***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 0.5108, 0.9163, 1.6094, Inf, NaN], 1e-4);
***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.4244, 2.7315, 3.1768, 4, NaN], 1e-4);
***** assert (iqr (pd), 1.0986, 1e-4);
***** assert (iqr (t), 0.8020, 1e-4);
***** assert (mean (pd), 1);
***** assert (mean (t), 2.6870, 1e-4);
***** assert (median (pd), 0.6931, 1e-4);
***** assert (median (t), 2.5662, 1e-4);
***** assert (pdf (pd, [0:5]), [1, 0.3679, 0.1353, 0.0498, 0.0183, 0.0067], 1e-4);
***** assert (pdf (t, [0:5]), [0, 0, 1.1565, 0.4255, 0.1565, 0], 1e-4);
***** assert (pdf (pd, [-1, 1:4, NaN]), [0, 0.3679, 0.1353, 0.0498, 0.0183, NaN], 1e-4);
***** assert (pdf (t, [-1, 1:4, NaN]), [0, 0, 1.1565, 0.4255, 0.1565, NaN], 1e-4);
***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
***** assert (any (random (t, 1000, 1) < 2), false);
***** assert (any (random (t, 1000, 1) > 4), false);
***** assert (std (pd), 1);
***** assert (std (t), 0.5253, 1e-4);
***** assert (var (pd), 1);
***** assert (var (t), 0.2759, 1e-4);
***** error <ExponentialDistribution: MU must be a positive real scalar.> ...
 ExponentialDistribution(0)
***** error <ExponentialDistribution: MU must be a positive real scalar.> ...
 ExponentialDistribution(-1)
***** error <ExponentialDistribution: MU must be a positive real scalar.> ...
 ExponentialDistribution(Inf)
***** error <ExponentialDistribution: MU must be a positive real scalar.> ...
 ExponentialDistribution(i)
***** error <ExponentialDistribution: MU must be a positive real scalar.> ...
 ExponentialDistribution("a")
***** error <ExponentialDistribution: MU must be a positive real scalar.> ...
 ExponentialDistribution([1, 2])
***** error <ExponentialDistribution: MU must be a positive real scalar.> ...
 ExponentialDistribution(NaN)
***** error <cdf: invalid argument for upper tail.> ...
 cdf (ExponentialDistribution, 2, "uper")
***** error <cdf: invalid argument for upper tail.> ...
 cdf (ExponentialDistribution, 2, 3)
***** shared x
 x = exprnd (1, [100, 1]);
***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
 paramci (ExponentialDistribution.fit (x), "alpha")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (ExponentialDistribution.fit (x), "alpha", 0)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (ExponentialDistribution.fit (x), "alpha", 1)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (ExponentialDistribution.fit (x), "alpha", [0.5 2])
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (ExponentialDistribution.fit (x), "alpha", "")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (ExponentialDistribution.fit (x), "alpha", {0.05})
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (ExponentialDistribution.fit (x), "parameter", "mu", ...
          "alpha", {0.05})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (ExponentialDistribution.fit (x), "parameter", {"mu", "param"})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (ExponentialDistribution.fit (x), "alpha", 0.01, ...
          "parameter", {"mu", "param"})
***** error <paramci: unknown distribution parameter.> ...
 paramci (ExponentialDistribution.fit (x), "parameter", "param")
***** error <paramci: unknown distribution parameter.> ...
 paramci (ExponentialDistribution.fit (x), "alpha", 0.01, "parameter", "parm")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (ExponentialDistribution.fit (x), "NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (ExponentialDistribution.fit (x), "alpha", 0.01, "NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (ExponentialDistribution.fit (x), "alpha", 0.01, ...
          "parameter", "mu", "NAME", "value")
***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
 plot (ExponentialDistribution, "Parent")
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (ExponentialDistribution, "PlotType", 12)
***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
 plot (ExponentialDistribution, "PlotType", {"pdf", "cdf"})
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (ExponentialDistribution, "PlotType", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (ExponentialDistribution, "Discrete", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (ExponentialDistribution, "Discrete", [1, 0])
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (ExponentialDistribution, "Discrete", {true})
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (ExponentialDistribution, "Parent", 12)
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (ExponentialDistribution, "Parent", "hax")
***** error <plot: invalid NAME for optional argument.> ...
 plot (ExponentialDistribution, "invalidNAME", "pdf")
***** error <plot: no fitted DATA to plot a probability plot.> ...
 plot (ExponentialDistribution, "PlotType", "probability")
***** error <proflik: no fitted data available.> ...
 proflik (ExponentialDistribution, 2)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (ExponentialDistribution.fit (x), 3)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (ExponentialDistribution.fit (x), [1, 2])
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (ExponentialDistribution.fit (x), {1})
***** error <proflik: SETPARAM must be a numeric vector.> ...
 proflik (ExponentialDistribution.fit (x), 1, ones (2))
***** error <proflik: missing VALUE for 'Display' argument.> ...
 proflik (ExponentialDistribution.fit (x), 1, "Display")
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (ExponentialDistribution.fit (x), 1, "Display", 1)
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (ExponentialDistribution.fit (x), 1, "Display", {1})
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (ExponentialDistribution.fit (x), 1, "Display", {"on"})
***** error <proflik: invalid VALUE size for 'Display' argument.> ...
 proflik (ExponentialDistribution.fit (x), 1, "Display", ["on"; "on"])
***** error <proflik: invalid VALUE for 'Display' argument.> ...
 proflik (ExponentialDistribution.fit (x), 1, "Display", "onnn")
***** error <proflik: invalid NAME for optional arguments.> ...
 proflik (ExponentialDistribution.fit (x), 1, "NAME", "on")
***** error <proflik: invalid optional argument.> ...
 proflik (ExponentialDistribution.fit (x), 1, {"NAME"}, "on")
***** error <proflik: invalid optional argument.> ...
 proflik (ExponentialDistribution.fit (x), 1, {[1 2 3 4]}, "Display", "on")
***** error <truncate: missing input argument.> ...
 truncate (ExponentialDistribution)
***** error <truncate: missing input argument.> ...
 truncate (ExponentialDistribution, 2)
***** error <truncate: invalid lower upper limits.> ...
 truncate (ExponentialDistribution, 4, 2)
***** shared pd
 pd = ExponentialDistribution(1);
 pd(2) = ExponentialDistribution(3);
***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
***** error <iqr: requires a scalar probability distribution.> iqr (pd)
***** error <mean: requires a scalar probability distribution.> mean (pd)
***** error <median: requires a scalar probability distribution.> median (pd)
***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
***** error <paramci: requires a scalar probability distribution.> paramci (pd)
***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
***** error <plot: requires a scalar probability distribution.> plot (pd)
***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
***** error <random: requires a scalar probability distribution.> random (pd)
***** error <std: requires a scalar probability distribution.> std (pd)
***** error <truncate: requires a scalar probability distribution.> ...
 truncate (pd, 2, 4)
***** error <var: requires a scalar probability distribution.> var (pd)
90 tests, 90 passed, 0 known failure, 0 skipped
[inst/dist_obj/RayleighDistribution.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_obj/RayleighDistribution.m
***** shared pd, t
 pd = RayleighDistribution;
 t = truncate (pd, 2, 4);
***** assert (cdf (pd, [0:5]), [0, 0.3935, 0.8647, 0.9889, 0.9997, 1], 1e-4);
***** assert (cdf (t, [0:5]), [0, 0, 0, 0.9202, 1, 1], 1e-4);
***** assert (cdf (pd, [1.5, 2, 3, 4, NaN]), [0.6753, 0.8647, 0.9889, 0.9997, NaN], 1e-4);
***** assert (cdf (t, [1.5, 2, 3, 4, NaN]), [0, 0, 0.9202, 1, NaN], 1e-4);
***** assert (icdf (pd, [0:0.2:1]), [0, 0.6680, 1.0108, 1.3537, 1.7941, Inf], 1e-4);
***** assert (icdf (t, [0:0.2:1]), [2, 2.1083, 2.2402, 2.4135, 2.6831, 4], 1e-4);
***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 1.0108, 1.3537, 1.7941, Inf, NaN], 1e-4);
***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.2402, 2.4135, 2.6831, 4, NaN], 1e-4);
***** assert (iqr (pd), 0.9066, 1e-4);
***** assert (iqr (t), 0.4609, 1e-4);
***** assert (mean (pd), 1.2533, 1e-4);
***** assert (mean (t), 2.4169, 1e-4);
***** assert (median (pd), 1.1774, 1e-4);
***** assert (median (t), 2.3198, 1e-4);
***** assert (pdf (pd, [0:5]), [0, 0.6065, 0.2707, 0.0333, 0.0013, 0], 1e-4);
***** assert (pdf (t, [0:5]), [0, 0, 2.0050, 0.2469, 0.0099, 0], 1e-4);
***** assert (pdf (pd, [-1, 1.5, NaN]), [0, 0.4870, NaN], 1e-4);
***** assert (pdf (t, [-1, 1.5, NaN]), [0, 0, NaN], 1e-4);
***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
***** assert (any (random (t, 1000, 1) < 2), false);
***** assert (any (random (t, 1000, 1) > 4), false);
***** assert (std (pd), 0.6551, 1e-4);
***** assert (std (t), 0.3591, 1e-4);
***** assert (var (pd), 0.4292, 1e-4);
***** assert (var (t), 0.1290, 1e-4);
***** error <RayleighDistribution: SIGMA must be a positive real scalar.> ...
 RayleighDistribution(0)
***** error <RayleighDistribution: SIGMA must be a positive real scalar.> ...
 RayleighDistribution(-1)
***** error <RayleighDistribution: SIGMA must be a positive real scalar.> ...
 RayleighDistribution(Inf)
***** error <RayleighDistribution: SIGMA must be a positive real scalar.> ...
 RayleighDistribution(i)
***** error <RayleighDistribution: SIGMA must be a positive real scalar.> ...
 RayleighDistribution("a")
***** error <RayleighDistribution: SIGMA must be a positive real scalar.> ...
 RayleighDistribution([1, 2])
***** error <RayleighDistribution: SIGMA must be a positive real scalar.> ...
 RayleighDistribution(NaN)
***** error <cdf: invalid argument for upper tail.> ...
 cdf (RayleighDistribution, 2, "uper")
***** error <cdf: invalid argument for upper tail.> ...
 cdf (RayleighDistribution, 2, 3)
***** shared x
 x = raylrnd (1, [1, 100]);
***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
 paramci (RayleighDistribution.fit (x), "alpha")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (RayleighDistribution.fit (x), "alpha", 0)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (RayleighDistribution.fit (x), "alpha", 1)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (RayleighDistribution.fit (x), "alpha", [0.5 2])
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (RayleighDistribution.fit (x), "alpha", "")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (RayleighDistribution.fit (x), "alpha", {0.05})
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (RayleighDistribution.fit (x), "parameter", "sigma", "alpha", {0.05})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (RayleighDistribution.fit (x), "parameter", {"sigma", "param"})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (RayleighDistribution.fit (x), "alpha", 0.01, ...
          "parameter", {"sigma", "param"})
***** error <paramci: unknown distribution parameter.> ...
 paramci (RayleighDistribution.fit (x), "parameter", "param")
***** error <paramci: unknown distribution parameter.> ...
 paramci (RayleighDistribution.fit (x), "alpha", 0.01, "parameter", "param")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (RayleighDistribution.fit (x), "NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (RayleighDistribution.fit (x), "alpha", 0.01, "NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (RayleighDistribution.fit (x), "alpha", 0.01, ...
          "parameter", "sigma", "NAME", "value")
***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
 plot (RayleighDistribution, "Parent")
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (RayleighDistribution, "PlotType", 12)
***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
 plot (RayleighDistribution, "PlotType", {"pdf", "cdf"})
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (RayleighDistribution, "PlotType", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (RayleighDistribution, "Discrete", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (RayleighDistribution, "Discrete", [1, 0])
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (RayleighDistribution, "Discrete", {true})
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (RayleighDistribution, "Parent", 12)
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (RayleighDistribution, "Parent", "hax")
***** error <plot: invalid NAME for optional argument.> ...
 plot (RayleighDistribution, "invalidNAME", "pdf")
***** error <plot: no fitted DATA to plot a probability plot.> ...
 plot (RayleighDistribution, "PlotType", "probability")
***** error <proflik: no fitted data available.> ...
 proflik (RayleighDistribution, 2)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (RayleighDistribution.fit (x), 3)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (RayleighDistribution.fit (x), [1, 2])
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (RayleighDistribution.fit (x), {1})
***** error <proflik: SETPARAM must be a numeric vector.> ...
 proflik (RayleighDistribution.fit (x), 1, ones (2))
***** error <proflik: missing VALUE for 'Display' argument.> ...
 proflik (RayleighDistribution.fit (x), 1, "Display")
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (RayleighDistribution.fit (x), 1, "Display", 1)
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (RayleighDistribution.fit (x), 1, "Display", {1})
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (RayleighDistribution.fit (x), 1, "Display", {"on"})
***** error <proflik: invalid VALUE size for 'Display' argument.> ...
 proflik (RayleighDistribution.fit (x), 1, "Display", ["on"; "on"])
***** error <proflik: invalid VALUE for 'Display' argument.> ...
 proflik (RayleighDistribution.fit (x), 1, "Display", "onnn")
***** error <proflik: invalid NAME for optional arguments.> ...
 proflik (RayleighDistribution.fit (x), 1, "NAME", "on")
***** error <proflik: invalid optional argument.> ...
 proflik (RayleighDistribution.fit (x), 1, {"NAME"}, "on")
***** error <proflik: invalid optional argument.> ...
 proflik (RayleighDistribution.fit (x), 1, {[1 2 3 4]}, "Display", "on")
***** error <truncate: missing input argument.> ...
 truncate (RayleighDistribution)
***** error <truncate: missing input argument.> ...
 truncate (RayleighDistribution, 2)
***** error <truncate: invalid lower upper limits.> ...
 truncate (RayleighDistribution, 4, 2)
***** shared pd
 pd = RayleighDistribution(1);
 pd(2) = RayleighDistribution(3);
***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
***** error <iqr: requires a scalar probability distribution.> iqr (pd)
***** error <mean: requires a scalar probability distribution.> mean (pd)
***** error <median: requires a scalar probability distribution.> median (pd)
***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
***** error <paramci: requires a scalar probability distribution.> paramci (pd)
***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
***** error <plot: requires a scalar probability distribution.> plot (pd)
***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
***** error <random: requires a scalar probability distribution.> random (pd)
***** error <std: requires a scalar probability distribution.> std (pd)
***** error <truncate: requires a scalar probability distribution.> ...
 truncate (pd, 2, 4)
***** error <var: requires a scalar probability distribution.> var (pd)
90 tests, 90 passed, 0 known failure, 0 skipped
[inst/dist_obj/GammaDistribution.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_obj/GammaDistribution.m
***** shared pd, t
 pd = GammaDistribution (1, 1);
 t = truncate (pd, 2, 4);
***** assert (cdf (pd, [0:5]), [0, 0.6321, 0.8647, 0.9502, 0.9817, 0.9933], 1e-4);
***** assert (cdf (t, [0:5]), [0, 0, 0, 0.7311, 1, 1], 1e-4);
***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.7769, 0.8647, 0.9502, 0.9817], 1e-4);
***** assert (cdf (t, [1.5, 2, 3, 4]), [0, 0, 0.7311, 1], 1e-4);
***** assert (icdf (pd, [0:0.2:1]), [0, 0.2231, 0.5108, 0.9163, 1.6094, Inf], 1e-4);
***** assert (icdf (t, [0:0.2:1]), [2, 2.1899, 2.4244, 2.7315, 3.1768, 4], 1e-4);
***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 0.5108, 0.9163, 1.6094, Inf, NaN], 1e-4);
***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.4244, 2.7315, 3.1768, 4, NaN], 1e-4);
***** assert (iqr (pd), 1.0986, 1e-4);
***** assert (iqr (t), 0.8020, 1e-4);
***** assert (mean (pd), 1);
***** assert (mean (t), 2.6870, 1e-4);
***** assert (median (pd), 0.6931, 1e-4);
***** assert (median (t), 2.5662, 1e-4);
***** assert (pdf (pd, [0:5]), [1, 0.3679, 0.1353, 0.0498, 0.0183, 0.0067], 1e-4);
***** assert (pdf (t, [0:5]), [0, 0, 1.1565, 0.4255, 0.1565, 0], 1e-4);
***** assert (pdf (pd, [-1, 1:4, NaN]), [0, 0.3679, 0.1353, 0.0498, 0.0183, NaN], 1e-4);
***** assert (pdf (t, [-1, 1:4, NaN]), [0, 0, 1.1565, 0.4255, 0.1565, NaN], 1e-4);
***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
***** assert (any (random (t, 1000, 1) < 2), false);
***** assert (any (random (t, 1000, 1) > 4), false);
***** assert (std (pd), 1);
***** assert (std (t), 0.5253, 1e-4);
***** assert (var (pd), 1);
***** assert (var (t), 0.2759, 1e-4);
***** error <GammaDistribution: A must be a positive real scalar.> ...
 GammaDistribution(0, 1)
***** error <GammaDistribution: A must be a positive real scalar.> ...
 GammaDistribution(Inf, 1)
***** error <GammaDistribution: A must be a positive real scalar.> ...
 GammaDistribution(i, 1)
***** error <GammaDistribution: A must be a positive real scalar.> ...
 GammaDistribution("a", 1)
***** error <GammaDistribution: A must be a positive real scalar.> ...
 GammaDistribution([1, 2], 1)
***** error <GammaDistribution: A must be a positive real scalar.> ...
 GammaDistribution(NaN, 1)
***** error <GammaDistribution: B must be a positive real scalar.> ...
 GammaDistribution(1, 0)
***** error <GammaDistribution: B must be a positive real scalar.> ...
 GammaDistribution(1, -1)
***** error <GammaDistribution: B must be a positive real scalar.> ...
 GammaDistribution(1, Inf)
***** error <GammaDistribution: B must be a positive real scalar.> ...
 GammaDistribution(1, i)
***** error <GammaDistribution: B must be a positive real scalar.> ...
 GammaDistribution(1, "a")
***** error <GammaDistribution: B must be a positive real scalar.> ...
 GammaDistribution(1, [1, 2])
***** error <GammaDistribution: B must be a positive real scalar.> ...
 GammaDistribution(1, NaN)
***** error <cdf: invalid argument for upper tail.> ...
 cdf (GammaDistribution, 2, "uper")
***** error <cdf: invalid argument for upper tail.> ...
 cdf (GammaDistribution, 2, 3)
***** shared x
 x = gamrnd (1, 1, [100, 1]);
***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
 paramci (GammaDistribution.fit (x), "alpha")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (GammaDistribution.fit (x), "alpha", 0)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (GammaDistribution.fit (x), "alpha", 1)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (GammaDistribution.fit (x), "alpha", [0.5 2])
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (GammaDistribution.fit (x), "alpha", "")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (GammaDistribution.fit (x), "alpha", {0.05})
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (GammaDistribution.fit (x), "parameter", "a", "alpha", {0.05})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (GammaDistribution.fit (x), "parameter", {"a", "b", "param"})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (GammaDistribution.fit (x), "alpha", 0.01, ...
          "parameter", {"a", "b", "param"})
***** error <paramci: unknown distribution parameter.> ...
 paramci (GammaDistribution.fit (x), "parameter", "param")
***** error <paramci: unknown distribution parameter.> ...
 paramci (GammaDistribution.fit (x), "alpha", 0.01, "parameter", "param")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (GammaDistribution.fit (x), "NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (GammaDistribution.fit (x), "alpha", 0.01, "NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (GammaDistribution.fit (x), "alpha", 0.01, "parameter", "a", ...
          "NAME", "value")
***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
 plot (GammaDistribution, "Parent")
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (GammaDistribution, "PlotType", 12)
***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
 plot (GammaDistribution, "PlotType", {"pdf", "cdf"})
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (GammaDistribution, "PlotType", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (GammaDistribution, "Discrete", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (GammaDistribution, "Discrete", [1, 0])
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (GammaDistribution, "Discrete", {true})
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (GammaDistribution, "Parent", 12)
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (GammaDistribution, "Parent", "hax")
***** error <plot: invalid NAME for optional argument.> ...
 plot (GammaDistribution, "invalidNAME", "pdf")
***** error <plot: no fitted DATA to plot a probability plot.> ...
 plot (GammaDistribution, "PlotType", "probability")
***** error <proflik: no fitted data available.> ...
 proflik (GammaDistribution, 2)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (GammaDistribution.fit (x), 3)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (GammaDistribution.fit (x), [1, 2])
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (GammaDistribution.fit (x), {1})
***** error <proflik: SETPARAM must be a numeric vector.> ...
 proflik (GammaDistribution.fit (x), 1, ones (2))
***** error <proflik: missing VALUE for 'Display' argument.> ...
 proflik (GammaDistribution.fit (x), 1, "Display")
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (GammaDistribution.fit (x), 1, "Display", 1)
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (GammaDistribution.fit (x), 1, "Display", {1})
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (GammaDistribution.fit (x), 1, "Display", {"on"})
***** error <proflik: invalid VALUE size for 'Display' argument.> ...
 proflik (GammaDistribution.fit (x), 1, "Display", ["on"; "on"])
***** error <proflik: invalid VALUE for 'Display' argument.> ...
 proflik (GammaDistribution.fit (x), 1, "Display", "onnn")
***** error <proflik: invalid NAME for optional arguments.> ...
 proflik (GammaDistribution.fit (x), 1, "NAME", "on")
***** error <proflik: invalid optional argument.> ...
 proflik (GammaDistribution.fit (x), 1, {"NAME"}, "on")
***** error <proflik: invalid optional argument.> ...
 proflik (GammaDistribution.fit (x), 1, {[1 2 3 4]}, "Display", "on")
***** error <truncate: missing input argument.> ...
 truncate (GammaDistribution)
***** error <truncate: missing input argument.> ...
 truncate (GammaDistribution, 2)
***** error <truncate: invalid lower upper limits.> ...
 truncate (GammaDistribution, 4, 2)
***** shared pd
 pd = GammaDistribution(1, 1);
 pd(2) = GammaDistribution(1, 3);
***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
***** error <iqr: requires a scalar probability distribution.> iqr (pd)
***** error <mean: requires a scalar probability distribution.> mean (pd)
***** error <median: requires a scalar probability distribution.> median (pd)
***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
***** error <paramci: requires a scalar probability distribution.> paramci (pd)
***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
***** error <plot: requires a scalar probability distribution.> plot (pd)
***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
***** error <random: requires a scalar probability distribution.> random (pd)
***** error <std: requires a scalar probability distribution.> std (pd)
***** error <truncate: requires a scalar probability distribution.> ...
 truncate (pd, 2, 4)
***** error <var: requires a scalar probability distribution.> var (pd)
96 tests, 96 passed, 0 known failure, 0 skipped
[inst/dist_obj/LoguniformDistribution.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_obj/LoguniformDistribution.m
***** shared pd, t
 pd = LoguniformDistribution (1, 4);
 t = truncate (pd, 2, 4);
***** assert (cdf (pd, [0, 1, 2, 3, 4, 5]), [0, 0, 0.5, 0.7925, 1, 1], 1e-4);
***** assert (cdf (t, [0, 1, 2, 3, 4, 5]), [0, 0, 0, 0.5850, 1, 1], 1e-4);
***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.2925, 0.5, 0.7925, 1], 1e-4);
***** assert (cdf (t, [1.5, 2, 3, 4]), [0, 0, 0.5850, 1], 1e-4);
***** assert (icdf (pd, [0:0.2:1]), [1, 1.3195, 1.7411, 2.2974, 3.0314, 4], 1e-4);
***** assert (icdf (t, [0:0.2:1]), [2, 2.2974, 2.6390, 3.0314, 3.4822, 4], 1e-4);
***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 1.7411, 2.2974, 3.0314, 4, NaN], 1e-4);
***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.6390, 3.0314, 3.4822, 4, NaN], 1e-4);
***** assert (iqr (pd), 1.4142, 1e-4);
***** assert (iqr (t), 0.9852, 1e-4);
***** assert (mean (pd), 2.1640, 1e-4);
***** assert (mean (t), 2.8854, 1e-4);
***** assert (median (pd), 2);
***** assert (median (t), 2.8284, 1e-4);
***** assert (pdf (pd, [0, 1, 2, 3, 4, 5]), [0, 0.7213, 0.3607, 0.2404, 0.1803, 0], 1e-4);
***** assert (pdf (t, [0, 1, 2, 3, 4, 5]), [0, 0, 0.7213, 0.4809, 0.3607, 0], 1e-4);
***** assert (pdf (pd, [-1, 1, 2, 3, 4, NaN]), [0, 0.7213, 0.3607, 0.2404, 0.1803, NaN], 1e-4);
***** assert (pdf (t, [-1, 1, 2, 3, 4, NaN]), [0, 0, 0.7213, 0.4809, 0.3607, NaN], 1e-4);
***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
***** assert (any (random (pd, 1000, 1) < 1), false);
***** assert (any (random (pd, 1000, 1) > 4), false);
***** assert (any (random (t, 1000, 1) < 2), false);
***** assert (any (random (t, 1000, 1) > 4), false);
***** assert (std (pd), 0.8527, 1e-4);
***** assert (std (t), 0.5751, 1e-4);
***** assert (var (pd), 0.7270, 1e-4);
***** assert (var (t), 0.3307, 1e-4);
***** error <LoguniformDistribution: LOWER must be a positive real scalar.> ...
 LoguniformDistribution (i, 1)
***** error <LoguniformDistribution: LOWER must be a positive real scalar.> ...
 LoguniformDistribution (Inf, 1)
***** error <LoguniformDistribution: LOWER must be a positive real scalar.> ...
 LoguniformDistribution ([1, 2], 1)
***** error <LoguniformDistribution: LOWER must be a positive real scalar.> ...
 LoguniformDistribution ("a", 1)
***** error <LoguniformDistribution: LOWER must be a positive real scalar.> ...
 LoguniformDistribution (NaN, 1)
***** error <LoguniformDistribution: UPPER must be a real scalar.> ...
 LoguniformDistribution (1, i)
***** error <LoguniformDistribution: UPPER must be a real scalar.> ...
 LoguniformDistribution (1, Inf)
***** error <LoguniformDistribution: UPPER must be a real scalar.> ...
 LoguniformDistribution (1, [1, 2])
***** error <LoguniformDistribution: UPPER must be a real scalar.> ...
 LoguniformDistribution (1, "a")
***** error <LoguniformDistribution: UPPER must be a real scalar.> ...
 LoguniformDistribution (1, NaN)
***** error <LoguniformDistribution: LOWER must be less than UPPER.> ...
 LoguniformDistribution (2, 1)
***** error <cdf: invalid argument for upper tail.> ...
 cdf (LoguniformDistribution, 2, "uper")
***** error <cdf: invalid argument for upper tail.> ...
 cdf (LoguniformDistribution, 2, 3)
***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
 plot (LoguniformDistribution, "Parent")
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (LoguniformDistribution, "PlotType", 12)
***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
 plot (LoguniformDistribution, "PlotType", {"pdf", "cdf"})
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (LoguniformDistribution, "PlotType", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (LoguniformDistribution, "Discrete", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (LoguniformDistribution, "Discrete", [1, 0])
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (LoguniformDistribution, "Discrete", {true})
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (LoguniformDistribution, "Parent", 12)
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (LoguniformDistribution, "Parent", "hax")
***** error <plot: invalid NAME for optional argument.> ...
 plot (LoguniformDistribution, "invalidNAME", "pdf")
***** error <plot: 'probability' PlotType is not supported for 'LoguniformDistribution'.> ...
 plot (LoguniformDistribution, "PlotType", "probability")
***** error <truncate: missing input argument.> ...
 truncate (LoguniformDistribution)
***** error <truncate: missing input argument.> ...
 truncate (LoguniformDistribution, 2)
***** error <truncate: invalid lower upper limits.> ...
 truncate (LoguniformDistribution, 4, 2)
***** shared pd
 pd = LoguniformDistribution(1, 4);
 pd(2) = LoguniformDistribution(2, 5);
***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
***** error <iqr: requires a scalar probability distribution.> iqr (pd)
***** error <mean: requires a scalar probability distribution.> mean (pd)
***** error <median: requires a scalar probability distribution.> median (pd)
***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
***** error <plot: requires a scalar probability distribution.> plot (pd)
***** error <random: requires a scalar probability distribution.> random (pd)
***** error <std: requires a scalar probability distribution.> std (pd)
***** error <truncate: requires a scalar probability distribution.> ...
 truncate (pd, 2, 4)
***** error <var: requires a scalar probability distribution.> var (pd)
65 tests, 65 passed, 0 known failure, 0 skipped
[inst/dist_obj/ExtremeValueDistribution.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_obj/ExtremeValueDistribution.m
***** shared pd, t
 pd = ExtremeValueDistribution (0, 1);
 t = truncate (pd, 2, 4);
***** assert (cdf (pd, [0:5]), [0.6321, 0.9340, 0.9994, 1, 1, 1], 1e-4);
***** assert (cdf (t, [0:5]), [0, 0, 0, 1, 1, 1], 1e-4);
***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.9887, 0.9994, 1, 1], 1e-4);
***** assert (cdf (t, [1.5, 2, 3, 4]), [0, 0, 1, 1], 1e-4);
***** assert (icdf (pd, [0:0.2:1]), [-Inf, -1.4999, -0.6717, -0.0874, 0.4759, Inf], 1e-4);
***** assert (icdf (t, [0:0.2:1]), [2, 2.0298, 2.0668, 2.1169, 2.1971, 4], 1e-4);
***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, -0.6717, -0.0874, 0.4759, Inf, NaN], 1e-4);
***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.0668, 2.1169, 2.1971, 4, NaN], 1e-4);
***** assert (iqr (pd), 1.5725, 1e-4);
***** assert (iqr (t), 0.1338, 1e-4);
***** assert (mean (pd), -0.5772, 1e-4);
***** assert (mean (t), 2.1206, 1e-4);
***** assert (median (pd), -0.3665, 1e-4);
***** assert (median (t), 2.0897, 1e-4);
***** assert (pdf (pd, [0:5]), [0.3679, 0.1794, 0.0046, 0, 0, 0], 1e-4);
***** assert (pdf (t, [0:5]), [0, 0, 7.3891, 0.0001, 0, 0], 1e-4);
***** assert (pdf (pd, [-1, 1:4, NaN]), [0.2546, 0.1794, 0.0046, 0, 0, NaN], 1e-4);
***** assert (pdf (t, [-1, 1:4, NaN]), [0, 0, 7.3891, 0.0001, 0, NaN], 1e-4);
***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
***** assert (any (random (t, 1000, 1) < 2), false);
***** assert (any (random (t, 1000, 1) > 4), false);
***** assert (std (pd), 1.2825, 1e-4);
***** assert (std (t), 0.1091, 1e-4);
***** assert (var (pd), 1.6449, 1e-4);
***** assert (var (t), 0.0119, 1e-4);
***** error <ExtremeValueDistribution: MU must be a real scalar.> ...
 ExtremeValueDistribution(Inf, 1)
***** error <ExtremeValueDistribution: MU must be a real scalar.> ...
 ExtremeValueDistribution(i, 1)
***** error <ExtremeValueDistribution: MU must be a real scalar.> ...
 ExtremeValueDistribution("a", 1)
***** error <ExtremeValueDistribution: MU must be a real scalar.> ...
 ExtremeValueDistribution([1, 2], 1)
***** error <ExtremeValueDistribution: MU must be a real scalar.> ...
 ExtremeValueDistribution(NaN, 1)
***** error <ExtremeValueDistribution: SIGMA must be a positive real scalar.> ...
 ExtremeValueDistribution(1, 0)
***** error <ExtremeValueDistribution: SIGMA must be a positive real scalar.> ...
 ExtremeValueDistribution(1, -1)
***** error <ExtremeValueDistribution: SIGMA must be a positive real scalar.> ...
 ExtremeValueDistribution(1, Inf)
***** error <ExtremeValueDistribution: SIGMA must be a positive real scalar.> ...
 ExtremeValueDistribution(1, i)
***** error <ExtremeValueDistribution: SIGMA must be a positive real scalar.> ...
 ExtremeValueDistribution(1, "a")
***** error <ExtremeValueDistribution: SIGMA must be a positive real scalar.> ...
 ExtremeValueDistribution(1, [1, 2])
***** error <ExtremeValueDistribution: SIGMA must be a positive real scalar.> ...
 ExtremeValueDistribution(1, NaN)
***** error <cdf: invalid argument for upper tail.> ...
 cdf (ExtremeValueDistribution, 2, "uper")
***** error <cdf: invalid argument for upper tail.> ...
 cdf (ExtremeValueDistribution, 2, 3)
***** shared x
 rand ("seed", 1);
 x = evrnd (1, 1, [1000, 1]);
***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
 paramci (ExtremeValueDistribution.fit (x), "alpha")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (ExtremeValueDistribution.fit (x), "alpha", 0)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (ExtremeValueDistribution.fit (x), "alpha", 1)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (ExtremeValueDistribution.fit (x), "alpha", [0.5 2])
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (ExtremeValueDistribution.fit (x), "alpha", "")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (ExtremeValueDistribution.fit (x), "alpha", {0.05})
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (ExtremeValueDistribution.fit (x), ...
          "parameter", "mu", "alpha", {0.05})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (ExtremeValueDistribution.fit (x), ...
          "parameter", {"mu", "sigma", "param"})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (ExtremeValueDistribution.fit (x), "alpha", 0.01, ...
          "parameter", {"mu", "sigma", "param"})
***** error <paramci: unknown distribution parameter.> ...
 paramci (ExtremeValueDistribution.fit (x), "parameter", "param")
***** error <paramci: unknown distribution parameter.> ...
 paramci (ExtremeValueDistribution.fit (x), "alpha", 0.01, ...
          "parameter", "param")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (ExtremeValueDistribution.fit (x), "NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (ExtremeValueDistribution.fit (x), "alpha", 0.01, "NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (ExtremeValueDistribution.fit (x), "alpha", 0.01, ...
          "parameter", "mu", "NAME", "value")
***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
 plot (ExtremeValueDistribution, "Parent")
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (ExtremeValueDistribution, "PlotType", 12)
***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
 plot (ExtremeValueDistribution, "PlotType", {"pdf", "cdf"})
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (ExtremeValueDistribution, "PlotType", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (ExtremeValueDistribution, "Discrete", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (ExtremeValueDistribution, "Discrete", [1, 0])
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (ExtremeValueDistribution, "Discrete", {true})
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (ExtremeValueDistribution, "Parent", 12)
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (ExtremeValueDistribution, "Parent", "hax")
***** error <plot: invalid NAME for optional argument.> ...
 plot (ExtremeValueDistribution, "invalidNAME", "pdf")
***** error <plot: no fitted DATA to plot a probability plot.> ...
 plot (ExtremeValueDistribution, "PlotType", "probability")
***** error <proflik: no fitted data available.> ...
 proflik (ExtremeValueDistribution, 2)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (ExtremeValueDistribution.fit (x), 3)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (ExtremeValueDistribution.fit (x), [1, 2])
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (ExtremeValueDistribution.fit (x), {1})
***** error <proflik: SETPARAM must be a numeric vector.> ...
 proflik (ExtremeValueDistribution.fit (x), 1, ones (2))
***** error <proflik: missing VALUE for 'Display' argument.> ...
 proflik (ExtremeValueDistribution.fit (x), 1, "Display")
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (ExtremeValueDistribution.fit (x), 1, "Display", 1)
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (ExtremeValueDistribution.fit (x), 1, "Display", {1})
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (ExtremeValueDistribution.fit (x), 1, "Display", {"on"})
***** error <proflik: invalid VALUE size for 'Display' argument.> ...
 proflik (ExtremeValueDistribution.fit (x), 1, "Display", ["on"; "on"])
***** error <proflik: invalid VALUE for 'Display' argument.> ...
 proflik (ExtremeValueDistribution.fit (x), 1, "Display", "onnn")
***** error <proflik: invalid NAME for optional arguments.> ...
 proflik (ExtremeValueDistribution.fit (x), 1, "NAME", "on")
***** error <proflik: invalid optional argument.> ...
 proflik (ExtremeValueDistribution.fit (x), 1, {"NAME"}, "on")
***** error <proflik: invalid optional argument.> ...
 proflik (ExtremeValueDistribution.fit (x), 1, {[1 2 3 4]}, "Display", "on")
***** error <truncate: missing input argument.> ...
 truncate (ExtremeValueDistribution)
***** error <truncate: missing input argument.> ...
 truncate (ExtremeValueDistribution, 2)
***** error <truncate: invalid lower upper limits.> ...
 truncate (ExtremeValueDistribution, 4, 2)
***** shared pd
 pd = ExtremeValueDistribution(1, 1);
 pd(2) = ExtremeValueDistribution(1, 3);
***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
***** error <iqr: requires a scalar probability distribution.> iqr (pd)
***** error <mean: requires a scalar probability distribution.> mean (pd)
***** error <median: requires a scalar probability distribution.> median (pd)
***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
***** error <paramci: requires a scalar probability distribution.> paramci (pd)
***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
***** error <plot: requires a scalar probability distribution.> plot (pd)
***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
***** error <random: requires a scalar probability distribution.> random (pd)
***** error <std: requires a scalar probability distribution.> std (pd)
***** error <truncate: requires a scalar probability distribution.> ...
 truncate (pd, 2, 4)
***** error <var: requires a scalar probability distribution.> var (pd)
95 tests, 95 passed, 0 known failure, 0 skipped
[inst/dist_obj/BetaDistribution.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_obj/BetaDistribution.m
***** demo
 ## Generate a data set of 5000 random samples from a Beta distribution with
 ## parameters a = 2 and b = 4.  Fit a Beta distribution to this data and plot
 ## a PDF of the fitted distribution superimposed on a histogram of the data

 pd = makedist ("Beta", "a", 2, "b", 4)
 randg ("seed", 21);
 data = random (pd, 5000, 1);
 pd = fitdist (data, "Beta")
 plot (pd)
 title (sprintf ("Fitted Beta distribution with a = %0.2f and b = %0.2f", ...
                 pd.a, pd.b))
***** demo
 ## Plot the PDF of a Beta distribution, with parameters a = 2 and b = 4,
 ## truncated at [0.1, 0.8] intervals.  Generate 10000 random samples from
 ## this truncated distribution and superimpose a histogram with 100 bins
 ## scaled accordingly

 pd = makedist ("Beta", "a", 2, "b", 4)
 t = truncate (pd, 0.1, 0.8)
 randg ("seed", 21);
 data = random (t, 10000, 1);
 plot (t)
 title ("Beta distribution (a = 2, b = 4) truncated at [0.1, 0.8]")
 hold on
 hist (data, 100, 140)
 hold off
***** demo
 ## Generate a data set of 100 random samples from a Beta distribution with
 ## parameters a = 2 and b = 4.  Fit a Beta distribution to this data and plot
 ## its CDF superimposed over an empirical CDF of the data

 pd = makedist ("Beta", "a", 2, "b", 4)
 randg ("seed", 21);
 data = random (pd, 100, 1);
 pd = fitdist (data, "Beta")
 plot (pd, "plottype", "cdf")
 title (sprintf ("Fitted Beta distribution with a = %0.2f and b = %0.2f", ...
                 pd.a, pd.b))
 legend ({"empirical CDF", "fitted CDF"}, "location", "east")
***** demo
 ## Generate a data set of 200 random samples from a Beta distribution with
 ## parameters a = 2 and b = 4.  Display a probability plot for the Beta
 ## distribution fit to the data.

 pd = makedist ("Beta", "a", 2, "b", 4)
 randg ("seed", 21);
 data = random (pd, 200, 1);
 pd = fitdist (data, "Beta")
 plot (pd, "plottype", "probability")
 title (sprintf ("Probability plot of a fitted Beta distribution with a = %0.2f and b = %0.2f", ...
                 pd.a, pd.b))
 legend ({"empirical CDF", "fitted CDF"}, "location", "southeast")
***** shared pd, t
 pd = BetaDistribution;
 t = truncate (pd, 0.2, 0.8);
***** assert (cdf (pd, [0:0.2:1]), [0, 0.2, 0.4, 0.6, 0.8, 1], 1e-4);
***** assert (cdf (t, [0:0.2:1]), [0, 0, 0.3333, 0.6667, 1, 1], 1e-4);
***** assert (cdf (pd, [-1, 1, NaN]), [0, 1, NaN], 1e-4);
***** assert (cdf (t, [-1, 1, NaN]), [0, 1, NaN], 1e-4);
***** assert (icdf (pd, [0:0.2:1]), [0, 0.2, 0.4, 0.6, 0.8, 1], 1e-4);
***** assert (icdf (t, [0:0.2:1]), [0.2, 0.32, 0.44, 0.56, 0.68, 0.8], 1e-4);
***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 0.4, 0.6, 0.8, 1, NaN], 1e-4);
***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 0.44, 0.56, 0.68, 0.8, NaN], 1e-4);
***** assert (iqr (pd), 0.5, 1e-4);
***** assert (iqr (t), 0.3, 1e-4);
***** assert (mean (pd), 0.5);
***** assert (mean (t), 0.5, 1e-6);
***** assert (median (pd), 0.5);
***** assert (median (t), 0.5, 1e-6);
***** assert (pdf (pd, [0:0.2:1]), [1, 1, 1, 1, 1, 1], 1e-4);
***** assert (pdf (t, [0:0.2:1]), [0, 1.6667, 1.6667, 1.6667, 1.6667, 0], 1e-4);
***** assert (pdf (pd, [-1, 1, NaN]), [0, 1, NaN], 1e-4);
***** assert (pdf (t, [-1, 1, NaN]), [0, 0, NaN], 1e-4);
***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
***** assert (any (random (t, 1000, 1) < 0.2), false);
***** assert (any (random (t, 1000, 1) > 0.8), false);
***** assert (std (pd), 0.2887, 1e-4);
***** assert (std (t), 0.1732, 1e-4);
***** assert (var (pd), 0.0833, 1e-4);
***** assert (var (t), 0.0300, 1e-4);
***** error <BetaDistribution: A must be a positive real scalar.> ...
 BetaDistribution(0, 1)
***** error <BetaDistribution: A must be a positive real scalar.> ...
 BetaDistribution(Inf, 1)
***** error <BetaDistribution: A must be a positive real scalar.> ...
 BetaDistribution(i, 1)
***** error <BetaDistribution: A must be a positive real scalar.> ...
 BetaDistribution("a", 1)
***** error <BetaDistribution: A must be a positive real scalar.> ...
 BetaDistribution([1, 2], 1)
***** error <BetaDistribution: A must be a positive real scalar.> ...
 BetaDistribution(NaN, 1)
***** error <BetaDistribution: B must be a positive real scalar.> ...
 BetaDistribution(1, 0)
***** error <BetaDistribution: B must be a positive real scalar.> ...
 BetaDistribution(1, -1)
***** error <BetaDistribution: B must be a positive real scalar.> ...
 BetaDistribution(1, Inf)
***** error <BetaDistribution: B must be a positive real scalar.> ...
 BetaDistribution(1, i)
***** error <BetaDistribution: B must be a positive real scalar.> ...
 BetaDistribution(1, "a")
***** error <BetaDistribution: B must be a positive real scalar.> ...
 BetaDistribution(1, [1, 2])
***** error <BetaDistribution: B must be a positive real scalar.> ...
 BetaDistribution(1, NaN)
***** error <cdf: invalid argument for upper tail.> ...
 cdf (BetaDistribution, 2, "uper")
***** error <cdf: invalid argument for upper tail.> ...
 cdf (BetaDistribution, 2, 3)
***** shared x
 randg ("seed", 1);
 x = betarnd (1, 1, [100, 1]);
***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
 paramci (BetaDistribution.fit (x), "alpha")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (BetaDistribution.fit (x), "alpha", 0)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (BetaDistribution.fit (x), "alpha", 1)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (BetaDistribution.fit (x), "alpha", [0.5 2])
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (BetaDistribution.fit (x), "alpha", "")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (BetaDistribution.fit (x), "alpha", {0.05})
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (BetaDistribution.fit (x), "parameter", "a", "alpha", {0.05})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (BetaDistribution.fit (x), "parameter", {"a", "b", "param"})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (BetaDistribution.fit (x), "alpha", 0.01, ...
          "parameter", {"a", "b", "param"})
***** error <paramci: unknown distribution parameter.> ...
 paramci (BetaDistribution.fit (x), "parameter", "param")
***** error <paramci: unknown distribution parameter.> ...
 paramci (BetaDistribution.fit (x), "alpha", 0.01, "parameter", "param")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (BetaDistribution.fit (x), "NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (BetaDistribution.fit (x), "alpha", 0.01, "NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (BetaDistribution.fit (x), "alpha", 0.01, "parameter", "a", ...
          "NAME", "value")
***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
 plot (BetaDistribution, "Parent")
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (BetaDistribution, "PlotType", 12)
***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
 plot (BetaDistribution, "PlotType", {"pdf", "cdf"})
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (BetaDistribution, "PlotType", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (BetaDistribution, "Discrete", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (BetaDistribution, "Discrete", [1, 0])
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (BetaDistribution, "Discrete", {true})
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (BetaDistribution, "Parent", 12)
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (BetaDistribution, "Parent", "hax")
***** error <plot: invalid NAME for optional argument.> ...
 plot (BetaDistribution, "invalidNAME", "pdf")
***** error <plot: no fitted DATA to plot a probability plot.> ...
 plot (BetaDistribution, "PlotType", "probability")
***** error <proflik: no fitted data available.> ...
 proflik (BetaDistribution, 2)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (BetaDistribution.fit (x), 3)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (BetaDistribution.fit (x), [1, 2])
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (BetaDistribution.fit (x), {1})
***** error <proflik: SETPARAM must be a numeric vector.> ...
 proflik (BetaDistribution.fit (x), 1, ones (2))
***** error <proflik: missing VALUE for 'Display' argument.> ...
 proflik (BetaDistribution.fit (x), 1, "Display")
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (BetaDistribution.fit (x), 1, "Display", 1)
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (BetaDistribution.fit (x), 1, "Display", {1})
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (BetaDistribution.fit (x), 1, "Display", {"on"})
***** error <proflik: invalid VALUE size for 'Display' argument.> ...
 proflik (BetaDistribution.fit (x), 1, "Display", ["on"; "on"])
***** error <proflik: invalid VALUE for 'Display' argument.> ...
 proflik (BetaDistribution.fit (x), 1, "Display", "onnn")
***** error <proflik: invalid NAME for optional arguments.> ...
 proflik (BetaDistribution.fit (x), 1, "NAME", "on")
***** error <proflik: invalid optional argument.> ...
 proflik (BetaDistribution.fit (x), 1, {"NAME"}, "on")
***** error <proflik: invalid optional argument.> ...
 proflik (BetaDistribution.fit (x), 1, {[1 2 3 4]}, "Display", "on")
***** error <truncate: missing input argument.> ...
 truncate (BetaDistribution)
***** error <truncate: missing input argument.> ...
 truncate (BetaDistribution, 2)
***** error <truncate: invalid lower upper limits.> ...
 truncate (BetaDistribution, 4, 2)
***** shared pd
 pd = BetaDistribution(1, 1);
 pd(2) = BetaDistribution(1, 3);
***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
***** error <iqr: requires a scalar probability distribution.> iqr (pd)
***** error <mean: requires a scalar probability distribution.> mean (pd)
***** error <median: requires a scalar probability distribution.> median (pd)
***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
***** error <paramci: requires a scalar probability distribution.> paramci (pd)
***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
***** error <plot: requires a scalar probability distribution.> plot (pd)
***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
***** error <random: requires a scalar probability distribution.> random (pd)
***** error <std: requires a scalar probability distribution.> std (pd)
***** error <truncate: requires a scalar probability distribution.> ...
 truncate (pd, 2, 4)
***** error <var: requires a scalar probability distribution.> var (pd)
96 tests, 96 passed, 0 known failure, 0 skipped
[inst/dist_obj/NormalDistribution.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_obj/NormalDistribution.m
***** shared pd, t
 pd = NormalDistribution;
 t = truncate (pd, -2, 2);
***** assert (cdf (pd, [0:5]), [0.5, 0.8413, 0.9772, 0.9987, 1, 1], 1e-4);
***** assert (cdf (t, [0:5]), [0.5, 0.8576, 1, 1, 1, 1], 1e-4);
***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.9332, 0.9772, 0.9987, 1], 1e-4);
***** assert (cdf (t, [1.5, 2, 3, 4]), [0.9538, 1, 1, 1], 1e-4);
***** assert (icdf (pd, [0:0.2:1]), [-Inf, -0.8416, -0.2533, 0.2533, 0.8416, Inf], 1e-4);
***** assert (icdf (t, [0:0.2:1]), [-2, -0.7938, -0.2416, 0.2416, 0.7938, 2], 1e-4);
***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, -0.2533, 0.2533, 0.8416, Inf, NaN], 1e-4);
***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, -0.2416, 0.2416, 0.7938, 2, NaN], 1e-4);
***** assert (iqr (pd), 1.3490, 1e-4);
***** assert (iqr (t), 1.2782, 1e-4);
***** assert (mean (pd), 0);
***** assert (mean (t), 0, 3e-16);
***** assert (median (pd), 0);
***** assert (median (t), 0, 3e-16);
***** assert (pdf (pd, [0:5]), [0.3989, 0.2420, 0.0540, 0.0044, 0.0001, 0], 1e-4);
***** assert (pdf (t, [0:5]), [0.4180, 0.2535, 0.0566, 0, 0, 0], 1e-4);
***** assert (pdf (pd, [-1, 1:4, NaN]), [0.2420, 0.2420, 0.0540, 0.0044, 0.0001, NaN], 1e-4);
***** assert (pdf (t, [-1, 1:4, NaN]), [0.2535, 0.2535, 0.0566, 0, 0, NaN], 1e-4);
***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
***** assert (any (random (t, 1000, 1) < -2), false);
***** assert (any (random (t, 1000, 1) > 2), false);
***** assert (std (pd), 1);
***** assert (std (t), 0.8796, 1e-4);
***** assert (var (pd), 1);
***** assert (var (t), 0.7737, 1e-4);
***** error <NormalDistribution: MU must be a real scalar.> ...
 NormalDistribution(Inf, 1)
***** error <NormalDistribution: MU must be a real scalar.> ...
 NormalDistribution(i, 1)
***** error <NormalDistribution: MU must be a real scalar.> ...
 NormalDistribution("a", 1)
***** error <NormalDistribution: MU must be a real scalar.> ...
 NormalDistribution([1, 2], 1)
***** error <NormalDistribution: MU must be a real scalar.> ...
 NormalDistribution(NaN, 1)
***** error <NormalDistribution: SIGMA must be a positive real scalar.> ...
 NormalDistribution(1, 0)
***** error <NormalDistribution: SIGMA must be a positive real scalar.> ...
 NormalDistribution(1, -1)
***** error <NormalDistribution: SIGMA must be a positive real scalar.> ...
 NormalDistribution(1, Inf)
***** error <NormalDistribution: SIGMA must be a positive real scalar.> ...
 NormalDistribution(1, i)
***** error <NormalDistribution: SIGMA must be a positive real scalar.> ...
 NormalDistribution(1, "a")
***** error <NormalDistribution: SIGMA must be a positive real scalar.> ...
 NormalDistribution(1, [1, 2])
***** error <NormalDistribution: SIGMA must be a positive real scalar.> ...
 NormalDistribution(1, NaN)
***** error <cdf: invalid argument for upper tail.> ...
 cdf (NormalDistribution, 2, "uper")
***** error <cdf: invalid argument for upper tail.> ...
 cdf (NormalDistribution, 2, 3)
***** shared x
 x = normrnd (1, 1, [1, 100]);
***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
 paramci (NormalDistribution.fit (x), "alpha")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (NormalDistribution.fit (x), "alpha", 0)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (NormalDistribution.fit (x), "alpha", 1)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (NormalDistribution.fit (x), "alpha", [0.5 2])
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (NormalDistribution.fit (x), "alpha", "")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (NormalDistribution.fit (x), "alpha", {0.05})
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (NormalDistribution.fit (x), "parameter", "mu", "alpha", {0.05})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (NormalDistribution.fit (x), "parameter", {"mu", "sigma", "param"})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (NormalDistribution.fit (x), "alpha", 0.01, ...
          "parameter", {"mu", "sigma", "param"})
***** error <paramci: unknown distribution parameter.> ...
 paramci (NormalDistribution.fit (x), "parameter", "param")
***** error <paramci: unknown distribution parameter.> ...
 paramci (NormalDistribution.fit (x), "alpha", 0.01, "parameter", "param")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (NormalDistribution.fit (x), "NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (NormalDistribution.fit (x), "alpha", 0.01, "NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (NormalDistribution.fit (x), "alpha", 0.01, "parameter", "mu", ...
          "NAME", "value")
***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
 plot (NormalDistribution, "Parent")
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (NormalDistribution, "PlotType", 12)
***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
 plot (NormalDistribution, "PlotType", {"pdf", "cdf"})
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (NormalDistribution, "PlotType", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (NormalDistribution, "Discrete", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (NormalDistribution, "Discrete", [1, 0])
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (NormalDistribution, "Discrete", {true})
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (NormalDistribution, "Parent", 12)
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (NormalDistribution, "Parent", "hax")
***** error <plot: invalid NAME for optional argument.> ...
 plot (NormalDistribution, "invalidNAME", "pdf")
***** error <plot: no fitted DATA to plot a probability plot.> ...
 plot (NormalDistribution, "PlotType", "probability")
***** error <proflik: no fitted data available.> ...
 proflik (NormalDistribution, 2)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (NormalDistribution.fit (x), 3)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (NormalDistribution.fit (x), [1, 2])
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (NormalDistribution.fit (x), {1})
***** error <proflik: SETPARAM must be a numeric vector.> ...
 proflik (NormalDistribution.fit (x), 1, ones (2))
***** error <proflik: missing VALUE for 'Display' argument.> ...
 proflik (NormalDistribution.fit (x), 1, "Display")
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (NormalDistribution.fit (x), 1, "Display", 1)
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (NormalDistribution.fit (x), 1, "Display", {1})
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (NormalDistribution.fit (x), 1, "Display", {"on"})
***** error <proflik: invalid VALUE size for 'Display' argument.> ...
 proflik (NormalDistribution.fit (x), 1, "Display", ["on"; "on"])
***** error <proflik: invalid VALUE for 'Display' argument.> ...
 proflik (NormalDistribution.fit (x), 1, "Display", "onnn")
***** error <proflik: invalid NAME for optional arguments.> ...
 proflik (NormalDistribution.fit (x), 1, "NAME", "on")
***** error <proflik: invalid optional argument.> ...
 proflik (NormalDistribution.fit (x), 1, {"NAME"}, "on")
***** error <proflik: invalid optional argument.> ...
 proflik (NormalDistribution.fit (x), 1, {[1 2 3 4]}, "Display", "on")
***** error <truncate: missing input argument.> ...
 truncate (NormalDistribution)
***** error <truncate: missing input argument.> ...
 truncate (NormalDistribution, 2)
***** error <truncate: invalid lower upper limits.> ...
 truncate (NormalDistribution, 4, 2)
***** shared pd
 pd = NormalDistribution(1, 1);
 pd(2) = NormalDistribution(1, 3);
***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
***** error <iqr: requires a scalar probability distribution.> iqr (pd)
***** error <mean: requires a scalar probability distribution.> mean (pd)
***** error <median: requires a scalar probability distribution.> median (pd)
***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
***** error <paramci: requires a scalar probability distribution.> paramci (pd)
***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
***** error <plot: requires a scalar probability distribution.> plot (pd)
***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
***** error <random: requires a scalar probability distribution.> random (pd)
***** error <std: requires a scalar probability distribution.> std (pd)
***** error <truncate: requires a scalar probability distribution.> ...
 truncate (pd, 2, 4)
***** error <var: requires a scalar probability distribution.> var (pd)
95 tests, 95 passed, 0 known failure, 0 skipped
[inst/dist_obj/LogisticDistribution.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_obj/LogisticDistribution.m
***** shared pd, t
 pd = LogisticDistribution (0, 1);
 t = truncate (pd, 2, 4);
***** assert (cdf (pd, [0:5]), [0.5, 0.7311, 0.8808, 0.9526, 0.9820, 0.9933], 1e-4);
***** assert (cdf (t, [0:5]), [0, 0, 0, 0.7091, 1, 1], 1e-4);
***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.8176, 0.8808, 0.9526, 0.9820], 1e-4);
***** assert (cdf (t, [1.5, 2, 3, 4]), [0, 0, 0.7091, 1], 1e-4);
***** assert (icdf (pd, [0:0.2:1]), [-Inf, -1.3863, -0.4055, 0.4055, 1.3863, Inf], 1e-4);
***** assert (icdf (t, [0:0.2:1]), [2, 2.2088, 2.4599, 2.7789, 3.2252, 4], 1e-4);
***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, -0.4055, 0.4055, 1.3863, Inf, NaN], 1e-4);
***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.4599, 2.7789, 3.2252, 4, NaN], 1e-4);
***** assert (iqr (pd), 2.1972, 1e-4);
***** assert (iqr (t), 0.8286, 1e-4);
***** assert (mean (pd), 0, 1e-4);
***** assert (mean (t), 2.7193, 1e-4);
***** assert (median (pd), 0);
***** assert (median (t), 2.6085, 1e-4);
***** assert (pdf (pd, [0:5]), [0.25, 0.1966, 0.1050, 0.0452, 0.0177, 0.0066], 1e-4);
***** assert (pdf (t, [0:5]), [0, 0, 1.0373, 0.4463, 0.1745, 0], 1e-4);
***** assert (pdf (pd, [-1, 1:4, NaN]), [0.1966, 0.1966, 0.1050, 0.0452, 0.0177, NaN], 1e-4);
***** assert (pdf (t, [-1, 1:4, NaN]), [0, 0, 1.0373, 0.4463, 0.1745, NaN], 1e-4);
***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
***** assert (any (random (t, 1000, 1) < 2), false);
***** assert (any (random (t, 1000, 1) > 4), false);
***** assert (std (pd), 1.8138, 1e-4);
***** assert (std (t), 0.5320, 1e-4);
***** assert (var (pd), 3.2899, 1e-4);
***** assert (var (t), 0.2830, 1e-4);
***** error <LogisticDistribution: MU must be a nonnegative real scalar.> ...
 LogisticDistribution(Inf, 1)
***** error <LogisticDistribution: MU must be a nonnegative real scalar.> ...
 LogisticDistribution(i, 1)
***** error <LogisticDistribution: MU must be a nonnegative real scalar.> ...
 LogisticDistribution("a", 1)
***** error <LogisticDistribution: MU must be a nonnegative real scalar.> ...
 LogisticDistribution([1, 2], 1)
***** error <LogisticDistribution: MU must be a nonnegative real scalar.> ...
 LogisticDistribution(NaN, 1)
***** error <LogisticDistribution: SIGMA must be a positive real scalar.> ...
 LogisticDistribution(1, 0)
***** error <LogisticDistribution: SIGMA must be a positive real scalar.> ...
 LogisticDistribution(1, -1)
***** error <LogisticDistribution: SIGMA must be a positive real scalar.> ...
 LogisticDistribution(1, Inf)
***** error <LogisticDistribution: SIGMA must be a positive real scalar.> ...
 LogisticDistribution(1, i)
***** error <LogisticDistribution: SIGMA must be a positive real scalar.> ...
 LogisticDistribution(1, "a")
***** error <LogisticDistribution: SIGMA must be a positive real scalar.> ...
 LogisticDistribution(1, [1, 2])
***** error <LogisticDistribution: SIGMA must be a positive real scalar.> ...
 LogisticDistribution(1, NaN)
***** error <cdf: invalid argument for upper tail.> ...
 cdf (LogisticDistribution, 2, "uper")
***** error <cdf: invalid argument for upper tail.> ...
 cdf (LogisticDistribution, 2, 3)
***** shared x
 x = logirnd (1, 1, [1, 100]);
***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
 paramci (LogisticDistribution.fit (x), "alpha")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (LogisticDistribution.fit (x), "alpha", 0)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (LogisticDistribution.fit (x), "alpha", 1)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (LogisticDistribution.fit (x), "alpha", [0.5 2])
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (LogisticDistribution.fit (x), "alpha", "")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (LogisticDistribution.fit (x), "alpha", {0.05})
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (LogisticDistribution.fit (x), "parameter", "mu", "alpha", {0.05})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (LogisticDistribution.fit (x), "parameter", {"mu", "sigma", "param"})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (LogisticDistribution.fit (x), "alpha", 0.01, ...
          "parameter", {"mu", "sigma", "param"})
***** error <paramci: unknown distribution parameter.> ...
 paramci (LogisticDistribution.fit (x), "parameter", "param")
***** error <paramci: unknown distribution parameter.> ...
 paramci (LogisticDistribution.fit (x), "alpha", 0.01, "parameter", "param")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (LogisticDistribution.fit (x), "NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (LogisticDistribution.fit (x), "alpha", 0.01, "NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (LogisticDistribution.fit (x), "alpha", 0.01, "parameter", "mu", ...
          "NAME", "value")
***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
 plot (LogisticDistribution, "Parent")
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (LogisticDistribution, "PlotType", 12)
***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
 plot (LogisticDistribution, "PlotType", {"pdf", "cdf"})
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (LogisticDistribution, "PlotType", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (LogisticDistribution, "Discrete", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (LogisticDistribution, "Discrete", [1, 0])
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (LogisticDistribution, "Discrete", {true})
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (LogisticDistribution, "Parent", 12)
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (LogisticDistribution, "Parent", "hax")
***** error <plot: invalid NAME for optional argument.> ...
 plot (LogisticDistribution, "invalidNAME", "pdf")
***** error <plot: no fitted DATA to plot a probability plot.> ...
 plot (LogisticDistribution, "PlotType", "probability")
***** error <proflik: no fitted data available.> ...
 proflik (LogisticDistribution, 2)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (LogisticDistribution.fit (x), 3)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (LogisticDistribution.fit (x), [1, 2])
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (LogisticDistribution.fit (x), {1})
***** error <proflik: SETPARAM must be a numeric vector.> ...
 proflik (LogisticDistribution.fit (x), 1, ones (2))
***** error <proflik: missing VALUE for 'Display' argument.> ...
 proflik (LogisticDistribution.fit (x), 1, "Display")
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (LogisticDistribution.fit (x), 1, "Display", 1)
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (LogisticDistribution.fit (x), 1, "Display", {1})
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (LogisticDistribution.fit (x), 1, "Display", {"on"})
***** error <proflik: invalid VALUE size for 'Display' argument.> ...
 proflik (LogisticDistribution.fit (x), 1, "Display", ["on"; "on"])
***** error <proflik: invalid VALUE for 'Display' argument.> ...
 proflik (LogisticDistribution.fit (x), 1, "Display", "onnn")
***** error <proflik: invalid NAME for optional arguments.> ...
 proflik (LogisticDistribution.fit (x), 1, "NAME", "on")
***** error <proflik: invalid optional argument.> ...
 proflik (LogisticDistribution.fit (x), 1, {"NAME"}, "on")
***** error <proflik: invalid optional argument.> ...
 proflik (LogisticDistribution.fit (x), 1, {[1 2 3 4]}, "Display", "on")
***** error <truncate: missing input argument.> ...
 truncate (LogisticDistribution)
***** error <truncate: missing input argument.> ...
 truncate (LogisticDistribution, 2)
***** error <truncate: invalid lower upper limits.> ...
 truncate (LogisticDistribution, 4, 2)
***** shared pd
 pd = LogisticDistribution(1, 1);
 pd(2) = LogisticDistribution(1, 3);
***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
***** error <iqr: requires a scalar probability distribution.> iqr (pd)
***** error <mean: requires a scalar probability distribution.> mean (pd)
***** error <median: requires a scalar probability distribution.> median (pd)
***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
***** error <paramci: requires a scalar probability distribution.> paramci (pd)
***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
***** error <plot: requires a scalar probability distribution.> plot (pd)
***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
***** error <random: requires a scalar probability distribution.> random (pd)
***** error <std: requires a scalar probability distribution.> std (pd)
***** error <truncate: requires a scalar probability distribution.> ...
 truncate (pd, 2, 4)
***** error <var: requires a scalar probability distribution.> var (pd)
95 tests, 95 passed, 0 known failure, 0 skipped
[inst/dist_obj/GeneralizedExtremeValueDistribution.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_obj/GeneralizedExtremeValueDistribution.m
***** shared pd, t
 pd = GeneralizedExtremeValueDistribution;
 t = truncate (pd, 2, 4);
***** assert (cdf (pd, [0:5]), [0.3679, 0.6922, 0.8734, 0.9514, 0.9819, 0.9933], 1e-4);
***** assert (cdf (t, [0:5]), [0, 0, 0, 0.7195, 1, 1], 1e-4);
***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.8, 0.8734, 0.9514, 0.9819], 1e-4);
***** assert (cdf (t, [1.5, 2, 3, 4]), [0, 0, 0.7195, 1], 1e-4);
***** assert (icdf (pd, [0:0.2:1]), [-Inf, -0.4759, 0.0874, 0.6717, 1.4999, Inf], 1e-4);
***** assert (icdf (t, [0:0.2:1]), [2, 2.1999, 2.4433, 2.7568, 3.2028, 4], 1e-4);
***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 0.0874, 0.6717, 1.4999, Inf, NaN], 1e-4);
***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.4433, 2.7568, 3.2028, 4, NaN], 1e-4);
***** assert (iqr (pd), 1.5725, 1e-4);
***** assert (iqr (t), 0.8164, 1e-4);
***** assert (mean (pd), 0.5772, 1e-4);
***** assert (mean (t), 2.7043, 1e-4);
***** assert (median (pd), 0.3665, 1e-4);
***** assert (median (t), 2.5887, 1e-4);
***** assert (pdf (pd, [0:5]), [0.3679, 0.2546, 0.1182, 0.0474, 0.0180, 0.0067], 1e-4);
***** assert (pdf (t, [0:5]), [0, 0, 1.0902, 0.4369, 0.1659, 0], 1e-4);
***** assert (pdf (pd, [-1, 1:4, NaN]), [0.1794, 0.2546, 0.1182, 0.0474, 0.0180, NaN], 1e-4);
***** assert (pdf (t, [-1, 1:4, NaN]), [0, 0, 1.0902, 0.4369, 0.1659, NaN], 1e-4);
***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
***** assert (any (random (t, 1000, 1) < 2), false);
***** assert (any (random (t, 1000, 1) > 4), false);
***** assert (std (pd), 1.2825, 1e-4);
***** assert (std (t), 0.5289, 1e-4);
***** assert (var (pd), 1.6449, 1e-4);
***** assert (var (t), 0.2798, 1e-4);
***** error <GeneralizedExtremeValueDistribution: MU must be a real scalar.> ...
 GeneralizedExtremeValueDistribution(Inf, 1, 1)
***** error <GeneralizedExtremeValueDistribution: MU must be a real scalar.> ...
 GeneralizedExtremeValueDistribution(i, 1, 1)
***** error <GeneralizedExtremeValueDistribution: MU must be a real scalar.> ...
 GeneralizedExtremeValueDistribution("a", 1, 1)
***** error <GeneralizedExtremeValueDistribution: MU must be a real scalar.> ...
 GeneralizedExtremeValueDistribution([1, 2], 1, 1)
***** error <GeneralizedExtremeValueDistribution: MU must be a real scalar.> ...
 GeneralizedExtremeValueDistribution(NaN, 1, 1)
***** error <GeneralizedExtremeValueDistribution: SIGMA must be a positive real scalar.> ...
 GeneralizedExtremeValueDistribution(1, 0, 1)
***** error <GeneralizedExtremeValueDistribution: SIGMA must be a positive real scalar.> ...
 GeneralizedExtremeValueDistribution(1, -1, 1)
***** error <GeneralizedExtremeValueDistribution: SIGMA must be a positive real scalar.> ...
 GeneralizedExtremeValueDistribution(1, Inf, 1)
***** error <GeneralizedExtremeValueDistribution: SIGMA must be a positive real scalar.> ...
 GeneralizedExtremeValueDistribution(1, i, 1)
***** error <GeneralizedExtremeValueDistribution: SIGMA must be a positive real scalar.> ...
 GeneralizedExtremeValueDistribution(1, "a", 1)
***** error <GeneralizedExtremeValueDistribution: SIGMA must be a positive real scalar.> ...
 GeneralizedExtremeValueDistribution(1, [1, 2], 1)
***** error <GeneralizedExtremeValueDistribution: SIGMA must be a positive real scalar.> ...
 GeneralizedExtremeValueDistribution(1, NaN, 1)
***** error <GeneralizedExtremeValueDistribution: MU must be a real scalar.> ...
 GeneralizedExtremeValueDistribution(1, 1, Inf)
***** error <GeneralizedExtremeValueDistribution: MU must be a real scalar.> ...
 GeneralizedExtremeValueDistribution(1, 1, i)
***** error <GeneralizedExtremeValueDistribution: MU must be a real scalar.> ...
 GeneralizedExtremeValueDistribution(1, 1, "a")
***** error <GeneralizedExtremeValueDistribution: MU must be a real scalar.> ...
 GeneralizedExtremeValueDistribution(1, 1, [1, 2])
***** error <GeneralizedExtremeValueDistribution: MU must be a real scalar.> ...
 GeneralizedExtremeValueDistribution(1, 1, NaN)
***** error <cdf: invalid argument for upper tail.> ...
 cdf (GeneralizedExtremeValueDistribution, 2, "uper")
***** error <cdf: invalid argument for upper tail.> ...
 cdf (GeneralizedExtremeValueDistribution, 2, 3)
***** shared x
 x = gevrnd (1, 1, 1, [1, 100]);
***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
 paramci (GeneralizedExtremeValueDistribution.fit (x), "alpha")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (GeneralizedExtremeValueDistribution.fit (x), "alpha", 0)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (GeneralizedExtremeValueDistribution.fit (x), "alpha", 1)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (GeneralizedExtremeValueDistribution.fit (x), "alpha", [0.5 2])
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (GeneralizedExtremeValueDistribution.fit (x), "alpha", "")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (GeneralizedExtremeValueDistribution.fit (x), "alpha", {0.05})
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (GeneralizedExtremeValueDistribution.fit (x), ...
          "parameter", "sigma", "alpha", {0.05})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (GeneralizedExtremeValueDistribution.fit (x), ...
          "parameter", {"k", "sigma", "mu", "param"})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (GeneralizedExtremeValueDistribution.fit (x), "alpha", 0.01, ...
          "parameter", {"k", "sigma", "mu", "param"})
***** error <paramci: unknown distribution parameter.> ...
 paramci (GeneralizedExtremeValueDistribution.fit (x), "parameter", "param")
***** error <paramci: unknown distribution parameter.> ...
 paramci (GeneralizedExtremeValueDistribution.fit (x), "alpha", 0.01, ...
          "parameter", "param")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (GeneralizedExtremeValueDistribution.fit (x), "NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (GeneralizedExtremeValueDistribution.fit (x), "alpha", 0.01, ...
          "NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (GeneralizedExtremeValueDistribution.fit (x), "alpha", 0.01, ...
          "parameter", "sigma", "NAME", "value")
***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
 plot (GeneralizedExtremeValueDistribution, "Parent")
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (GeneralizedExtremeValueDistribution, "PlotType", 12)
***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
 plot (GeneralizedExtremeValueDistribution, "PlotType", {"pdf", "cdf"})
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (GeneralizedExtremeValueDistribution, "PlotType", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (GeneralizedExtremeValueDistribution, "Discrete", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (GeneralizedExtremeValueDistribution, "Discrete", [1, 0])
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (GeneralizedExtremeValueDistribution, "Discrete", {true})
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (GeneralizedExtremeValueDistribution, "Parent", 12)
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (GeneralizedExtremeValueDistribution, "Parent", "hax")
***** error <plot: invalid NAME for optional argument.> ...
 plot (GeneralizedExtremeValueDistribution, "invalidNAME", "pdf")
***** error <plot: no fitted DATA to plot a probability plot.> ...
 plot (GeneralizedExtremeValueDistribution, "PlotType", "probability")
***** error <proflik: no fitted data available.> ...
 proflik (GeneralizedExtremeValueDistribution, 2)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (GeneralizedExtremeValueDistribution.fit (x), 4)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (GeneralizedExtremeValueDistribution.fit (x), [1, 2])
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (GeneralizedExtremeValueDistribution.fit (x), {1})
***** error <proflik: SETPARAM must be a numeric vector.> ...
 proflik (GeneralizedExtremeValueDistribution.fit (x), 1, ones (2))
***** error <proflik: missing VALUE for 'Display' argument.> ...
 proflik (GeneralizedExtremeValueDistribution.fit (x), 1, "Display")
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (GeneralizedExtremeValueDistribution.fit (x), 1, "Display", 1)
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (GeneralizedExtremeValueDistribution.fit (x), 1, "Display", {1})
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (GeneralizedExtremeValueDistribution.fit (x), 1, "Display", {"on"})
***** error <proflik: invalid VALUE size for 'Display' argument.> ...
 proflik (GeneralizedExtremeValueDistribution.fit (x), 1, ...
          "Display", ["on"; "on"])
***** error <proflik: invalid VALUE for 'Display' argument.> ...
 proflik (GeneralizedExtremeValueDistribution.fit (x), 1, "Display", "onnn")
***** error <proflik: invalid NAME for optional arguments.> ...
 proflik (GeneralizedExtremeValueDistribution.fit (x), 1, "NAME", "on")
***** error <proflik: invalid optional argument.> ...
 proflik (GeneralizedExtremeValueDistribution.fit (x), 1, {"NAME"}, "on")
***** error <proflik: invalid optional argument.> ...
 proflik (GeneralizedExtremeValueDistribution.fit (x), 1, {[1 2 3 4]}, ...
          "Display", "on")
***** error <truncate: missing input argument.> ...
 truncate (GeneralizedExtremeValueDistribution)
***** error <truncate: missing input argument.> ...
 truncate (GeneralizedExtremeValueDistribution, 2)
***** error <truncate: invalid lower upper limits.> ...
 truncate (GeneralizedExtremeValueDistribution, 4, 2)
***** shared pd
 pd = GeneralizedExtremeValueDistribution(1, 1, 1);
 pd(2) = GeneralizedExtremeValueDistribution(1, 3, 1);
***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
***** error <iqr: requires a scalar probability distribution.> iqr (pd)
***** error <mean: requires a scalar probability distribution.> mean (pd)
***** error <median: requires a scalar probability distribution.> median (pd)
***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
***** error <paramci: requires a scalar probability distribution.> paramci (pd)
***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
***** error <plot: requires a scalar probability distribution.> plot (pd)
***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
***** error <random: requires a scalar probability distribution.> random (pd)
***** error <std: requires a scalar probability distribution.> std (pd)
***** error <truncate: requires a scalar probability distribution.> ...
 truncate (pd, 2, 4)
***** error <var: requires a scalar probability distribution.> var (pd)
100 tests, 100 passed, 0 known failure, 0 skipped
[inst/rangesearch.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/rangesearch.m
***** demo
 ## Generate 1000 random 2D points from each of five distinct multivariate
 ## normal distributions that form five separate classes
 N = 1000;
 d = 10;
 randn ("seed", 5);
 X1 = mvnrnd (d * [0, 0], eye (2), 1000);
 randn ("seed", 6);
 X2 = mvnrnd (d * [1, 1], eye (2), 1000);
 randn ("seed", 7);
 X3 = mvnrnd (d * [-1, -1], eye (2), 1000);
 randn ("seed", 8);
 X4 = mvnrnd (d * [1, -1], eye (2), 1000);
 randn ("seed", 8);
 X5 = mvnrnd (d * [-1, 1], eye (2), 1000);
 X = [X1; X2; X3; X4; X5];

 ## For each point in X, find the points in X that are within a radius d
 ## away from the points in X.
 Idx = rangesearch (X, X, d, "NSMethod", "exhaustive");

 ## Select the first point in X (corresponding to the first class) and find
 ## its nearest neighbors within the radius d.  Display these points in
 ## one color and the remaining points in a different color.
 x = X(1,:);
 nearestPoints = X (Idx{1},:);
 nonNearestIdx = true (size (X, 1), 1);
 nonNearestIdx(Idx{1}) = false;

 scatter (X(nonNearestIdx,1), X(nonNearestIdx,2))
 hold on
 scatter (nearestPoints(:,1),nearestPoints(:,2))
 scatter (x(1), x(2), "black", "filled")
 hold off

 ## Select the last point in X (corresponding to the fifth class) and find
 ## its nearest neighbors within the radius d.  Display these points in
 ## one color and the remaining points in a different color.
 x = X(end,:);
 nearestPoints = X (Idx{1},:);
 nonNearestIdx = true (size (X, 1), 1);
 nonNearestIdx(Idx{1}) = false;

 figure
 scatter (X(nonNearestIdx,1), X(nonNearestIdx,2))
 hold on
 scatter (nearestPoints(:,1),nearestPoints(:,2))
 scatter (x(1), x(2), "black", "filled")
 hold off
***** shared x, y, X, Y
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = [2, 3, 4; 1, 4, 3];
 X = [1, 2, 3, 4; 2, 3, 4, 5; 3, 4, 5, 6];
 Y = [1, 2, 2, 3; 2, 3, 3, 4];
***** test
 [idx, D] = rangesearch (x, y, 4);
 assert (idx, {[1, 4, 2]; [1, 4]});
 assert (D, {[1.7321, 3.3166, 3.4641]; [2, 3.4641]}, 1e-4);
***** test
 [idx, D] = rangesearch (x, y, 4, "NSMethod", "exhaustive");
 assert (idx, {[1, 4, 2]; [1, 4]});
 assert (D, {[1.7321, 3.3166, 3.4641]; [2, 3.4641]}, 1e-4);
***** test
 [idx, D] = rangesearch (x, y, 4, "NSMethod", "kdtree");
 assert (idx, {[1, 4, 2]; [1, 4]});
 assert (D, {[1.7321, 3.3166, 3.4641]; [2, 3.4641]}, 1e-4);
***** test
 [idx, D] = rangesearch (x, y, 4, "SortIndices", true);
 assert (idx, {[1, 4, 2]; [1, 4]});
 assert (D, {[1.7321, 3.3166, 3.4641]; [2, 3.4641]}, 1e-4);
***** test
 [idx, D] = rangesearch (x, y, 4, "SortIndices", false);
 assert (idx, {[1, 2, 4]; [1, 4]});
 assert (D, {[1.7321, 3.4641, 3.3166]; [2, 3.4641]}, 1e-4);
***** test
 [idx, D] = rangesearch (x, y, 4, "NSMethod", "exhaustive", ...
                         "SortIndices", false);
 assert (idx, {[1, 2, 4]; [1, 4]});
 assert (D, {[1.7321, 3.4641, 3.3166]; [2, 3.4641]}, 1e-4);
***** test
 eucldist = @(v,m) sqrt(sumsq(repmat(v,rows(m),1)-m,2));
 [idx, D] = rangesearch (x, y, 4, "Distance", eucldist);
 assert (idx, {[1, 4, 2]; [1, 4]});
 assert (D, {[1.7321, 3.3166, 3.4641]; [2, 3.4641]}, 1e-4);
***** test
 eucldist = @(v,m) sqrt(sumsq(repmat(v,rows(m),1)-m,2));
 [idx, D] = rangesearch (x, y, 4, "Distance", eucldist, ...
                         "NSMethod", "exhaustive");
 assert (idx, {[1, 4, 2]; [1, 4]});
 assert (D, {[1.7321, 3.3166, 3.4641]; [2, 3.4641]}, 1e-4);
***** test
 [idx, D] = rangesearch (x, y, 1.5, "Distance", "seuclidean", ...
                         "NSMethod", "exhaustive");
 assert (idx, {[1, 4, 2]; [1, 4]});
 assert (D, {[0.6024, 1.0079, 1.2047]; [0.6963, 1.2047]}, 1e-4);
***** test
 [idx, D] = rangesearch (x, y, 1.5, "Distance", "seuclidean", ...
                         "NSMethod", "exhaustive", "SortIndices", false);
 assert (idx, {[1, 2, 4]; [1, 4]});
 assert (D, {[0.6024, 1.2047, 1.0079]; [0.6963, 1.2047]}, 1e-4);
***** test
 [idx, D] = rangesearch (X, Y, 4);
 assert (idx, {[1, 2]; [1, 2, 3]});
 assert (D, {[1.4142, 3.1623]; [1.4142, 1.4142, 3.1623]}, 1e-4);
***** test
 [idx, D] = rangesearch (X, Y, 2);
 assert (idx, {[1]; [1, 2]});
 assert (D, {[1.4142]; [1.4142, 1.4142]}, 1e-4);
***** test
 eucldist = @(v,m) sqrt(sumsq(repmat(v,rows(m),1)-m,2));
 [idx, D] = rangesearch (X, Y, 4, "Distance", eucldist);
 assert (idx, {[1, 2]; [1, 2, 3]});
 assert (D, {[1.4142, 3.1623]; [1.4142, 1.4142, 3.1623]}, 1e-4);
***** test
 [idx, D] = rangesearch (X, Y, 4, "SortIndices", false);
 assert (idx, {[1, 2]; [1, 2, 3]});
 assert (D, {[1.4142, 3.1623]; [1.4142, 1.4142, 3.1623]}, 1e-4);
***** test
 [idx, D] = rangesearch (X, Y, 4, "Distance", "seuclidean", ...
                         "NSMethod", "exhaustive");
 assert (idx, {[1, 2]; [1, 2, 3]});
 assert (D, {[1.4142, 3.1623]; [1.4142, 1.4142, 3.1623]}, 1e-4);
***** error<rangesearch: too few input arguments.> rangesearch (1)
***** error<rangesearch: number of columns in X and Y must match.> ...
 rangesearch (ones (4, 5), ones (4))
***** error<rangesearch: invalid NAME in optional pairs of arguments.> ...
 rangesearch (ones (4, 2), ones (3, 2), 1, "Distance", "euclidean", "some", "some")
***** error<rangesearch: only a single distance parameter can be defined.> ...
 rangesearch (ones (4, 5), ones (1, 5), 1, "scale", ones (1, 5), "P", 3)
***** error<rangesearch: invalid value of Minkowski Exponent.> ...
 rangesearch (ones (4, 5), ones (1, 5), 1, "P",-2)
***** error<rangesearch: invalid value in Scale or the size of Scale.> ...
 rangesearch (ones (4, 5), ones (1, 5), 1, "scale", ones(4,5), "distance", "euclidean")
***** error<rangesearch: invalid value in Cov, Cov can only be given for mahalanobis distance.> ...
 rangesearch (ones (4, 5), ones (1, 5), 1, "cov", ["some" "some"])
***** error<rangesearch: invalid value in Cov, Cov can only be given for mahalanobis distance.> ...
 rangesearch (ones (4, 5), ones (1, 5), 1, "cov", ones(4,5), "distance", "euclidean")
***** error<rangesearch: invalid value of bucketsize.> ...
 rangesearch (ones (4, 5), ones (1, 5), 1, "bucketsize", -1)
***** error<rangesearch: 'kdtree' cannot be used with the given distance metric.> ...
 rangesearch (ones (4, 5), ones (1, 5), 1, "NSmethod", "kdtree", "distance", "cosine")
***** error<rangesearch: 'kdtree' cannot be used with the given distance metric.> ...
 rangesearch (ones (4, 5), ones (1, 5), 1, "NSmethod", "kdtree", "distance", "mahalanobis")
***** error<rangesearch: 'kdtree' cannot be used with the given distance metric.> ...
 rangesearch (ones (4, 5), ones (1, 5), 1, "NSmethod", "kdtree", "distance", "correlation")
***** error<rangesearch: 'kdtree' cannot be used with the given distance metric.> ...
 rangesearch (ones (4, 5), ones (1, 5), 1, "NSmethod", "kdtree", "distance", "seuclidean")
***** error<rangesearch: 'kdtree' cannot be used with the given distance metric.> ...
 rangesearch (ones (4, 5), ones (1, 5), 1, "NSmethod", "kdtree", "distance", "spearman")
***** error<rangesearch: 'kdtree' cannot be used with the given distance metric.> ...
 rangesearch (ones (4, 5), ones (1, 5), 1, "NSmethod", "kdtree", "distance", "hamming")
***** error<rangesearch: 'kdtree' cannot be used with the given distance metric.> ...
 rangesearch (ones (4, 5), ones (1, 5), 1, "NSmethod", "kdtree", "distance", "jaccard")
31 tests, 31 passed, 0 known failure, 0 skipped
[inst/cmdscale.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/cmdscale.m
***** shared m, n, X, D
 m = randi(100) + 1; n = randi(100) + 1; X = rand(m, n); D = pdist(X);
***** assert(norm(pdist(cmdscale(D))), norm(D), sqrt(eps))
***** assert(norm(pdist(cmdscale(squareform(D)))), norm(D), sqrt(eps))
2 tests, 2 passed, 0 known failure, 0 skipped
[inst/fitcgam.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/fitcgam.m
***** demo
 ## Train a GAM classifier for binary classification
 ## using specific data and plot the decision boundaries.

 ## Define specific data
 X = [1, 2; 2, 3; 3, 3; 4, 5; 5, 5; ...
     6, 7; 7, 8; 8, 8; 9, 9; 10, 10];
 Y = [0; 0; 0; 0; 0; ...
     1; 1; 1; 1; 1];

 ## Train the GAM model
 obj = fitcgam (X, Y, "Interactions", "all");

 ## Create a grid of values for prediction
 x1 = [min(X(:,1)):0.1:max(X(:,1))];
 x2 = [min(X(:,2)):0.1:max(X(:,2))];
 [x1G, x2G] = meshgrid (x1, x2);
 XGrid = [x1G(:), x2G(:)];
 pred = predict (obj, XGrid);

 ## Plot decision boundaries and data points
 predNumeric = str2double (pred);
 gidx = predNumeric > 0.5;

 figure
 scatter(XGrid(gidx,1), XGrid(gidx,2), "markerfacecolor", "magenta");
 hold on
 scatter(XGrid(!gidx,1), XGrid(!gidx,2), "markerfacecolor", "red");
 plot(X(Y == 0, 1), X(Y == 0, 2), "ko", X(Y == 1, 1), X(Y == 1, 2), "kx");
 xlabel("Feature 1");
 ylabel("Feature 2");
 title("Generalized Additive Model (GAM) Decision Boundary");
 legend({"Class 1 Region", "Class 0 Region", ...
       "Class 1 Samples", "Class 0 Samples"}, ...
       "location", "northwest")
 axis tight
 hold off
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = [0; 0; 1; 1];
 PredictorNames = {'Feature1', 'Feature2', 'Feature3'};
 a = fitcgam (x, y, "PredictorNames", PredictorNames);
 assert (class (a), "ClassificationGAM");
 assert ({a.X, a.Y, a.NumObservations}, {x, y, 4})
 assert ({a.NumPredictors, a.ResponseName}, {3, "Y"})
 assert (a.ClassNames, {'0'; '1'})
 assert (a.PredictorNames, PredictorNames)
 assert (a.BaseModel.Intercept, 0)
***** test
 x = [1, 2; 3, 4; 5, 6; 7, 8; 9, 10];
 y = [1; 0; 1; 0; 1];
 a = fitcgam (x, y, "interactions", "all");
 assert (class (a), "ClassificationGAM");
 assert ({a.X, a.Y, a.NumObservations}, {x, y, 5})
 assert ({a.NumPredictors, a.ResponseName}, {2, "Y"})
 assert (a.ClassNames, {'1'; '0'})
 assert (a.PredictorNames, {'x1', 'x2'})
 assert (a.ModelwInt.Intercept, 0.4055, 1e-1)
***** test
 load fisheriris
 inds = strcmp (species,'versicolor') | strcmp (species,'virginica');
 X = meas(inds, :);
 Y = species(inds, :)';
 Y = strcmp (Y, 'virginica')';
 a = fitcgam (X, Y, 'Formula', 'Y ~ x1 + x2 + x3 + x4 + x1:x2 + x2:x3');
 assert (class (a), "ClassificationGAM");
 assert ({a.X, a.Y, a.NumObservations}, {X, Y, 100})
 assert ({a.NumPredictors, a.ResponseName}, {4, "Y"})
 assert (a.ClassNames, {'0'; '1'})
 assert (a.Formula, 'Y ~ x1 + x2 + x3 + x4 + x1:x2 + x2:x3')
 assert (a.PredictorNames, {'x1', 'x2', 'x3', 'x4'})
 assert (a.ModelwInt.Intercept, 0)
***** error<fitcgam: too few arguments.> fitcgam ()
***** error<fitcgam: too few arguments.> fitcgam (ones (4,1))
***** error<fitcgam: name-value arguments must be in pairs.>
 fitcgam (ones (4,2), ones (4, 1), "K")
***** error<fitcgam: number of rows in X and Y must be equal.>
 fitcgam (ones (4,2), ones (3, 1))
***** error<fitcgam: number of rows in X and Y must be equal.>
 fitcgam (ones (4,2), ones (3, 1), "K", 2)
8 tests, 8 passed, 0 known failure, 0 skipped
[inst/Classification/ConfusionMatrixChart.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/Classification/ConfusionMatrixChart.m
***** demo
 ## Create a simple ConfusionMatrixChart Object

 cm = ConfusionMatrixChart (gca, [1 2; 1 2], {"A","B"}, {"XLabel","LABEL A"})
 NormalizedValues = cm.NormalizedValues
 ClassLabels = cm.ClassLabels
***** test
 hf = figure ("visible", "off");
 unwind_protect
   cm = ConfusionMatrixChart (gca, [1 2; 1 2], {"A","B"}, {"XLabel","LABEL A"});
   assert (isa (cm, "ConfusionMatrixChart"), true);
 unwind_protect_cleanup
   close (hf);
 end_unwind_protect
1 test, 1 passed, 0 known failure, 0 skipped
[inst/Classification/ClassificationPartitionedModel.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/Classification/ClassificationPartitionedModel.m
***** demo

 load fisheriris
 x = meas;
 y = species;

 ## Create a KNN classifier model
 obj = fitcknn (x, y, "NumNeighbors", 5, "Standardize", 1);

 ## Create a partition for 5-fold cross-validation
 partition = cvpartition (y, "KFold", 5);

 ## Create the ClassificationPartitionedModel object
 cvModel = crossval (obj, 'cvPartition', partition)
***** demo

 load fisheriris
 x = meas;
 y = species;

 ## Create a KNN classifier model
 obj = fitcknn (x, y, "NumNeighbors", 5, "Standardize", 1);

 ## Create the ClassificationPartitionedModel object
 cvModel = crossval (obj);

 ## Predict the class labels for the observations not used for training
 [label, score, cost] = kfoldPredict (cvModel);
 fprintf ("Cross-validated accuracy = %1.2f%% (%d/%d)\n", ...
          sum (strcmp (label, y)) / numel (y) *100, ...
          sum (strcmp (label, y)), numel (y))
***** test
 load fisheriris
 a = fitcdiscr (meas, species, "gamma", 0.3);
 cvModel = crossval (a, "KFold", 5);
 assert (class (cvModel), "ClassificationPartitionedModel");
 assert (cvModel.NumObservations, 150);
 assert (numel (cvModel.Trained), 5);
 assert (class (cvModel.Trained{1}), "CompactClassificationDiscriminant");
 assert (cvModel.CrossValidatedModel, "ClassificationDiscriminant");
 assert (cvModel.KFold, 5);
***** test
 load fisheriris
 a = fitcdiscr (meas, species, "gamma", 0.5, "fillcoeffs", "off");
 cvModel = crossval (a, "HoldOut", 0.3);
 assert (class (cvModel), "ClassificationPartitionedModel");
 assert ({cvModel.X, cvModel.Y}, {meas, species});
 assert (cvModel.NumObservations, 150);
 assert (numel (cvModel.Trained), 1);
 assert (class (cvModel.Trained{1}), "CompactClassificationDiscriminant");
 assert (cvModel.CrossValidatedModel, "ClassificationDiscriminant");
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "b"; "b"];
 a = fitcgam (x, y, "Interactions", "all");
 cvModel = crossval (a, "KFold", 5);
 assert (class (cvModel), "ClassificationPartitionedModel");
 assert (cvModel.NumObservations, 4);
 assert (numel (cvModel.Trained), 5);
 assert (class (cvModel.Trained{1}), "CompactClassificationGAM");
 assert (cvModel.CrossValidatedModel, "ClassificationGAM");
 assert (cvModel.KFold, 5);
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "b"; "b"];
 a = fitcgam (x, y);
 cvModel = crossval (a, "LeaveOut", "on");
 assert (class (cvModel), "ClassificationPartitionedModel");
 assert ({cvModel.X, cvModel.Y}, {x, y});
 assert (cvModel.NumObservations, 4);
 assert (numel (cvModel.Trained), 4);
 assert (class (cvModel.Trained{1}), "CompactClassificationGAM");
 assert (cvModel.CrossValidatedModel, "ClassificationGAM");
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "b"; "b"];
 a = fitcknn (x, y);
 partition = cvpartition (y, "KFold", 5);
 cvModel = ClassificationPartitionedModel (a, partition);
 assert (class (cvModel), "ClassificationPartitionedModel");
 assert (class (cvModel.Trained{1}), "ClassificationKNN");
 assert (cvModel.NumObservations, 4);
 assert (cvModel.ModelParameters.NumNeighbors, 1);
 assert (cvModel.ModelParameters.NSMethod, "kdtree");
 assert (cvModel.ModelParameters.Distance, "euclidean");
 assert (! cvModel.ModelParameters.Standardize);
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "b"; "b"];
 a = fitcknn (x, y, "NSMethod", "exhaustive");
 partition = cvpartition (y, "HoldOut", 0.2);
 cvModel = ClassificationPartitionedModel (a, partition);
 assert (class (cvModel), "ClassificationPartitionedModel");
 assert (class (cvModel.Trained{1}), "ClassificationKNN");
 assert ({cvModel.X, cvModel.Y}, {x, y});
 assert (cvModel.NumObservations, 4);
 assert (cvModel.ModelParameters.NumNeighbors, 1);
 assert (cvModel.ModelParameters.NSMethod, "exhaustive");
 assert (cvModel.ModelParameters.Distance, "euclidean");
 assert (! cvModel.ModelParameters.Standardize);
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "b"; "b"];
 k = 3;
 a = fitcknn (x, y, "NumNeighbors" ,k);
 partition = cvpartition (y, "LeaveOut");
 cvModel = ClassificationPartitionedModel (a, partition);
 assert (class (cvModel), "ClassificationPartitionedModel");
 assert (class (cvModel.Trained{1}), "ClassificationKNN");
 assert ({cvModel.X, cvModel.Y}, {x, y});
 assert (cvModel.NumObservations, 4);
 assert (cvModel.ModelParameters.NumNeighbors, k);
 assert (cvModel.ModelParameters.NSMethod, "kdtree");
 assert (cvModel.ModelParameters.Distance, "euclidean");
 assert (! cvModel.ModelParameters.Standardize);
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = {"a"; "a"; "b"; "b"};
 a = fitcnet (x, y, "IterationLimit", 50);
 cvModel = crossval (a, "KFold", 5);
 assert (class (cvModel), "ClassificationPartitionedModel");
 assert (cvModel.NumObservations, 4);
 assert (numel (cvModel.Trained), 5);
 assert (class (cvModel.Trained{1}), "CompactClassificationNeuralNetwork");
 assert (cvModel.CrossValidatedModel, "ClassificationNeuralNetwork");
 assert (cvModel.KFold, 5);
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = {"a"; "a"; "b"; "b"};
 a = fitcnet (x, y, "LayerSizes", [5, 3]);
 cvModel = crossval (a, "LeaveOut", "on");
 assert (class (cvModel), "ClassificationPartitionedModel");
 assert ({cvModel.X, cvModel.Y}, {x, y});
 assert (cvModel.NumObservations, 4);
 assert (numel (cvModel.Trained), 4);
 assert (class (cvModel.Trained{1}), "CompactClassificationNeuralNetwork");
 assert (cvModel.CrossValidatedModel, "ClassificationNeuralNetwork");
***** test
 load fisheriris
 inds = ! strcmp (species, 'setosa');
 x = meas(inds, 3:4);
 y = grp2idx (species(inds));
 SVMModel = fitcsvm (x,y);
 CVMdl = crossval (SVMModel, "KFold", 5);
 assert (class (CVMdl), "ClassificationPartitionedModel")
 assert ({CVMdl.X, CVMdl.Y}, {x, y})
 assert (CVMdl.KFold == 5)
 assert (class (CVMdl.Trained{1}), "CompactClassificationSVM")
 assert (CVMdl.CrossValidatedModel, "ClassificationSVM");
***** test
 load fisheriris
 inds = ! strcmp (species, 'setosa');
 x = meas(inds, 3:4);
 y = grp2idx (species(inds));
 obj = fitcsvm (x, y);
 CVMdl = crossval (obj, "HoldOut", 0.2);
 assert (class (CVMdl), "ClassificationPartitionedModel")
 assert ({CVMdl.X, CVMdl.Y}, {x, y})
 assert (class (CVMdl.Trained{1}), "CompactClassificationSVM")
 assert (CVMdl.CrossValidatedModel, "ClassificationSVM");
***** test
 load fisheriris
 inds = ! strcmp (species, 'setosa');
 x = meas(inds, 3:4);
 y = grp2idx (species(inds));
 obj = fitcsvm (x, y);
 CVMdl = crossval (obj, "LeaveOut", 'on');
 assert (class (CVMdl), "ClassificationPartitionedModel")
 assert ({CVMdl.X, CVMdl.Y}, {x, y})
 assert (class (CVMdl.Trained{1}), "CompactClassificationSVM")
 assert (CVMdl.CrossValidatedModel, "ClassificationSVM");
***** error<ClassificationPartitionedModel: too few input arguments.> ...
 ClassificationPartitionedModel ()
***** error<ClassificationPartitionedModel: too few input arguments.> ...
 ClassificationPartitionedModel (ClassificationKNN (ones (4,2), ones (4,1)))
***** test
 load fisheriris
 a = fitcdiscr (meas, species, "gamma", 0.5, "fillcoeffs", "off");
 cvModel = crossval (a, "Kfold", 4);
 [label, score, cost] = kfoldPredict (cvModel);
 assert (class(cvModel), "ClassificationPartitionedModel");
 assert ({cvModel.X, cvModel.Y}, {meas, species});
 assert (cvModel.NumObservations, 150);
***** # assert (label, {"b"; "b"; "a"; "a"});
***** # assert (score, [4.5380e-01, 5.4620e-01; 2.4404e-01, 7.5596e-01; ...
***** #         9.9392e-01, 6.0844e-03; 9.9820e-01, 1.8000e-03], 1e-4);
***** # assert (cost, [5.4620e-01, 4.5380e-01; 7.5596e-01, 2.4404e-01; ...
***** #         6.0844e-03, 9.9392e-01; 1.8000e-03, 9.9820e-01], 1e-4);
***** test
 x = ones(4, 11);
 y = {"a"; "a"; "b"; "b"};
 k = 3;
 a = fitcknn (x, y, "NumNeighbors", k);
 partition = cvpartition (y, "LeaveOut");
 cvModel = ClassificationPartitionedModel (a, partition);
 [label, score, cost] = kfoldPredict (cvModel);
 assert (class(cvModel), "ClassificationPartitionedModel");
 assert ({cvModel.X, cvModel.Y}, {x, y});
 assert (cvModel.NumObservations, 4);
 assert (cvModel.ModelParameters.NumNeighbors, k);
 assert (cvModel.ModelParameters.NSMethod, "exhaustive");
 assert (cvModel.ModelParameters.Distance, "euclidean");
 assert (! cvModel.ModelParameters.Standardize);
 assert (label, {"b"; "b"; "a"; "a"});
 assert (score, [0.3333, 0.6667; 0.3333, 0.6667; 0.6667, 0.3333; ...
          0.6667, 0.3333], 1e-4);
 assert (cost, [0.6667, 0.3333; 0.6667, 0.3333; 0.3333, 0.6667; ...
          0.3333, 0.6667], 1e-4);
***** error<ClassificationPartitionedModel.kfoldPredict: 'Cost' output is not supported for ClassificationSVM cross validated models.> ...
 [label, score, cost] = kfoldPredict (crossval (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1))))
***** error<ClassificationPartitionedModel.kfoldPredict: 'Cost' output is not supported for ClassificationNeuralNetwork cross validated models.> ...
 [label, score, cost] = kfoldPredict (crossval (ClassificationNeuralNetwork (ones (40,2), randi ([1, 2], 40, 1))))
18 tests, 18 passed, 0 known failure, 0 skipped
[inst/Classification/CompactClassificationGAM.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/Classification/CompactClassificationGAM.m
***** demo
 ## Create a generalized additive model classifier and its compact version
 # and compare their size

 load fisheriris
 X = meas;
 Y = species;

 Mdl = fitcdiscr (X, Y, 'ClassNames', unique (species))
 CMdl = crossval (Mdl);

 whos ('Mdl', 'CMdl')
***** test
 Mdl = CompactClassificationGAM ();
 assert (class (Mdl), "CompactClassificationGAM")
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = [0; 0; 1; 1];
 PredictorNames = {'Feature1', 'Feature2', 'Feature3'};
 Mdl = fitcgam (x, y, "PredictorNames", PredictorNames);
 CMdl = compact (Mdl);
 assert (class (CMdl), "CompactClassificationGAM");
 assert ({CMdl.NumPredictors, CMdl.ResponseName}, {3, "Y"})
 assert (CMdl.ClassNames, {'0'; '1'})
 assert (CMdl.PredictorNames, PredictorNames)
 assert (CMdl.BaseModel.Intercept, 0)
***** test
 load fisheriris
 inds = strcmp (species,'versicolor') | strcmp (species,'virginica');
 X = meas(inds, :);
 Y = species(inds, :)';
 Y = strcmp (Y, 'virginica')';
 Mdl = fitcgam (X, Y, 'Formula', 'Y ~ x1 + x2 + x3 + x4 + x1:x2 + x2:x3');
 CMdl = compact (Mdl);
 assert (class (CMdl), "CompactClassificationGAM");
 assert ({CMdl.NumPredictors, CMdl.ResponseName}, {4, "Y"})
 assert (CMdl.ClassNames, {'0'; '1'})
 assert (CMdl.Formula, 'Y ~ x1 + x2 + x3 + x4 + x1:x2 + x2:x3')
 assert (CMdl.PredictorNames, {'x1', 'x2', 'x3', 'x4'})
 assert (CMdl.ModelwInt.Intercept, 0)
***** test
 X = [2, 3, 5; 4, 6, 8; 1, 2, 3; 7, 8, 9; 5, 4, 3];
 Y = [0; 1; 0; 1; 1];
 Mdl = fitcgam (X, Y, 'Knots', [4, 4, 4], 'Order', [3, 3, 3]);
 CMdl = compact (Mdl);
 assert (class (CMdl), "CompactClassificationGAM");
 assert ({CMdl.NumPredictors, CMdl.ResponseName}, {3, "Y"})
 assert (CMdl.ClassNames, {'0'; '1'})
 assert (CMdl.PredictorNames, {'x1', 'x2', 'x3'})
 assert (CMdl.Knots, [4, 4, 4])
 assert (CMdl.Order, [3, 3, 3])
 assert (CMdl.DoF, [7, 7, 7])
 assert (CMdl.BaseModel.Intercept, 0.4055, 1e-1)
***** error<CompactClassificationGAM: invalid classification object.> ...
 CompactClassificationGAM (1)
***** test
 x = [1, 2; 3, 4; 5, 6; 7, 8; 9, 10];
 y = [1; 0; 1; 0; 1];
 Mdl = fitcgam (x, y, "interactions", "all");
 CMdl = compact (Mdl);
 l = {'0'; '0'; '0'; '0'; '0'};
 s = [0.3760, 0.6240; 0.4259, 0.5741; 0.3760, 0.6240; ...
      0.4259, 0.5741; 0.3760, 0.6240];
 [labels, scores] = predict (CMdl, x);
 assert (class (CMdl), "CompactClassificationGAM");
 assert ({CMdl.NumPredictors, CMdl.ResponseName}, {2, "Y"})
 assert (CMdl.ClassNames, {'1'; '0'})
 assert (CMdl.PredictorNames, {'x1', 'x2'})
 assert (CMdl.ModelwInt.Intercept, 0.4055, 1e-1)
 assert (labels, l)
 assert (scores, s, 1e-1)
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = [0; 0; 1; 1];
 interactions = [false, true, false; true, false, true; false, true, false];
 Mdl = fitcgam (x, y, "learningrate", 0.2, "interactions", interactions);
 CMdl = compact (Mdl);
 [label, score] = predict (CMdl, x, "includeinteractions", true);
 l = {'0'; '0'; '1'; '1'};
 s = [0.5106, 0.4894; 0.5135, 0.4865; 0.4864, 0.5136; 0.4847, 0.5153];
 assert (class (CMdl), "CompactClassificationGAM");
 assert ({CMdl.NumPredictors, CMdl.ResponseName}, {3, "Y"})
 assert (CMdl.ClassNames, {'0'; '1'})
 assert (CMdl.PredictorNames, {'x1', 'x2', 'x3'})
 assert (CMdl.ModelwInt.Intercept, 0)
 assert (label, l)
 assert (score, s, 1e-1)
***** shared CMdl
 Mdl = fitcgam (ones (4,2), ones (4,1));
 CMdl = compact (Mdl);
***** error<CompactClassificationGAM.predict: too few input arguments.> ...
 predict (CMdl)
***** error<CompactClassificationGAM.predict: XC is empty.> ...
 predict (CMdl, [])
***** error<CompactClassificationGAM.predict: XC must have the same number of features as the trained model.> ...
 predict (CMdl, 1)
10 tests, 10 passed, 0 known failure, 0 skipped
[inst/Classification/CompactClassificationSVM.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/Classification/CompactClassificationSVM.m
***** demo
 ## Create a support vectors machine classifier and its compact version
 # and compare their size

 load fisheriris
 X = meas;
 Y = species;

 Mdl = fitcsvm (X, Y, 'ClassNames', unique (species))
 CMdl = crossval (Mdl);

 whos ('Mdl', 'CMdl')
***** error<CompactClassificationSVM: invalid classification object.> ...
 CompactClassificationSVM (1)
***** shared x, y, CMdl
 load fisheriris
 inds = ! strcmp (species, 'setosa');
 x = meas(inds, 3:4);
 y = grp2idx (species(inds));
***** test
 xc = [min(x); mean(x); max(x)];
 Mdl = fitcsvm (x, y, 'KernelFunction', 'rbf', 'Tolerance', 1e-7);
 CMdl = compact (Mdl);
 assert (isempty (CMdl.Alpha), true)
 assert (sum (CMdl.IsSupportVector), numel (CMdl.Beta))
 [label, score] = predict (CMdl, xc);
 assert (label, [1; 2; 2]);
 assert (score(:,1), [0.99285; -0.080296; -0.93694], 1e-5);
 assert (score(:,1), -score(:,2), eps)
***** test
 Mdl = fitcsvm (x, y);
 CMdl = compact (Mdl);
 assert (isempty (CMdl.Beta), true)
 assert (sum (CMdl.IsSupportVector), numel (CMdl.Alpha))
 assert (numel (CMdl.Alpha), 24)
 assert (CMdl.Bias, -14.415, 1e-3)
 xc = [min(x); mean(x); max(x)];
 label = predict (CMdl, xc);
 assert (label, [1; 2; 2]);
***** error<CompactClassificationSVM.predict: too few input arguments.> ...
 predict (CMdl)
***** error<CompactClassificationSVM.predict: XC is empty.> ...
 predict (CMdl, [])
***** error<CompactClassificationSVM.predict: XC must have the same number of predictors as the trained SVM model.> ...
 predict (CMdl, 1)
***** test
 CMdl.ScoreTransform = "a";
***** error<CompactClassificationSVM.predict: 'ScoreTransform' must be a 'function_handle' object.> ...
 [labels, scores] = predict (CMdl, x);
***** test
 rand ("seed", 1);
 C = cvpartition (y, 'HoldOut', 0.15);
 Mdl = fitcsvm (x(training (C),:), y(training (C)), ...
                'KernelFunction', 'rbf', 'Tolerance', 1e-7);
 CMdl = compact (Mdl);
 testInds = test (C);
 expected_margin = [2.0000;  0.8579;  1.6690;  3.4141;  3.4552; ...
                    2.6605;  3.5251; -4.0000; -6.3411; -6.4511; ...
                   -3.0532; -7.5054; -1.6700; -5.6227; -7.3640];
 computed_margin = margin (CMdl, x(testInds,:), y(testInds,:));
 assert (computed_margin, expected_margin, 1e-4);
***** error<CompactClassificationSVM.margin: too few input arguments.> ...
 margin (CMdl)
***** error<CompactClassificationSVM.margin: too few input arguments.> ...
 margin (CMdl, zeros (2))
***** error<CompactClassificationSVM.margin: X is empty.> ...
 margin (CMdl, [], 1)
***** error<CompactClassificationSVM.margin: X must have the same number of predictors as the trained SVM model.> ...
 margin (CMdl, 1, 1)
***** error<CompactClassificationSVM.margin: Y is empty.> ...
 margin (CMdl, [1, 2], [])
***** error<CompactClassificationSVM.margin: Y must have the same number of rows as X.> ...
 margin (CMdl, [1, 2], [1; 2])
***** test
 rand ("seed", 1);
 C = cvpartition (y, 'HoldOut', 0.15);
 Mdl = fitcsvm (x(training (C),:), y(training (C)), ...
                'KernelFunction', 'rbf', 'Tolerance', 1e-7);
 CMdl = compact (Mdl);
 testInds = test (C);
 L1 = loss (CMdl, x(testInds,:), y(testInds,:), 'LossFun', 'binodeviance');
 L2 = loss (CMdl, x(testInds,:), y(testInds,:), 'LossFun', 'classiferror');
 L3 = loss (CMdl, x(testInds,:), y(testInds,:), 'LossFun', 'exponential');
 L4 = loss (CMdl, x(testInds,:), y(testInds,:), 'LossFun', 'hinge');
 L5 = loss (CMdl, x(testInds,:), y(testInds,:), 'LossFun', 'logit');
 L6 = loss (CMdl, x(testInds,:), y(testInds,:), 'LossFun', 'quadratic');
 assert (L1, 2.8711, 1e-4);
 assert (L2, 0.5333, 1e-4);
 assert (L3, 10.9685, 1e-4);
 assert (L4, 1.9827, 1e-4);
 assert (L5, 1.5849, 1e-4);
 assert (L6, 7.6739, 1e-4);
***** error<CompactClassificationSVM.loss: too few input arguments.> ...
 loss (CMdl)
***** error<CompactClassificationSVM.loss: too few input arguments.> ...
 loss (CMdl, zeros (2))
***** error<CompactClassificationSVM.loss: Name-Value arguments must be in pairs.> ...
 loss (CMdl, [1, 2], 1, "LossFun")
***** error<CompactClassificationSVM.loss: X is empty.> ...
 loss (CMdl, [], zeros (2))
***** error<CompactClassificationSVM.loss: X must have the same number of predictors as the trained SVM model.> ...
 loss (CMdl, 1, zeros (2))
***** error<CompactClassificationSVM.loss: Y is empty.> ...
 loss (CMdl, [1, 2], [])
***** error<CompactClassificationSVM.loss: Y must have the same number of rows as X.> ...
 loss (CMdl, [1, 2], [1; 2])
***** error<CompactClassificationSVM.loss: 'LossFun' must be a character vector.> ...
 loss (CMdl, [1, 2], 1, "LossFun", 1)
***** error<CompactClassificationSVM.loss: unsupported Loss function.> ...
 loss (CMdl, [1, 2], 1, "LossFun", "some")
***** error<CompactClassificationSVM.loss: 'Weights' must be a numeric vector.> ...
 loss (CMdl, [1, 2], 1, "Weights", ['a', 'b'])
***** error<CompactClassificationSVM.loss: 'Weights' must be a numeric vector.> ...
 loss (CMdl, [1, 2], 1, "Weights", 'a')
***** error<CompactClassificationSVM.loss: size of 'Weights' must be equal to the number of rows in X.> ...
 loss (CMdl, [1, 2], 1, "Weights", [1, 2])
***** error<CompactClassificationSVM.loss: invalid parameter name in optional pair arguments.> ...
 loss (CMdl, [1, 2], 1, "some", "some")
29 tests, 29 passed, 0 known failure, 0 skipped
[inst/Classification/ClassificationKNN.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/Classification/ClassificationKNN.m
***** demo
 ## Create a k-nearest neighbor classifier for Fisher's iris data with k = 5.
 ## Evaluate some model predictions on new data.

 load fisheriris
 x = meas;
 y = species;
 xc = [min(x); mean(x); max(x)];
 obj = fitcknn (x, y, "NumNeighbors", 5, "Standardize", 1);
 [label, score, cost] = predict (obj, xc)
***** demo
 load fisheriris
 x = meas;
 y = species;
 obj = fitcknn (x, y, "NumNeighbors", 5, "Standardize", 1);

 ## Create a cross-validated model
 CVMdl = crossval (obj)
***** demo
 load fisheriris
 x = meas;
 y = species;
 covMatrix = cov (x);

 ## Fit the k-NN model using the 'mahalanobis' distance
 ## and the custom covariance matrix
 obj = fitcknn(x, y, 'NumNeighbors', 5, 'Distance','mahalanobis', ...
 'Cov', covMatrix);

 ## Create a partition model using cvpartition
 Partition = cvpartition (size (x, 1), 'kfold', 12);

 ## Create cross-validated model using 'cvPartition' name-value argument
 CVMdl = crossval (obj, 'cvPartition', Partition)

 ## Access the trained model from first fold of cross-validation
 CVMdl.Trained{1}
***** demo
 X = [1, 2; 3, 4; 5, 6];
 Y = {'A'; 'B'; 'A'};
 model = fitcknn (X, Y);
 customLossFun = @(C, S, W, Cost) sum (W .* sum (abs (C - S), 2));
 ## Calculate loss using custom loss function
 L = loss (model, X, Y, 'LossFun', customLossFun)
***** demo
 X = [1, 2; 3, 4; 5, 6];
 Y = {'A'; 'B'; 'A'};
 model = fitcknn (X, Y);
 ## Calculate loss using 'mincost' loss function
 L = loss (model, X, Y, 'LossFun', 'mincost')
***** demo
 X = [1, 2; 3, 4; 5, 6];
 Y = ['1'; '2'; '3'];
 model = fitcknn (X, Y);
 X_test = [3, 3; 5, 7];
 Y_test = ['1'; '2'];
 ## Specify custom Weights
 W = [1; 2];
 L = loss (model, X_test, Y_test, 'LossFun', 'logit', 'Weights', W);
***** demo
 load fisheriris
 mdl = fitcknn (meas, species);
 X = mean (meas);
 Y = {'versicolor'};
 m = margin (mdl, X, Y)
***** demo
 X = [1, 2; 4, 5; 7, 8; 3, 2];
 Y = [2; 1; 3; 2];
 ## Train the model
 mdl = fitcknn (X, Y);
 ## Specify Vars and Labels
 Vars = 1;
 Labels = 2;
 ## Calculate partialDependence
 [pd, x, y] = partialDependence (mdl, Vars, Labels);
***** demo
 X = [1, 2; 4, 5; 7, 8; 3, 2];
 Y = [2; 1; 3; 2];
 ## Train the model
 mdl = fitcknn (X, Y);
 ## Specify Vars and Labels
 Vars = 1;
 Labels = 1;
 queryPoints = [linspace(0, 1, 3)', linspace(0, 1, 3)'];
 ## Calculate partialDependence using queryPoints
 [pd, x, y] = partialDependence (mdl, Vars, Labels, 'QueryPoints', ...
 queryPoints)
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "b"; "b"];
 a = ClassificationKNN (x, y);
 assert (class (a), "ClassificationKNN");
 assert ({a.X, a.Y, a.NumNeighbors}, {x, y, 1})
 assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
 assert ({a.BucketSize}, {50})
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "b"; "b"];
 a = ClassificationKNN (x, y, "NSMethod", "exhaustive");
 assert (class (a), "ClassificationKNN");
 assert ({a.X, a.Y, a.NumNeighbors}, {x, y, 1})
 assert ({a.NSMethod, a.Distance}, {"exhaustive", "euclidean"})
 assert ({a.BucketSize}, {50})
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "b"; "b"];
 k = 10;
 a = ClassificationKNN (x, y, "NumNeighbors" ,k);
 assert (class (a), "ClassificationKNN");
 assert ({a.X, a.Y, a.NumNeighbors}, {x, y, 10})
 assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
 assert ({a.BucketSize}, {50})
***** test
 x = ones (4, 11);
 y = ["a"; "a"; "b"; "b"];
 k = 10;
 a = ClassificationKNN (x, y, "NumNeighbors" ,k);
 assert (class (a), "ClassificationKNN");
 assert ({a.X, a.Y, a.NumNeighbors}, {x, y, 10})
 assert ({a.NSMethod, a.Distance}, {"exhaustive", "euclidean"})
 assert ({a.BucketSize}, {50})
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "b"; "b"];
 k = 10;
 a = ClassificationKNN (x, y, "NumNeighbors" ,k, "NSMethod", "exhaustive");
 assert (class (a), "ClassificationKNN");
 assert ({a.X, a.Y, a.NumNeighbors}, {x, y, 10})
 assert ({a.NSMethod, a.Distance}, {"exhaustive", "euclidean"})
 assert ({a.BucketSize}, {50})
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "b"; "b"];
 k = 10;
 a = ClassificationKNN (x, y, "NumNeighbors" ,k, "Distance", "hamming");
 assert (class (a), "ClassificationKNN");
 assert ({a.X, a.Y, a.NumNeighbors}, {x, y, 10})
 assert ({a.NSMethod, a.Distance}, {"exhaustive", "hamming"})
 assert ({a.BucketSize}, {50})
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "b"; "b"];
 weights = ones (4,1);
 a = ClassificationKNN (x, y, "Standardize", 1);
 assert (class (a), "ClassificationKNN");
 assert ({a.X, a.Y, a.NumNeighbors}, {x, y, 1})
 assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
 assert ({a.Standardize}, {true})
 assert ({a.Sigma}, {std(x, [], 1)})
 assert ({a.Mu}, {[3.75, 4.25, 4.75]})
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "b"; "b"];
 weights = ones (4,1);
 a = ClassificationKNN (x, y, "Standardize", false);
 assert (class (a), "ClassificationKNN");
 assert ({a.X, a.Y, a.NumNeighbors}, {x, y, 1})
 assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
 assert ({a.Standardize}, {false})
 assert ({a.Sigma}, {[]})
 assert ({a.Mu}, {[]})
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "b"; "b"];
 s = ones (1, 3);
 a = ClassificationKNN (x, y, "Scale" , s, "Distance", "seuclidean");
 assert (class (a), "ClassificationKNN");
 assert ({a.DistParameter}, {s})
 assert ({a.NSMethod, a.Distance}, {"exhaustive", "seuclidean"})
 assert ({a.BucketSize}, {50})
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "b"; "b"];
 a = ClassificationKNN (x, y, "Exponent" , 5, "Distance", "minkowski");
 assert (class (a), "ClassificationKNN");
 assert (a.DistParameter, 5)
 assert ({a.NSMethod, a.Distance}, {"kdtree", "minkowski"})
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "b"; "b"];
 a = ClassificationKNN (x, y, "Exponent" , 5, "Distance", "minkowski", ...
                        "NSMethod", "exhaustive");
 assert (class (a), "ClassificationKNN");
 assert (a.DistParameter, 5)
 assert ({a.NSMethod, a.Distance}, {"exhaustive", "minkowski"})
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "b"; "b"];
 a = ClassificationKNN (x, y, "BucketSize" , 20, "distance", "mahalanobis");
 assert (class (a), "ClassificationKNN");
 assert ({a.NSMethod, a.Distance}, {"exhaustive", "mahalanobis"})
 assert ({a.BucketSize}, {20})
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "b"; "b"];
 a = ClassificationKNN (x, y, "IncludeTies", true);
 assert (class (a), "ClassificationKNN");
 assert (a.IncludeTies, true);
 assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "b"; "b"];
 a = ClassificationKNN (x, y);
 assert (class (a), "ClassificationKNN");
 assert (a.IncludeTies, false);
 assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "b"; "b"];
 a = ClassificationKNN (x, y);
 assert (class (a), "ClassificationKNN")
 assert (a.Prior, [0.5; 0.5])
 assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
 assert ({a.BucketSize}, {50})
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "b"; "b"];
 prior = [0.5; 0.5];
 a = ClassificationKNN (x, y, "Prior", "empirical");
 assert (class (a), "ClassificationKNN")
 assert (a.Prior, prior)
 assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
 assert ({a.BucketSize}, {50})
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "a"; "b"];
 prior = [0.75; 0.25];
 a = ClassificationKNN (x, y, "Prior", "empirical");
 assert (class (a), "ClassificationKNN")
 assert (a.Prior, prior)
 assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
 assert ({a.BucketSize}, {50})
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "a"; "b"];
 prior = [0.5; 0.5];
 a = ClassificationKNN (x, y, "Prior", "uniform");
 assert (class (a), "ClassificationKNN")
 assert (a.Prior, prior)
 assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
 assert ({a.BucketSize}, {50})
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "b"; "b"];
 cost = eye (2);
 a = ClassificationKNN (x, y, "Cost", cost);
 assert (class (a), "ClassificationKNN")
 assert (a.Cost, [1, 0; 0, 1])
 assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
 assert ({a.BucketSize}, {50})
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "b"; "b"];
 cost = eye (2);
 a = ClassificationKNN (x, y, "Cost", cost, "Distance", "hamming" );
 assert (class (a), "ClassificationKNN")
 assert (a.Cost, [1, 0; 0, 1])
 assert ({a.NSMethod, a.Distance}, {"exhaustive", "hamming"})
 assert ({a.BucketSize}, {50})
***** test
 x = [1, 2; 3, 4; 5,6; 5, 8];
 y = {'9'; '9'; '6'; '7'};
 a = ClassificationKNN (x, y);
 assert (a.Prior, [0.5; 0.25; 0.25])
***** test
 load fisheriris
 x = meas;
 y = species;
 ClassNames = {'setosa', 'versicolor', 'virginica'};
 a = ClassificationKNN (x, y, 'ClassNames', ClassNames);
 assert (a.ClassNames, ClassNames')
***** error<ClassificationKNN: too few input arguments.> ClassificationKNN ()
***** error<ClassificationKNN: too few input arguments.> ...
 ClassificationKNN (ones(4, 1))
***** error<ClassificationKNN: number of rows in X and Y must be equal.> ...
 ClassificationKNN (ones (4,2), ones (1,4))
***** error<ClassificationKNN: 'Standardize' must be either true or false.> ...
 ClassificationKNN (ones (5,3), ones (5,1), "standardize", "a")
***** error<ClassificationKNN: 'Standardize' cannot simultaneously be specified with> ...
 ClassificationKNN (ones (5,2), ones (5,1), "scale", [1 1], "standardize", true)
***** error<ClassificationKNN: 'PredictorNames' must be supplied as a cellstring array.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "PredictorNames", ["A"])
***** error<ClassificationKNN: 'PredictorNames' must be supplied as a cellstring array.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "PredictorNames", "A")
***** error<ClassificationKNN: 'PredictorNames' must have the same number of columns as X.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "PredictorNames", {"A", "B", "C"})
***** error<ClassificationKNN: 'ResponseName' must be a character vector.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "ResponseName", {"Y"})
***** error<ClassificationKNN: 'ResponseName' must be a character vector.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "ResponseName", 1)
***** error<ClassificationKNN: 'ClassNames' must be a cellstring, logical or numeric vector.> ...
 ClassificationKNN (ones(10,2), ones (10,1), "ClassNames", @(x)x)
***** error<ClassificationKNN: 'ClassNames' must be a cellstring, logical or numeric vector.> ...
 ClassificationKNN (ones(10,2), ones (10,1), "ClassNames", ['a'])
***** error<ClassificationKNN: not all 'ClassNames' are present in Y.> ...
 ClassificationKNN (ones(10,2), ones (10,1), "ClassNames", [1, 2])
***** error<ClassificationKNN: not all 'ClassNames' are present in Y.> ...
 ClassificationKNN (ones(5,2), {'a';'b';'a';'a';'b'}, "ClassNames", {'a','c'})
***** error<ClassificationKNN: not all 'ClassNames' are present in Y.> ...
 ClassificationKNN (ones(10,2), logical (ones (10,1)), "ClassNames", [true, false])
***** error<ClassificationKNN: 'BreakTies' must be a character vector.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "BreakTies", 1)
***** error<ClassificationKNN: 'BreakTies' must be a character vector.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "BreakTies", {"1"})
***** error<ClassificationKNN: invalid value for 'BreakTies'.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "BreakTies", "some")
***** error<ClassificationKNN: 'Prior' must be either a numeric vector or a character vector.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "Prior", {"1", "2"})
***** error<ClassificationKNN: 'Cost' must be a numeric square matrix.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "Cost", [1, 2])
***** error<ClassificationKNN: 'Cost' must be a numeric square matrix.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "Cost", "string")
***** error<ClassificationKNN: 'Cost' must be a numeric square matrix.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "Cost", {eye(2)})
***** error<ClassificationKNN: 'NumNeighbors' must be a positive integer.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "NumNeighbors", 0)
***** error<ClassificationKNN: 'NumNeighbors' must be a positive integer.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "NumNeighbors", 15.2)
***** error<ClassificationKNN: 'NumNeighbors' must be a positive integer.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "NumNeighbors", "asd")
***** error<ClassificationKNN: unsupported distance metric.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "Distance", "somemetric")
***** error<ClassificationKNN: invalid function handle for distance metric.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "Distance", ...
                    @(v,m)sqrt(repmat(v,rows(m),1)-m,2))
***** error<ClassificationKNN: custom distance function produces wrong output size.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "Distance", ...
                    @(v,m)sqrt(sum(sumsq(repmat(v,rows(m),1)-m,2))))
***** error<ClassificationKNN: invalid distance metric.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "Distance", [1 2 3])
***** error<ClassificationKNN: invalid distance metric.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "Distance", {"mahalanobis"})
***** error<ClassificationKNN: invalid distance metric.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "Distance", logical (5))
***** error<ClassificationKNN: function handle for distance weight must return the> ...
 ClassificationKNN (ones (5,2), ones (5,1), "DistanceWeight", @(x)sum(x))
***** error<ClassificationKNN: invalid distance weight.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "DistanceWeight", "text")
***** error<ClassificationKNN: invalid distance weight.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "DistanceWeight", [1 2 3])
***** error<ClassificationKNN: 'Scale' must be a numeric vector.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "Scale", "scale")
***** error<ClassificationKNN: 'Scale' must be a numeric vector.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "Scale", {[1 2 3]})
***** error<ClassificationKNN: 'Scale' cannot simultaneously be specified with> ...
 ClassificationKNN (ones (5,2), ones (5,1), "standardize", true, "scale", [1 1])
***** error<ClassificationKNN: 'Cov' must be a symmetric positive definite matrix.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "Cov", ones (2), "Distance", "mahalanobis")
***** error<ClassificationKNN: 'Cov' cannot simultaneously be specified with> ...
 ClassificationKNN (ones (5,2), ones (5,1), "scale", [1 1], "Cov", ones (2))
***** error<ClassificationKNN: 'Exponent' must be a positive integer.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "Exponent", 12.5)
***** error<ClassificationKNN: 'Exponent' must be a positive integer.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "Exponent", -3)
***** error<ClassificationKNN: 'Exponent' must be a positive integer.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "Exponent", "three")
***** error<ClassificationKNN: 'Exponent' must be a positive integer.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "Exponent", {3})
***** error<ClassificationKNN: 'NSMethod' must be a character vector.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "NSMethod", {"kdtree"})
***** error<ClassificationKNN: 'NSMethod' must be a character vector.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "NSMethod", 3)
***** error<ClassificationKNN: 'NSMethod' must be either 'kdtree' or 'exhaustive'.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "NSMethod", "some")
***** error<ClassificationKNN: 'IncludeTies' must be either true or false.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "IncludeTies", "some")
***** error<ClassificationKNN: 'BucketSize' must be a positive integer.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "BucketSize", 42.5)
***** error<ClassificationKNN: 'BucketSize' must be a positive integer.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "BucketSize", -50)
***** error<ClassificationKNN: 'BucketSize' must be a positive integer.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "BucketSize", "some")
***** error<ClassificationKNN: 'BucketSize' must be a positive integer.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "BucketSize", {50})
***** error<ClassificationKNN: invalid parameter name in optional pair arguments.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "some", "some")
***** error<ClassificationKNN: invalid values in X.> ...
 ClassificationKNN ([1;2;3;'a';4], ones (5,1))
***** error<ClassificationKNN: invalid values in X.> ...
 ClassificationKNN ([1;2;3;Inf;4], ones (5,1))
***** error<ClassificationKNN: the elements in 'Prior' do not correspond to selected classes in Y.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "Prior", [1 2])
***** error<ClassificationKNN: the number of rows and columns in 'Cost' must correspond to selected classes in Y.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "Cost", [1 2; 1 3])
***** error<ClassificationKNN: 'Scale' is only valid when distance metric is seuclidean.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "Scale", [1 1])
***** error<ClassificationKNN: 'Scale' vector must have equal length to the number of columns in X.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "Scale", [1 1 1], "Distance", "seuclidean")
***** error<ClassificationKNN: 'Scale' vector must contain nonnegative scalar values.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "Scale", [1 -1], "Distance", "seuclidean")
***** error<ClassificationKNN: 'Cov' is only valid when distance metric is 'mahalanobis'.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "Cov", eye (2))
***** error<ClassificationKNN: 'Cov' matrix must have equal columns as X.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "Cov", eye (3), "Distance", "mahalanobis")
***** error<ClassificationKNN: 'Exponent' is only valid when distance metric is 'minkowski'.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "Exponent", 3)
***** error<ClassificationKNN: 'kdtree' method is only valid for 'euclidean', 'cityblock', 'manhattan', 'minkowski', and 'chebychev' distance metrics.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "Distance", "hamming", "NSMethod", "kdtree")
***** shared x, y
 load fisheriris
 x = meas;
 y = species;
***** test
 xc = [min(x); mean(x); max(x)];
 obj = fitcknn (x, y, "NumNeighbors", 5);
 [l, s, c] = predict (obj, xc);
 assert (l, {"setosa"; "versicolor"; "virginica"})
 assert (s, [1, 0, 0; 0, 1, 0; 0, 0, 1])
 assert (c, [0, 1, 1; 1, 0, 1; 1, 1, 0])
***** test
 xc = [min(x); mean(x); max(x)];
 obj = fitcknn (x, y, "NumNeighbors", 5, "Standardize", 1);
 [l, s, c] = predict (obj, xc);
 assert (l, {"versicolor"; "versicolor"; "virginica"})
 assert (s, [0.4, 0.6, 0; 0, 1, 0; 0, 0, 1])
 assert (c, [0.6, 0.4, 1; 1, 0, 1; 1, 1, 0])
***** test
 xc = [min(x); mean(x); max(x)];
 obj = fitcknn (x, y, "NumNeighbors", 10, "distance", "mahalanobis");
 [l, s, c] = predict (obj, xc);
 assert (s, [0.3, 0.7, 0; 0, 0.9, 0.1; 0.2, 0.2, 0.6], 1e-4)
 assert (c, [0.7, 0.3, 1; 1, 0.1, 0.9; 0.8, 0.8, 0.4], 1e-4)
***** test
 xc = [min(x); mean(x); max(x)];
 obj = fitcknn (x, y, "NumNeighbors", 10, "distance", "cosine");
 [l, s, c] = predict (obj, xc);
 assert (l, {"setosa"; "versicolor"; "virginica"})
 assert (s, [1, 0, 0; 0, 1, 0; 0, 0.3, 0.7], 1e-4)
 assert (c, [0, 1, 1; 1, 0, 1; 1, 0.7, 0.3], 1e-4)
***** test
 xc = [5.2, 4.1, 1.5, 0.1; 5.1, 3.8, 1.9, 0.4; ...
         5.1, 3.8, 1.5, 0.3; 4.9, 3.6, 1.4, 0.1];
 obj = fitcknn (x, y, "NumNeighbors", 5);
 [l, s, c] = predict (obj, xc);
 assert (l, {"setosa"; "setosa"; "setosa"; "setosa"})
 assert (s, [1, 0, 0; 1, 0, 0; 1, 0, 0; 1, 0, 0])
 assert (c, [0, 1, 1; 0, 1, 1; 0, 1, 1; 0, 1, 1])
***** test
 xc = [5, 3, 5, 1.45];
 obj = fitcknn (x, y, "NumNeighbors", 5);
 [l, s, c] = predict (obj, xc);
 assert (l, {"versicolor"})
 assert (s, [0, 0.6, 0.4], 1e-4)
 assert (c, [1, 0.4, 0.6], 1e-4)
***** test
 xc = [5, 3, 5, 1.45];
 obj = fitcknn (x, y, "NumNeighbors", 10, "distance", "minkowski", "Exponent", 5);
 [l, s, c] = predict (obj, xc);
 assert (l, {"versicolor"})
 assert (s, [0, 0.5, 0.5], 1e-4)
 assert (c, [1, 0.5, 0.5], 1e-4)
***** test
 xc = [5, 3, 5, 1.45];
 obj = fitcknn (x, y, "NumNeighbors", 10, "distance", "jaccard");
 [l, s, c] = predict (obj, xc);
 assert (l, {"setosa"})
 assert (s, [0.9, 0.1, 0], 1e-4)
 assert (c, [0.1, 0.9, 1], 1e-4)
***** test
 xc = [5, 3, 5, 1.45];
 obj = fitcknn (x, y, "NumNeighbors", 10, "distance", "mahalanobis");
 [l, s, c] = predict (obj, xc);
 assert (l, {"versicolor"})
 assert (s, [0.1000, 0.5000, 0.4000], 1e-4)
 assert (c, [0.9000, 0.5000, 0.6000], 1e-4)
***** test
 xc = [5, 3, 5, 1.45];
 obj = fitcknn (x, y, "NumNeighbors", 5, "distance", "jaccard");
 [l, s, c] = predict (obj, xc);
 assert (l, {"setosa"})
 assert (s, [0.8, 0.2, 0], 1e-4)
 assert (c, [0.2, 0.8, 1], 1e-4)
***** test
 xc = [5, 3, 5, 1.45];
 obj = fitcknn (x, y, "NumNeighbors", 5, "distance", "seuclidean");
 [l, s, c] = predict (obj, xc);
 assert (l, {"versicolor"})
 assert (s, [0, 1, 0], 1e-4)
 assert (c, [1, 0, 1], 1e-4)
***** test
 xc = [5, 3, 5, 1.45];
 obj = fitcknn (x, y, "NumNeighbors", 10, "distance", "chebychev");
 [l, s, c] = predict (obj, xc);
 assert (l, {"versicolor"})
 assert (s, [0, 0.7, 0.3], 1e-4)
 assert (c, [1, 0.3, 0.7], 1e-4)
***** test
 xc = [5, 3, 5, 1.45];
 obj = fitcknn (x, y, "NumNeighbors", 10, "distance", "cityblock");
 [l, s, c] = predict (obj, xc);
 assert (l, {"versicolor"})
 assert (s, [0, 0.6, 0.4], 1e-4)
 assert (c, [1, 0.4, 0.6], 1e-4)
***** test
 xc = [5, 3, 5, 1.45];
 obj = fitcknn (x, y, "NumNeighbors", 10, "distance", "cosine");
 [l, s, c] = predict (obj, xc);
 assert (l, {"virginica"})
 assert (s, [0, 0.1, 0.9], 1e-4)
 assert (c, [1, 0.9, 0.1], 1e-4)
***** test
 xc = [5, 3, 5, 1.45];
 obj = fitcknn (x, y, "NumNeighbors", 10, "distance", "correlation");
 [l, s, c] = predict (obj, xc);
 assert (l, {"virginica"})
 assert (s, [0, 0.1, 0.9], 1e-4)
 assert (c, [1, 0.9, 0.1], 1e-4)
***** test
 xc = [5, 3, 5, 1.45];
 obj = fitcknn (x, y, "NumNeighbors", 30, "distance", "spearman");
 [l, s, c] = predict (obj, xc);
 assert (l, {"versicolor"})
 assert (s, [0, 1, 0], 1e-4)
 assert (c, [1, 0, 1], 1e-4)
***** test
 xc = [5, 3, 5, 1.45];
 obj = fitcknn (x, y, "NumNeighbors", 30, "distance", "hamming");
 [l, s, c] = predict (obj, xc);
 assert (l, {"setosa"})
 assert (s, [0.4333, 0.3333, 0.2333], 1e-4)
 assert (c, [0.5667, 0.6667, 0.7667], 1e-4)
***** test
 xc = [5, 3, 5, 1.45];
 obj = fitcknn (x, y, "NumNeighbors", 5, "distance", "hamming");
 [l, s, c] = predict (obj, xc);
 assert (l, {"setosa"})
 assert (s, [0.8, 0.2, 0], 1e-4)
 assert (c, [0.2, 0.8, 1], 1e-4)
***** test
 xc = [min(x); mean(x); max(x)];
 obj = fitcknn (x, y, "NumNeighbors", 10, "distance", "correlation");
 [l, s, c] = predict (obj, xc);
 assert (l, {"setosa"; "versicolor"; "virginica"})
 assert (s, [1, 0, 0; 0, 1, 0; 0, 0.4, 0.6], 1e-4)
 assert (c, [0, 1, 1; 1, 0, 1; 1, 0.6, 0.4], 1e-4)
***** test
 xc = [min(x); mean(x); max(x)];
 obj = fitcknn (x, y, "NumNeighbors", 10, "distance", "hamming");
 [l, s, c] = predict (obj, xc);
 assert (l, {"setosa";"setosa";"setosa"})
 assert (s, [0.9, 0.1, 0; 1, 0, 0; 0.5, 0, 0.5], 1e-4)
 assert (c, [0.1, 0.9, 1; 0, 1, 1; 0.5, 1, 0.5], 1e-4)
***** error<ClassificationKNN.predict: too few input arguments.> ...
 predict (ClassificationKNN (ones (4,2), ones (4,1)))
***** error<ClassificationKNN.predict: XC is empty.> ...
 predict (ClassificationKNN (ones (4,2), ones (4,1)), [])
***** error<ClassificationKNN.predict: XC must have the same number of predictors as the trained model.> ...
 predict (ClassificationKNN (ones (4,2), ones (4,1)), 1)
***** test
 load fisheriris
 model = fitcknn (meas, species, 'NumNeighbors', 5);
 X = mean (meas);
 Y = {'versicolor'};
 L = loss (model, X, Y);
 assert (L, 0)
***** test
 X = [1, 2; 3, 4; 5, 6];
 Y = {'A'; 'B'; 'A'};
 model = fitcknn (X, Y);
 X_test = [1, 6; 3, 3];
 Y_test = {'A'; 'B'};
 L = loss (model, X_test, Y_test);
 assert (abs (L - 0.6667) > 1e-5)
***** test
 X = [1, 2; 3, 4; 5, 6];
 Y = {'A'; 'B'; 'A'};
 model = fitcknn (X, Y);
 X_with_nan = [1, 2; NaN, 4];
 Y_test = {'A'; 'B'};
 L = loss (model, X_with_nan, Y_test);
 assert (abs (L - 0.3333) < 1e-4)
***** test
 X = [1, 2; 3, 4; 5, 6];
 Y = {'A'; 'B'; 'A'};
 model = fitcknn (X, Y);
 X_with_nan = [1, 2; NaN, 4];
 Y_test = {'A'; 'B'};
 L = loss (model, X_with_nan, Y_test, 'LossFun', 'logit');
 assert (isnan (L))
***** test
 X = [1, 2; 3, 4; 5, 6];
 Y = {'A'; 'B'; 'A'};
 model = fitcknn (X, Y);
 customLossFun = @(C, S, W, Cost) sum (W .* sum (abs (C - S), 2));
 L = loss (model, X, Y, 'LossFun', customLossFun);
 assert (L, 0)
***** test
 X = [1, 2; 3, 4; 5, 6];
 Y = [1; 2; 1];
 model = fitcknn (X, Y);
 L = loss (model, X, Y, 'LossFun', 'classiferror');
 assert (L, 0)
***** test
 X = [1, 2; 3, 4; 5, 6];
 Y = [true; false; true];
 model = fitcknn (X, Y);
 L = loss (model, X, Y, 'LossFun', 'binodeviance');
 assert (abs (L - 0.1269) < 1e-4)
***** test
 X = [1, 2; 3, 4; 5, 6];
 Y = ['1'; '2'; '1'];
 model = fitcknn (X, Y);
 L = loss (model, X, Y, 'LossFun', 'classiferror');
 assert (L, 0)
***** test
 X = [1, 2; 3, 4; 5, 6];
 Y = ['1'; '2'; '3'];
 model = fitcknn (X, Y);
 X_test = [3, 3];
 Y_test = ['1'];
 L = loss (model, X_test, Y_test, 'LossFun', 'quadratic');
 assert (L, 1)
***** test
 X = [1, 2; 3, 4; 5, 6];
 Y = ['1'; '2'; '3'];
 model = fitcknn (X, Y);
 X_test = [3, 3; 5, 7];
 Y_test = ['1'; '2'];
 L = loss (model, X_test, Y_test, 'LossFun', 'classifcost');
 assert (L, 1)
***** test
 X = [1, 2; 3, 4; 5, 6];
 Y = ['1'; '2'; '3'];
 model = fitcknn (X, Y);
 X_test = [3, 3; 5, 7];
 Y_test = ['1'; '2'];
 L = loss (model, X_test, Y_test, 'LossFun', 'hinge');
 assert (L, 1)
***** test
 X = [1, 2; 3, 4; 5, 6];
 Y = ['1'; '2'; '3'];
 model = fitcknn (X, Y);
 X_test = [3, 3; 5, 7];
 Y_test = ['1'; '2'];
 W = [1; 2];
 L = loss (model, X_test, Y_test, 'LossFun', 'logit', 'Weights', W);
 assert (abs (L - 0.6931) < 1e-4)
***** error<ClassificationKNN.loss: too few input arguments.> ...
 loss (ClassificationKNN (ones (4,2), ones (4,1)))
***** error<ClassificationKNN.loss: too few input arguments.> ...
 loss (ClassificationKNN (ones (4,2), ones (4,1)), ones (4,2))
***** error<ClassificationKNN.loss: X is empty.> ...
 loss (ClassificationKNN (ones (40,2), randi ([1, 2], 40, 1)), [], zeros (2))
***** error<ClassificationKNN.loss: X must have the same number of predictors as the trained model.> ...
 loss (ClassificationKNN (ones (40,2), randi ([1, 2], 40, 1)), 1, zeros (2))
***** error<ClassificationKNN.loss: name-value arguments must be in pairs.> ...
 loss (ClassificationKNN (ones (4,2), ones (4,1)), ones (4,2), ...
        ones (4,1), 'LossFun')
***** error<ClassificationKNN.loss: Y must have the same number of rows as X.> ...
 loss (ClassificationKNN (ones (4,2), ones (4,1)), ones (4,2), ones (3,1))
***** error<ClassificationKNN.loss: invalid loss function.> ...
 loss (ClassificationKNN (ones (4,2), ones (4,1)), ones (4,2), ...
        ones (4,1), 'LossFun', 'a')
***** error<ClassificationKNN.loss: invalid Weights.> ...
 loss (ClassificationKNN (ones (4,2), ones (4,1)), ones (4,2), ...
        ones (4,1), 'Weights', 'w')
***** test
 load fisheriris
 mdl = fitcknn (meas, species, 'NumNeighbors', 5);
 X = mean (meas);
 Y = {'versicolor'};
 m = margin (mdl, X, Y);
 assert (m, 1)
***** test
 X = [1, 2; 3, 4; 5, 6];
 Y = [1; 2; 3];
 mdl = fitcknn (X, Y);
 m = margin (mdl, X, Y);
 assert (m, [1; 1; 1])
***** test
 X = [7, 8; 9, 10];
 Y = ['1'; '2'];
 mdl = fitcknn (X, Y);
 m = margin (mdl, X, Y);
 assert (m, [1; 1])
***** test
 X = [11, 12];
 Y = {'1'};
 mdl = fitcknn (X, Y);
 m = margin (mdl, X, Y);
 assert (isnan (m))
***** test
 X = [1, 2; 3, 4; 5, 6];
 Y = [1; 2; 3];
 mdl = fitcknn (X, Y);
 X1 = [15, 16];
 Y1 = [1];
 m = margin (mdl, X1, Y1);
 assert (m, -1)
***** error<ClassificationKNN.margin: too few input arguments.> ...
 margin (ClassificationKNN (ones (4,2), ones (4,1)))
***** error<ClassificationKNN.margin: too few input arguments.> ...
 margin (ClassificationKNN (ones (4,2), ones (4,1)), ones (4,2))
***** error<ClassificationKNN.margin: X is empty.> ...
 margin (ClassificationKNN (ones (40,2), randi ([1, 2], 40, 1)), [], zeros (2))
***** error<ClassificationKNN.margin: X must have the same number of predictors as the trained model.> ...
 margin (ClassificationKNN (ones (40,2), randi ([1, 2], 40, 1)), 1, zeros (2))
***** error<ClassificationKNN.margin: Y must have the same number of rows as X.> ...
 margin (ClassificationKNN (ones (4,2), ones (4,1)), ones (4,2), ones (3,1))
***** shared X, Y, mdl
 X = [1, 2; 4, 5; 7, 8; 3, 2];
 Y = [2; 1; 3; 2];
 mdl = fitcknn (X, Y);
***** test
 Vars = 1;
 Labels = 2;
 [pd, x, y] = partialDependence (mdl, Vars, Labels);
 pdm = [0.7500, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
 0.5000, 0.5000];
 assert (pd, pdm)
***** test
 Vars = 1;
 Labels = 2;
 [pd, x, y] = partialDependence (mdl, Vars, Labels, ...
 'NumObservationsToSample', 5);
 pdm = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
 0, 0, 0, 0, 0, 0, 0, 0, 0, 0];
 assert (abs (pdm - pd) < 1)
***** test
 Vars = 1;
 Labels = 2;
 [pd, x, y] = partialDependence (mdl, Vars, Labels, 'UseParallel', true);
 pdm = [0.7500, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
 0.5000, 0.5000];
 assert (pd, pdm)
***** test
 Vars = [1, 2];
 Labels = 1;
 queryPoints = {linspace(0, 1, 3)', linspace(0, 1, 3)'};
 [pd, x, y] = partialDependence (mdl, Vars, Labels, 'QueryPoints', ...
                            queryPoints, 'UseParallel', true);
 pdm = [0, 0, 0; 0, 0, 0; 0, 0, 0];
 assert (pd, pdm)
***** test
 Vars = 1;
 Labels = [1; 2];
 [pd, x, y] = partialDependence (mdl, Vars, Labels);
 pdm = [0.2500, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.2500, 0.2500, 0.2500, ...
 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, ...
 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, ...
 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, ...
 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, ...
 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, ...
 0.2500, 0.2500; 0.7500, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
 0.5000, 0.5000, 0.5000];
 assert (pd, pdm)
***** test
 Vars = [1, 2];
 Labels = [1; 2];
 queryPoints = {linspace(0, 1, 3)', linspace(0, 1, 3)'};
 [pd, x, y] = partialDependence (mdl, Vars, Labels, 'QueryPoints', queryPoints);
 pdm(:,:,1) = [0, 0, 0; 1, 1, 1];
 pdm(:,:,2) = [0, 0, 0; 1, 1, 1];
 pdm(:,:,3) = [0, 0, 0; 1, 1, 1];
 assert (pd, pdm)
***** test
 X1 = [1; 2; 4; 5; 7; 8; 3; 2];
 X2 = ['2'; '3'; '1'; '3'; '1'; '3'; '2'; '2'];
 X = [X1, double(X2)];
 Y = [1; 2; 3; 3; 2; 1; 2; 1];
 mdl = fitcknn (X, Y, 'ClassNames', {'1', '2', '3'});
 Vars = 1;
 Labels = 1;
 [pd, x, y] = partialDependence (mdl, Vars, Labels);
 pdm = [1.0000, 0.6250, 0.6250, 0.6250, 0.6250, 0.6250, 0.6250, 0.6250, ...
 0.6250, 0.6250, 0.6250, 0.6250, 0.6250, 0.6250, 0.6250, 0.6250, 0.6250, ...
 0.6250, 0.6250, 0.6250, 0.6250, 0.6250, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.3750, ...
 0.3750, 0.3750, 0.3750, 0.3750, 0.3750, 0.3750, 0.3750, 0.3750, 0.3750, ...
 0.3750, 0.3750, 0.3750, 0.3750, 0.7500, 0.7500, 0.7500, 0.7500, 0.7500, ...
 0.7500, 0.7500, 0.7500];
 assert (pd, pdm)
***** test
 X1 = [1; 2; 4; 5; 7; 8; 3; 2];
 X2 = ['2'; '3'; '1'; '3'; '1'; '3'; '2'; '2'];
 X = [X1, double(X2)];
 Y = [1; 2; 3; 3; 2; 1; 2; 1];
 predictorNames = {'Feature1', 'Feature2'};
 mdl = fitcknn (X, Y, 'PredictorNames', predictorNames);
 Vars = 'Feature1';
 Labels = 1;
 [pd, x, y] = partialDependence (mdl, Vars, Labels);
 pdm = [1.0000, 0.6250, 0.6250, 0.6250, 0.6250, 0.6250, 0.6250, 0.6250, ...
 0.6250, 0.6250, 0.6250, 0.6250, 0.6250, 0.6250, 0.6250, 0.6250, 0.6250, ...
 0.6250, 0.6250, 0.6250, 0.6250, 0.6250, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.3750, ...
 0.3750, 0.3750, 0.3750, 0.3750, 0.3750, 0.3750, 0.3750, 0.3750, 0.3750, ...
 0.3750, 0.3750, 0.3750, 0.3750, 0.7500, 0.7500, 0.7500, 0.7500, 0.7500, ...
 0.7500, 0.7500, 0.7500];
 assert (pd, pdm)
***** test
 X1 = [1; 2; 4; 5; 7; 8; 3; 2];
 X2 = ['2'; '3'; '1'; '3'; '1'; '3'; '2'; '2'];
 X = [X1, double(X2)];
 Y = [1; 2; 3; 3; 2; 1; 2; 1];
 predictorNames = {'Feature1', 'Feature2'};
 mdl = fitcknn (X, Y, 'PredictorNames', predictorNames);
 new_X1 = [10; 5; 6; 8; 9; 20; 35; 6];
 new_X2 = ['2'; '2'; '1'; '2'; '1'; '3'; '3'; '2'];
 new_X = [new_X1, double(new_X2)];
 Vars = 'Feature1';
 Labels = 1;
 [pd, x, y] = partialDependence (mdl, Vars, Labels, new_X);
 pdm = [0, 0, 0, 0, 0, 0.2500, 0.2500, 0.2500, 0.2500, 0.7500, 0.7500, ...
 0.7500, 0.7500, 0.7500, 0.7500, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, ...
 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, ...
 1.0000, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1];
 assert (pd, pdm)
***** error<ClassificationKNN.partialDependence: too few input arguments.> ...
 partialDependence (ClassificationKNN (ones (4,2), ones (4,1)))
***** error<ClassificationKNN.partialDependence: too few input arguments.> ...
 partialDependence (ClassificationKNN (ones (4,2), ones (4,1)), 1)
***** error<ClassificationKNN.partialDependence: name-value arguments must be in pairs.> ...
 partialDependence (ClassificationKNN (ones (4,2), ones (4,1)), 1, ...
          ones (4,1), 'NumObservationsToSample')
***** error<ClassificationKNN.partialDependence: name-value arguments must be in pairs.> ...
 partialDependence (ClassificationKNN (ones (4,2), ones (4,1)), 1, ...
          ones (4,1), 2)
***** shared x, y, obj
 load fisheriris
 x = meas;
 y = species;
 covMatrix = cov (x);
 obj = fitcknn (x, y, 'NumNeighbors', 5, 'Distance', ...
      'mahalanobis', 'Cov', covMatrix);
***** test
 CVMdl = crossval (obj);
 assert (class (CVMdl), "ClassificationPartitionedModel")
 assert ({CVMdl.X, CVMdl.Y}, {x, y})
 assert (CVMdl.KFold == 10)
 assert (CVMdl.ModelParameters.NumNeighbors == 5)
 assert (strcmp (CVMdl.ModelParameters.Distance, "mahalanobis"))
 assert (class (CVMdl.Trained{1}), "ClassificationKNN")
 assert (!CVMdl.ModelParameters.Standardize)
***** test
 CVMdl = crossval (obj, "KFold", 5);
 assert (class (CVMdl), "ClassificationPartitionedModel")
 assert ({CVMdl.X, CVMdl.Y}, {x, y})
 assert (CVMdl.KFold == 5)
 assert (CVMdl.ModelParameters.NumNeighbors == 5)
 assert (strcmp (CVMdl.ModelParameters.Distance, "mahalanobis"))
 assert (class (CVMdl.Trained{1}), "ClassificationKNN")
 assert (CVMdl.ModelParameters.Standardize == obj.Standardize)
***** test
 obj = fitcknn (x, y, "NumNeighbors", 5, "Distance", "cityblock");
 CVMdl = crossval (obj, "HoldOut", 0.2);
 assert (class (CVMdl), "ClassificationPartitionedModel")
 assert ({CVMdl.X, CVMdl.Y}, {x, y})
 assert (CVMdl.ModelParameters.NumNeighbors == 5)
 assert (strcmp (CVMdl.ModelParameters.Distance, "cityblock"))
 assert (class (CVMdl.Trained{1}), "ClassificationKNN")
 assert (CVMdl.ModelParameters.Standardize == obj.Standardize)
***** test
 obj = fitcknn (x, y, "NumNeighbors", 10, "Distance", "cityblock");
 CVMdl = crossval (obj, "LeaveOut", 'on');
 assert (class (CVMdl), "ClassificationPartitionedModel")
 assert ({CVMdl.X, CVMdl.Y}, {x, y})
 assert (CVMdl.ModelParameters.NumNeighbors == 10)
 assert (strcmp (CVMdl.ModelParameters.Distance, "cityblock"))
 assert (class (CVMdl.Trained{1}), "ClassificationKNN")
 assert (CVMdl.ModelParameters.Standardize == obj.Standardize)
***** test
 obj = fitcknn (x, y, "NumNeighbors", 10, "Distance", "cityblock");
 partition = cvpartition (y, 'KFold', 3);
 CVMdl = crossval (obj, 'cvPartition', partition);
 assert (class (CVMdl), "ClassificationPartitionedModel")
 assert (CVMdl.KFold == 3)
 assert (CVMdl.ModelParameters.NumNeighbors == 10)
 assert (strcmp (CVMdl.ModelParameters.Distance, "cityblock"))
 assert (class (CVMdl.Trained{1}), "ClassificationKNN")
 assert (CVMdl.ModelParameters.Standardize == obj.Standardize)
***** error<ClassificationKNN.crossval: Name-Value arguments must be in pairs.> ...
 crossval (ClassificationKNN (ones (4,2), ones (4,1)), "kfold")
***** error<ClassificationKNN.crossval: specify only one of the optional Name-Value paired arguments.>...
 crossval (ClassificationKNN (ones (4,2), ones (4,1)), "kfold", 12, "holdout", 0.2)
***** error<ClassificationKNN.crossval: 'KFold' must be an integer value greater than 1.> ...
 crossval (ClassificationKNN (ones (4,2), ones (4,1)), "kfold", 'a')
***** error<ClassificationKNN.crossval: 'Holdout' must be a numeric value between 0 and 1.> ...
 crossval (ClassificationKNN (ones (4,2), ones (4,1)), "holdout", 2)
***** error<ClassificationKNN.crossval: 'Leaveout' must be either 'on' or 'off'.> ...
 crossval (ClassificationKNN (ones (4,2), ones (4,1)), "leaveout", 1)
***** error<ClassificationKNN.crossval: 'CVPartition' must be a 'cvpartition' object.> ...
 crossval (ClassificationKNN (ones (4,2), ones (4,1)), "cvpartition", 1)
162 tests, 162 passed, 0 known failure, 0 skipped
[inst/Classification/ClassificationSVM.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/Classification/ClassificationSVM.m
***** demo
 ## Create a Support Vector Machine classifier and determine margin for test
 ## data.
 load fisheriris
 rng(1);  ## For reproducibility

 ## Select indices of the non-setosa species
 inds = !strcmp(species, 'setosa');

  ## Select features and labels for non-setosa species
 X = meas(inds, 3:4);
 Y = grp2idx(species(inds));

 ##  Convert labels to +1 and -1
 unique_classes = unique(Y);
 Y(Y == unique_classes(1)) = -1;
 Y(Y == unique_classes(2)) = 1;

 ## Partition data for training and testing
 cv = cvpartition(Y, 'HoldOut', 0.15);
 X_train = X(training(cv), :);
 Y_train = Y(training(cv));
 X_test = X(test(cv), :);
 Y_test = Y(test(cv));

 ## Train the SVM model
 CVSVMModel = fitcsvm(X_train, Y_train);

 ## Calculate margins
 m = margin(CVSVMModel, X_test, Y_test);
 disp(m);
***** demo
 ## Create a Support Vector Machine classifier and determine loss for test
 ## data.
 load fisheriris
 rng(1);  ## For reproducibility

  ## Select indices of the non-setosa species
 inds = !strcmp(species, 'setosa');

  ## Select features and labels for non-setosa species
 X = meas(inds, 3:4);
 Y = grp2idx(species(inds));

 ##  Convert labels to +1 and -1
 unique_classes = unique(Y);
 Y(Y == unique_classes(1)) = -1;
 Y(Y == unique_classes(2)) = 1;

 ## Randomly partition the data into training and testing sets
 cv = cvpartition(Y, 'HoldOut', 0.3); # 30% data for testing, 60% for training

 X_train = X(training(cv), :);
 Y_train = Y(training(cv));

 X_test = X(test(cv), :);
 Y_test = Y(test(cv));

 ## Train the SVM model
 SVMModel = fitcsvm(X_train, Y_train);

 ## Calculate loss

 L = loss(SVMModel,X_test,Y_test,'LossFun','binodeviance')
 L = loss(SVMModel,X_test,Y_test,'LossFun','classiferror')
 L = loss(SVMModel,X_test,Y_test,'LossFun','exponential')
 L = loss(SVMModel,X_test,Y_test,'LossFun','hinge')
 L = loss(SVMModel,X_test,Y_test,'LossFun','logit')
 L = loss(SVMModel,X_test,Y_test,'LossFun','quadratic')
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1; 4, 5, 6; 7, 8, 9; ...
 3, 2, 1; 4, 5, 6; 7, 8, 9; 3, 2, 1; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = [1; 2; 3; 4; 2; 3; 4; 2; 3; 4; 2; 3; 4];
 a = ClassificationSVM (x, y, "ClassNames", [1, 2]);
 assert (class (a), "ClassificationSVM");
 assert (a.RowsUsed, [1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0]');
 assert ({a.X, a.Y}, {x, y})
 assert (a.NumObservations, 5)
 assert ({a.ResponseName, a.PredictorNames}, {"Y", {"x1", "x2", "x3"}})
 assert ({a.ClassNames, a.ModelParameters.SVMtype}, {[1; 2], "c_svc"})
***** test
 x = [1, 2; 2, 3; 3, 4; 4, 5; 2, 3; 3, 4; 2, 3; 3, 4; 2, 3; 3, 4];
 y = [1; 1; -1; -1; 1; -1; -1; -1; -1; -1];
 a = ClassificationSVM (x, y);
 assert (class (a), "ClassificationSVM");
 assert ({a.X, a.Y, a.ModelParameters.KernelFunction}, {x, y, "linear"})
 assert (a.ModelParameters.BoxConstraint, 1)
 assert (a.ClassNames, [1; -1])
 assert (a.ModelParameters.KernelOffset, 0)
***** test
 x = [1, 2; 2, 3; 3, 4; 4, 5; 2, 3; 3, 4; 2, 3; 3, 4; 2, 3; 3, 4];
 y = [1; 1; -1; -1; 1; -1; -1; -1; -1; -1];
 a = ClassificationSVM (x, y, "KernelFunction", "rbf", "BoxConstraint", 2, ...
 "KernelOffset", 2);
 assert (class (a), "ClassificationSVM");
 assert ({a.X, a.Y, a.ModelParameters.KernelFunction}, {x, y, "rbf"})
 assert (a.ModelParameters.BoxConstraint, 2)
 assert (a.ModelParameters.KernelOffset, 2)
***** test
 x = [1, 2; 2, 3; 3, 4; 4, 5; 2, 3; 3, 4; 2, 3; 3, 4; 2, 3; 3, 4];
 y = [1; 1; -1; -1; 1; -1; -1; -1; -1; -1];
 a = ClassificationSVM (x, y, "KernelFunction", "polynomial", ...
 "PolynomialOrder", 3);
 assert (class (a), "ClassificationSVM");
 assert ({a.X, a.Y, a.ModelParameters.KernelFunction}, {x, y, "polynomial"})
 assert (a.ModelParameters.PolynomialOrder, 3)
***** error<ClassificationSVM: too few input arguments.> ClassificationSVM ()
***** error<ClassificationSVM: too few input arguments.> ...
 ClassificationSVM (ones(10,2))
***** error<ClassificationSVM: number of rows in X and Y must be equal.> ...
 ClassificationSVM (ones(10,2), ones (5,1))
***** error<ClassificationSVM: 'Standardize' must be either true or false.> ...
 ClassificationSVM (ones(10,2), ones (10,1), "Standardize", 'a')
***** error<ClassificationSVM: 'PredictorNames' must be supplied as a cellstring array.> ...
 ClassificationSVM (ones(10,2), ones (10,1), "PredictorNames", ['x1';'x2'])
***** error<ClassificationSVM: 'PredictorNames' must have the same number of columns as X.> ...
 ClassificationSVM (ones(10,2), ones (10,1), "PredictorNames", {'x1','x2','x3'})
***** error<ClassificationSVM: 'ResponseName' must be a character vector.> ...
 ClassificationSVM (ones(10,2), ones (10,1), "ResponseName", {'Y'})
***** error<ClassificationSVM: 'ResponseName' must be a character vector.> ...
 ClassificationSVM (ones(10,2), ones (10,1), "ResponseName", 21)
***** error<ClassificationSVM: 'ClassNames' must be a cellstring, logical or numeric vector.> ...
 ClassificationSVM (ones(10,2), ones (10,1), "ClassNames", @(x)x)
***** error<ClassificationSVM: 'ClassNames' must be a cellstring, logical or numeric vector.> ...
 ClassificationSVM (ones(10,2), ones (10,1), "ClassNames", ['a'])
***** error<ClassificationSVM: not all 'ClassNames' are present in Y.> ...
 ClassificationSVM (ones(10,2), ones (10,1), "ClassNames", [1, 2])
***** error<ClassificationSVM: not all 'ClassNames' are present in Y.> ...
 ClassificationSVM (ones(5,2), {'a';'b';'a';'a';'b'}, "ClassNames", {'a','c'})
***** error<ClassificationSVM: not all 'ClassNames' are present in Y.> ...
 ClassificationSVM (ones(10,2), logical (ones (10,1)), "ClassNames", [true, false])
***** error<ClassificationSVM: 'Prior' must be either a numeric vector or a character vector.> ...
 ClassificationSVM (ones(10,2), ones(10,1), "Prior", {"asd"})
***** error<ClassificationSVM: 'Prior' must be either a numeric vector or a character vector.> ...
 ClassificationSVM (ones(10,2), ones(10,1), "Prior", ones (2))
***** error<ClassificationSVM: 'Cost' must be a numeric square matrix.> ...
 ClassificationSVM (ones(10,2), ones(10,1), "Cost", [1:4])
***** error<ClassificationSVM: 'Cost' must be a numeric square matrix.> ...
 ClassificationSVM (ones(10,2), ones(10,1), "Cost", {0,1;1,0})
***** error<ClassificationSVM: 'Cost' must be a numeric square matrix.> ...
 ClassificationSVM (ones(10,2), ones(10,1), "Cost", 'a')
***** error<ClassificationSVM: 'SVMtype' must be 'c_svc', 'nu_svc', or 'one_class_svm'.> ...
 ClassificationSVM (ones(10,2), ones(10,1), "svmtype", 123)
***** error<ClassificationSVM: 'SVMtype' must be 'c_svc', 'nu_svc', or 'one_class_svm'.> ...
 ClassificationSVM (ones(10,2), ones(10,1), "svmtype", 'some_type')
***** error<ClassificationSVM: 'OutlierFraction' must be a positive scalar in the range 0 =< OutlierFraction < 1.> ...
 ClassificationSVM (ones(10,2), ones(10,1), "OutlierFraction", -1)
***** error<ClassificationSVM: 'KernelFunction' must be a character vector.> ...
 ClassificationSVM (ones(10,2), ones(10,1), "KernelFunction", 123)
***** error<ClassificationSVM: unsupported Kernel function.> ...
 ClassificationSVM (ones(10,2), ones(10,1), "KernelFunction", "fcn")
***** error<ClassificationSVM: 'PolynomialOrder' must be a positive integer.> ...
 ClassificationSVM (ones(10,2), ones(10,1), "PolynomialOrder", -1)
***** error<ClassificationSVM: 'PolynomialOrder' must be a positive integer.> ...
 ClassificationSVM (ones(10,2), ones(10,1), "PolynomialOrder", 0.5)
***** error<ClassificationSVM: 'PolynomialOrder' must be a positive integer.> ...
 ClassificationSVM (ones(10,2), ones(10,1), "PolynomialOrder", [1,2])
***** error<ClassificationSVM: 'KernelScale' must be a positive scalar.> ...
 ClassificationSVM (ones(10,2), ones (10,1), "KernelScale", -1)
***** error<ClassificationSVM: 'KernelScale' must be a positive scalar.> ...
 ClassificationSVM (ones(10,2), ones (10,1), "KernelScale", 0)
***** error<ClassificationSVM: 'KernelScale' must be a positive scalar.> ...
 ClassificationSVM (ones(10,2), ones (10,1), "KernelScale", [1, 2])
***** error<ClassificationSVM: 'KernelScale' must be a positive scalar.> ...
 ClassificationSVM (ones(10,2), ones (10,1), "KernelScale", "invalid")
***** error<ClassificationSVM: 'KernelOffset' must be a non-negative scalar.> ...
 ClassificationSVM (ones(10,2), ones(10,1), "KernelOffset", -1)
***** error<ClassificationSVM: 'KernelOffset' must be a non-negative scalar.> ...
 ClassificationSVM (ones(10,2), ones(10,1), "KernelOffset", [1,2])
***** error<ClassificationSVM: 'BoxConstraint' must be a positive scalar.> ...
 ClassificationSVM (ones(10,2), ones (10,1), "BoxConstraint", -1)
***** error<ClassificationSVM: 'BoxConstraint' must be a positive scalar.> ...
 ClassificationSVM (ones(10,2), ones (10,1), "BoxConstraint", 0)
***** error<ClassificationSVM: 'BoxConstraint' must be a positive scalar.> ...
 ClassificationSVM (ones(10,2), ones (10,1), "BoxConstraint", [1, 2])
***** error<ClassificationSVM: 'BoxConstraint' must be a positive scalar.> ...
 ClassificationSVM (ones(10,2), ones (10,1), "BoxConstraint", "invalid")
***** error<ClassificationSVM: 'Nu' must be a positive scalar in the range 0 < Nu <= 1.> ...
 ClassificationSVM (ones(10,2), ones(10,1), "nu", -0.5)
***** error<ClassificationSVM: 'Nu' must be a positive scalar in the range 0 < Nu <= 1.> ...
 ClassificationSVM (ones(10,2), ones(10,1), "nu", 0)
***** error<ClassificationSVM: 'Nu' must be a positive scalar in the range 0 < Nu <= 1.> ...
 ClassificationSVM (ones(10,2), ones(10,1), "nu", 1.5)
***** error<ClassificationSVM: 'CacheSize' must be a positive scalar.> ...
 ClassificationSVM (ones(10,2), ones(10,1), "CacheSize", -1)
***** error<ClassificationSVM: 'CacheSize' must be a positive scalar.> ...
 ClassificationSVM (ones(10,2), ones(10,1), "CacheSize", [1,2])
***** error<ClassificationSVM: 'Tolerance' must be a positive scalar.> ...
 ClassificationSVM (ones(10,2), ones(10,1), "Tolerance", -0.1)
***** error<ClassificationSVM: 'Tolerance' must be a positive scalar.> ...
 ClassificationSVM (ones(10,2), ones(10,1), "Tolerance", [0.1,0.2])
***** error<ClassificationSVM: 'Shrinking' must be either 0 or 1.> ...
 ClassificationSVM (ones(10,2), ones(10,1), "shrinking", 2)
***** error<ClassificationSVM: 'Shrinking' must be either 0 or 1.> ...
 ClassificationSVM (ones(10,2), ones(10,1), "shrinking", -1)
***** error<ClassificationSVM: 'Shrinking' must be either 0 or 1.> ...
 ClassificationSVM (ones(10,2), ones(10,1), "shrinking", [1 0])
***** error<ClassificationSVM: invalid parameter name in optional pair arguments.> ...
 ClassificationSVM (ones(10,2), ones(10,1), "invalid_name", 'c_svc')
***** error<ClassificationSVM: cannot train a binary problem with only one class available.> ...
 ClassificationSVM (ones(10,2), ones(10,1), "SVMtype", 'c_svc')
***** error<ClassificationSVM: can only be used for one-class or two-class learning.> ...
 ClassificationSVM (ones(10,2), [1;1;1;1;2;2;2;2;3;3])
***** error<ClassificationSVM: invalid values in X.> ...
 ClassificationSVM ([ones(9,2);2,Inf], ones(10,1))
***** error<ClassificationSVM: the elements in 'Prior' do not correspond to selected classes in Y.> ...
 ClassificationSVM (ones (5,2), ones (5,1), "Prior", [0,1])
***** error<ClassificationSVM: the elements in 'Prior' do not correspond to selected classes in Y.> ...
 ClassificationSVM (ones (5,2), [1;1;2;2;3], "ClassNames", [1,2], "Prior", [0,0.4,0.6])
***** error<ClassificationSVM: the number of rows and columns in 'Cost' must correspond to the selected classes in Y.> ...
 ClassificationSVM (ones (5,2), [1;1;2;2;3], "ClassNames", [1,2], "Cost", ones (3))
***** shared x, y, x_train, x_test, y_train, y_test, objST
 load fisheriris
 inds = ! strcmp (species, 'setosa');
 x = meas(inds, 3:4);
 y = grp2idx (species(inds));
***** test
 xc = [min(x); mean(x); max(x)];
 obj = fitcsvm (x, y, 'KernelFunction', 'rbf', 'Tolerance', 1e-7);
 assert (isempty (obj.Alpha), true)
 assert (sum (obj.IsSupportVector), numel (obj.Beta))
 [label, score] = predict (obj, xc);
 assert (label, [1; 2; 2]);
 assert (score(:,1), [0.99285; -0.080296; -0.93694], 1e-5);
 assert (score(:,1), -score(:,2), eps)
 obj = fitPosterior (obj);
 [label, probs] = predict (obj, xc);
 assert (probs(:,2), [0.97555; 0.428164; 0.030385], 1e-5);
 assert (probs(:,1) + probs(:,2), [1; 1; 1], 0.05)
***** test
 obj = fitcsvm (x, y);
 assert (isempty (obj.Beta), true)
 assert (sum (obj.IsSupportVector), numel (obj.Alpha))
 assert (numel (obj.Alpha), 24)
 assert (obj.Bias, -14.415, 1e-3)
 xc = [min(x); mean(x); max(x)];
 label = predict (obj, xc);
 assert (label, [1; 2; 2]);
***** error<ClassificationSVM.predict: too few input arguments.> ...
 predict (ClassificationSVM (ones (40,2), ones (40,1)))
***** error<ClassificationSVM.predict: XC is empty.> ...
 predict (ClassificationSVM (ones (40,2), ones (40,1)), [])
***** error<ClassificationSVM.predict: XC must have the same number of predictors as the trained model.> ...
 predict (ClassificationSVM (ones (40,2), ones (40,1)), 1)
***** test
 objST = fitcsvm (x, y);
 objST.ScoreTransform = "a";
***** error<ClassificationSVM.predict: 'ScoreTransform' must be a 'function_handle' object.> ...
 [labels, scores] = predict (objST, x);
***** error<ClassificationSVM.resubPredict: 'ScoreTransform' must be a 'function_handle' object.> ...
 [labels, scores] = resubPredict (objST);
***** test
 rand ("seed", 1);
 CVSVMModel = fitcsvm (x, y, 'KernelFunction', 'rbf', 'HoldOut', 0.15, ...
                       'Tolerance', 1e-7);
 obj = CVSVMModel.Trained{1};
 testInds = test (CVSVMModel.Partition);
 expected_margin = [2.0000;  0.8579;  1.6690;  3.4141;  3.4552; ...
                    2.6605;  3.5251; -4.0000; -6.3411; -6.4511; ...
                   -3.0532; -7.5054; -1.6700; -5.6227; -7.3640];
 computed_margin = margin (obj, x(testInds,:), y(testInds,:));
 assert (computed_margin, expected_margin, 1e-4);
***** error<ClassificationSVM.margin: too few input arguments.> ...
 margin (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)))
***** error<ClassificationSVM.margin: too few input arguments.> ...
 margin (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), zeros (2))
***** error<ClassificationSVM.margin: X is empty.> ...
 margin (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), [], zeros (2))
***** error<ClassificationSVM.margin: X must have the same number of predictors as the trained model.> ...
 margin (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), 1, zeros (2))
***** error<ClassificationSVM.margin: Y is empty.> ...
 margin (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), zeros (2), [])
***** error<ClassificationSVM.margin: Y must have the same number of rows as X.> ...
 margin (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), zeros (2), 1)
***** test
 rand ("seed", 1);
 CVSVMModel = fitcsvm (x, y, 'KernelFunction', 'rbf', 'HoldOut', 0.15);
 obj = CVSVMModel.Trained{1};
 testInds = test (CVSVMModel.Partition);
 L1 = loss (obj, x(testInds,:), y(testInds,:), 'LossFun', 'binodeviance');
 L2 = loss (obj, x(testInds,:), y(testInds,:), 'LossFun', 'classiferror');
 L3 = loss (obj, x(testInds,:), y(testInds,:), 'LossFun', 'exponential');
 L4 = loss (obj, x(testInds,:), y(testInds,:), 'LossFun', 'hinge');
 L5 = loss (obj, x(testInds,:), y(testInds,:), 'LossFun', 'logit');
 L6 = loss (obj, x(testInds,:), y(testInds,:), 'LossFun', 'quadratic');
 assert (L1, 2.8711, 1e-4);
 assert (L2, 0.5333, 1e-4);
 assert (L3, 10.9685, 1e-4);
 assert (L4, 1.9827, 1e-4);
 assert (L5, 1.5849, 1e-4);
 assert (L6, 7.6739, 1e-4);
***** error<ClassificationSVM.loss: too few input arguments.> ...
 loss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)))
***** error<ClassificationSVM.loss: too few input arguments.> ...
 loss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), zeros (2))
***** error<ClassificationSVM.loss: Name-Value arguments must be in pairs.> ...
 loss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), zeros (2), ...
 ones(2,1), "LossFun")
***** error<ClassificationSVM.loss: X is empty.> ...
 loss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), [], zeros (2))
***** error<ClassificationSVM.loss: X must have the same number of predictors as the trained model.> ...
 loss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), 1, zeros (2))
***** error<ClassificationSVM.loss: Y is empty.> ...
 loss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), zeros (2), [])
***** error<ClassificationSVM.loss: Y must have the same number of rows as X.> ...
 loss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), zeros (2), 1)
***** error<ClassificationSVM.loss: 'LossFun' must be a character vector.> ...
 loss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), zeros (2), ...
 ones (2,1), "LossFun", 1)
***** error<ClassificationSVM.loss: unsupported Loss function.> ...
 loss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), zeros (2), ...
 ones (2,1), "LossFun", "some")
***** error<ClassificationSVM.loss: 'Weights' must be a numeric vector.> ...
 loss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), zeros (2), ...
 ones (2,1), "Weights", ['a','b'])
***** error<ClassificationSVM.loss: 'Weights' must be a numeric vector.> ...
 loss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), zeros (2), ...
 ones (2,1), "Weights", 'a')
***** error<ClassificationSVM.loss: size of 'Weights' must be equal to the number> ...
 loss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), zeros (2), ...
 ones (2,1), "Weights", [1,2,3])
***** error<ClassificationSVM.loss: size of 'Weights' must be equal to the number> ...
 loss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), zeros (2), ...
 ones (2,1), "Weights", 3)
***** error<ClassificationSVM.loss: invalid parameter name in optional pair arg> ...
 loss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), zeros (2), ...
 ones (2,1), "some", "some")
***** error<ClassificationSVM.resubLoss: Name-Value arguments must be in pairs.> ...
 resubLoss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), "LossFun")
***** error<ClassificationSVM.resubLoss: 'LossFun' must be a character vector.> ...
 resubLoss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), "LossFun", 1)
***** error<ClassificationSVM.resubLoss: unsupported Loss function.> ...
 resubLoss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), "LossFun", "some")
***** error<ClassificationSVM.resubLoss: 'Weights' must be a numeric vector.> ...
 resubLoss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), "Weights", ['a','b'])
***** error<ClassificationSVM.resubLoss: 'Weights' must be a numeric vector.> ...
 resubLoss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), "Weights", 'a')
***** error<ClassificationSVM.resubLoss: size of 'Weights' must be equal to the n> ...
 resubLoss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), "Weights", [1,2,3])
***** error<ClassificationSVM.resubLoss: size of 'Weights' must be equal to the n> ...
 resubLoss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), "Weights", 3)
***** error<ClassificationSVM.resubLoss: invalid parameter name in optional pai> ...
 resubLoss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), "some", "some")
***** test
 SVMModel = fitcsvm (x, y);
 CVMdl = crossval (SVMModel, "KFold", 5);
 assert (class (CVMdl), "ClassificationPartitionedModel")
 assert ({CVMdl.X, CVMdl.Y}, {x, y})
 assert (CVMdl.KFold == 5)
 assert (class (CVMdl.Trained{1}), "CompactClassificationSVM")
 assert (CVMdl.CrossValidatedModel, "ClassificationSVM")
***** test
 obj = fitcsvm (x, y);
 CVMdl = crossval (obj, "HoldOut", 0.2);
 assert (class (CVMdl), "ClassificationPartitionedModel")
 assert ({CVMdl.X, CVMdl.Y}, {x, y})
 assert (class (CVMdl.Trained{1}), "CompactClassificationSVM")
 assert (CVMdl.CrossValidatedModel, "ClassificationSVM")
***** test
 obj = fitcsvm (x, y);
 CVMdl = crossval (obj, "LeaveOut", 'on');
 assert (class (CVMdl), "ClassificationPartitionedModel")
 assert ({CVMdl.X, CVMdl.Y}, {x, y})
 assert (class (CVMdl.Trained{1}), "CompactClassificationSVM")
 assert (CVMdl.CrossValidatedModel, "ClassificationSVM")
***** error<ClassificationSVM.crossval: Name-Value arguments must be in pairs.> ...
 crossval (ClassificationSVM (ones (40,2),randi([1, 2], 40, 1)), "KFold")
***** error<ClassificationSVM.crossval: specify only one of the optional Name-Value paired arguments.> ...
 crossval (ClassificationSVM (ones (40,2),randi([1, 2], 40, 1)), ...
 "KFold", 5, "leaveout", 'on')
***** error<ClassificationSVM.crossval: 'KFold' must be an integer value greater than 1.> ...
 crossval (ClassificationSVM (ones (40,2),randi([1, 2], 40, 1)), "KFold", 'a')
***** error<ClassificationSVM.crossval: 'KFold' must be an integer value greater than 1.> ...
 crossval (ClassificationSVM (ones (40,2),randi([1, 2], 40, 1)), "KFold", 1)
***** error<ClassificationSVM.crossval: 'KFold' must be an integer value greater than 1.> ...
 crossval (ClassificationSVM (ones (40,2),randi([1, 2], 40, 1)), "KFold", -1)
***** error<ClassificationSVM.crossval: 'KFold' must be an integer value greater than 1.> ...
 crossval (ClassificationSVM (ones (40,2),randi([1, 2], 40, 1)), "KFold", 11.5)
***** error<ClassificationSVM.crossval: 'KFold' must be an integer value greater than 1.> ...
 crossval (ClassificationSVM (ones (40,2),randi([1, 2], 40, 1)), "KFold", [1,2])
***** error<ClassificationSVM.crossval: 'Holdout' must be a numeric value between 0 and 1.> ...
 crossval (ClassificationSVM (ones (40,2),randi([1, 2], 40, 1)), "Holdout", 'a')
***** error<ClassificationSVM.crossval: 'Holdout' must be a numeric value between 0 and 1.> ...
 crossval (ClassificationSVM (ones (40,2),randi([1, 2], 40, 1)), "Holdout", 11.5)
***** error<ClassificationSVM.crossval: 'Holdout' must be a numeric value between 0 and 1.> ...
 crossval (ClassificationSVM (ones (40,2),randi([1, 2], 40, 1)), "Holdout", -1)
***** error<ClassificationSVM.crossval: 'Holdout' must be a numeric value between 0 and 1.> ...
 crossval (ClassificationSVM (ones (40,2),randi([1, 2], 40, 1)), "Holdout", 0)
***** error<ClassificationSVM.crossval: 'Holdout' must be a numeric value between 0 and 1.> ...
 crossval (ClassificationSVM (ones (40,2),randi([1, 2], 40, 1)), "Holdout", 1)
***** error<ClassificationSVM.crossval: 'Leaveout' must be either 'on' or 'off'.> ...
 crossval (ClassificationSVM (ones (40,2),randi([1, 2], 40, 1)), "Leaveout", 1)
***** error<ClassificationSVM.crossval: 'CVPartition' must be a 'cvpartition' object.> ...
 crossval (ClassificationSVM (ones (40,2),randi([1, 2], 40, 1)), "CVPartition", 1)
***** error<ClassificationSVM.crossval: 'CVPartition' must be a 'cvpartition' object.> ...
 crossval (ClassificationSVM (ones (40,2),randi([1, 2], 40, 1)), "CVPartition", 'a')
***** error<ClassificationSVM.crossval: invalid parameter name in optional paired arguments> ...
 crossval (ClassificationSVM (ones (40,2),randi([1, 2], 40, 1)), "some", "some")
114 tests, 114 passed, 0 known failure, 0 skipped
[inst/Classification/CompactClassificationDiscriminant.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/Classification/CompactClassificationDiscriminant.m
***** demo
 ## Create a discriminant analysis classifier and its compact version
 # and compare their size

 load fisheriris
 X = meas;
 Y = species;

 Mdl = fitcdiscr (X, Y, 'ClassNames', unique (species))
 CMdl = crossval (Mdl);

 whos ('Mdl', 'CMdl')
***** test
 load fisheriris
 x = meas;
 y = species;
 PredictorNames = {'Sepal Length', 'Sepal Width', 'Petal Length', 'Petal Width'};
 Mdl = fitcdiscr (x, y, "PredictorNames", PredictorNames);
 CMdl = compact (Mdl);
 sigma = [0.265008, 0.092721, 0.167514, 0.038401; ...
          0.092721, 0.115388, 0.055244, 0.032710; ...
          0.167514, 0.055244, 0.185188, 0.042665; ...
          0.038401, 0.032710, 0.042665, 0.041882];
 mu = [5.0060, 3.4280, 1.4620, 0.2460; ...
       5.9360, 2.7700, 4.2600, 1.3260; ...
       6.5880, 2.9740, 5.5520, 2.0260];
 xCentered = [ 9.4000e-02,  7.2000e-02, -6.2000e-02, -4.6000e-02; ...
              -1.0600e-01, -4.2800e-01, -6.2000e-02, -4.6000e-02; ...
              -3.0600e-01, -2.2800e-01, -1.6200e-01, -4.6000e-02];
 assert (class (CMdl), "CompactClassificationDiscriminant");
 assert ({CMdl.DiscrimType, CMdl.ResponseName}, {"linear", "Y"})
 assert ({CMdl.Gamma, CMdl.MinGamma}, {0, 0}, 1e-15)
 assert (CMdl.ClassNames, unique (species))
 assert (CMdl.Sigma, sigma, 1e-6)
 assert (CMdl.Mu, mu, 1e-14)
 assert (CMdl.XCentered([1:3],:), xCentered, 1e-14)
 assert (CMdl.LogDetSigma, -9.9585, 1e-4)
 assert (CMdl.PredictorNames, PredictorNames)
***** test
 load fisheriris
 x = meas;
 y = species;
 Mdl = fitcdiscr (x, y, "Gamma", 0.5);
 CMdl = compact (Mdl);
 sigma = [0.265008, 0.046361, 0.083757, 0.019201; ...
          0.046361, 0.115388, 0.027622, 0.016355; ...
          0.083757, 0.027622, 0.185188, 0.021333; ...
          0.019201, 0.016355, 0.021333, 0.041882];
 mu = [5.0060, 3.4280, 1.4620, 0.2460; ...
       5.9360, 2.7700, 4.2600, 1.3260; ...
       6.5880, 2.9740, 5.5520, 2.0260];
 xCentered = [ 9.4000e-02,  7.2000e-02, -6.2000e-02, -4.6000e-02; ...
              -1.0600e-01, -4.2800e-01, -6.2000e-02, -4.6000e-02; ...
              -3.0600e-01, -2.2800e-01, -1.6200e-01, -4.6000e-02];
 assert (class (CMdl), "CompactClassificationDiscriminant");
 assert ({CMdl.DiscrimType, CMdl.ResponseName}, {"linear", "Y"})
 assert ({CMdl.Gamma, CMdl.MinGamma}, {0.5, 0})
 assert (CMdl.ClassNames, unique (species))
 assert (CMdl.Sigma, sigma, 1e-6)
 assert (CMdl.Mu, mu, 1e-14)
 assert (CMdl.XCentered([1:3],:), xCentered, 1e-14)
 assert (CMdl.LogDetSigma, -8.6884, 1e-4)
***** error<CompactClassificationDiscriminant: invalid classification object.> ...
 CompactClassificationDiscriminant (1)
***** test
 load fisheriris
 x = meas;
 y = species;
 Mdl = fitcdiscr (meas, species, "Gamma", 0.5);
 CMdl = compact (Mdl);
 [label, score, cost] = predict (CMdl, [2, 2, 2, 2]);
 assert (label, {'versicolor'})
 assert (score, [0, 0.9999, 0.0001], 1e-4)
 assert (cost, [1, 0.0001, 0.9999], 1e-4)
 [label, score, cost] = predict (CMdl, [2.5, 2.5, 2.5, 2.5]);
 assert (label, {'versicolor'})
 assert (score, [0, 0.6368, 0.3632], 1e-4)
 assert (cost, [1, 0.3632, 0.6368], 1e-4)
***** test
 load fisheriris
 x = meas;
 y = species;
 xc = [min(x); mean(x); max(x)];
 Mdl = fitcdiscr (x, y);
 CMdl = compact (Mdl);
 [label, score, cost] = predict (CMdl, xc);
 l = {'setosa'; 'versicolor'; 'virginica'};
 s = [1, 0, 0; 0, 1, 0; 0, 0, 1];
 c = [0, 1, 1; 1, 0, 1; 1, 1, 0];
 assert (label, l)
 assert (score, s, 1e-4)
 assert (cost, c, 1e-4)
***** shared MODEL
 X = rand (10,2);
 Y = [ones(5,1);2*ones(5,1)];
 MODEL = compact (ClassificationDiscriminant (X, Y));
***** error<CompactClassificationDiscriminant.predict: too few input arguments.> ...
 predict (MODEL)
***** error<CompactClassificationDiscriminant.predict: XC is empty.> ...
 predict (MODEL, [])
***** error<CompactClassificationDiscriminant.predict: XC must have the same number of features as the trained model.> ...
 predict (MODEL, 1)
***** test
 load fisheriris
 model = fitcdiscr (meas, species);
 x = mean (meas);
 y = {'versicolor'};
 L = loss (model, x, y);
 assert (L, 0)
***** test
 x = [1, 2; 3, 4; 5, 6];
 y = {'A'; 'B'; 'A'};
 model = fitcdiscr (x, y, "Gamma", 0.4);
 x_test = [1, 6; 3, 3];
 y_test = {'A'; 'B'};
 L = loss (model, x_test, y_test);
 assert (L, 0.3333, 1e-4)
***** test
 x = [1, 2; 3, 4; 5, 6; 7, 8];
 y = ['1'; '2'; '3'; '1'];
 model = fitcdiscr (x, y, "gamma" , 0.5);
 x_test = [3, 3];
 y_test = ['1'];
 L = loss (model, x_test, y_test, 'LossFun', 'quadratic');
 assert (L, 0.2423, 1e-4)
***** test
 x = [1, 2; 3, 4; 5, 6; 7, 8];
 y = ['1'; '2'; '3'; '1'];
 model = fitcdiscr (x, y, "gamma" , 0.5);
 x_test = [3, 3; 5, 7];
 y_test = ['1'; '2'];
 L = loss (model, x_test, y_test, 'LossFun', 'classifcost');
 assert (L, 0.3333, 1e-4)
***** test
 x = [1, 2; 3, 4; 5, 6; 7, 8];
 y = ['1'; '2'; '3'; '1'];
 model = fitcdiscr (x, y, "gamma" , 0.5);
 x_test = [3, 3; 5, 7];
 y_test = ['1'; '2'];
 L = loss (model, x_test, y_test, 'LossFun', 'hinge');
 assert (L, 0.5886, 1e-4)
***** test
 x = [1, 2; 3, 4; 5, 6; 7, 8];
 y = ['1'; '2'; '3'; '1'];
 model = fitcdiscr (x, y, "gamma" , 0.5);
 x_test = [3, 3; 5, 7];
 y_test = ['1'; '2'];
 W = [1; 2];
 L = loss (model, x_test, y_test, 'LossFun', 'logit', 'Weights', W);
 assert (L, 0.5107, 1e-4)
***** test
 x = [1, 2; 3, 4; 5, 6];
 y = {'A'; 'B'; 'A'};
 model = fitcdiscr (x, y, "gamma" , 0.5);
 x_with_nan = [1, 2; NaN, 4];
 y_test = {'A'; 'B'};
 L = loss (model, x_with_nan, y_test);
 assert (L, 0.3333, 1e-4)
***** test
 x = [1, 2; 3, 4; 5, 6];
 y = {'A'; 'B'; 'A'};
 model = fitcdiscr (x, y);
 x_with_nan = [1, 2; NaN, 4];
 y_test = {'A'; 'B'};
 L = loss (model, x_with_nan, y_test, 'LossFun', 'logit');
 assert (isnan (L))
***** test
 x = [1, 2; 3, 4; 5, 6];
 y = {'A'; 'B'; 'A'};
 model = fitcdiscr (x, y);
 customLossFun = @(C, S, W, Cost) sum (W .* sum (abs (C - S), 2));
 L = loss (model, x, y, 'LossFun', customLossFun);
 assert (L, 0.8889, 1e-4)
***** test
 x = [1, 2; 3, 4; 5, 6];
 y = [1; 2; 1];
 model = fitcdiscr (x, y);
 L = loss (model, x, y, 'LossFun', 'classiferror');
 assert (L, 0.3333, 1e-4)
***** error<CompactClassificationDiscriminant.loss: too few input arguments.> ...
 loss (MODEL)
***** error<CompactClassificationDiscriminant.loss: too few input arguments.> ...
 loss (MODEL, ones (4,2))
***** error<CompactClassificationDiscriminant.loss: name-value arguments must be in pairs.> ...
 loss (MODEL, ones (4,2), ones (4,1), 'LossFun')
***** error<CompactClassificationDiscriminant.loss: Y must have the same number of rows as X.> ...
 loss (MODEL, ones (4,2), ones (3,1))
***** error<CompactClassificationDiscriminant.loss: invalid loss function.> ...
 loss (MODEL, ones (4,2), ones (4,1), 'LossFun', 'a')
***** error<CompactClassificationDiscriminant.loss: invalid 'Weights'.> ...
 loss (MODEL, ones (4,2), ones (4,1), 'Weights', 'w')
 load fisheriris
 mdl = fitcdiscr (meas, species);
 X = mean (meas);
 Y = {'versicolor'};
 m = margin (mdl, X, Y);
 assert (m, 1, 1e-6)
***** test
 X = [1, 2; 3, 4; 5, 6];
 Y = [1; 2; 1];
 mdl = fitcdiscr (X, Y, "gamma", 0.5);
 m = margin (mdl, X, Y);
 assert (m, [0.3333; -0.3333; 0.3333], 1e-4)
***** error<CompactClassificationDiscriminant.margin: too few input arguments.> ...
 margin (MODEL)
***** error<CompactClassificationDiscriminant.margin: too few input arguments.> ...
 margin (MODEL, ones (4,2))
***** error<CompactClassificationDiscriminant.margin: Y must have the same number of rows as X.> ...
 margin (MODEL, ones (4,2), ones (3,1))
28 tests, 28 passed, 0 known failure, 0 skipped
[inst/Classification/CompactClassificationNeuralNetwork.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/Classification/CompactClassificationNeuralNetwork.m
***** demo
 ## Create a neural network classifier and its compact version
 # and compare their size

 load fisheriris
 X = meas;
 Y = species;

 Mdl = fitcnet (X, Y, 'ClassNames', unique (species))
 CMdl = crossval (Mdl);

 whos ('Mdl', 'CMdl')
***** error<CompactClassificationDiscriminant: invalid classification object.> ...
 CompactClassificationDiscriminant (1)
***** shared x, y, CMdl
 load fisheriris
 x = meas;
 y = grp2idx (species);
 Mdl = fitcnet (x, y, "IterationLimit", 100);
 CMdl = compact (Mdl);
***** error<CompactClassificationNeuralNetwork.predict: too few input arguments.> ...
 predict (CMdl)
***** error<CompactClassificationNeuralNetwork.predict: XC is empty.> ...
 predict (CMdl, [])
***** error<CompactClassificationNeuralNetwork.predict: XC must have the same number of predictors as the trained neural network.> ...
 predict (CMdl, 1)
***** test
 CMdl.ScoreTransform = "a";
***** error<CompactClassificationNeuralNetwork.predict: 'ScoreTransform' must be a 'function_handle' object.> ...
 [labels, scores] = predict (CMdl, x);
6 tests, 6 passed, 0 known failure, 0 skipped
[inst/Classification/ClassificationGAM.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/Classification/ClassificationGAM.m
***** demo
 ## Train a GAM classifier for binary classification
 ## using specific data and plot the decision boundaries.

 ## Define specific data
 X = [1, 2; 2, 3; 3, 3; 4, 5; 5, 5; ...
     6, 7; 7, 8; 8, 8; 9, 9; 10, 10];
 Y = [0; 0; 0; 0; 0; ...
     1; 1; 1; 1; 1];

 ## Train the GAM model
 obj = fitcgam (X, Y, "Interactions", "all")

 ## Create a grid of values for prediction
 x1 = [min(X(:,1)):0.1:max(X(:,1))];
 x2 = [min(X(:,2)):0.1:max(X(:,2))];
 [x1G, x2G] = meshgrid (x1, x2);
 XGrid = [x1G(:), x2G(:)];
 [labels, score] = predict (obj, XGrid);
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = [0; 0; 1; 1];
 PredictorNames = {'Feature1', 'Feature2', 'Feature3'};
 a = ClassificationGAM (x, y, "PredictorNames", PredictorNames);
 assert (class (a), "ClassificationGAM");
 assert ({a.X, a.Y, a.NumObservations}, {x, y, 4})
 assert ({a.NumPredictors, a.ResponseName}, {3, "Y"})
 assert (a.ClassNames, {'0'; '1'})
 assert (a.PredictorNames, PredictorNames)
 assert (a.BaseModel.Intercept, 0)
***** test
 load fisheriris
 inds = strcmp (species,'versicolor') | strcmp (species,'virginica');
 X = meas(inds, :);
 Y = species(inds, :)';
 Y = strcmp (Y, 'virginica')';
 a = ClassificationGAM (X, Y, 'Formula', 'Y ~ x1 + x2 + x3 + x4 + x1:x2 + x2:x3');
 assert (class (a), "ClassificationGAM");
 assert ({a.X, a.Y, a.NumObservations}, {X, Y, 100})
 assert ({a.NumPredictors, a.ResponseName}, {4, "Y"})
 assert (a.ClassNames, {'0'; '1'})
 assert (a.Formula, 'Y ~ x1 + x2 + x3 + x4 + x1:x2 + x2:x3')
 assert (a.PredictorNames, {'x1', 'x2', 'x3', 'x4'})
 assert (a.ModelwInt.Intercept, 0)
***** test
 X = [2, 3, 5; 4, 6, 8; 1, 2, 3; 7, 8, 9; 5, 4, 3];
 Y = [0; 1; 0; 1; 1];
 a = ClassificationGAM (X, Y, 'Knots', [4, 4, 4], 'Order', [3, 3, 3]);
 assert (class (a), "ClassificationGAM");
 assert ({a.X, a.Y, a.NumObservations}, {X, Y, 5})
 assert ({a.NumPredictors, a.ResponseName}, {3, "Y"})
 assert (a.ClassNames, {'0'; '1'})
 assert (a.PredictorNames, {'x1', 'x2', 'x3'})
 assert (a.Knots, [4, 4, 4])
 assert (a.Order, [3, 3, 3])
 assert (a.DoF, [7, 7, 7])
 assert (a.BaseModel.Intercept, 0.4055, 1e-1)
***** error<ClassificationGAM: too few input arguments.> ClassificationGAM ()
***** error<ClassificationGAM: too few input arguments.> ...
 ClassificationGAM (ones(4, 1))
***** error<ClassificationGAM: number of rows in X and Y must be equal.> ...
 ClassificationGAM (ones (4,2), ones (1,4))
***** error<ClassificationGAM: 'PredictorNames' must be supplied as a cellstring array.> ...
 ClassificationGAM (ones (5,2), ones (5,1), "PredictorNames", ["A"])
***** error<ClassificationGAM: 'PredictorNames' must be supplied as a cellstring array.> ...
 ClassificationGAM (ones (5,2), ones (5,1), "PredictorNames", "A")
***** error<ClassificationGAM: 'PredictorNames' must equal the number of columns in X.> ...
 ClassificationGAM (ones (5,2), ones (5,1), "PredictorNames", {"A", "B", "C"})
***** error<ClassificationGAM: 'ResponseName' must be a character vector.> ...
 ClassificationGAM (ones (5,2), ones (5,1), "ResponseName", {"Y"})
***** error<ClassificationGAM: 'ResponseName' must be a character vector.> ...
 ClassificationGAM (ones (5,2), ones (5,1), "ResponseName", 1)
***** error<ClassificationGAM: 'ClassNames' must be a cellstring, logical or numeric vector.> ...
 ClassificationGAM (ones(10,2), ones (10,1), "ClassNames", @(x)x)
***** error<ClassificationGAM: 'ClassNames' must be a cellstring, logical or numeric vector.> ...
 ClassificationGAM (ones(10,2), ones (10,1), "ClassNames", ['a'])
***** error<ClassificationGAM: not all 'ClassNames' are present in Y.> ...
 ClassificationGAM (ones(10,2), ones (10,1), "ClassNames", [1, 2])
***** error<ClassificationGAM: not all 'ClassNames' are present in Y.> ...
 ClassificationGAM (ones(5,2), {'a';'b';'a';'a';'b'}, "ClassNames", {'a','c'})
***** error<ClassificationGAM: not all 'ClassNames' are present in Y.> ...
 ClassificationGAM (ones(10,2), logical (ones (10,1)), "ClassNames", [true, false])
***** error<ClassificationGAM: 'Cost' must be a numeric square matrix.> ...
 ClassificationGAM (ones (5,2), ones (5,1), "Cost", [1, 2])
***** error<ClassificationGAM: 'Cost' must be a numeric square matrix.> ...
 ClassificationGAM (ones (5,2), ones (5,1), "Cost", "string")
***** error<ClassificationGAM: 'Cost' must be a numeric square matrix.> ...
 ClassificationGAM (ones (5,2), ones (5,1), "Cost", {eye(2)})
***** test
 x = [1, 2; 3, 4; 5, 6; 7, 8; 9, 10];
 y = [1; 0; 1; 0; 1];
 a = ClassificationGAM (x, y, "interactions", "all");
 l = {'0'; '0'; '0'; '0'; '0'};
 s = [0.3760, 0.6240; 0.4259, 0.5741; 0.3760, 0.6240; ...
      0.4259, 0.5741; 0.3760, 0.6240];
 [labels, scores] = predict (a, x);
 assert (class (a), "ClassificationGAM");
 assert ({a.X, a.Y, a.NumObservations}, {x, y, 5})
 assert ({a.NumPredictors, a.ResponseName}, {2, "Y"})
 assert (a.ClassNames, {'1'; '0'})
 assert (a.PredictorNames, {'x1', 'x2'})
 assert (a.ModelwInt.Intercept, 0.4055, 1e-1)
 assert (labels, l)
 assert (scores, s, 1e-1)
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = [0; 0; 1; 1];
 interactions = [false, true, false; true, false, true; false, true, false];
 a = fitcgam (x, y, "learningrate", 0.2, "interactions", interactions);
 [label, score] = predict (a, x, "includeinteractions", true);
 l = {'0'; '0'; '1'; '1'};
 s = [0.5106, 0.4894; 0.5135, 0.4865; 0.4864, 0.5136; 0.4847, 0.5153];
 assert (class (a), "ClassificationGAM");
 assert ({a.X, a.Y, a.NumObservations}, {x, y, 4})
 assert ({a.NumPredictors, a.ResponseName}, {3, "Y"})
 assert (a.ClassNames, {'0'; '1'})
 assert (a.PredictorNames, {'x1', 'x2', 'x3'})
 assert (a.ModelwInt.Intercept, 0)
 assert (label, l)
 assert (score, s, 1e-1)
***** error<ClassificationGAM.predict: too few input arguments.> ...
 predict (ClassificationGAM (ones (4,2), ones (4,1)))
***** error<ClassificationGAM.predict: XC is empty.> ...
 predict (ClassificationGAM (ones (4,2), ones (4,1)), [])
***** error<ClassificationGAM.predict: XC must have the same number of predictors as the trained model.> ...
 predict (ClassificationGAM (ones (4,2), ones (4,1)), 1)
***** shared x, y, obj
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = [0; 0; 1; 1];
 obj = fitcgam (x, y);
***** test
 CVMdl = crossval (obj);
 assert (class (CVMdl), "ClassificationPartitionedModel")
 assert ({CVMdl.X, CVMdl.Y}, {x, y})
 assert (CVMdl.KFold == 10)
 assert (class (CVMdl.Trained{1}), "CompactClassificationGAM")
 assert (CVMdl.CrossValidatedModel, "ClassificationGAM")
***** test
 CVMdl = crossval (obj, "KFold", 5);
 assert (class (CVMdl), "ClassificationPartitionedModel")
 assert ({CVMdl.X, CVMdl.Y}, {x, y})
 assert (CVMdl.KFold == 5)
 assert (class (CVMdl.Trained{1}), "CompactClassificationGAM")
 assert (CVMdl.CrossValidatedModel, "ClassificationGAM")
***** test
 CVMdl = crossval (obj, "HoldOut", 0.2);
 assert (class (CVMdl), "ClassificationPartitionedModel")
 assert ({CVMdl.X, CVMdl.Y}, {x, y})
 assert (class (CVMdl.Trained{1}), "CompactClassificationGAM")
 assert (CVMdl.CrossValidatedModel, "ClassificationGAM")
***** test
 partition = cvpartition (y, 'KFold', 3);
 CVMdl = crossval (obj, 'cvPartition', partition);
 assert (class (CVMdl), "ClassificationPartitionedModel")
 assert (CVMdl.KFold == 3)
 assert (class (CVMdl.Trained{1}), "CompactClassificationGAM")
 assert (CVMdl.CrossValidatedModel, "ClassificationGAM")
***** error<ClassificationGAM.crossval: Name-Value arguments must be in pairs.> ...
 crossval (obj, "kfold")
***** error<ClassificationGAM.crossval: specify only one of the optional Name-Value paired arguments.>...
 crossval (obj, "kfold", 12, "holdout", 0.2)
***** error<ClassificationGAM.crossval: 'KFold' must be an integer value greater than 1.> ...
 crossval (obj, "kfold", 'a')
***** error<ClassificationGAM.crossval: 'Holdout' must be a numeric value between 0 and 1.> ...
 crossval (obj, "holdout", 2)
***** error<ClassificationGAM.crossval: 'Leaveout' must be either 'on' or 'off'.> ...
 crossval (obj, "leaveout", 1)
***** error<ClassificationGAM.crossval: 'CVPartition' must be a 'cvpartition' object.> ...
 crossval (obj, "cvpartition", 1)
34 tests, 34 passed, 0 known failure, 0 skipped
[inst/Classification/ClassificationDiscriminant.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/Classification/ClassificationDiscriminant.m
***** demo
 ## Create discriminant classifier
 ## Evaluate some model predictions on new data.

 load fisheriris
 x = meas;
 y = species;
 xc = [min(x); mean(x); max(x)];
 obj = fitcdiscr (x, y);
 [label, score, cost] = predict (obj, xc);
***** demo
 load fisheriris
 model = fitcdiscr (meas, species);
 X = mean (meas);
 Y = {'versicolor'};
 ## Compute loss for discriminant model
 L = loss (model, X, Y)
***** demo
 load fisheriris
 mdl = fitcdiscr (meas, species);
 X = mean (meas);
 Y = {'versicolor'};
 ## Margin for discriminant model
 m = margin (mdl, X, Y)
***** demo
 load fisheriris
 x = meas;
 y = species;
 obj = fitcdiscr (x, y, "gamma", 0.4);
 ## Cross-validation for discriminant model
 CVMdl = crossval (obj)
***** test
 load fisheriris
 x = meas;
 y = species;
 PredictorNames = {'Sepal Length', 'Sepal Width', 'Petal Length', 'Petal Width'};
 Mdl = ClassificationDiscriminant (x, y, "PredictorNames", PredictorNames);
 sigma = [0.265008, 0.092721, 0.167514, 0.038401; ...
          0.092721, 0.115388, 0.055244, 0.032710; ...
          0.167514, 0.055244, 0.185188, 0.042665; ...
          0.038401, 0.032710, 0.042665, 0.041882];
 mu = [5.0060, 3.4280, 1.4620, 0.2460; ...
       5.9360, 2.7700, 4.2600, 1.3260; ...
       6.5880, 2.9740, 5.5520, 2.0260];
 xCentered = [ 9.4000e-02,  7.2000e-02, -6.2000e-02, -4.6000e-02; ...
              -1.0600e-01, -4.2800e-01, -6.2000e-02, -4.6000e-02; ...
              -3.0600e-01, -2.2800e-01, -1.6200e-01, -4.6000e-02];
 assert (class (Mdl), "ClassificationDiscriminant");
 assert ({Mdl.X, Mdl.Y, Mdl.NumObservations}, {x, y, 150})
 assert ({Mdl.DiscrimType, Mdl.ResponseName}, {"linear", "Y"})
 assert ({Mdl.Gamma, Mdl.MinGamma}, {0, 0}, 1e-15)
 assert (Mdl.ClassNames, unique (species))
 assert (Mdl.Sigma, sigma, 1e-6)
 assert (Mdl.Mu, mu, 1e-14)
 assert (Mdl.XCentered([1:3],:), xCentered, 1e-14)
 assert (Mdl.LogDetSigma, -9.9585, 1e-4)
 assert (Mdl.PredictorNames, PredictorNames)
***** test
 load fisheriris
 x = meas;
 y = species;
 Mdl = ClassificationDiscriminant (x, y, "Gamma", 0.5);
 sigma = [0.265008, 0.046361, 0.083757, 0.019201; ...
          0.046361, 0.115388, 0.027622, 0.016355; ...
          0.083757, 0.027622, 0.185188, 0.021333; ...
          0.019201, 0.016355, 0.021333, 0.041882];
 mu = [5.0060, 3.4280, 1.4620, 0.2460; ...
       5.9360, 2.7700, 4.2600, 1.3260; ...
       6.5880, 2.9740, 5.5520, 2.0260];
 xCentered = [ 9.4000e-02,  7.2000e-02, -6.2000e-02, -4.6000e-02; ...
              -1.0600e-01, -4.2800e-01, -6.2000e-02, -4.6000e-02; ...
              -3.0600e-01, -2.2800e-01, -1.6200e-01, -4.6000e-02];
 assert (class (Mdl), "ClassificationDiscriminant");
 assert ({Mdl.X, Mdl.Y, Mdl.NumObservations}, {x, y, 150})
 assert ({Mdl.DiscrimType, Mdl.ResponseName}, {"linear", "Y"})
 assert ({Mdl.Gamma, Mdl.MinGamma}, {0.5, 0})
 assert (Mdl.ClassNames, unique (species))
 assert (Mdl.Sigma, sigma, 1e-6)
 assert (Mdl.Mu, mu, 1e-14)
 assert (Mdl.XCentered([1:3],:), xCentered, 1e-14)
 assert (Mdl.LogDetSigma, -8.6884, 1e-4)
***** shared X, Y, MODEL
 X = rand (10,2);
 Y = [ones(5,1);2*ones(5,1)];
 MODEL = ClassificationDiscriminant (X, Y);
***** error<ClassificationDiscriminant: too few input arguments.> ClassificationDiscriminant ()
***** error<ClassificationDiscriminant: too few input arguments.> ...
 ClassificationDiscriminant (ones(4, 1))
***** error<ClassificationDiscriminant: number of rows in X and Y must be equal.> ...
 ClassificationDiscriminant (ones (4,2), ones (1,4))
***** error<ClassificationDiscriminant: 'PredictorNames' must be supplied as a cellstring array.> ...
 ClassificationDiscriminant (X, Y, "PredictorNames", ["A"])
***** error<ClassificationDiscriminant: 'PredictorNames' must be supplied as a cellstring array.> ...
 ClassificationDiscriminant (X, Y, "PredictorNames", "A")
***** error<ClassificationDiscriminant: 'PredictorNames' must equal the number of columns in X.> ...
 ClassificationDiscriminant (X, Y, "PredictorNames", {"A", "B", "C"})
***** error<ClassificationDiscriminant: 'ResponseName' must be a character vector.> ...
 ClassificationDiscriminant (X, Y, "ResponseName", {"Y"})
***** error<ClassificationDiscriminant: 'ResponseName' must be a character vector.> ...
 ClassificationDiscriminant (X, Y, "ResponseName", 1)
***** error<ClassificationDiscriminant: 'ClassNames' must be a cellstring, logical or numeric vector.> ...
 ClassificationDiscriminant (X, Y, "ClassNames", @(x)x)
***** error<ClassificationDiscriminant: 'ClassNames' must be a cellstring, logical or numeric vector.> ...
 ClassificationDiscriminant (X, Y, "ClassNames", ['a'])
***** error<ClassificationDiscriminant: not all 'ClassNames' are present in Y.> ...
 ClassificationDiscriminant (X, ones (10,1), "ClassNames", [1, 2])
***** error<ClassificationDiscriminant: not all 'ClassNames' are present in Y.> ...
 ClassificationDiscriminant ([1;2;3;4;5], {'a';'b';'a';'a';'b'}, "ClassNames", {'a','c'})
***** error<ClassificationDiscriminant: not all 'ClassNames' are present in Y.> ...
 ClassificationDiscriminant (X, logical (ones (10,1)), "ClassNames", [true, false])
***** error<ClassificationDiscriminant: 'Prior' must be either a numeric or a character vector.> ...
 ClassificationDiscriminant (X, Y, "Prior", {"1", "2"})
***** error<ClassificationDiscriminant: the elements in 'Prior' do not correspond to the selected classes in Y.> ...
 ClassificationDiscriminant (X, ones (10,1), "Prior", [1 2])
***** error<ClassificationDiscriminant: 'Cost' must be a numeric square matrix.> ...
 ClassificationDiscriminant (X, Y, "Cost", [1, 2])
***** error<ClassificationDiscriminant: 'Cost' must be a numeric square matrix.> ...
 ClassificationDiscriminant (X, Y, "Cost", "string")
***** error<ClassificationDiscriminant: 'Cost' must be a numeric square matrix.> ...
 ClassificationDiscriminant (X, Y, "Cost", {eye(2)})
***** error<ClassificationDiscriminant: the number of rows and columns in 'Cost' must correspond to selected classes in Y.> ...
 ClassificationDiscriminant (X, Y, "Cost", ones (3))
***** error<ClassificationDiscriminant: Predictor 'x1' has zero within-class variance.> ...
 ClassificationDiscriminant (ones (5,2), [1; 1; 2; 2; 2])
***** error<ClassificationDiscriminant: Predictor 'A' has zero within-class variance.> ...
 ClassificationDiscriminant (ones (5,2), [1; 1; 2; 2; 2], "PredictorNames", {"A", "B"})
***** error<ClassificationDiscriminant: Predictor 'x2' has zero within-class variance.> ...
 ClassificationDiscriminant ([1,2;2,2;3,2;4,2;5,2], ones (5, 1))
***** error<ClassificationDiscriminant: Predictor 'B' has zero within-class variance.> ...
 ClassificationDiscriminant ([1,2;2,2;3,2;4,2;5,2], ones (5, 1), "PredictorNames", {"A", "B"})
***** test
 load fisheriris
 x = meas;
 y = species;
 Mdl = fitcdiscr (meas, species, "Gamma", 0.5);
 [label, score, cost] = predict (Mdl, [2, 2, 2, 2]);
 assert (label, {'versicolor'})
 assert (score, [0, 0.9999, 0.0001], 1e-4)
 assert (cost, [1, 0.0001, 0.9999], 1e-4)
 [label, score, cost] = predict (Mdl, [2.5, 2.5, 2.5, 2.5]);
 assert (label, {'versicolor'})
 assert (score, [0, 0.6368, 0.3632], 1e-4)
 assert (cost, [1, 0.3632, 0.6368], 1e-4)
***** test
 load fisheriris
 x = meas;
 y = species;
 xc = [min(x); mean(x); max(x)];
 Mdl = fitcdiscr (x, y);
 [label, score, cost] = predict (Mdl, xc);
 l = {'setosa'; 'versicolor'; 'virginica'};
 s = [1, 0, 0; 0, 1, 0; 0, 0, 1];
 c = [0, 1, 1; 1, 0, 1; 1, 1, 0];
 assert (label, l)
 assert (score, s, 1e-4)
 assert (cost, c, 1e-4)
***** error<ClassificationDiscriminant.predict: too few input arguments.> ...
 predict (MODEL)
***** error<ClassificationDiscriminant.predict: XC is empty.> ...
 predict (MODEL, [])
***** error<ClassificationDiscriminant.predict: XC must have the same number of predictors as the trained model.> ...
 predict (MODEL, 1)
***** test
 load fisheriris
 model = fitcdiscr (meas, species);
 x = mean (meas);
 y = {'versicolor'};
 L = loss (model, x, y);
 assert (L, 0)
***** test
 x = [1, 2; 3, 4; 5, 6];
 y = {'A'; 'B'; 'A'};
 model = fitcdiscr (x, y, "Gamma", 0.4);
 x_test = [1, 6; 3, 3];
 y_test = {'A'; 'B'};
 L = loss (model, x_test, y_test);
 assert (L, 0.3333, 1e-4)
***** test
 x = [1, 2; 3, 4; 5, 6; 7, 8];
 y = ['1'; '2'; '3'; '1'];
 model = fitcdiscr (x, y, "gamma" , 0.5);
 x_test = [3, 3];
 y_test = ['1'];
 L = loss (model, x_test, y_test, 'LossFun', 'quadratic');
 assert (L, 0.2423, 1e-4)
***** test
 x = [1, 2; 3, 4; 5, 6; 7, 8];
 y = ['1'; '2'; '3'; '1'];
 model = fitcdiscr (x, y, "gamma" , 0.5);
 x_test = [3, 3; 5, 7];
 y_test = ['1'; '2'];
 L = loss (model, x_test, y_test, 'LossFun', 'classifcost');
 assert (L, 0.3333, 1e-4)
***** test
 x = [1, 2; 3, 4; 5, 6; 7, 8];
 y = ['1'; '2'; '3'; '1'];
 model = fitcdiscr (x, y, "gamma" , 0.5);
 x_test = [3, 3; 5, 7];
 y_test = ['1'; '2'];
 L = loss (model, x_test, y_test, 'LossFun', 'hinge');
 assert (L, 0.5886, 1e-4)
***** test
 x = [1, 2; 3, 4; 5, 6; 7, 8];
 y = ['1'; '2'; '3'; '1'];
 model = fitcdiscr (x, y, "gamma" , 0.5);
 x_test = [3, 3; 5, 7];
 y_test = ['1'; '2'];
 W = [1; 2];
 L = loss (model, x_test, y_test, 'LossFun', 'logit', 'Weights', W);
 assert (L, 0.5107, 1e-4)
***** test
 x = [1, 2; 3, 4; 5, 6];
 y = {'A'; 'B'; 'A'};
 model = fitcdiscr (x, y, "gamma" , 0.5);
 x_with_nan = [1, 2; NaN, 4];
 y_test = {'A'; 'B'};
 L = loss (model, x_with_nan, y_test);
 assert (L, 0.3333, 1e-4)
***** test
 x = [1, 2; 3, 4; 5, 6];
 y = {'A'; 'B'; 'A'};
 model = fitcdiscr (x, y);
 x_with_nan = [1, 2; NaN, 4];
 y_test = {'A'; 'B'};
 L = loss (model, x_with_nan, y_test, 'LossFun', 'logit');
 assert (isnan (L))
***** test
 x = [1, 2; 3, 4; 5, 6];
 y = {'A'; 'B'; 'A'};
 model = fitcdiscr (x, y);
 customLossFun = @(C, S, W, Cost) sum (W .* sum (abs (C - S), 2));
 L = loss (model, x, y, 'LossFun', customLossFun);
 assert (L, 0.8889, 1e-4)
***** test
 x = [1, 2; 3, 4; 5, 6];
 y = [1; 2; 1];
 model = fitcdiscr (x, y);
 L = loss (model, x, y, 'LossFun', 'classiferror');
 assert (L, 0.3333, 1e-4)
***** error<ClassificationDiscriminant.loss: too few input arguments.> ...
 loss (MODEL)
***** error<ClassificationDiscriminant.loss: too few input arguments.> ...
 loss (MODEL, ones (4,2))
***** error<ClassificationDiscriminant.loss: X is empty.> ...
 loss (MODEL, [], zeros (2))
***** error<ClassificationDiscriminant.loss: X must have the same number of predictors as the trained model.> ...
 loss (MODEL, 1, zeros (2))
***** error<ClassificationDiscriminant.loss: name-value arguments must be in pairs.> ...
 loss (MODEL, ones (4,2), ones (4,1), 'LossFun')
***** error<ClassificationDiscriminant.loss: Y must have the same number of rows as X.> ...
 loss (MODEL, ones (4,2), ones (3,1))
***** error<ClassificationDiscriminant.loss: invalid loss function.> ...
 loss (MODEL, ones (4,2), ones (4,1), 'LossFun', 'a')
***** error<ClassificationDiscriminant.loss: invalid 'Weights'.> ...
 loss (MODEL, ones (4,2), ones (4,1), 'Weights', 'w')
 load fisheriris
 mdl = fitcdiscr (meas, species);
 X = mean (meas);
 Y = {'versicolor'};
 m = margin (mdl, X, Y);
 assert (m, 1, 1e-6)
***** test
 X = [1, 2; 3, 4; 5, 6];
 Y = [1; 2; 1];
 mdl = fitcdiscr (X, Y, "gamma", 0.5);
 m = margin (mdl, X, Y);
 assert (m, [0.3333; -0.3333; 0.3333], 1e-4)
***** error<ClassificationDiscriminant.margin: too few input arguments.> ...
 margin (MODEL)
***** error<ClassificationDiscriminant.margin: too few input arguments.> ...
 margin (MODEL, ones (4,2))
***** error<ClassificationDiscriminant.margin: X is empty.> ...
 margin (MODEL, [], zeros (2))
***** error<ClassificationDiscriminant.margin: X must have the same number of predictors as the trained model.> ...
 margin (MODEL, 1, zeros (2))
***** error<ClassificationDiscriminant.margin: Y must have the same number of rows as X.> ...
 margin (MODEL, ones (4,2), ones (3,1))
***** shared x, y, obj
 load fisheriris
 x = meas;
 y = species;
 obj = fitcdiscr (x, y, "gamma", 0.4);
***** test
 CVMdl = crossval (obj);
 assert (class (CVMdl), "ClassificationPartitionedModel")
 assert ({CVMdl.X, CVMdl.Y}, {x, y})
 assert (CVMdl.KFold == 10)
 assert (class (CVMdl.Trained{1}), "CompactClassificationDiscriminant")
 assert (CVMdl.CrossValidatedModel, "ClassificationDiscriminant")
***** test
 CVMdl = crossval (obj, "KFold", 3);
 assert (class (CVMdl), "ClassificationPartitionedModel")
 assert ({CVMdl.X, CVMdl.Y}, {x, y})
 assert (CVMdl.KFold == 3)
 assert (class (CVMdl.Trained{1}), "CompactClassificationDiscriminant")
 assert (CVMdl.CrossValidatedModel, "ClassificationDiscriminant")
***** test
 CVMdl = crossval (obj, "HoldOut", 0.2);
 assert (class (CVMdl), "ClassificationPartitionedModel")
 assert ({CVMdl.X, CVMdl.Y}, {x, y})
 assert (class (CVMdl.Trained{1}), "CompactClassificationDiscriminant")
 assert (CVMdl.CrossValidatedModel, "ClassificationDiscriminant")
***** test
 CVMdl = crossval (obj, "LeaveOut", 'on');
 assert (class (CVMdl), "ClassificationPartitionedModel")
 assert ({CVMdl.X, CVMdl.Y}, {x, y})
 assert (class (CVMdl.Trained{1}), "CompactClassificationDiscriminant")
 assert (CVMdl.CrossValidatedModel, "ClassificationDiscriminant")
***** test
 partition = cvpartition (y, 'KFold', 3);
 CVMdl = crossval (obj, 'cvPartition', partition);
 assert (class (CVMdl), "ClassificationPartitionedModel")
 assert (CVMdl.KFold == 3)
 assert (class (CVMdl.Trained{1}), "CompactClassificationDiscriminant")
 assert (CVMdl.CrossValidatedModel, "ClassificationDiscriminant")
***** error<ClassificationDiscriminant.crossval: Name-Value arguments must be in pairs.> ...
 crossval (obj, "kfold")
***** error<ClassificationDiscriminant.crossval: specify only one of the optional Name-Value paired arguments.>...
 crossval (obj, "kfold", 12, "holdout", 0.2)
***** error<ClassificationDiscriminant.crossval: 'KFold' must be an integer value greater than 1.> ...
 crossval (obj, "kfold", 'a')
***** error<ClassificationDiscriminant.crossval: 'Holdout' must be a numeric value between 0 and 1.> ...
 crossval (obj, "holdout", 2)
***** error<ClassificationDiscriminant.crossval: 'Leaveout' must be either 'on' or 'off'.> ...
 crossval (obj, "leaveout", 1)
***** error<ClassificationDiscriminant.crossval: 'CVPartition' must be a 'cvpartition' object.> ...
 crossval (obj, "cvpartition", 1)
65 tests, 65 passed, 0 known failure, 0 skipped
[inst/Classification/ClassificationNeuralNetwork.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/Classification/ClassificationNeuralNetwork.m
***** error<ClassificationNeuralNetwork: too few input arguments.> ...
 ClassificationNeuralNetwork ()
***** error<ClassificationNeuralNetwork: too few input arguments.> ...
 ClassificationNeuralNetwork (ones(10,2))
***** error<ClassificationNeuralNetwork: number of rows in X and Y must be equal.> ...
 ClassificationNeuralNetwork (ones(10,2), ones (5,1))
***** error<ClassificationNeuralNetwork: 'Standardize' must be either true or false.> ...
 ClassificationNeuralNetwork (ones (5,3), ones (5,1), "standardize", "a")
***** error<ClassificationNeuralNetwork: 'PredictorNames' must be supplied as a cellstring array.> ...
 ClassificationNeuralNetwork (ones (5,2), ones (5,1), "PredictorNames", ["A"])
***** error<ClassificationNeuralNetwork: 'PredictorNames' must be supplied as a cellstring array.> ...
 ClassificationNeuralNetwork (ones (5,2), ones (5,1), "PredictorNames", "A")
***** error<ClassificationNeuralNetwork: 'PredictorNames' must have the same number of columns as X.> ...
 ClassificationNeuralNetwork (ones (5,2), ones (5,1), "PredictorNames", {"A", "B", "C"})
***** error<ClassificationNeuralNetwork: 'ResponseName' must be a character vector.> ...
 ClassificationNeuralNetwork (ones (5,2), ones (5,1), "ResponseName", {"Y"})
***** error<ClassificationNeuralNetwork: 'ResponseName' must be a character vector.> ...
 ClassificationNeuralNetwork (ones (5,2), ones (5,1), "ResponseName", 1)
***** error<ClassificationNeuralNetwork: 'ClassNames' must be a cellstring, logical or numeric vector.> ...
 ClassificationNeuralNetwork (ones(10,2), ones (10,1), "ClassNames", @(x)x)
***** error<ClassificationNeuralNetwork: 'ClassNames' must be a cellstring, logical or numeric vector.> ...
 ClassificationNeuralNetwork (ones(10,2), ones (10,1), "ClassNames", ['a'])
***** error<ClassificationNeuralNetwork: not all 'ClassNames' are present in Y.> ...
 ClassificationNeuralNetwork (ones(10,2), ones (10,1), "ClassNames", [1, 2])
***** error<ClassificationNeuralNetwork: not all 'ClassNames' are present in Y.> ...
 ClassificationNeuralNetwork (ones(5,2), {'a';'b';'a';'a';'b'}, "ClassNames", {'a','c'})
***** error<ClassificationNeuralNetwork: not all 'ClassNames' are present in Y.> ...
 ClassificationNeuralNetwork (ones(10,2), logical (ones (10,1)), "ClassNames", [true, false])
***** error<ClassificationNeuralNetwork: 'LayerSizes' must be a positive integer vector.> ...
 ClassificationNeuralNetwork (ones(10,2), ones(10,1), "LayerSizes", -1)
***** error<ClassificationNeuralNetwork: 'LayerSizes' must be a positive integer vector.> ...
 ClassificationNeuralNetwork (ones(10,2), ones(10,1), "LayerSizes", 0.5)
***** error<ClassificationNeuralNetwork: 'LayerSizes' must be a positive integer vector.> ...
 ClassificationNeuralNetwork (ones(10,2), ones(10,1), "LayerSizes", [1,-2])
***** error<ClassificationNeuralNetwork: 'LayerSizes' must be a positive integer vector.> ...
 ClassificationNeuralNetwork (ones(10,2), ones(10,1), "LayerSizes", [10,20,30.5])
***** error<ClassificationNeuralNetwork: 'LearningRate' must be a positive scalar.> ...
 ClassificationNeuralNetwork (ones(10,2), ones(10,1), "LearningRate", -0.1)
***** error<ClassificationNeuralNetwork: 'LearningRate' must be a positive scalar.> ...
 ClassificationNeuralNetwork (ones(10,2), ones(10,1), "LearningRate", [0.1, 0.01])
***** error<ClassificationNeuralNetwork: 'LearningRate' must be a positive scalar.> ...
 ClassificationNeuralNetwork (ones(10,2), ones(10,1), "LearningRate", "a")
***** error<ClassificationNeuralNetwork: 'Activations' must be a character vector or a cellstring vector.> ...
 ClassificationNeuralNetwork (ones(10,2), ones(10,1), "Activations", 123)
***** error<ClassificationNeuralNetwork: unsupported 'Activation' function.> ...
 ClassificationNeuralNetwork (ones(10,2), ones(10,1), "Activations", "unsupported_type")
***** error<ClassificationNeuralNetwork: unsupported 'Activation' functions.> ...
 ClassificationNeuralNetwork (ones(10,2), ones(10,1), "LayerSizes", [10, 5], ...
 "Activations", {"sigmoid", "unsupported_type"})
***** error<ClassificationNeuralNetwork: 'Activations' vector does not match the number of layers.> ...
 ClassificationNeuralNetwork (ones(10,2), ones(10,1), "Activations", {"sigmoid", "relu", "softmax"})
***** error<ClassificationNeuralNetwork: 'OutputLayerActivation' must be a character vector.> ...
 ClassificationNeuralNetwork (ones(10,2), ones(10,1), "OutputLayerActivation", 123)
***** error<ClassificationNeuralNetwork: unsupported 'OutputLayerActivation' function.> ...
 ClassificationNeuralNetwork (ones(10,2), ones(10,1), "OutputLayerActivation", "unsupported_type")
***** error<ClassificationNeuralNetwork: 'IterationLimit' must be a positive integer.> ...
 ClassificationNeuralNetwork (ones(10,2), ones(10,1), "IterationLimit", -1)
***** error<ClassificationNeuralNetwork: 'IterationLimit' must be a positive integer.> ...
 ClassificationNeuralNetwork (ones(10,2), ones(10,1), "IterationLimit", 0.5)
***** error<ClassificationNeuralNetwork: 'IterationLimit' must be a positive integer.> ...
 ClassificationNeuralNetwork (ones(10,2), ones(10,1), "IterationLimit", [1,2])
***** error<ClassificationNeuralNetwork: 'ScoreTransform' must be a character vector or a function handle.> ...
 ClassificationNeuralNetwork (ones(10,2), ones(10,1), "ScoreTransform", [1,2])
***** error<ClassificationNeuralNetwork: unrecognized 'ScoreTransform' function.> ...
 ClassificationNeuralNetwork (ones(10,2), ones(10,1), "ScoreTransform", "unsupported_type")
***** error<ClassificationNeuralNetwork: invalid parameter name in optional pair arguments.> ...
 ClassificationNeuralNetwork (ones(10,2), ones(10,1), "some", "some")
***** error<ClassificationNeuralNetwork: invalid values in X.> ...
 ClassificationNeuralNetwork ([1;2;3;'a';4], ones (5,1))
***** error<ClassificationNeuralNetwork: invalid values in X.> ...
 ClassificationNeuralNetwork ([1;2;3;Inf;4], ones (5,1))
***** shared x, y, objST, Mdl
 load fisheriris
 x = meas;
 y = grp2idx (species);
 Mdl = fitcnet (x, y, "IterationLimit", 100);
***** error<ClassificationNeuralNetwork.predict: too few input arguments.> ...
 predict (Mdl)
***** error<ClassificationNeuralNetwork.predict: XC is empty.> ...
 predict (Mdl, [])
***** error<ClassificationNeuralNetwork.predict: XC must have the same number of predictors as the trained model.> ...
 predict (Mdl, 1)
***** test
 objST = fitcnet (x, y, "IterationLimit", 100);
 objST.ScoreTransform = "a";
***** error<ClassificationNeuralNetwork.predict: 'ScoreTransform' must be a 'function_handle' object.> ...
 [labels, scores] = predict (objST, x);
***** error<ClassificationNeuralNetwork.resubPredict: 'ScoreTransform' must be a 'function_handle' object.> ...
 [labels, scores] = resubPredict (objST);
***** test
 CVMdl = crossval (Mdl, "KFold", 5);
 assert (class (CVMdl), "ClassificationPartitionedModel")
 assert ({CVMdl.X, CVMdl.Y}, {x, y})
 assert (CVMdl.KFold == 5)
 assert (class (CVMdl.Trained{1}), "CompactClassificationNeuralNetwork")
 assert (CVMdl.CrossValidatedModel, "ClassificationNeuralNetwork")
***** test
 CVMdl = crossval (Mdl, "HoldOut", 0.2);
 assert (class (CVMdl), "ClassificationPartitionedModel")
 assert ({CVMdl.X, CVMdl.Y}, {x, y})
 assert (class (CVMdl.Trained{1}), "CompactClassificationNeuralNetwork")
 assert (CVMdl.CrossValidatedModel, "ClassificationNeuralNetwork")
***** error<ClassificationNeuralNetwork.crossval: Name-Value arguments must be in pairs.> ...
 crossval (Mdl, "KFold")
***** error<ClassificationNeuralNetwork.crossval: specify only one of the optional Name-Value paired arguments.> ...
 crossval (Mdl, "KFold", 5, "leaveout", 'on')
***** error<ClassificationNeuralNetwork.crossval: 'KFold' must be an integer value greater than 1.> ...
 crossval (Mdl, "KFold", 'a')
***** error<ClassificationNeuralNetwork.crossval: 'KFold' must be an integer value greater than 1.> ...
 crossval (Mdl, "KFold", 1)
***** error<ClassificationNeuralNetwork.crossval: 'KFold' must be an integer value greater than 1.> ...
 crossval (Mdl, "KFold", -1)
***** error<ClassificationNeuralNetwork.crossval: 'KFold' must be an integer value greater than 1.> ...
 crossval (Mdl, "KFold", 11.5)
***** error<ClassificationNeuralNetwork.crossval: 'KFold' must be an integer value greater than 1.> ...
 crossval (Mdl, "KFold", [1,2])
***** error<ClassificationNeuralNetwork.crossval: 'Holdout' must be a numeric value between 0 and 1.> ...
 crossval (Mdl, "Holdout", 'a')
***** error<ClassificationNeuralNetwork.crossval: 'Holdout' must be a numeric value between 0 and 1.> ...
 crossval (Mdl, "Holdout", 11.5)
***** error<ClassificationNeuralNetwork.crossval: 'Holdout' must be a numeric value between 0 and 1.> ...
 crossval (Mdl, "Holdout", -1)
***** error<ClassificationNeuralNetwork.crossval: 'Holdout' must be a numeric value between 0 and 1.> ...
 crossval (Mdl, "Holdout", 0)
***** error<ClassificationNeuralNetwork.crossval: 'Holdout' must be a numeric value between 0 and 1.> ...
 crossval (Mdl, "Holdout", 1)
***** error<ClassificationNeuralNetwork.crossval: 'Leaveout' must be either 'on' or 'off'.> ...
 crossval (Mdl, "Leaveout", 1)
***** error<ClassificationNeuralNetwork.crossval: 'CVPartition' must be a 'cvpartition' object.> ...
 crossval (Mdl, "CVPartition", 1)
***** error<ClassificationNeuralNetwork.crossval: 'CVPartition' must be a 'cvpartition' object.> ...
 crossval (Mdl, "CVPartition", 'a')
***** error<ClassificationNeuralNetwork.crossval: invalid parameter name in optional paired arguments> ...
 crossval (Mdl, "some", "some")
59 tests, 59 passed, 0 known failure, 0 skipped
[inst/fitcsvm.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/fitcsvm.m
***** demo
 ## Use a subset of Fisher's iris data set

 load fisheriris
 inds = ! strcmp (species, 'setosa');
 X = meas(inds, [3,4]);
 Y = species(inds);

 ## Train a linear SVM classifier
 SVMModel = fitcsvm (X, Y)

 ## Plot a scatter diagram of the data and circle the support vectors.
 sv = SVMModel.SupportVectors;
 figure
 gscatter (X(:,1), X(:,2), Y)
 hold on
 plot (sv(:,1), sv(:,2), 'ko', 'MarkerSize', 10)
 legend ('versicolor', 'virginica', 'Support Vector')
 hold off
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = {"a"; "a"; "b"; "b"};
 a = fitcsvm (x, y);
 assert (class (a), "ClassificationSVM");
 assert ({a.X, a.Y}, {x, y})
 assert (a.NumObservations, 4)
 assert ({a.ResponseName, a.PredictorNames}, {"Y", {"x1", "x2", "x3"}})
 assert (a.ModelParameters.SVMtype, "c_svc")
 assert (a.ClassNames, {"a"; "b"})
***** test
 x = [1, 2; 2, 3; 3, 4; 4, 5; 2, 3; 3, 4; 2, 3; 3, 4; 2, 3; 3, 4];
 y = [1; 1; -1; -1; 1; -1; -1; -1; -1; -1];
 a = fitcsvm (x, y);
 assert (class (a), "ClassificationSVM");
 assert ({a.X, a.Y, a.ModelParameters.KernelFunction}, {x, y, "linear"})
 assert (a.ModelParameters.BoxConstraint, 1)
 assert (a.ModelParameters.KernelOffset, 0)
 assert (a.ClassNames, [1; -1])
***** test
 x = [1, 2; 2, 3; 3, 4; 4, 5; 2, 3; 3, 4; 2, 3; 3, 4; 2, 3; 3, 4];
 y = [1; 1; -1; -1; 1; -1; -1; -1; -1; -1];
 a = fitcsvm (x, y, "KernelFunction", "rbf", "BoxConstraint", 2, ...
 "KernelOffset", 2);
 assert (class (a), "ClassificationSVM");
 assert ({a.X, a.Y, a.ModelParameters.KernelFunction}, {x, y, "rbf"})
 assert (a.ModelParameters.BoxConstraint, 2)
 assert (a.ModelParameters.KernelOffset, 2)
 assert (isempty (a.Alpha), true)
 assert (isempty (a.Beta), false)
***** test
 x = [1, 2; 2, 3; 3, 4; 4, 5; 2, 3; 3, 4; 2, 3; 3, 4; 2, 3; 3, 4];
 y = [1; 1; -1; -1; 1; -1; -1; -1; -1; -1];
 a = fitcsvm (x, y, "KernelFunction", "polynomial", "PolynomialOrder", 3);
 assert (class (a), "ClassificationSVM");
 assert ({a.X, a.Y, a.ModelParameters.KernelFunction}, {x, y, "polynomial"})
 assert (a.ModelParameters.PolynomialOrder, 3)
 assert (isempty (a.Alpha), true)
 assert (isempty (a.Beta), false)
***** test
 x = [1, 2; 2, 3; 3, 4; 4, 5; 2, 3; 3, 4; 2, 3; 3, 4; 2, 3; 3, 4];
 y = [1; 1; -1; -1; 1; -1; -1; -1; -1; -1];
 a = fitcsvm (x, y, "KernelFunction", "linear", "PolynomialOrder", 3);
 assert (class (a), "ClassificationSVM");
 assert ({a.X, a.Y, a.ModelParameters.KernelFunction}, {x, y, "linear"})
 assert (a.ModelParameters.PolynomialOrder, 3)
 assert (isempty (a.Alpha), false)
 assert (isempty (a.Beta), true)
***** test
 x = [1, 2; 2, 3; 3, 4; 4, 5; 2, 3; 3, 4; 2, 3; 3, 4; 2, 3; 3, 4];
 y = [1; 1; -1; -1; 1; -1; -1; -1; -1; -1];
 a = fitcsvm (x, y, "KernelFunction", "linear", "CrossVal", 'on');
 assert (class (a), "ClassificationPartitionedModel");
 assert ({a.X, a.Y, a.ModelParameters.KernelFunction}, {x, y, "linear"})
 assert (a.ModelParameters.PolynomialOrder, 3)
 assert (isempty (a.Trained{1}.Alpha), false)
 assert (isempty (a.Trained{1}.Beta), true)
***** error<fitcsvm: too few arguments.> fitcsvm ()
***** error<fitcsvm: too few arguments.> fitcsvm (ones (4,1))
***** error<fitcsvm: Name-Value arguments must be in pairs.>
 fitcsvm (ones (4,2), ones (4, 1), 'KFold')
***** error<fitcsvm: number of rows in X and Y must be equal.>
 fitcsvm (ones (4,2), ones (3, 1))
***** error<fitcsvm: number of rows in X and Y must be equal.>
 fitcsvm (ones (4,2), ones (3, 1), 'KFold', 2)
***** error <fitcsvm: 'CrossVal' must be either 'off' or 'on'.>
 fitcsvm (ones (4,2), ones (4, 1), "CrossVal", 2)
***** error <fitcsvm: 'CrossVal' must be either 'off' or 'on'.>
 fitcsvm (ones (4,2), ones (4, 1), "CrossVal", 'a')
***** error <fitcsvm: You can use only one cross-validation name-value pair argument> ...
 fitcsvm (ones (4,2), ones (4, 1), "KFold", 10, "Holdout", 0.3)
14 tests, 14 passed, 0 known failure, 0 skipped
[inst/kstest.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/kstest.m
***** error kstest ()
***** error kstest (ones(2,4))
***** error kstest ([2,3,5,7,3+3i])
***** error kstest ([2,3,4,5,6],"tail")
***** error kstest ([2,3,4,5,6],"tail", "whatever")
***** error kstest ([2,3,4,5,6],"badoption", 0.51)
***** error kstest ([2,3,4,5,6],"tail", 0)
***** error kstest ([2,3,4,5,6],"alpha", 0)
***** error kstest ([2,3,4,5,6],"alpha", NaN)
***** error kstest ([NaN,NaN,NaN,NaN,NaN],"tail", "unequal")
***** error kstest ([2,3,4,5,6],"alpha", 0.05, "CDF", [2,3,4;1,3,4;1,2,1])
***** test
 load examgrades
 [h, p] = kstest (grades(:,1));
 assert (h, true);
 assert (p, 7.58603305206105e-107, 1e-14);
***** test
 load stockreturns
 x = stocks(:,3);
 [h,p,k,c] = kstest (x, "Tail", "larger");
 assert (h, true);
 assert (p, 5.085438806199252e-05, 1e-14);
 assert (k, 0.2197, 1e-4);
 assert (c, 0.1207, 1e-4);
13 tests, 13 passed, 0 known failure, 0 skipped
[inst/grp2idx.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/grp2idx.m
***** test
 in = [true false false true];
 out = {[1; 2; 2; 1] {"1"; "0"} [true; false]};
 assert (nthargout (1:3, @grp2idx, in), out)
 assert (nthargout (1:3, @grp2idx, in), nthargout (1:3, @grp2idx, in'))
***** test
 assert (nthargout (1:3, @grp2idx, [false, true]),
         {[1; 2] {"0"; "1"} [false; true]});
 assert (nthargout (1:3, @grp2idx, [true, false]),
         {[1; 2] {"1"; "0"} [true; false]});
***** assert (nthargout (1:3, @grp2idx, ["oct"; "sci"; "oct"; "oct"; "sci"]),
        {[1; 2; 1; 1; 2] {"oct"; "sci"} ["oct"; "sci"]});
***** assert (nthargout (1:3, @grp2idx, {"oct"; "sci"; "oct"; "oct"; "sci"}),
        {[1; 2; 1; 1; 2] {"oct"; "sci"} {"oct"; "sci"}});
***** assert (nthargout (1:3, @grp2idx, [ 1 -3 -2 -3 -3  2  1 -1  3 -3]),
        {[1; 2; 3; 2; 2; 4; 1; 5; 6; 2], {"1"; "-3"; "-2"; "2"; "-1"; "3"}, ...
         [1; -3; -2; 2; -1; 3]});
***** assert (nthargout (1:3, @grp2idx, [2 2 3 NaN 2 3]),
        {[1; 1; 2; NaN; 1; 2] {"2"; "3"} [2; 3]})
***** assert (nthargout (1:3, @grp2idx, {"et" "sa" "sa" "" "et"}),
        {[1; 2; 2; NaN; 1] {"et"; "sa"} {"et"; "sa"}})
***** test assert (nthargout (1:3, @grp2idx, ["sci"; "oct"; "sci"; "oct"; "oct"]),
        {[1; 2; 1; 2; 2] {"sci"; "oct"} ["sci"; "oct"]});
***** test assert (nthargout (1:3, @grp2idx, {"sci"; "oct"; "sci"; "oct"; "oct"}),
        {[1; 2; 1; 2; 2] {"sci"; "oct"} {"sci"; "oct"}});
***** test assert (nthargout (1:3, @grp2idx, {"sa" "et" "et" "" "sa"}),
        {[1; 2; 2; NaN; 1] {"sa"; "et"} {"sa"; "et"}})
10 tests, 10 passed, 0 known failure, 0 skipped
[inst/fillmissing.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/fillmissing.m
***** assert (fillmissing ([1, 2, 3], "constant", 99), [1, 2, 3])
***** assert (fillmissing ([1, 2, NaN], "constant", 99), [1, 2, 99])
***** assert (fillmissing ([NaN, 2, NaN], "constant", 99), [99, 2, 99])
***** assert (fillmissing ([1, 2, 3]', "constant", 99), [1, 2, 3]')
***** assert (fillmissing ([1, 2, NaN]', "constant", 99), [1, 2, 99]')
***** assert (fillmissing ([1, 2, 3; 4, 5, 6], "constant", 99), [1, 2, 3; 4, 5, 6])
***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "constant", 99), [1, 2, 99; 4, 99, 6])
***** assert (fillmissing ([NaN, 2, NaN; 4, NaN, 6], "constant", [97, 98, 99]), [97, 2, 99; 4, 98, 6])
***** test
 x = cat (3, [1, 2, NaN; 4, NaN, 6], [NaN, 2, 3; 4, 5, NaN]);
 y = cat (3, [1, 2, 99; 4, 99, 6], [99, 2, 3; 4, 5, 99]);
 assert (fillmissing (x, "constant", 99), y);
 y = cat (3, [1, 2, 96; 4, 95, 6], [97, 2, 3; 4, 5, 99]);
 assert (fillmissing (x, "constant", [94:99]), y);
 assert (fillmissing (x, "constant", [94:99]'), y);
 assert (fillmissing (x, "constant", permute ([94:99], [1 3 2])), y);
 assert (fillmissing (x, "constant", [94, 96, 98; 95, 97, 99]), y);
 assert (fillmissing (x, "constant", [94:99], 1), y);
 y = cat (3, [1, 2, 96; 4, 97, 6], [98, 2, 3; 4, 5, 99]);
 assert (fillmissing (x, "constant", [96:99], 2), y);
 y = cat (3, [1, 2, 98; 4, 97, 6], [94, 2, 3; 4, 5, 99]);
 assert (fillmissing (x, "constant", [94:99], 3), y);
 y = cat (3, [1, 2, 92; 4, 91, 6], [94, 2, 3; 4, 5, 99]);
 assert (fillmissing (x, "constant", [88:99], 99), y);
***** test
 x = reshape ([1:24], 4, 3, 2);
 x([1, 6, 7, 9, 12, 14, 16, 19, 22, 23]) = NaN;
 y = x;
 y([1, 6, 7, 9, 12, 14, 16, 19, 22, 23]) = [94, 95, 95, 96, 96, 97, 97, 98, 99, 99];
 assert (fillmissing (x, "constant", [94:99], 1), y);
 y([1, 6, 7, 9, 12, 14, 16, 19, 22, 23]) = [92, 93, 94, 92, 95, 97, 99, 98, 97, 98];
 assert (fillmissing (x, "constant", [92:99], 2), y);
 y([1, 6, 7, 9, 12, 14, 16, 19, 22, 23]) = [88, 93, 94, 96, 99, 89, 91, 94, 97, 98];
 assert (fillmissing (x, "constant", [88:99], 3), y);
 y([1, 6, 7, 9, 12, 14, 16, 19, 22, 23]) = [76, 81, 82, 84, 87, 89, 91, 94, 97, 98];
 assert (fillmissing (x, "constant", [76:99], 99), y);
***** assert (fillmissing ([1, 2, 3], "constant", 99, "endvalues", 88), [1, 2, 3])
***** assert (fillmissing ([1, NaN, 3], "constant", 99, "endvalues", 88), [1, 99, 3])
***** assert (fillmissing ([1, 2, NaN], "constant", 99, "endvalues", 88), [1, 2, 88])
***** assert (fillmissing ([NaN, 2, 3], "constant", 99, "endvalues", 88), [88, 2, 3])
***** assert (fillmissing ([NaN, NaN, 3], "constant", 99, "endvalues", 88), [88, 88, 3])
***** assert (fillmissing ([1, NaN, NaN], "constant", 99, "endvalues", 88), [1, 88, 88])
***** assert (fillmissing ([NaN, 2, NaN], "constant", 99, "endvalues", 88), [88, 2, 88])
***** assert (fillmissing ([NaN, 2, NaN]', "constant", 99, "endvalues", 88), [88, 2, 88]')
***** assert (fillmissing ([1, NaN, 3, NaN, 5], "constant", 99, "endvalues", 88), [1, 99, 3, 99, 5])
***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "constant", 99, "endvalues", 88), [1, 99, 99, 99, 5])
***** assert (fillmissing ([NaN, NaN, NaN, NaN, 5], "constant", 99, "endvalues", 88), [88, 88, 88, 88, 5])
***** assert (fillmissing ([1, NaN, 3, 4, NaN], "constant", 99, "endvalues", 88), [1, 99, 3, 4, 88])
***** assert (fillmissing ([1, NaN, 3, 4, NaN], "constant", 99, 1, "endvalues", 88), [1, 88, 3, 4, 88])
***** assert (fillmissing ([1, NaN, 3, 4, NaN], "constant", 99, 1, "endvalues", "extrap"), [1, 99, 3, 4, 99])
***** test
 x = reshape ([1:24], 3, 4, 2);
 y = x;
 x([1, 2, 5, 6, 8, 10, 13, 16, 18, 19, 20, 21, 22]) = NaN;
 y([1, 2, 5, 6, 10, 13, 16, 18, 19, 20, 21, 22]) = 88;
 y([8]) = 99;
 assert (fillmissing (x, "constant", 99, "endvalues", 88), y);
 assert (fillmissing (x, "constant", 99, 1, "endvalues", 88), y);
 y = x;
 y([1, 2, 5, 8, 10, 13, 16, 19, 22]) = 88;
 y([6, 18, 20, 21]) = 99;
 assert (fillmissing (x, "constant", 99, 2, "endvalues", 88), y);
 y(y == 99) = 88;
 assert (fillmissing (x, "constant", 99, 3, "endvalues", 88), y);
 assert (fillmissing (x, "constant", 99, 4, "endvalues", 88), y);
 assert (fillmissing (x, "constant", 99, 99, "endvalues", 88), y);
 y([8]) = 94;
 assert (fillmissing (x, "constant", [92:99], 1, "endvalues", 88), y);
 y([6, 8, 18, 20, 21]) = [96, 88, 99, 98, 99];
 assert (fillmissing (x, "constant", [94:99], 2, "endvalues", 88), y);
 y = x;
 y(isnan (y)) = 88;
 assert (fillmissing (x, "constant", [88:99], 3, "endvalues", 88), y);
 y = x;
 y(isnan (y)) = [82, 82, 83, 83, 94, 85, 86, 87, 87, 88, 88, 88, 89];
 assert (fillmissing (x, "constant", [92:99], 1, "endvalues", [82:89]), y);
 y = x;
 y(isnan (y)) = [84, 85, 85, 96, 85, 84, 87, 87, 99, 87, 98, 99, 87];
 assert (fillmissing (x, "constant", [94:99], 2, "endvalues", [84:89]), y);
 y = x;
 y(isnan (y)) = [68, 69, 72, 73, 75, 77, 68, 71, 73, 74, 75, 76, 77];
 assert (fillmissing (x, "constant", [88:99], 3, "endvalues", [68:79]), y);
 assert (fillmissing (x, "constant", [88:93; 94:99]', 3, "endvalues", [68:73; 74:79]'), y)
***** test
 x = reshape ([1:24],4,3,2);
 x([1, 6, 7, 9, 12, 14, 16, 19, 22, 23]) = NaN;
 y = x;
 y([1, 6, 7, 9, 12, 14, 16, 19, 22, 23]) = [94, 95, 95, 96, 96, 97, 97, 98, 99, 99];
 assert (fillmissing (x, "constant", [94:99], 1), y);
 y([1, 6, 7, 9, 12, 14, 16, 19, 22, 23]) = [92, 93, 94, 92, 95, 97, 99, 98, 97, 98];
 assert (fillmissing (x, "constant", [92:99], 2), y);
 y([1, 6, 7, 9, 12, 14, 16, 19, 22, 23]) = [88, 93, 94, 96, 99, 89, 91, 94, 97, 98];
 assert (fillmissing (x, "constant", [88:99], 3), y);
 y([1, 6, 7, 9, 12, 14, 16, 19, 22, 23]) = [76, 81, 82, 84, 87, 89, 91, 94, 97, 98];
 assert (fillmissing (x, "constant", [76:99], 99), y);
***** assert (fillmissing ([1, 2, 3], "previous"), [1, 2, 3])
***** assert (fillmissing ([1, 2, 3], "next"), [1, 2, 3])
***** assert (fillmissing ([1, 2, 3]', "previous"), [1, 2, 3]')
***** assert (fillmissing ([1, 2, 3]', "next"), [1, 2, 3]')
***** assert (fillmissing ([1, 2, NaN], "previous"), [1, 2, 2])
***** assert (fillmissing ([1, 2, NaN], "next"), [1, 2, NaN])
***** assert (fillmissing ([NaN, 2, NaN], "previous"), [NaN, 2, 2])
***** assert (fillmissing ([NaN, 2, NaN], "next"), [2, 2, NaN])
***** assert (fillmissing ([1, NaN, 3], "previous"), [1, 1, 3])
***** assert (fillmissing ([1, NaN, 3], "next"), [1, 3, 3])
***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "previous", 1), [1, 2, NaN; 4, 2, 6])
***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "previous", 2), [1, 2, 2; 4, 4, 6])
***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "previous", 3), [1, 2, NaN; 4, NaN, 6])
***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "next", 1), [1, 2, 6; 4, NaN, 6])
***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "next", 2), [1, 2, NaN; 4, 6, 6])
***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "next", 3), [1, 2, NaN; 4, NaN, 6])
***** test
 x = reshape ([1:24], 4, 3, 2);
 x([1, 6, 7, 9, 12, 14, 16, 19, 22, 23]) = NaN;
 y = x;
 y([1, 6, 7, 9, 14, 19, 22, 23]) = [2, 8, 8, 10, 15, 20, 24, 24];
 assert (fillmissing (x, "next", 1), y);
 y = x;
 y([1, 6, 7, 14, 16]) = [5, 10, 11, 18, 20];
 assert (fillmissing (x, "next", 2), y);
 y = x;
 y([1, 6, 9, 12]) = [13, 18, 21, 24];
 assert (fillmissing (x, "next", 3), y);
 assert (fillmissing (x, "next", 99), x);
 y = x;
 y([6, 7, 12, 14, 16, 19, 22, 23]) = [5, 5, 11, 13, 15, 18, 21, 21];
 assert (fillmissing (x, "previous", 1), y);
 y = x;
 y([6, 7, 9, 12, 19, 22, 23]) = [2, 3, 5, 8, 15, 18, 15];
 assert (fillmissing (x, "previous", 2), y);
 y = x;
 y([14, 16, 22, 23]) = [2, 4, 10, 11];
 assert (fillmissing (x, "previous", 3), y);
 assert (fillmissing (x, "previous", 99), x);
***** assert (fillmissing ([1, 2, 3], "constant", 0, "endvalues", "previous"), [1, 2, 3])
***** assert (fillmissing ([1, 2, 3], "constant", 0, "endvalues", "next"), [1, 2, 3])
***** assert (fillmissing ([1, NaN, 3], "constant", 0, "endvalues", "previous"), [1, 0, 3])
***** assert (fillmissing ([1, NaN, 3], "constant", 0, "endvalues", "next"), [1, 0, 3])
***** assert (fillmissing ([1, 2, NaN], "constant", 0, "endvalues", "previous"), [1, 2, 2])
***** assert (fillmissing ([1, 2, NaN], "constant", 0, "endvalues", "next"), [1, 2, NaN])
***** assert (fillmissing ([1, NaN, NaN], "constant", 0, "endvalues", "previous"), [1, 1, 1])
***** assert (fillmissing ([1, NaN, NaN], "constant", 0, "endvalues", "next"), [1, NaN, NaN])
***** assert (fillmissing ([NaN, 2, 3], "constant", 0, "endvalues", "previous"), [NaN, 2, 3])
***** assert (fillmissing ([NaN, 2, 3], "constant", 0, "endvalues", "next"), [2, 2, 3])
***** assert (fillmissing ([NaN, NaN, 3], "constant", 0, "endvalues", "previous"), [NaN, NaN, 3])
***** assert (fillmissing ([NaN, NaN, 3], "constant", 0, "endvalues", "next"), [3, 3, 3])
***** assert (fillmissing ([NaN, NaN, NaN], "constant", 0, "endvalues", "previous"), [NaN, NaN, NaN])
***** assert (fillmissing ([NaN, NaN, NaN], "constant", 0, "endvalues", "next"), [NaN, NaN, NaN])
***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 0, "endvalues", "previous"), [NaN, 2, 0, 4, 4])
***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 0, "endvalues", "next"), [2, 2, 0, 4, NaN])
***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 0, 1, "endvalues", "previous"), [NaN, 2, NaN, 4, NaN])
***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 0, 1, "endvalues", "next"), [NaN, 2, NaN, 4, NaN])
***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 0, 2, "endvalues", "previous"), [NaN, 2, 0, 4, 4])
***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 0, 2, "endvalues", "next"), [2, 2, 0, 4, NaN])
***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 0, 3, "endvalues", "previous"), [NaN, 2, NaN, 4, NaN])
***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 0, 3, "endvalues", "next"), [NaN, 2, NaN, 4, NaN])
***** test
 x = reshape ([1:24], 3, 4, 2);
 x([1, 2, 5, 6, 8, 10, 13, 16, 18, 19, 20, 21, 22]) = NaN;
 y = x;
 y([5, 6, 8, 18]) = [4, 4, 0, 17];
 assert (fillmissing (x, "constant", 0, "endvalues", "previous"), y);
 assert (fillmissing (x, "constant", 0, 1, "endvalues", "previous"), y);
 y = x;
 y([6, 10, 18, 20, 21]) = [0, 7, 0, 0, 0];
 assert (fillmissing (x, "constant", 0, 2, "endvalues", "previous"), y);
 y = x;
 y([16, 19, 21]) = [4, 7, 9];
 assert (fillmissing (x, "constant", 0, 3, "endvalues", "previous"), y);
 assert (fillmissing (x, "constant", 0, 4, "endvalues", "previous"), x);
 assert (fillmissing (x, "constant", 0, 99, "endvalues", "previous"), x);
 y = x;
 y([1, 2, 8, 10, 13, 16, 22]) = [3, 3, 0, 11, 14, 17, 23];
 assert (fillmissing (x, "constant", 0, "endvalues", "next"), y);
 assert (fillmissing (x, "constant", 0, 1, "endvalues", "next"), y);
 y = x;
 y([1, 2, 5, 6, 8, 18, 20, 21]) = [4, 11, 11, 0, 11, 0, 0, 0];
 assert (fillmissing (x, "constant", 0, 2, "endvalues", "next"), y);
 y = x;
 y([2, 5]) = [14, 17];
 assert (fillmissing (x, "constant", 0, 3, "endvalues", "next"), y);
 assert (fillmissing (x, "constant", 0, 4, "endvalues", "next"), x);
 assert (fillmissing (x, "constant", 0, 99, "endvalues", "next"), x);
***** assert (fillmissing ([1, 2, 3], "nearest"), [1, 2, 3])
***** assert (fillmissing ([1, 2, 3]', "nearest"), [1, 2, 3]')
***** assert (fillmissing ([1, 2, NaN], "nearest"), [1, 2, 2])
***** assert (fillmissing ([NaN, 2, NaN], "nearest"), [2, 2, 2])
***** assert (fillmissing ([1, NaN, 3], "nearest"), [1, 3, 3])
***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "nearest", 1), [1, 2, 6; 4, 2, 6])
***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "nearest", 2), [1, 2, 2; 4, 6, 6])
***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "nearest", 3), [1, 2, NaN; 4, NaN, 6])
***** assert (fillmissing ([1, NaN, 3, NaN, 5], "nearest"), [1, 3, 3, 5, 5])
***** assert (fillmissing ([1, NaN, 3, NaN, 5], "nearest", "samplepoints", [0, 1, 2, 3, 4]), [1, 3, 3, 5, 5])
***** assert (fillmissing ([1, NaN, 3, NaN, 5], "nearest", "samplepoints", [0.5, 1, 2, 3, 5]), [1, 1, 3, 3, 5])
***** test
 x = reshape ([1:24], 4, 3, 2);
 x([1, 6, 7, 9, 12, 14, 16, 19, 22, 23]) = NaN;
 y = x;
 y([1, 6, 7, 9, 12, 14, 16, 19, 22, 23]) = [2, 5, 8, 10, 11, 15, 15, 20, 21, 24];
 assert (fillmissing (x, "nearest", 1), y);
 y = x;
 y([1, 6, 7, 9, 12, 14, 16, 19, 22, 23]) = [5, 10, 11, 5, 8, 18, 20, 15, 18, 15];
 assert (fillmissing (x, "nearest", 2), y);
 y = x;
 y([1, 6, 9, 12, 14, 16, 22, 23]) = [13, 18, 21, 24, 2, 4, 10, 11];
 assert (fillmissing (x, "nearest", 3), y);
 assert (fillmissing (x, "nearest", 99), x);
***** assert (fillmissing ([1, 2, 3], "constant", 0, "endvalues", "nearest"), [1, 2, 3])
***** assert (fillmissing ([1, NaN, 3], "constant", 0, "endvalues", "nearest"), [1 0 3])
***** assert (fillmissing ([1, 2, NaN], "constant", 0, "endvalues", "nearest"), [1, 2, 2])
***** assert (fillmissing ([1, NaN, NaN], "constant", 0, "endvalues", "nearest"), [1, 1, 1])
***** assert (fillmissing ([NaN, 2, 3], "constant", 0, "endvalues", "nearest"), [2, 2, 3])
***** assert (fillmissing ([NaN, NaN, 3], "constant", 0, "endvalues", "nearest"), [3, 3, 3])
***** assert (fillmissing ([NaN, NaN, NaN], "constant", 0, "endvalues", "nearest"), [NaN, NaN, NaN])
***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 0, "endvalues", "nearest"), [2, 2, 0, 4, 4])
***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 0, 1, "endvalues", "nearest"), [NaN, 2, NaN, 4, NaN])
***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 0, 2, "endvalues", "nearest"), [2, 2, 0, 4, 4])
***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 0, 3, "endvalues", "nearest"), [NaN, 2, NaN, 4, NaN])
***** test
 x = reshape ([1:24], 3, 4, 2);
 x([1, 2, 5, 6, 8, 10, 13, 16, 18, 19, 20, 21, 22]) = NaN;
 y = x;
 y([1, 2, 5, 6, 8, 10, 13, 16, 18, 22]) = [3, 3, 4, 4, 0, 11, 14, 17, 17, 23];
 assert (fillmissing (x, "constant", 0, "endvalues", "nearest"), y);
 assert (fillmissing (x, "constant", 0, 1, "endvalues", "nearest"), y);
 y = x;
 y([1, 2, 5, 6, 8, 10, 18, 20, 21]) = [4, 11, 11, 0, 11, 7, 0, 0, 0];
 assert (fillmissing (x, "constant", 0, 2, "endvalues", "nearest"), y);
 y = x;
 y([2, 5, 16, 19, 21]) = [14, 17, 4, 7, 9];
 assert (fillmissing (x, "constant", 0, 3, "endvalues", "nearest"), y);
 assert (fillmissing (x, "constant", 0, 99, "endvalues", "nearest"), x);
***** assert (fillmissing ([1, 2, 3], "linear"), [1, 2, 3])
***** assert (fillmissing ([1, 2, 3]', "linear"), [1, 2, 3]')
***** assert (fillmissing ([1, 2, NaN], "linear"), [1, 2, 3])
***** assert (fillmissing ([NaN, 2, NaN], "linear"), [NaN, 2, NaN])
***** assert (fillmissing ([1, NaN, 3], "linear"), [1, 2, 3])
***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "linear", 1), [1, 2, NaN; 4, NaN, 6])
***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "linear", 2), [1, 2, 3; 4, 5, 6])
***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "linear", 3), [1, 2, NaN; 4, NaN, 6])
***** assert (fillmissing ([1, NaN, 3, NaN, 5], "linear"), [1, 2, 3, 4, 5])
***** assert (fillmissing ([1, NaN, 3, NaN, 5], "linear", "samplepoints", [0, 1, 2, 3, 4]), [1, 2, 3, 4, 5])
***** assert (fillmissing ([1, NaN, 3, NaN, 5], "linear", "samplepoints", [0, 1.5, 2, 5, 14]), [1, 2.5, 3, 3.5, 5], eps)
***** test
 x = reshape ([1:24], 4, 3, 2);
 x([1, 6, 7, 9, 12, 14, 16, 19, 22, 23]) = NaN;
 assert (fillmissing (x, "linear", 1), reshape ([1:24], 4, 3, 2));
 y = reshape ([1:24], 4, 3, 2);
 y([1, 9, 14, 19, 22, 23]) = NaN;
 assert (fillmissing (x, "linear", 2), y);
 y = reshape ([1:24], 4, 3, 2);
 y([1, 6, 7, 9, 12, 14, 16, 19, 22, 23]) = NaN;
 assert (fillmissing (x, "linear", 3), y);
 assert (fillmissing (x, "linear", 99), x);
***** assert (fillmissing ([1, 2, 3], "linear", "endvalues", 0), [1, 2, 3])
***** assert (fillmissing ([1, NaN, 3], "linear", "endvalues", 0), [1, 2, 3])
***** assert (fillmissing ([1, 2, NaN], "linear", "endvalues", 0), [1, 2, 0])
***** assert (fillmissing ([1, NaN, NaN], "linear", "endvalues", 0), [1, 0, 0])
***** assert (fillmissing ([NaN, 2, 3], "linear", "endvalues", 0), [0, 2, 3])
***** assert (fillmissing ([NaN, NaN, 3], "linear", "endvalues", 0), [0, 0, 3])
***** assert (fillmissing ([NaN, NaN, NaN], "linear", "endvalues", 0), [0, 0, 0])
***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "linear", "endvalues", 0), [0, 2, 3, 4, 0])
***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "linear", 1, "endvalues", 0), [0, 2, 0, 4, 0])
***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "linear", 2, "endvalues", 0), [0, 2, 3, 4, 0])
***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "linear", 3, "endvalues", 0), [0, 2, 0, 4, 0])
***** test
 x = reshape ([1:24], 3, 4, 2);
 x([1, 2, 5, 6, 8, 10, 13, 16, 18, 19, 20, 21, 22]) = NaN;
 y = x;
 y([1, 2, 5, 6, 10, 13, 16, 18, 19, 20, 21, 22]) = 0;
 y(8) = 8;
 assert (fillmissing (x, "linear", "endvalues", 0), y);
 assert (fillmissing (x, "linear", 1, "endvalues", 0), y);
 y = x;
 y([1, 2, 5, 8, 10, 13, 16, 19, 22]) = 0;
 y([6, 18, 20, 21]) = [6, 18, 20, 21];
 assert (fillmissing (x, "linear", 2, "endvalues", 0), y);
 y = x;
 y(isnan(y)) = 0;
 assert (fillmissing (x, "linear", 3, "endvalues", 0), y);
 assert (fillmissing (x, "linear", 99, "endvalues", 0), y);
***** assert (fillmissing ([1, 2, 3], "constant", 99, "endvalues", "linear"), [1, 2, 3])
***** assert (fillmissing ([1, NaN, 3], "constant", 99, "endvalues", "linear"), [1, 99, 3])
***** assert (fillmissing ([1, NaN, 3, NaN], "constant", 99, "endvalues", "linear"), [1, 99, 3, 4])
***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 99, "endvalues", "linear"), [1, 2, 99, 4, 5])
***** assert (fillmissing ([NaN, 2, NaN, NaN], "constant", 99, "endvalues", "linear"), [NaN, 2, NaN, NaN])
***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 99, "endvalues", "linear", "samplepoints", [1, 2, 3, 4, 5]), [1, 2, 99, 4, 5])
***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 99, "endvalues", "linear", "samplepoints", [0, 2, 3, 4, 10]), [0, 2, 99, 4, 10])
***** test
 x = reshape ([1:24], 3, 4, 2);
 x([1, 2, 5, 6, 8, 10, 13, 16, 18, 19, 20, 21, 22]) = NaN;
 y = x;
 y([1, 6, 10, 18, 20, 21]) = [2.5, 5, 8.5, 17.25, 21, 21.75];
 assert (fillmissing (x, "linear", 2, "samplepoints", [2 4 8 10]), y, eps);
 y([1, 6, 10, 18, 20, 21]) = [2.5, 4.5, 8.5, 17.25, 21.5, 21.75];
 assert (fillmissing (x, "spline", 2, "samplepoints", [2, 4, 8, 10]), y, eps);
 y([1, 6, 10, 18, 20, 21]) = [2.5, 4.559386973180077, 8.5, 17.25, 21.440613026819925, 21.75];
 assert (fillmissing (x, "pchip", 2, "samplepoints", [2, 4, 8, 10]), y, 10*eps);
***** test <60965>
 x = reshape ([1:24], 3, 4, 2);
 x([1, 2, 5, 6, 8, 10, 13, 16, 18, 19, 20, 21, 22]) = NaN;
 y = x;
 y([1, 6, 10, 18, 20, 21]) = [2.5, 4.609523809523809, 8.5, 17.25, 21.390476190476186, 21.75];
 assert (fillmissing (x, "makima", 2, "samplepoints", [2, 4, 8, 10]), y, 10*eps);
!!!!! known bug: https://octave.org/testfailure/?60965
interp1: invalid METHOD 'makima'
***** assert (fillmissing ([1, 2, 3], "constant", 99, "endvalues", "spline"), [1, 2, 3])
***** assert (fillmissing ([1, NaN, 3], "constant", 99, "endvalues", "spline"), [1, 99, 3])
***** assert (fillmissing ([1, NaN, 3, NaN], "constant", 99, "endvalues", "spline"), [1, 99, 3, 4])
***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 99, "endvalues", "spline"), [1, 2, 99, 4, 5])
***** assert (fillmissing ([NaN, 2, NaN, NaN], "constant", 99, "endvalues", "spline"), [NaN, 2, NaN, NaN])
***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 99, "endvalues", "spline", "samplepoints", [1, 2, 3, 4, 5]), [1, 2, 99, 4, 5])
***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 99, "endvalues", "spline", "samplepoints", [0, 2, 3, 4, 10]), [0, 2, 99, 4, 10])
***** assert (fillmissing ([1, 2, 3], "movmean", 1), [1, 2, 3])
***** assert (fillmissing ([1, 2, NaN], "movmean", 1), [1, 2, NaN])
***** assert (fillmissing ([1, 2, 3], "movmean", 2), [1, 2, 3])
***** assert (fillmissing ([1, 2, 3], "movmean", [1, 0]), [1, 2, 3])
***** assert (fillmissing ([1, 2, 3]', "movmean", 2), [1, 2, 3]')
***** assert (fillmissing ([1, 2, NaN], "movmean", 2), [1, 2, 2])
***** assert (fillmissing ([1, 2, NaN], "movmean", [1, 0]), [1, 2, 2])
***** assert (fillmissing ([1, 2, NaN], "movmean", [1, 0]'), [1, 2, 2])
***** assert (fillmissing ([NaN, 2, NaN], "movmean", 2), [NaN, 2, 2])
***** assert (fillmissing ([NaN, 2, NaN], "movmean", [1, 0]), [NaN, 2, 2])
***** assert (fillmissing ([NaN, 2, NaN], "movmean", [0, 1]), [2, 2, NaN])
***** assert (fillmissing ([NaN, 2, NaN], "movmean", [0, 1.1]), [2, 2, NaN])
***** assert (fillmissing ([1, NaN, 3, NaN, 5], "movmean", [3, 0]), [1, 1, 3, 2, 5])
***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "movmean", 3, 1), [1, 2, 6; 4, 2, 6])
***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "movmean", 3, 2), [1, 2, 2; 4, 5, 6])
***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "movmean", 3, 3), [1, 2, NaN; 4, NaN, 6])
***** assert (fillmissing ([1, NaN, 3, NaN, 5], "movmean", 99), [1, 3, 3, 3, 5])
***** assert (fillmissing ([1, NaN, 3, NaN, 5], "movmean", 99, 1), [1, NaN, 3, NaN, 5])
***** assert (fillmissing ([1, NaN, 3, NaN, 5]', "movmean", 99, 1), [1, 3, 3, 3, 5]')
***** assert (fillmissing ([1, NaN, 3, NaN, 5], "movmean", 99, 2), [1, 3, 3, 3, 5])
***** assert (fillmissing ([1, NaN, 3, NaN, 5]', "movmean", 99, 2), [1, NaN, 3, NaN, 5]')
***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmean", 3, "samplepoints", [1, 2, 3, 4, 5]), [1, 1, NaN, 5, 5])
***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmean", [1, 1], "samplepoints", [1, 2, 3, 4, 5]), [1, 1, NaN, 5, 5])
***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmean", [1.5, 1.5], "samplepoints", [1, 2, 3, 4, 5]), [1, 1, NaN, 5, 5])
***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmean", 4, "samplepoints", [1, 2, 3, 4, 5]), [1, 1, 1, 5, 5])
***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmean", [2, 2], "samplepoints", [1, 2, 3, 4, 5]), [1, 1, 3, 5, 5])
***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmean", 4.0001, "samplepoints", [1, 2, 3, 4, 5]), [1, 1, 3, 5, 5])
***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmean", 3, "samplepoints", [1.5, 2, 3, 4, 5]), [1, 1, 1, 5, 5])
***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmean", 3, "samplepoints", [1 2, 3, 4, 4.5]), [1, 1, NaN, 5, 5])
***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmean", 3, "samplepoints", [1.5, 2, 3, 4, 4.5]), [1, 1, 1, 5, 5])
***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmean", [1.5, 1.5], "samplepoints", [1.5, 2, 3, 4, 5]), [1, 1, 1, 5, 5])
***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmean", [1.5, 1.5], "samplepoints", [1, 2, 3, 4, 4.5]), [1, 1, 5, 5, 5])
***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmean", [1.5, 1.5], "samplepoints", [1.5, 2 3, 4, 4.5]), [1, 1, 3, 5, 5])
***** test
 x = reshape ([1:24], 3, 4, 2);
 x([1, 2, 5, 6, 8, 10, 13, 16, 18, 19, 20, 21, 22]) = NaN;
 y = x;
 y([2, 5, 8, 10, 13, 16, 18, 22]) = [3, 4, 8, 11, 14, 17, 17, 23];
 assert (fillmissing (x, "movmean", 3), y);
 assert (fillmissing (x, "movmean", [1, 1]), y);
 assert (fillmissing (x, "movmean", 3, "endvalues", "extrap"), y);
 assert (fillmissing (x, "movmean", 3, "samplepoints", [1, 2, 3]), y);
 y = x;
 y([1, 6, 8, 10, 18, 20, 21]) = [4, 6, 11, 7, 15, 20, 24];
 assert (fillmissing (x, "movmean", 3, 2), y);
 assert (fillmissing (x, "movmean", [1, 1], 2), y);
 assert (fillmissing (x, "movmean", 3, 2, "endvalues", "extrap"), y);
 assert (fillmissing (x, "movmean", 3, 2, "samplepoints", [1, 2, 3, 4]), y);
 y([1, 18]) = NaN;
 y(6) = 9;
 assert (fillmissing (x, "movmean", 3, 2, "samplepoints", [0, 2, 3, 4]), y);
 y = x;
 y([1, 2, 5, 6, 10, 13, 16, 18, 19, 20, 21, 22]) = 99;
 y(8) = 8;
 assert (fillmissing (x, "movmean", 3, "endvalues", 99), y);
 y = x;
 y([1, 2, 5, 8, 10, 13, 16, 19, 22]) = 99;
 y([6, 18, 20, 21]) = [6, 15, 20, 24];
 assert (fillmissing (x, "movmean", 3, 2, "endvalues", 99), y);
***** assert (fillmissing ([1, 2, 3], "movmedian", 1), [1, 2, 3])
***** assert (fillmissing ([1, 2, NaN], "movmedian", 1), [1, 2, NaN])
***** assert (fillmissing ([1, 2, 3], "movmedian", 2), [1, 2, 3])
***** assert (fillmissing ([1, 2, 3], "movmedian", [1, 0]), [1, 2, 3])
***** assert (fillmissing ([1, 2, 3]', "movmedian", 2), [1, 2, 3]')
***** assert (fillmissing ([1, 2, NaN], "movmedian", 2), [1, 2, 2])
***** assert (fillmissing ([1, 2, NaN], "movmedian", [1, 0]), [1, 2, 2])
***** assert (fillmissing ([1, 2, NaN], "movmedian", [1, 0]'), [1, 2, 2])
***** assert (fillmissing ([NaN, 2, NaN], "movmedian", 2), [NaN, 2, 2])
***** assert (fillmissing ([NaN, 2, NaN], "movmedian", [1, 0]), [NaN, 2, 2])
***** assert (fillmissing ([NaN, 2, NaN], "movmedian", [0, 1]), [2, 2, NaN])
***** assert (fillmissing ([NaN, 2, NaN], "movmedian", [0, 1.1]), [2, 2, NaN])
***** assert (fillmissing ([1, NaN, 3, NaN, 5], "movmedian", [3, 0]), [1, 1, 3, 2, 5])
***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "movmedian", 3, 1), [1, 2, 6; 4, 2, 6])
***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "movmedian", 3, 2), [1, 2, 2; 4, 5, 6])
***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "movmedian", 3, 3), [1, 2, NaN; 4, NaN, 6])
***** assert (fillmissing ([1, NaN, 3, NaN, 5], "movmedian", 99), [1, 3, 3, 3, 5])
***** assert (fillmissing ([1, NaN, 3, NaN, 5], "movmedian", 99, 1), [1, NaN, 3, NaN, 5])
***** assert (fillmissing ([1, NaN, 3, NaN, 5]', "movmedian", 99, 1), [1, 3, 3, 3, 5]')
***** assert (fillmissing ([1, NaN, 3, NaN, 5], "movmedian", 99, 2), [1, 3, 3, 3, 5])
***** assert (fillmissing ([1, NaN, 3, NaN, 5]', "movmedian", 99, 2), [1, NaN, 3, NaN, 5]')
***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmedian", 3, "samplepoints", [1, 2, 3, 4, 5]), [1, 1, NaN, 5, 5])
***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmedian", [1, 1], "samplepoints", [1, 2, 3, 4, 5]), [1, 1, NaN, 5, 5])
***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmedian", [1.5, 1.5], "samplepoints", [1, 2, 3, 4, 5]), [1, 1, NaN, 5, 5])
***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmedian", 4, "samplepoints", [1, 2, 3, 4, 5]), [1, 1, 1, 5, 5])
***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmedian", [2, 2], "samplepoints", [1, 2, 3, 4, 5]), [1, 1, 3, 5, 5])
***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmedian", 4.0001, "samplepoints", [1, 2, 3, 4, 5]), [1, 1, 3, 5, 5])
***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmedian", 3, "samplepoints", [1.5 2 3 4 5]), [1, 1, 1, 5, 5])
***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmedian", 3, "samplepoints", [1 2 3 4 4.5]), [1, 1, NaN, 5, 5])
***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmedian", 3, "samplepoints", [1.5 2 3 4 4.5]), [1, 1, 1, 5, 5])
***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmedian", [1.5, 1.5], "samplepoints", [1.5 2 3 4 5]), [1, 1, 1, 5, 5])
***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmedian", [1.5, 1.5], "samplepoints", [1 2 3 4 4.5]), [1, 1, 5, 5, 5])
***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmedian", [1.5, 1.5], "samplepoints", [1.5 2 3 4 4.5]), [1, 1, 3, 5, 5])
***** test
 x = reshape ([1:24], 3, 4, 2);
 x([1, 2, 5, 6, 8, 10, 13, 16, 18, 19, 20, 21, 22]) = NaN;
 y = x;
 y([2, 5, 8, 10, 13, 16, 18, 22]) = [3, 4, 8, 11, 14, 17, 17, 23];
 assert (fillmissing (x, "movmedian", 3), y);
 assert (fillmissing (x, "movmedian", [1, 1]), y);
 assert (fillmissing (x, "movmedian", 3, "endvalues", "extrap"), y);
 assert (fillmissing (x, "movmedian", 3, "samplepoints", [1, 2, 3]), y);
 y = x;
 y([1, 6, 8, 10, 18, 20, 21]) = [4, 6, 11, 7, 15, 20, 24];
 assert (fillmissing (x, "movmedian", 3, 2), y);
 assert (fillmissing (x, "movmedian", [1, 1], 2), y);
 assert (fillmissing (x, "movmedian", 3, 2, "endvalues", "extrap"), y);
 assert (fillmissing (x, "movmedian", 3, 2, "samplepoints", [1, 2, 3, 4]), y);
 y([1,18]) = NaN;
 y(6) = 9;
 assert (fillmissing (x, "movmedian", 3, 2, "samplepoints", [0, 2, 3, 4]), y);
 y = x;
 y([1, 2, 5, 6, 10, 13, 16, 18, 19, 20, 21, 22]) = 99;
 y(8) = 8;
 assert (fillmissing (x, "movmedian", 3, "endvalues", 99), y);
 y = x;
 y([1, 2, 5, 8, 10, 13, 16, 19, 22]) = 99;
 y([6, 18, 20, 21]) = [6, 15, 20, 24];
 assert (fillmissing (x, "movmedian", 3, 2, "endvalues", 99), y);
***** assert (fillmissing ([1, 2, 3], @(x,y,z) x+y+z, 2), [1, 2, 3])
***** assert (fillmissing ([1, 2, NaN], @(x,y,z) x+y+z, 1), [1, 2, NaN])
***** assert (fillmissing ([1, 2, 3], @(x,y,z) x+y+z, 2), [1, 2, 3])
***** assert (fillmissing ([1, 2, 3], @(x,y,z) x+y+z, [1, 0]), [1, 2, 3])
***** assert (fillmissing ([1, 2, 3]', @(x,y,z) x+y+z, 2), [1, 2, 3]')
***** assert (fillmissing ([1, 2, NaN], @(x,y,z) x+y+z, 2), [1, 2, 7])
***** assert (fillmissing ([1, 2, NaN], @(x,y,z) x+y+z, [1, 0]), [1, 2, 7])
***** assert (fillmissing ([1, 2, NaN], @(x,y,z) x+y+z, [1, 0]'), [1, 2, 7])
***** assert (fillmissing ([NaN, 2, NaN], @(x,y,z) x+y+z, 2), [5, 2, 7])
***** assert (fillmissing ([NaN, 2, NaN], @(x,y,z) x+y+z, [1, 0]), [NaN, 2, 7])
***** assert (fillmissing ([NaN, 2, NaN], @(x,y,z) x+y+z, [0, 1]), [5, 2, NaN])
***** assert (fillmissing ([NaN, 2, NaN], @(x,y,z) x+y+z, [0, 1.1]), [5, 2, NaN])
***** assert (fillmissing ([1, 2, NaN, NaN, 3, 4], @(x,y,z) x+y+z, 2), [1, 2, 7, 12, 3, 4])
***** assert (fillmissing ([1, 2, NaN, NaN, 3, 4], @(x,y,z) x+y+z, 0.5), [1, 2, NaN, NaN, 3, 4])
***** function A = testfcn (x, y, z)
  if (isempty (y))
    A = z;
  elseif (numel (y) == 1)
    A = repelem (x(1), numel(z));
  else
    A = interp1 (y, x, z, "linear", "extrap");
  endif
***** endfunction
***** assert (fillmissing ([1, NaN, 3, NaN, 5], @testfcn, [3, 0]), [1, 1, 3, NaN, 5])
***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], @testfcn, 3, 1), [1, 2, 6; 4, 2, 6])
***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], @testfcn, 3, 2), [1, 2, 2; 4, 5, 6])
***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], @testfcn, 3, 3), [1, 2, NaN; 4, NaN, 6])
***** assert (fillmissing ([1, NaN, 3, NaN, 5], @testfcn, 99), [1, 2, 3, 4, 5])
***** assert (fillmissing ([1, NaN, 3, NaN, 5], @testfcn, 99, 1), [1, NaN, 3, NaN, 5]) ##known not-compatible. matlab bug ML2022a: [1, 1, 3, 1, 5]
***** assert (fillmissing ([1, NaN, 3, NaN, 5]', @testfcn, 99, 1), [1, 2, 3, 4, 5]')
***** assert (fillmissing ([1, NaN, 3, NaN, 5], @testfcn, 99, 2), [1, 2, 3, 4, 5])
***** assert (fillmissing ([1, NaN, 3, NaN, 5]', @testfcn, 99, 2), [1, NaN, 3, NaN, 5]') ##known not-compatible. matlab bug ML2022a: [1, 1, 3, 1, 5]'
***** assert (fillmissing ([1, NaN, 3, NaN, 5], @testfcn, 99, 3), [1, NaN, 3, NaN, 5])
***** assert (fillmissing ([1, NaN, 3, NaN, 5]', @testfcn, 99, 3), [1, NaN, 3, NaN, 5]')
***** assert (fillmissing ([1, NaN, NaN, NaN, 5], @testfcn, 3, "samplepoints", [1, 2, 3, 4, 5]), [1, 2, 3, 4, 5])
***** assert (fillmissing ([1, NaN, NaN, NaN, 5], @testfcn, [1, 1], "samplepoints", [1, 2, 3, 4, 5]), [1, 2, 3, 4, 5])
***** assert (fillmissing ([1, NaN, NaN, NaN, 5], @testfcn, [1.5, 1.5], "samplepoints", [1, 2, 3, 4, 5]), [1, 2, 3, 4, 5])
***** assert (fillmissing ([1, NaN, NaN, NaN, 5], @testfcn, 4, "samplepoints", [1, 2, 3, 4, 5]), [1, 2, 3, 4, 5])
***** assert (fillmissing ([1, NaN, NaN, NaN, 5], @testfcn, [2, 2], "samplepoints", [1, 2, 3, 4, 5]), [1, 2, 3, 4, 5])
***** assert (fillmissing ([1, NaN, NaN, NaN, 5], @testfcn, 3, "samplepoints", [1, 2, 2.5, 3, 3.5]), [1, 2.6, 3.4, 4.2, 5], 10*eps)
***** assert (fillmissing ([NaN, NaN, 3, NaN, 5], @testfcn, 99, 1), [NaN, NaN, 3, NaN, 5]) ##known not-compatible. matlab bug ML2022a: [1, 1, 3, 1, 5]
***** test
***** function A = testfcn (x, y, z)
  if (isempty (y))
    A = z;
  elseif (numel (y) == 1)
    A = repelem (x(1), numel(z));
  else
    A = interp1 (y, x, z, "linear", "extrap");
  endif
***** endfunction
 x = reshape ([1:24], 3, 4, 2);
 x([1, 2, 5, 6, 8, 10, 13, 16, 18, 19, 20, 21, 22]) = NaN;
 y = x;
 y([1, 2, 5, 6, 8, 10, 13, 16, 18, 22]) = [3, 3, 4, 4, 8, 11, 14, 17, 17, 23];
 assert (fillmissing (x, @testfcn, 3), y);
 assert (fillmissing (x, @testfcn, [1, 1]), y);
 assert (fillmissing (x, @testfcn, 3, "endvalues", "extrap"), y);
 assert (fillmissing (x, @testfcn, 3, "samplepoints", [1, 2, 3]), y);
 y= x;
 y(isnan (x)) = 99;
 y(8) = 8;
 assert (fillmissing (x, @testfcn, 3, "endvalues", 99), y)
 y = x;
 y([1, 2, 5, 6, 8, 10, 18, 20, 21]) = [4, 11, 11, 6, 11, 7, 18, 20, 21];
 assert (fillmissing (x, @testfcn, 3, 2), y);
 assert (fillmissing (x, @testfcn, [1, 1], 2), y);
 assert (fillmissing (x, @testfcn, 3, 2, "endvalues", "extrap"), y);
 assert (fillmissing (x, @testfcn, 3, 2, "samplepoints", [1, 2, 3, 4]), y);
 y(1) = NaN;
 y([6, 18, 21]) = [9, 24, 24];
 assert (fillmissing (x, @testfcn, 3, 2, "samplepoints", [0, 2, 3, 4]), y);
 y = x;
 y([1, 2, 5, 6, 10, 13, 16, 18, 19, 20, 21, 22]) = 99;
 y(8) = 8;
 assert (fillmissing (x, @testfcn, 3, "endvalues", 99), y);
 y([6, 18, 20, 21]) = [6, 18, 20, 21];
 y(8) = 99;
 assert (fillmissing (x, @testfcn, 3, 2, "endvalues", 99), y);
 y([6, 18, 20, 21]) = 99;
 assert (fillmissing (x, @testfcn, 3, 3, "endvalues", 99), y);
***** assert (fillmissing ([1, 2, 3], "constant", 0, "maxgap", 1), [1, 2, 3])
***** assert (fillmissing ([1, 2, 3], "constant", 0, "maxgap", 99), [1, 2, 3])
***** assert (fillmissing ([1, NaN, 3], "constant", 0, "maxgap", 1), [1, NaN, 3])
***** assert (fillmissing ([1, NaN, 3], "constant", 0, "maxgap", 1.999), [1, NaN, 3])
***** assert (fillmissing ([1, NaN, 3], "constant", 0, "maxgap", 2), [1, 0, 3])
***** assert (fillmissing ([1, NaN, NaN, 4], "constant", 0, "maxgap", 2), [1, NaN, NaN, 4])
***** assert (fillmissing ([1, NaN, NaN, 4], "constant", 0, "maxgap", 3), [1, 0, 0, 4])
***** assert (fillmissing ([1, NaN, 3, NaN, 5], "constant", 0, "maxgap", 2), [1, 0, 3, 0, 5])
***** assert (fillmissing ([NaN, 2, NaN], "constant", 0, "maxgap", 0.999), [NaN, 2, NaN])
***** assert (fillmissing ([NaN, 2, NaN], "constant", 0, "maxgap", 1), [0, 2, 0])
***** assert (fillmissing ([NaN, 2, NaN, NaN], "constant", 0, "maxgap", 1), [0, 2, NaN, NaN])
***** assert (fillmissing ([NaN, 2, NaN, NaN], "constant", 0, "maxgap", 2), [0, 2, 0, 0])
***** assert (fillmissing ([NaN, NaN, NaN], "constant", 0, "maxgap", 1), [NaN, NaN, NaN])
***** assert (fillmissing ([NaN, NaN, NaN], "constant", 0, "maxgap", 3), [NaN, NaN, NaN])
***** assert (fillmissing ([NaN, NaN, NaN], "constant", 0, "maxgap", 999), [NaN, NaN, NaN])
***** assert (fillmissing ([1, NaN, 3, NaN, 5], "constant", 0, "maxgap", 2, "samplepoints", [0, 1, 2, 3, 5]), [1, 0, 3, NaN, 5])
***** assert (fillmissing ([1, NaN, 3, NaN, 5]', "constant", 0, "maxgap", 2, "samplepoints", [0, 1, 2, 3, 5]), [1, 0, 3, NaN, 5]')
***** assert (fillmissing ([1, NaN, 3, NaN, 5], "constant", 0, "maxgap", 2, "samplepoints", [0, 2, 3, 4, 5]), [1, NaN, 3, 0, 5])
***** assert (fillmissing ([1, NaN, 3, NaN, 5; 1, NaN, 3, NaN, 5], "constant", 0, 2, "maxgap", 2, "samplepoints", [0, 2, 3, 4, 5]), [1, NaN, 3, 0, 5; 1, NaN, 3, 0, 5])
***** test
 x = cat (3, [1, 2, NaN; 4, NaN, NaN], [NaN, 2, 3; 4, 5, NaN]);
 assert (fillmissing (x, "constant", 0, "maxgap", 0.1), x);
 y = x;
 y([4, 7, 12]) = 0;
 assert (fillmissing (x, "constant", 0, "maxgap", 1), y);
 assert (fillmissing (x, "constant", 0, 1, "maxgap", 1), y);
 y = x;
 y([5, 7, 12]) = 0;
 assert (fillmissing (x, "constant", 0, 2, "maxgap", 1), y);
 y = x;
 y([4, 5, 7]) = 0;
 assert (fillmissing (x, "constant", 0, 3, "maxgap", 1), y);
***** test
 x = cat (3, [1, 2, NaN; 4, NaN, NaN], [NaN, 2, 3; 4, 5, NaN]);
 [~, idx] = fillmissing (x, "constant", 0, "maxgap", 1);
 assert (idx, logical (cat (3, [0, 0, 0; 0, 1, 0], [1, 0, 0; 0, 0, 1])));
 [~, idx] = fillmissing (x, "constant", 0, 1, "maxgap", 1);
 assert (idx, logical (cat (3, [0, 0, 0; 0, 1, 0], [1, 0, 0; 0, 0, 1])));
 [~, idx] = fillmissing (x, "constant", 0, 2, "maxgap", 1);
 assert (idx, logical (cat (3, [0, 0, 1; 0, 0, 0], [1, 0, 0; 0, 0, 1])));
 [~, idx] = fillmissing (x, "constant", 0, 3, "maxgap", 1);
 assert (idx, logical (cat (3, [0, 0, 1; 0, 1, 0], [1, 0, 0; 0, 0, 0])));
***** test
 x = [NaN, 2, 3];
 [~, idx] = fillmissing (x, "previous");
 assert (idx, logical ([0, 0, 0]));
 [~, idx] = fillmissing (x, "movmean", 1);
 assert (idx, logical ([0, 0, 0]));
 x = [1:3; 4:6; 7:9];
 x([2, 4, 7, 9]) = NaN;
 [~, idx] = fillmissing (x, "linear");
 assert (idx, logical ([0, 1, 0; 1, 0, 0; 0, 0, 0]));
 [~, idx] = fillmissing (x, "movmean", 2);
 assert (idx, logical ([0, 0, 0; 1, 0, 0; 0, 0, 1]));
 [A, idx] = fillmissing ([1, 2, 3, NaN, NaN], "movmean",2);
 assert (A, [1, 2, 3, 3, NaN]);
 assert (idx, logical ([0, 0, 0, 1, 0]));
 [A, idx] = fillmissing ([1, 2, 3, NaN, NaN], "movmean",3);
 assert (A, [1, 2, 3, 3, NaN]);
 assert (idx, logical ([0, 0, 0, 1, 0]));
 [A, idx] = fillmissing ([1, 2, NaN, NaN, NaN], "movmedian", 2);
 assert (A, [1, 2, 2, NaN, NaN]);
 assert (idx, logical ([0, 0, 1, 0, 0]));
 [A, idx] = fillmissing ([1, 2, 3, NaN, NaN], "movmedian", 3);
 assert (A, [1, 2, 3, 3, NaN]);
 assert (idx, logical ([0, 0, 0, 1, 0]));
 [A, idx] = fillmissing ([1, NaN, 1, NaN, 1],  @(x,y,z) z, 3);
 assert (A, [1, 2, 1, 4, 1]);
 assert (idx, logical ([0, 1, 0, 1, 0]));
 [A, idx] = fillmissing ([1, NaN, 1, NaN, 1],  @(x,y,z) NaN (size (z)), 3);
 assert (A, [1, NaN, 1, NaN, 1]);
 assert (idx, logical ([0, 0, 0, 0, 0]));
***** assert (fillmissing ([1, 2, 3], "constant", 99, "missinglocations", logical ([0, 0, 0])), [1, 2, 3])
***** assert (fillmissing ([1, 2, 3], "constant", 99, "missinglocations", logical ([1, 1, 1])), [99, 99, 99])
***** assert (fillmissing ([1, NaN, 2, 3, NaN], "constant", 99, "missinglocations", logical ([1, 0, 1, 0, 1])), [99, NaN, 99, 3, 99])
***** assert (fillmissing ([1, NaN, 3, NaN, 5], "constant", NaN, "missinglocations", logical ([0, 1, 1, 1, 0])), [1, NaN, NaN, NaN, 5])
***** assert (fillmissing (["foo "; " bar"], "constant", "X", "missinglocations", logical ([0, 0, 0, 0; 0, 0, 0, 0])), ["foo "; " bar"])
***** assert (fillmissing (["foo "; " bar"], "constant", "X", "missinglocations", logical ([1, 0, 1, 0; 0, 1, 1, 0])), ["XoX "; " XXr"])
***** assert (fillmissing ({"foo", "", "bar"}, "constant", "X", "missinglocations", logical ([0, 0, 0])), {"foo", "", "bar"})
***** assert (fillmissing ({"foo", "", "bar"}, "constant", "X", "missinglocations", logical ([1, 1, 0])), {"X", "X", "bar"})
***** test
 [~, idx] = fillmissing ([1, NaN, 3, NaN, 5], "constant", NaN);
 assert (idx, logical ([0, 0, 0, 0, 0]));
 [~, idx] = fillmissing ([1 NaN 3 NaN 5], "constant", NaN, "missinglocations", logical ([0, 1, 1, 1, 0]));
 assert (idx, logical ([0, 1, 1, 1, 0]));
 [A, idx] = fillmissing ([1, 2, NaN, 1, NaN], "movmean", 3.1, "missinglocations", logical ([0, 0, 1, 1, 0]));
 assert (A, [1, 2, 2, NaN, NaN]);
 assert (idx, logical ([0, 0, 1, 0, 0]));
 [A, idx] = fillmissing ([1, 2, NaN, NaN, NaN], "movmean", 2, "missinglocations", logical ([0, 0, 1, 1, 0]));
 assert (A, [1, 2, 2, NaN, NaN]);
 assert (idx, logical ([0, 0, 1, 0, 0]));
 [A, idx] = fillmissing ([1, 2, NaN, 1, NaN], "movmean", 3, "missinglocations", logical ([0, 0, 1, 1, 0]));
 assert (A, [1, 2, 2, NaN, NaN]);
 assert (idx, logical ([0, 0, 1, 0, 0]));
 [A, idx] = fillmissing ([1, 2, NaN, NaN, NaN], "movmean", 3, "missinglocations", logical ([0, 0, 1, 1, 0]));
 assert (A, [1, 2, 2, NaN, NaN]);
 assert (idx, logical ([0, 0, 1, 0, 0]));
 [A, idx] = fillmissing ([1, 2, NaN, NaN, NaN], "movmedian", 2, "missinglocations", logical ([0, 0, 1, 1, 0]));
 assert (A, [1, 2, 2, NaN, NaN]);
 assert (idx, logical ([0, 0, 1, 0, 0]));
 [A, idx] = fillmissing ([1, 2, NaN, NaN, NaN], "movmedian", 3, "missinglocations", logical ([0, 0, 1, 1, 0]));
 assert (A, [1, 2, 2, NaN, NaN]);
 assert (idx, logical ([0, 0, 1, 0, 0]));
 [A, idx] = fillmissing ([1, 2, NaN, NaN, NaN], "movmedian", 3.1, "missinglocations", logical ([0, 0, 1, 1, 0]));
 assert (A, [1, 2, 2, NaN, NaN]);
 assert (idx, logical ([0, 0, 1, 0, 0]));
 [A, idx] = fillmissing ([1, NaN, 1, NaN, 1],  @(x,y,z) ones (size (z)), 3, "missinglocations", logical ([0, 1, 0, 1, 1]));
 assert (A, [1, 1, 1, 1, 1]);
 assert (idx, logical ([0, 1, 0, 1, 1]));
 [A, idx] = fillmissing ([1, NaN, 1, NaN, 1],  @(x,y,z) NaN (size (z)), 3, "missinglocations", logical ([0, 1, 0, 1, 1]));
 assert (A, [1, NaN, 1, NaN, NaN]);
 assert (idx, logical ([0, 0, 0, 0, 0]));
***** test
 [A, idx] = fillmissing ([1, 2, 5], "movmedian", 3, "missinglocations", logical ([0, 1, 0]));
 assert (A, [1, 3, 5]);
 assert (idx, logical ([0, 1, 0]));
***** assert (fillmissing (" foo bar ", "constant", "X"), "XfooXbarX")
***** assert (fillmissing ([" foo"; "bar "], "constant", "X"), ["Xfoo"; "barX"])
***** assert (fillmissing ([" foo"; "bar "], "next"), ["bfoo"; "bar "])
***** assert (fillmissing ([" foo"; "bar "], "next", 1), ["bfoo"; "bar "])
***** assert (fillmissing ([" foo"; "bar "], "previous"), [" foo"; "baro"])
***** assert (fillmissing ([" foo"; "bar "], "previous", 1), [" foo"; "baro"])
***** assert (fillmissing ([" foo"; "bar "], "nearest"), ["bfoo"; "baro"])
***** assert (fillmissing ([" foo"; "bar "], "nearest", 1), ["bfoo"; "baro"])
***** assert (fillmissing ([" foo"; "bar "], "next", 2), ["ffoo"; "bar "])
***** assert (fillmissing ([" foo"; "bar "], "previous", 2), [" foo"; "barr"])
***** assert (fillmissing ([" foo"; "bar "], "nearest", 2), ["ffoo"; "barr"])
***** assert (fillmissing ([" foo"; "bar "], "next", 3), [" foo"; "bar "])
***** assert (fillmissing ([" foo"; "bar "], "previous", 3), [" foo"; "bar "])
***** assert (fillmissing ([" foo"; "bar "], "nearest", 3), [" foo"; "bar "])
***** assert (fillmissing ({"foo", "bar"}, "constant", "a"), {"foo", "bar"})
***** assert (fillmissing ({"foo", "bar"}, "constant", {"a"}), {"foo", "bar"})
***** assert (fillmissing ({"foo", "", "bar"}, "constant", "a"), {"foo", "a", "bar"})
***** assert (fillmissing ({"foo", "", "bar"}, "constant", {"a"}), {"foo", "a", "bar"})
***** assert (fillmissing ({"foo", "", "bar"}, "previous"), {"foo", "foo", "bar"})
***** assert (fillmissing ({"foo", "", "bar"}, "next"), {"foo", "bar", "bar"})
***** assert (fillmissing ({"foo", "", "bar"}, "nearest"), {"foo", "bar", "bar"})
***** assert (fillmissing ({"foo", "", "bar"}, "previous", 2), {"foo", "foo", "bar"})
***** assert (fillmissing ({"foo", "", "bar"}, "next", 2), {"foo", "bar", "bar"})
***** assert (fillmissing ({"foo", "", "bar"}, "nearest", 2), {"foo", "bar", "bar"})
***** assert (fillmissing ({"foo", "", "bar"}, "previous", 1), {"foo", "", "bar"})
***** assert (fillmissing ({"foo", "", "bar"}, "previous", 1), {"foo", "", "bar"})
***** assert (fillmissing ({"foo", "", "bar"}, "next", 1), {"foo", "", "bar"})
***** assert (fillmissing ({"foo", "", "bar"}, "nearest", 1), {"foo", "", "bar"})
***** assert (fillmissing ("abc ", @(x,y,z) x+y+z, 2), "abcj")
***** assert (fillmissing ({"foo", "", "bar"}, @(x,y,z) x(1), 3), {"foo", "foo", "bar"})
***** test
 [A, idx] = fillmissing (" a b c", "constant", " ");
 assert (A, " a b c");
 assert (idx, logical ([0, 0, 0, 0, 0, 0]));
 [A, idx] = fillmissing ({"foo", "", "bar", ""}, "constant", "");
 assert (A, {"foo", "", "bar", ""});
 assert (idx, logical ([0, 0, 0, 0]));
 [A, idx] = fillmissing ({"foo", "", "bar", ""}, "constant", {""});
 assert (A, {"foo", "", "bar", ""});
 assert (idx, logical ([0, 0, 0, 0]));
 [A,idx] = fillmissing (" f o o ", @(x,y,z) repelem ("a", numel (z)), 3);
 assert (A, "afaoaoa");
 assert (idx, logical ([1, 0, 1, 0, 1, 0, 1]));
 [A,idx] = fillmissing (" f o o ", @(x,y,z) repelem (" ", numel (z)), 3);
 assert (A, " f o o ");
 assert (idx, logical ([0, 0, 0, 0, 0, 0, 0]));
 [A,idx] = fillmissing ({"", "foo", ""}, @(x,y,z) repelem ({"a"}, numel (z)), 3);
 assert (A, {"a", "foo", "a"});
 assert (idx, logical ([1, 0, 1]));
 [A,idx] = fillmissing ({"", "foo", ""}, @(x,y,z) repelem ({""}, numel (z)), 3);
 assert (A, {"", "foo", ""});
 assert (idx, logical ([0, 0, 0]));
***** assert (fillmissing (logical ([1, 0, 1, 0, 1]), "constant", true), logical ([1, 0, 1, 0, 1]))
***** assert (fillmissing (logical ([1, 0, 1, 0, 1]), "constant", false, "missinglocations", logical ([1, 0, 1, 0, 1])), logical ([0, 0, 0, 0, 0]))
***** assert (fillmissing (logical ([1, 0, 1, 0, 1]), "previous",  "missinglocations", logical ([1, 0, 1, 0, 1])), logical ([1, 0, 0, 0, 0]))
***** assert (fillmissing (logical ([1, 0, 1, 0, 1]), "next",  "missinglocations", logical ([1, 0, 1, 0, 1])), logical ([0, 0, 0, 0, 1]))
***** assert (fillmissing (logical ([1, 0, 1, 0, 1]), "nearest", "missinglocations", logical ([1, 0, 1, 0, 1])), logical ([0, 0, 0, 0, 0]))
***** assert (fillmissing (logical ([1, 0, 1, 0, 1]),  @(x,y,z) false(size(z)), 3), logical ([1, 0, 1, 0, 1]))
***** assert (fillmissing (logical ([1, 0, 1, 0, 1]),  @(x,y,z) false(size(z)), 3, "missinglocations", logical ([1, 0, 1, 0, 1])), logical ([0, 0, 0, 0, 0]))
***** assert (fillmissing (logical ([1, 0, 1, 0, 1]),  @(x,y,z) false(size(z)), [2, 0], "missinglocations", logical ([1, 0, 1, 0, 1])), logical ([1, 0, 0, 0, 0]))
***** test
 x = logical ([1, 0, 1, 0, 1]);
 [~, idx] = fillmissing (x, "constant", true);
 assert (idx, logical ([0, 0, 0, 0, 0]));
 [~, idx] = fillmissing (x, "constant", false, "missinglocations", logical ([1, 0, 1, 0, 1]));
 assert (idx, logical ([1, 0, 1, 0, 1]));
 [~, idx] = fillmissing (x, "constant", true, "missinglocations", logical ([1, 0, 1, 0, 1]));
 assert (idx, logical ([1, 0, 1, 0, 1]));
 [~, idx] = fillmissing (x, "previous", "missinglocations", logical ([1, 0, 1, 0, 1]));
 assert (idx, logical ([0, 0, 1, 0, 1]));
 [~, idx] = fillmissing (x, "next",  "missinglocations", logical ([1, 0, 1, 0, 1]));
 assert (idx, logical ([1, 0, 1, 0, 0]));
 [~, idx] = fillmissing (x, "nearest", "missinglocations", logical ([1, 0, 1, 0, 1]));
 assert (idx, logical ([1, 0, 1, 0, 1]));
 [~, idx] = fillmissing (x, @(x,y,z) false(size(z)), 3);
 assert (idx, logical ([0, 0, 0, 0, 0]))
 [~, idx] = fillmissing (x, @(x,y,z) false(size(z)), 3, "missinglocations", logical ([1, 0, 1, 0, 1]));
 assert (idx, logical ([1, 0, 1, 0, 1]))
 [~, idx] = fillmissing (x, @(x,y,z) false(size(z)), [2 0], "missinglocations", logical ([1, 0, 1, 0, 1]));
 assert (idx, logical ([0, 0, 1, 0, 1]))
***** assert (fillmissing (int32 ([1, 2, 3, 4, 5]), "constant", 0), int32 ([1, 2, 3, 4, 5]))
***** assert (fillmissing (int32 ([1, 2, 3, 4, 5]), "constant", 0, "missinglocations", logical ([1, 0, 1, 0, 1])), int32 ([0, 2, 0, 4, 0]))
***** assert (fillmissing (int32 ([1, 2, 3, 4, 5]), "previous", "missinglocations", logical ([1, 0, 1, 0, 1])), int32 ([1, 2, 2, 4, 4]))
***** assert (fillmissing (int32 ([1, 2, 3, 4, 5]), "next", "missinglocations", logical ([1, 0, 1, 0, 1])), int32 ([2, 2, 4, 4, 5]))
***** assert (fillmissing (int32 ([1, 2, 3, 4, 5]), "nearest", "missinglocations", logical ([1, 0, 1, 0, 1])), int32 ([2, 2, 4, 4, 4]))
***** assert (fillmissing (int32 ([1, 2, 3, 4, 5]), @(x,y,z) z+10, 3), int32 ([1, 2, 3, 4, 5]))
***** assert (fillmissing (int32 ([1, 2, 3, 4, 5]), @(x,y,z) z+10, 3, "missinglocations", logical ([1, 0, 1, 0, 1])), int32 ([11, 2, 13, 4, 15]))
***** assert (fillmissing (int32 ([1, 2, 3, 4, 5]), @(x,y,z) z+10, [2, 0], "missinglocations", logical ([1, 0, 1, 0, 1])), int32 ([1, 2, 13, 4, 15]))
***** test
 x = int32 ([1, 2, 3, 4, 5]);
 [~, idx] = fillmissing (x, "constant", 0);
 assert (idx, logical ([0, 0, 0, 0, 0]));
 [~, idx] = fillmissing (x, "constant", 0, "missinglocations", logical ([1, 0, 1, 0, 1]));
 assert (idx, logical ([1, 0, 1, 0, 1]));
 [~, idx] = fillmissing (x, "constant", 3, "missinglocations", logical ([0, 0, 1, 0, 0]));
 assert (idx, logical ([0, 0, 1, 0, 0]));
 [~, idx] = fillmissing (x, "previous", "missinglocations", logical ([1, 0, 1, 0, 1]));
 assert (idx, logical ([0, 0, 1, 0, 1]));
 [~, idx] = fillmissing (x, "next", "missinglocations", logical ([1, 0, 1, 0, 1]));
 assert (idx, logical ([1, 0, 1, 0, 0]));
 [~, idx] = fillmissing (x, "nearest", "missinglocations", logical ([1, 0, 1, 0, 1]));
 assert (idx, logical ([1, 0, 1, 0, 1]));
 [~, idx] = fillmissing (x, @(x,y,z) z+10, 3);
 assert (idx, logical ([0, 0, 0, 0, 0]));
 [~, idx] = fillmissing (x, @(x,y,z) z+10, 3, "missinglocations", logical ([1, 0, 1, 0, 1]));
 assert (idx, logical ([1, 0, 1, 0, 1]));
 [~, idx] = fillmissing (x, @(x,y,z) z+10, [2 0], "missinglocations", logical ([1, 0, 1, 0, 1]));
 assert (idx, logical ([0, 0, 1, 0, 1]));
***** test
 [A, idx] = fillmissing ([struct, struct], "constant", 1);
 assert (A, [struct, struct])
 assert (idx, [false, false])
***** error <Invalid call> fillmissing ()
***** error <Invalid call> fillmissing (1)
***** error <Invalid call> fillmissing (1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13)
***** error <second input must be a> fillmissing (1, 2)
***** error <unknown fill method 'foo'> fillmissing (1, "foo")
***** error <fill function must accept at least> fillmissing (1, @(x) x, 1)
***** error <fill function must accept at least> fillmissing (1, @(x,y) x+y, 1)
***** error <interpolation methods only valid for numeric> fillmissing ("a b c", "linear")
***** error <interpolation methods only valid for numeric> fillmissing ({"a", "b"}, "linear")
***** error <'movmean' and 'movmedian' methods only valid for numeric> fillmissing ("a b c", "movmean", 2)
***** error <'movmean' and 'movmedian' methods only valid for numeric> fillmissing ({"a", "b"}, "movmean", 2)
***** error <'constant' method must be followed by> fillmissing (1, "constant")
***** error <a numeric fill value cannot be emtpy> fillmissing (1, "constant", [])
***** error <fill value must be the same data type> fillmissing (1, "constant", "a")
***** error <fill value must be the same data type> fillmissing ("a", "constant", 1)
***** error <fill value must be the same data type> fillmissing ("a", "constant", {"foo"})
***** error <fill value must be the same data type> fillmissing ({"foo"}, "constant", 1)
***** error <moving window method must be followed by> fillmissing (1, "movmean")
***** error <moving window method must be followed by> fillmissing (1, "movmedian")
***** error <DIM must be a positive scalar> fillmissing (1, "constant", 1, 0)
***** error <DIM must be a positive scalar> fillmissing (1, "constant", 1, -1)
***** error <DIM must be a positive scalar> fillmissing (1, "constant", 1, [1, 2])
***** error <properties must be given as> fillmissing (1, "constant", 1, "samplepoints")
***** error <properties must be given as> fillmissing (1, "constant", 1, "foo")
***** error <properties must be given as> fillmissing (1, "constant", 1, 1, "foo")
***** error <invalid parameter name specified> fillmissing (1, "constant", 1, 2, {1}, 4)
***** error <SamplePoints must be a> fillmissing ([1, 2, 3], "constant", 1, 2, "samplepoints", [1, 2])
***** error <SamplePoints must be a> fillmissing ([1, 2, 3], "constant", 1, 2, "samplepoints", [3, 1, 2])
***** error <SamplePoints must be a> fillmissing ([1, 2, 3], "constant", 1, 2, "samplepoints", [1, 1, 2])
***** error <SamplePoints must be a> fillmissing ([1, 2, 3], "constant", 1, 2, "samplepoints", "abc")
***** error <SamplePoints must be a> fillmissing ([1, 2, 3], "constant", 1, 2, "samplepoints", logical ([1, 1, 1]))
***** error <SamplePoints must be a> fillmissing ([1, 2, 3], "constant", 1, 1, "samplepoints", [1, 2, 3])
***** error <EndValues method 'constant' only valid> fillmissing ("foo", "next", "endvalues", 1)
***** error <invalid EndValues method 'foo'> fillmissing (1, "constant", 1, 1, "endvalues", "foo")
***** error <EndValues must be a scalar or a 1 element array> fillmissing ([1, 2, 3], "constant", 1, 2, "endvalues", [1, 2, 3])
***** error <EndValues must be a scalar or a 3 element array> fillmissing ([1, 2, 3], "constant", 1, 1, "endvalues", [1, 2])
***** error <EndValues must be a scalar or a 12 element array> fillmissing (randi(5,4,3,2), "constant", 1, 3, "endvalues", [1, 2])
***** error <EndValues must be numeric or a> fillmissing (1, "constant", 1, 1, "endvalues", {1})
***** error <invalid parameter name 'foo'> fillmissing (1, "constant", 1, 2, "foo", 4)
***** error <MissingLocations option is not compatible with> fillmissing (struct, "constant", 1, "missinglocations", false)
***** error <MissingLocations and MaxGap options> fillmissing (1, "constant", 1, 2, "maxgap", 1, "missinglocations", false)
***** error <MissingLocations and MaxGap options> fillmissing (1, "constant", 1, 2, "missinglocations", false, "maxgap", 1)
***** error <the 'replacevalues' option has not> fillmissing (1, "constant", 1, "replacevalues", true)
***** error <the 'datavariables' option has not> fillmissing (1, "constant", 1, "datavariables", "Varname")
***** error <MissingLocations must be a> fillmissing (1, "constant", 1, 2, "missinglocations", 1)
***** error <MissingLocations must be a> fillmissing (1, "constant", 1, 2, "missinglocations", "a")
***** error <MissingLocations must be a> fillmissing (1, "constant", 1, 2, "missinglocations", [true, false])
***** error <MissingLocations cannot be used with method> fillmissing (true, "linear", "missinglocations", true)
***** error <MissingLocations cannot be used with method> fillmissing (int8 (1), "linear", "missinglocations", true)
***** error <MissingLocations cannot be used with EndValues method> fillmissing (true, "next", "missinglocations", true, "EndValues", "linear")
***** error <MissingLocations cannot be used with EndValues method> fillmissing (true, "next", "EndValues", "linear", "missinglocations", true)
***** error <MissingLocations cannot be used with EndValues method> fillmissing (int8 (1), "next", "missinglocations", true, "EndValues", "linear")
***** error <MissingLocations cannot be used with EndValues method> fillmissing (int8 (1), "next", "EndValues", "linear", "missinglocations", true)
***** error <MaxGap must be a positive numeric scalar> fillmissing (1, "constant", 1, 2, "maxgap", true)
***** error <MaxGap must be a positive numeric scalar> fillmissing (1, "constant", 1, 2, "maxgap", "a")
***** error <MaxGap must be a positive numeric scalar> fillmissing (1, "constant", 1, 2, "maxgap", [1, 2])
***** error <MaxGap must be a positive numeric scalar> fillmissing (1, "constant", 1, 2, "maxgap", 0)
***** error <MaxGap must be a positive numeric scalar> fillmissing (1, "constant", 1, 2, "maxgap", -1)
***** error <fill value 'V' must be a scalar or a 1> fillmissing ([1, 2, 3], "constant", [1, 2, 3])
***** error <fill value 'V' must be a scalar or a 1> fillmissing ([1, 2, 3]', "constant", [1, 2, 3])
***** error <fill value 'V' must be a scalar or a 1> fillmissing ([1, 2, 3]', "constant", [1, 2, 3], 1)
***** error <fill value 'V' must be a scalar or a 1> fillmissing ([1, 2, 3], "constant", [1, 2, 3], 2)
***** error <fill value 'V' must be a scalar or a 6> fillmissing (randi (5, 4, 3, 2), "constant", [1, 2], 1)
***** error <fill value 'V' must be a scalar or a 8> fillmissing (randi (5, 4, 3, 2), "constant", [1, 2], 2)
***** error <fill value 'V' must be a scalar or a 12> fillmissing (randi (5, 4, 3, 2), "constant", [1, 2], 3)
***** error <fill function handle must be followed by> fillmissing (1, @(x,y,z) x+y+z)
***** error <fill function return values must be the same size> fillmissing ([1, NaN, 2], @(x,y,z) [1, 2], 2)
380 tests, 379 passed, 0 known failure, 1 skipped
[inst/cophenet.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/cophenet.m
***** demo
 randn ("seed", 5)  # for reproducibility
 X = randn (10,2);
 y = pdist (X);
 Z = linkage (y, "average");
 cophenet (Z, y)
***** error<cophenet: function called with too few input arguments.> cophenet ()
***** error<cophenet: function called with too few input arguments.> cophenet (1)
***** error<cophenet: Z must be a matrix as generated by the linkage function.> ...
 cophenet (ones (2,2), 1)
***** error<cophenet: Y must be a vector of euclidean distances.> ...
 cophenet ([1 2 1], "a")
***** error<cophenet: Y must be a vector of euclidean distances.> ...
 cophenet ([1 2 1], [1 2])
5 tests, 5 passed, 0 known failure, 0 skipped
[inst/pcacov.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/pcacov.m
***** demo
 x = [ 7    26     6    60;
       1    29    15    52;
      11    56     8    20;
      11    31     8    47;
       7    52     6    33;
      11    55     9    22;
       3    71    17     6;
       1    31    22    44;
       2    54    18    22;
      21    47     4    26;
       1    40    23    34;
      11    66     9    12;
      10    68     8    12
     ];
 Kxx = cov (x);
 [coeff, latent, explained] = pcacov (Kxx)
***** test
 load hald
 Kxx = cov (ingredients);
 [coeff,latent,explained] = pcacov(Kxx);
 c_out = [-0.0678, -0.6460,  0.5673, 0.5062; ...
          -0.6785, -0.0200, -0.5440, 0.4933; ...
           0.0290,  0.7553,  0.4036, 0.5156; ...
           0.7309, -0.1085, -0.4684, 0.4844];
 l_out = [517.7969; 67.4964; 12.4054; 0.2372];
 e_out = [ 86.5974; 11.2882;  2.0747; 0.0397];
 assert (coeff, c_out, 1e-4);
 assert (latent, l_out, 1e-4);
 assert (explained, e_out, 1e-4);
***** error<pcacov: K must be a square matrix.> pcacov (ones (2,3))
***** error<pcacov: K must be a square matrix.> pcacov (ones (3,3,3))
3 tests, 3 passed, 0 known failure, 0 skipped
[inst/cl_multinom.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/cl_multinom.m
***** demo
 CL = cl_multinom ([27; 43; 19; 11], 10000, 0.05)
***** error<Invalid call to cl_multinom.  Correct usage> cl_multinom ();
***** error cl_multinom (1, 2, 3, 4, 5);
***** error<cl_multinom: argument method must be a string.> ...
 cl_multinom (1, 2, 3, 4);
***** error<cl_multinom: unknown calculation type.> ...
 cl_multinom (1, 2, 3, "some string");
4 tests, 4 passed, 0 known failure, 0 skipped
[inst/cdfcalc.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/cdfcalc.m
***** test
 x = [2, 4, 3, 2, 4, 3, 2, 5, 6, 4];
 [yCDF, xCDF, n, emsg, eid] = cdfcalc (x);
 assert (yCDF, [0, 0.3, 0.5, 0.8, 0.9, 1]');
 assert (xCDF, [2, 3, 4, 5, 6]');
 assert (n, 10);
***** shared x
 x = [2, 4, 3, 2, 4, 3, 2, 5, 6, 4];
***** error yCDF = cdfcalc (x);
***** error [yCDF, xCDF] = cdfcalc ();
***** error [yCDF, xCDF] = cdfcalc (x, x);
***** warning [yCDF, xCDF] = cdfcalc (ones(10,2));
5 tests, 5 passed, 0 known failure, 0 skipped
[inst/geomean.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/geomean.m
***** test
 x = [0:10];
 y = [x;x+5;x+10];
 assert (geomean (x), 0);
 m = [0 9.462942809849169 14.65658770861967];
 assert (geomean (y, 2), m', 4e-14);
 assert (geomean (y, "all"), 0);
 y(2,4) = NaN;
 m(2) = 9.623207231679554;
 assert (geomean (y, 2), [0 NaN m(3)]', 4e-14);
 assert (geomean (y', "omitnan"), m, 4e-14);
 z = y + 20;
 assert (geomean (z, "all"), NaN);
 assert (geomean (z, "all", "includenan"), NaN);
 assert (geomean (z, "all", "omitnan"), 29.59298474535024, 4e-14);
 m = [24.79790781765634 NaN 34.85638839503932];
 assert (geomean (z'), m, 4e-14);
 assert (geomean (z', "includenan"), m, 4e-14);
 m(2) = 30.02181156156319;
 assert (geomean (z', "omitnan"), m, 4e-14);
 assert (geomean (z, 2, "omitnan"), m', 4e-14);
***** test
 x = repmat ([1:20;6:25], [5 2 6 3]);
 assert (size (geomean (x, [3 2])), [10 1 1 3]);
 assert (size (geomean (x, [1 2])), [1 1 6 3]);
 assert (size (geomean (x, [1 2 4])), [1 1 6]);
 assert (size (geomean (x, [1 4 3])), [1 40]);
 assert (size (geomean (x, [1 2 3 4])), [1 1]);
***** test
 x = repmat ([1:20;6:25], [5 2 6 3]);
 m = repmat ([8.304361203739333;14.3078118884256], [5 1 1 3]);
 assert (geomean (x, [3 2]), m, 4e-13);
 x(2,5,6,3) = NaN;
 m(2,3) = NaN;
 assert (geomean (x, [3 2]), m, 4e-13);
 m(2,3) = 14.3292729579901;
 assert (geomean (x, [3 2], "omitnan"), m, 4e-13);
***** error <geomean: X must contain real nonnegative values.> geomean ("char")
***** error <geomean: X must contain real nonnegative values.> geomean ([1 -1 3])
***** error <geomean: DIM must be a positive integer scalar or vector.> ...
 geomean (repmat ([1:20;6:25], [5 2 6 3 5]), -1)
***** error <geomean: DIM must be a positive integer scalar or vector.> ...
 geomean (repmat ([1:20;6:25], [5 2 6 3 5]), 0)
***** error <geomean: VECDIM must contain non-repeating positive integers.> ...
 geomean (repmat ([1:20;6:25], [5 2 6 3 5]), [1 1])
8 tests, 8 passed, 0 known failure, 0 skipped
[inst/fitcknn.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/fitcknn.m
***** demo
 ## Train a k-nearest neighbor classifier for k = 10
 ## and plot the decision boundaries.

 load fisheriris
 idx = ! strcmp (species, "setosa");
 X = meas(idx,3:4);
 Y = cast (strcmpi (species(idx), "virginica"), "double");
 obj = fitcknn (X, Y, "Standardize", 1, "NumNeighbors", 10, "NSMethod", "exhaustive")
 x1 = [min(X(:,1)):0.03:max(X(:,1))];
 x2 = [min(X(:,2)):0.02:max(X(:,2))];
 [x1G, x2G] = meshgrid (x1, x2);
 XGrid = [x1G(:), x2G(:)];
 pred = predict (obj, XGrid);
 gidx = logical (str2num (cell2mat (pred)));

 figure
 scatter (XGrid(gidx,1), XGrid(gidx,2), "markerfacecolor", "magenta");
 hold on
 scatter (XGrid(!gidx,1), XGrid(!gidx,2), "markerfacecolor", "red");
 plot (X(Y == 0, 1), X(Y == 0, 2), "ko", X(Y == 1, 1), X(Y == 1, 2), "kx");
 xlabel ("Petal length (cm)");
 ylabel ("Petal width (cm)");
 title ("5-Nearest Neighbor Classifier Decision Boundary");
 legend ({"Versicolor Region", "Virginica Region", ...
         "Sampled Versicolor", "Sampled Virginica"}, ...
         "location", "northwest")
 axis tight
 hold off
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "b"; "b"];
 a = fitcknn (x, y);
 assert (class (a), "ClassificationKNN");
 assert ({a.X, a.Y, a.NumNeighbors}, {x, y, 1})
 assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
 assert ({a.BucketSize}, {50})
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "b"; "b"];
 a = fitcknn (x, y, "NSMethod", "exhaustive");
 assert (class (a), "ClassificationKNN");
 assert ({a.X, a.Y, a.NumNeighbors}, {x, y, 1})
 assert ({a.NSMethod, a.Distance}, {"exhaustive", "euclidean"})
 assert ({a.BucketSize}, {50})
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "b"; "b"];
 k = 10;
 a = fitcknn (x, y, "NumNeighbors" ,k);
 assert (class (a), "ClassificationKNN");
 assert ({a.X, a.Y, a.NumNeighbors}, {x, y, 10})
 assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
 assert ({a.BucketSize}, {50})
***** test
 x = ones (4, 11);
 y = ["a"; "a"; "b"; "b"];
 k = 10;
 a = fitcknn (x, y, "NumNeighbors" ,k);
 assert (class (a), "ClassificationKNN");
 assert ({a.X, a.Y, a.NumNeighbors}, {x, y, 10})
 assert ({a.NSMethod, a.Distance}, {"exhaustive", "euclidean"})
 assert ({a.BucketSize}, {50})
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "b"; "b"];
 k = 10;
 a = fitcknn (x, y, "NumNeighbors" ,k, "NSMethod", "exhaustive");
 assert (class (a), "ClassificationKNN");
 assert ({a.X, a.Y, a.NumNeighbors}, {x, y, 10})
 assert ({a.NSMethod, a.Distance}, {"exhaustive", "euclidean"})
 assert ({a.BucketSize}, {50})
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "b"; "b"];
 k = 10;
 a = fitcknn (x, y, "NumNeighbors" ,k, "Distance", "hamming");
 assert (class (a), "ClassificationKNN");
 assert ({a.X, a.Y, a.NumNeighbors}, {x, y, 10})
 assert ({a.NSMethod, a.Distance}, {"exhaustive", "hamming"})
 assert ({a.BucketSize}, {50})
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "b"; "b"];
 weights = ones (4,1);
 a = fitcknn (x, y, "Standardize", 1);
 assert (class (a), "ClassificationKNN");
 assert ({a.X, a.Y, a.NumNeighbors}, {x, y, 1})
 assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
 assert ({a.Standardize}, {true})
 assert ({a.Sigma}, {std(x, [], 1)})
 assert ({a.Mu}, {[3.75, 4.25, 4.75]})
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "b"; "b"];
 weights = ones (4,1);
 a = fitcknn (x, y, "Standardize", false);
 assert (class (a), "ClassificationKNN");
 assert ({a.X, a.Y, a.NumNeighbors}, {x, y, 1})
 assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
 assert ({a.Standardize}, {false})
 assert ({a.Sigma}, {[]})
 assert ({a.Mu}, {[]})
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "b"; "b"];
 s = ones (1, 3);
 a = fitcknn (x, y, "Scale" , s, "Distance", "seuclidean");
 assert (class (a), "ClassificationKNN");
 assert ({a.DistParameter}, {s})
 assert ({a.NSMethod, a.Distance}, {"exhaustive", "seuclidean"})
 assert ({a.BucketSize}, {50})
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "b"; "b"];
 a = fitcknn (x, y, "Exponent" , 5, "Distance", "minkowski");
 assert (class (a), "ClassificationKNN");
 assert (a.DistParameter, 5)
 assert ({a.NSMethod, a.Distance}, {"kdtree", "minkowski"})
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "b"; "b"];
 a = fitcknn (x, y, "Exponent" , 5, "Distance", "minkowski", ...
                    "NSMethod", "exhaustive");
 assert (class (a), "ClassificationKNN");
 assert (a.DistParameter, 5)
 assert ({a.NSMethod, a.Distance}, {"exhaustive", "minkowski"})
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "b"; "b"];
 a = fitcknn (x, y, "BucketSize" , 20, "distance", "mahalanobis");
 assert (class (a), "ClassificationKNN");
 assert ({a.NSMethod, a.Distance}, {"exhaustive", "mahalanobis"})
 assert ({a.BucketSize}, {20})
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "b"; "b"];
 a = fitcknn (x, y, "IncludeTies", true);
 assert (class (a), "ClassificationKNN");
 assert (a.IncludeTies, true);
 assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "b"; "b"];
 a = fitcknn (x, y);
 assert (class (a), "ClassificationKNN");
 assert (a.IncludeTies, false);
 assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "b"; "b"];
 a = fitcknn (x, y);
 assert (class (a), "ClassificationKNN")
 assert (a.Prior, [0.5; 0.5])
 assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
 assert ({a.BucketSize}, {50})
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "b"; "b"];
 prior = [0.5; 0.5];
 a = fitcknn (x, y, "Prior", "empirical");
 assert (class (a), "ClassificationKNN")
 assert (a.Prior, prior)
 assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
 assert ({a.BucketSize}, {50})
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "a"; "b"];
 prior = [0.75; 0.25];
 a = fitcknn (x, y, "Prior", "empirical");
 assert (class (a), "ClassificationKNN")
 assert (a.Prior, prior)
 assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
 assert ({a.BucketSize}, {50})
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "a"; "b"];
 prior = [0.5; 0.5];
 a = fitcknn (x, y, "Prior", "uniform");
 assert (class (a), "ClassificationKNN")
 assert (a.Prior, prior)
 assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
 assert ({a.BucketSize}, {50})
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "b"; "b"];
 cost = eye (2);
 a = fitcknn (x, y, "Cost", cost);
 assert (class (a), "ClassificationKNN")
 assert (a.Cost, [1, 0; 0, 1])
 assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
 assert ({a.BucketSize}, {50})
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "b"; "b"];
 cost = eye (2);
 a = fitcknn (x, y, "Cost", cost, "Distance", "hamming" );
 assert (class (a), "ClassificationKNN")
 assert (a.Cost, [1, 0; 0, 1])
 assert ({a.NSMethod, a.Distance}, {"exhaustive", "hamming"})
 assert ({a.BucketSize}, {50})
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "b"; "b"];
 a = fitcknn (x, y, "NSMethod", "exhaustive", "CrossVal", "on");
 assert (class (a), "ClassificationPartitionedModel");
 assert ({a.X, a.Y, a.Trained{1}.NumNeighbors}, {x, y, 1})
 assert (a.ModelParameters.NSMethod, "exhaustive")
 assert (a.ModelParameters.Distance, "euclidean")
 assert ({a.Trained{1}.BucketSize}, {50})
***** error<fitcknn: too few arguments.> fitcknn ()
***** error<fitcknn: too few arguments.> fitcknn (ones (4,1))
***** error<fitcknn: Name-Value arguments must be in pairs.>
 fitcknn (ones (4,2), ones (4, 1), "K")
***** error<fitcknn: number of rows in X and Y must be equal.>
 fitcknn (ones (4,2), ones (3, 1))
***** error<fitcknn: number of rows in X and Y must be equal.>
 fitcknn (ones (4,2), ones (3, 1), "K", 2)
***** error <fitcknn: 'CrossVal' must be either 'off' or 'on'.>
 fitcknn (ones (4,2), ones (4, 1), "CrossVal", 2)
***** error <fitcknn: 'CrossVal' must be either 'off' or 'on'.>
 fitcknn (ones (4,2), ones (4, 1), "CrossVal", 'a')
***** error <fitcknn: You can use only one cross-validation name-value pair argument> ...
 fitcknn (ones (4,2), ones (4, 1), "KFold", 10, "Holdout", 0.3)
29 tests, 29 passed, 0 known failure, 0 skipped
[inst/vartest2.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/vartest2.m
***** error<vartest2: too few input arguments.> vartest2 ();
***** error<vartest2: too few input arguments.> vartest2 (ones (20,1));
***** error<vartest2: X and Y must be vectors or matrices or N-D arrays.> ...
 vartest2 (rand (20,1), 5);
***** error<vartest2: invalid value for alpha.> ...
 vartest2 (rand (20,1), rand (25,1)*2, "alpha", 0);
***** error<vartest2: invalid value for alpha.> ...
 vartest2 (rand (20,1), rand (25,1)*2, "alpha", 1.2);
***** error<vartest2: invalid value for alpha.> ...
 vartest2 (rand (20,1), rand (25,1)*2, "alpha", "some");
***** error<vartest2: invalid value for alpha.> ...
 vartest2 (rand (20,1), rand (25,1)*2, "alpha", [0.05, 0.001]);
***** error<vartest2: invalid value for tail.> ...
 vartest2 (rand (20,1), rand (25,1)*2, "tail", [0.05, 0.001]);
***** error<vartest2: invalid value for tail.> ...
 vartest2 (rand (20,1), rand (25,1)*2, "tail", "some");
***** error<vartest2: invalid value for operating dimension.> ...
 vartest2 (rand (20,1), rand (25,1)*2, "dim", 3);
***** error<vartest2: invalid value for operating dimension.> ...
 vartest2 (rand (20,1), rand (25,1)*2, "alpha", 0.001, "dim", 3);
***** error<vartest2: invalid name for optional arguments.> ...
 vartest2 (rand (20,1), rand (25,1)*2, "some", 3);
***** error<vartest2: optional arguments must be in name/value pairs.> ...
 vartest2 (rand (20,1), rand (25,1)*2, "some");
***** test
 load carsmall
 [h, pval, ci, stat] = vartest2 (MPG(Model_Year==82), MPG(Model_Year==76));
 assert (h, 0);
 assert (pval, 0.6288022362718455, 1e-13);
 assert (ci, [0.4139; 1.7193], 1e-4);
 assert (stat.fstat, 0.8384, 1e-4);
 assert (stat.df1, 30);
 assert (stat.df2, 33);
14 tests, 14 passed, 0 known failure, 0 skipped
[inst/bar3h.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/bar3h.m
***** demo
 ## Ploting 5 bars in the same series.

 y = [50; 40; 30; 20; 10];
 bar3h (y);
***** demo
 ## Ploting 5 bars in different groups.

 y = [50, 40, 30, 20, 10];
 bar3h (y);
***** demo
 ## A 3D bar graph with each series corresponding to a column in y.

 y = [1, 4, 7; 2, 5, 8; 3, 6, 9; 4, 7, 10];
 bar3h (y);
***** demo
 ## Specify z-axis locations as tick names. z must be a column vector!

 z = [1950, 1960, 1970, 1980, 1990]';
 y = [16, 8, 4, 2, 1]';
 bar3h (z, y);
***** demo
 ## Plot 3 series as a grouped plot without any space between the grouped bars

 y = [70 50 33 10; 75 55 35 15; 80 60 40 20];
 bar3h (y, 1, 'grouped');
***** demo
 ## Plot a stacked style 3D bar graph

 y = [19, 30, 21, 30; 40, 16, 32, 12];
 b = bar3h (y, 0.5, 'stacked');
***** error <bar3h: Z must be numeric.> bar3h ("A")
***** error <bar3h: Z must be numeric.> bar3h ({2,3,4,5})
***** error <bar3h: inconsistent size in Y and Z input arguments.> ...
 bar3h ([1,2,3]', ones (2))
***** error <bar3h: WIDTH must be a scalar in the range> ...
 bar3h ([1:5], 1.2)
***** error <bar3h: WIDTH must be a scalar in the range> ...
 bar3h ([1:5]', ones (5), 1.2)
***** error <bar3h: numeric COLOR must be a 1x3 vector of an Nx3 matrix> ...
 bar3h ([1:5]', ones (5), [0.8, 0.7])
***** error <bar3h: missing value for optional argument 'width'.> ...
 bar3h (ones (5), 'width')
***** error <bar3h: invalid value for optional argument 'width'.> ...
 bar3h (ones (5), 'width', 1.2)
***** error <bar3h: invalid value for optional argument 'width'.> ...
 bar3h (ones (5), 'width', [0.8, 0.8, 0.8])
***** error <bar3h: missing value for optional argument 'color'.> ...
 bar3h (ones (5), 'color')
***** error <bar3h: numeric COLOR must be a 1x3 vector of an Nx3 matrix> ...
 bar3h (ones (5), 'color', [0.8, 0.8])
***** error <bar3h: invalid value for optional argument 'color'.> ...
 bar3h (ones (5), 'color', "brown")
***** error <bar3h: invalid value for optional argument 'color'.> ...
 bar3h (ones (5), 'color', {"r", "k", "c", "m", "brown"})
***** error <bar3h: missing value for optional argument 'xlabel'.> ...
 bar3h (ones (5), 'xlabel')
***** error <bar3h: invalid value for optional argument 'xlabel'.> ...
 bar3h (ones (5), 'xlabel', 4)
***** error <bar3h: missing value for optional argument 'zlabel'.> ...
 bar3h (ones (5), 'zlabel')
***** error <bar3h: invalid value for optional argument 'zlabel'.> ...
 bar3h (ones (5), 'zlabel', 4)
***** error <bar3h: invalid optional argument.> bar3h (ones (5), 'this', 4)
***** error <bar3h: the elements in 'xlabel' must equal the columns in Z.> ...
 bar3h (ones (5), 'xlabel', {"A", "B", "C"})
***** error <bar3h: the elements in 'zlabel' must equal the rows in Z.> ...
 bar3h (ones (5), 'zlabel', {"A", "B", "C"})
20 tests, 20 passed, 0 known failure, 0 skipped
[inst/kstest2.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/kstest2.m
***** error kstest2 ([1,2,3,4,5,5])
***** error kstest2 (ones(2,4), [1,2,3,4,5,5])
***** error kstest2 ([2,3,5,7,3+3i], [1,2,3,4,5,5])
***** error kstest2 ([2,3,4,5,6],[3;5;7;8;7;6;5],"tail")
***** error kstest2 ([2,3,4,5,6],[3;5;7;8;7;6;5],"tail", "whatever")
***** error kstest2 ([2,3,4,5,6],[3;5;7;8;7;6;5],"badoption", 0.51)
***** error kstest2 ([2,3,4,5,6],[3;5;7;8;7;6;5],"tail", 0)
***** error kstest2 ([2,3,4,5,6],[3;5;7;8;7;6;5],"alpha", 0)
***** error kstest2 ([2,3,4,5,6],[3;5;7;8;7;6;5],"alpha", NaN)
***** error kstest2 ([NaN,NaN,NaN,NaN,NaN],[3;5;7;8;7;6;5],"tail", "unequal")
***** test
 load examgrades
 [h, p] = kstest2 (grades(:,1), grades(:,2));
 assert (h, false);
 assert (p, 0.1222791870137312, 1e-14);
***** test
 load examgrades
 [h, p] = kstest2 (grades(:,1), grades(:,2), "tail", "larger");
 assert (h, false);
 assert (p, 0.1844421391011258, 1e-14);
***** test
 load examgrades
 [h, p] = kstest2 (grades(:,1), grades(:,2), "tail", "smaller");
 assert (h, false);
 assert (p, 0.06115357930171663, 1e-14);
***** test
 load examgrades
 [h, p] = kstest2 (grades(:,1), grades(:,2), "tail", "smaller", "alpha", 0.1);
 assert (h, true);
 assert (p, 0.06115357930171663, 1e-14);
14 tests, 14 passed, 0 known failure, 0 skipped
[inst/monotone_smooth.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/monotone_smooth.m
***** error<Invalid call to monotone_smooth.  Correct usage is> ...
 monotone_smooth (1)
***** error<monotone_smooth: X must be a numeric vector.> ...
 monotone_smooth ("char", 1)
***** error<monotone_smooth: X must be a numeric vector.> ...
 monotone_smooth ({1,2,3}, 1)
***** error<monotone_smooth: X must be a numeric vector.> ...
 monotone_smooth (ones(20,3), 1)
***** error<monotone_smooth: Y must be a numeric vector.> ...
 monotone_smooth (1, "char")
***** error<monotone_smooth: Y must be a numeric vector.> ...
 monotone_smooth (1, {1,2,3})
***** error<monotone_smooth: Y must be a numeric vector.> ...
 monotone_smooth (1, ones(20,3))
***** error<monotone_smooth: H>  monotone_smooth (ones (10,1), ones(10,1), [1, 2])
***** error<monotone_smooth: H>  monotone_smooth (ones (10,1), ones(10,1), {2})
***** error<monotone_smooth: H>  monotone_smooth (ones (10,1), ones(10,1), "char")
10 tests, 10 passed, 0 known failure, 0 skipped
[inst/ranksum.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/ranksum.m
***** test
 mileage = [33.3, 34.5, 37.4; 33.4, 34.8, 36.8; ...
            32.9, 33.8, 37.6; 32.6, 33.4, 36.6; ...
            32.5, 33.7, 37.0; 33.0, 33.9, 36.7];
 [p,h,stats] = ranksum(mileage(:,1),mileage(:,2));
 assert (p, 0.004329004329004329, 1e-14);
 assert (h, true);
 assert (stats.ranksum, 21.5);
***** test
 year1 = [51 52 62 62 52 52 51 53 59 63 59 56 63 74 68 86 82 70 69 75 73 ...
          49 47 50 60 59 60 62 61 71]';
 year2 = [54 53 64 66 57 53 54 54 62 66 59 59 67 76 75 86 82 67 74 80 75 ...
          54 50 53 62 62 62 72 60 67]';
 [p,h,stats] = ranksum(year1, year2, "alpha", 0.01, "tail", "left");
 assert (p, 0.1270832752950605, 1e-14);
 assert (h, false);
 assert (stats.ranksum, 837.5);
 assert (stats.zval, -1.140287483634606, 1e-14);
 [p,h,stats] = ranksum(year1, year2, "alpha", 0.01, "tail", "left", ...
                       "method", "exact");
 assert (p, 0.127343916432862, 1e-14);
 assert (h, false);
 assert (stats.ranksum, 837.5);
2 tests, 2 passed, 0 known failure, 0 skipped
[inst/optimalleaforder.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/optimalleaforder.m
***** demo
 randn ("seed", 5)  # for reproducibility
 X = randn (10, 2);
 D = pdist (X);
 tree = linkage(D, 'average');
 optimalleaforder (tree, D, 'Transformation', 'linear')
***** error optimalleaforder ()
***** error optimalleaforder (1)
***** error <tree must be .*> optimalleaforder (ones (2, 2), 1)
***** error <character inputs expected> optimalleaforder ([1 2 3], [1 2; 3 4], "criteria", 5)
***** error <D must be .*> optimalleaforder ([1 2 1], [1 2 3])
***** error <unknown property .*> optimalleaforder ([1 2 1], 1, "xxx", "xxx")
***** error optimalleaforder ([1 2 1], 1, "Transformation", "xxx")
7 tests, 7 passed, 0 known failure, 0 skipped
[inst/chi2test.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/chi2test.m
***** error chi2test ();
***** error chi2test ([1, 2, 3, 4, 5]);
***** error chi2test ([1, 2; 2, 1+3i]);
***** error chi2test ([NaN, 6; 34, 12]);
***** error<chi2test: optional arguments are not supported for 2-way> ...
 p = chi2test (ones (3, 3), "mutual", []);
***** error<chi2test: invalid model name for testing a 3-way table.> ...
 p = chi2test (ones (3, 3, 3), "testtype", 2);
***** error<chi2test: optional arguments must be in pairs.> ...
 p = chi2test (ones (3, 3, 3), "mutual");
***** error<chi2test: value must be numeric in optional argument> ...
 p = chi2test (ones (3, 3, 3), "joint", ["a"]);
***** error<chi2test: value must be empty or scalar in optional argument> ...
 p = chi2test (ones (3, 3, 3), "joint", [2, 3]);
***** error<chi2test: optional arguments are not supported for k> ...
 p = chi2test (ones (3, 3, 3, 4), "mutual", [])
***** warning<chi2test: Expected values less than 5.> p = chi2test (ones (2));
***** warning<chi2test: Expected values less than 5.> p = chi2test (ones (3, 2));
***** warning<chi2test: Expected values less than 1.> p = chi2test (0.4 * ones (3));
***** test
 x = [11, 3, 8; 2, 9, 14; 12, 13, 28];
 p = chi2test (x);
 assert (p, 0.017787, 1e-6);
***** test
 x = [11, 3, 8; 2, 9, 14; 12, 13, 28];
 [p, chisq] = chi2test (x);
 assert (chisq, 11.9421, 1e-4);
***** test
 x = [11, 3, 8; 2, 9, 14; 12, 13, 28];
 [p, chisq, df] = chi2test (x);
 assert (df, 4);
***** test
***** shared x
 x(:,:,1) = [59, 32; 9,16];
 x(:,:,2) = [55, 24;12,33];
 x(:,:,3) = [107,80;17,56];%!
***** assert (chi2test (x), 2.282063427117009e-11, 1e-14);
***** assert (chi2test (x, "mutual", []), 2.282063427117009e-11, 1e-14);
***** assert (chi2test (x, "joint", 1), 1.164834895206468e-11, 1e-14);
***** assert (chi2test (x, "joint", 2), 7.771350230001417e-11, 1e-14);
***** assert (chi2test (x, "joint", 3), 0.07151361728026107, 1e-14);
***** assert (chi2test (x, "marginal", 1), 0, 1e-14);
***** assert (chi2test (x, "marginal", 2), 6.347555814301131e-11, 1e-14);
***** assert (chi2test (x, "marginal", 3), 0, 1e-14);
***** assert (chi2test (x, "conditional", 1), 0.2303114201312508, 1e-14);
***** assert (chi2test (x, "conditional", 2), 0.0958810684407079, 1e-14);
***** assert (chi2test (x, "conditional", 3), 2.648037344954446e-11, 1e-14);
***** assert (chi2test (x, "homogeneous", []), 0.4485579470993741, 1e-14);
***** test
 [pval, chisq, df, E] = chi2test (x);
 assert (chisq, 64.0982, 1e-4);
 assert (df, 7);
 assert (E(:,:,1), [42.903, 39.921; 17.185, 15.991], ones (2, 2) * 1e-3);
***** test
 [pval, chisq, df, E] = chi2test (x, "joint", 2);
 assert (chisq, 56.0943, 1e-4);
 assert (df, 5);
 assert (E(:,:,2), [40.922, 23.310; 38.078, 21.690], ones (2, 2) * 1e-3);
***** test
 [pval, chisq, df, E] = chi2test (x, "marginal", 3);
 assert (chisq, 146.6058, 1e-4);
 assert (df, 9);
 assert (E(:,1,1), [61.642; 57.358], ones (2, 1) * 1e-3);
***** test
 [pval, chisq, df, E] = chi2test (x, "conditional", 3);
 assert (chisq, 52.2509, 1e-4);
 assert (df, 3);
 assert (E(:,:,1), [53.345, 37.655; 14.655, 10.345], ones (2, 2) * 1e-3);
***** test
 [pval, chisq, df, E] = chi2test (x, "homogeneous", []);
 assert (chisq, 1.6034, 1e-4);
 assert (df, 2);
 assert (E(:,:,1), [60.827, 31.382; 7.173, 16.618], ones (2, 2) * 1e-3);
34 tests, 34 passed, 0 known failure, 0 skipped
[inst/mcnemar_test.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/mcnemar_test.m
***** test
 [h, pval, chisq] = mcnemar_test ([101,121;59,33]);
 assert (h, 1);
 assert (pval, 3.8151e-06, 1e-10);
 assert (chisq, 21.356, 1e-3);
***** test
 [h, pval, chisq] = mcnemar_test ([59,6;16,80]);
 assert (h, 1);
 assert (pval, 0.034690, 1e-6);
 assert (isempty (chisq), true);
***** test
 [h, pval, chisq] = mcnemar_test ([59,6;16,80], 0.01);
 assert (h, 0);
 assert (pval, 0.034690, 1e-6);
 assert (isempty (chisq), true);
***** test
 [h, pval, chisq] = mcnemar_test ([59,6;16,80], "mid-p");
 assert (h, 1);
 assert (pval, 0.034690, 1e-6);
 assert (isempty (chisq), true);
***** test
 [h, pval, chisq] = mcnemar_test ([59,6;16,80], "asymptotic");
 assert (h, 1);
 assert (pval, 0.033006, 1e-6);
 assert (chisq, 4.5455, 1e-4);
***** test
 [h, pval, chisq] = mcnemar_test ([59,6;16,80], "exact");
 assert (h, 0);
 assert (pval, 0.052479, 1e-6);
 assert (isempty (chisq), true);
***** test
 [h, pval, chisq] = mcnemar_test ([59,6;16,80], "corrected");
 assert (h, 0);
 assert (pval, 0.055009, 1e-6);
 assert (chisq, 3.6818, 1e-4);
***** test
 [h, pval, chisq] = mcnemar_test ([59,6;16,80], 0.1, "corrected");
 assert (h, 1);
 assert (pval, 0.055009, 1e-6);
 assert (chisq, 3.6818, 1e-4);
***** error<mcnemar_test: too many input arguments.> mcnemar_test (59, 6, 16, 80)
***** error<mcnemar_test: X must be a 2x2 matrix.> mcnemar_test (ones (3, 3))
***** error<mcnemar_test: all entries of X must be non-negative integers.> ...
 mcnemar_test ([59,6;16,-80])
***** error<mcnemar_test: all entries of X must be non-negative integers.> ...
 mcnemar_test ([59,6;16,4.5])
***** error<mcnemar_test: invalid 2nd input argument.> ...
 mcnemar_test ([59,6;16,80], {""})
***** error<mcnemar_test: invalid value for ALPHA.> ...
 mcnemar_test ([59,6;16,80], -0.2)
***** error<mcnemar_test: invalid value for ALPHA.> ...
 mcnemar_test ([59,6;16,80], [0.05, 0.1])
***** error<mcnemar_test: invalid value for ALPHA.> ...
 mcnemar_test ([59,6;16,80], 1)
***** error<mcnemar_test: invalid value for TESTTYPE.> ...
 mcnemar_test ([59,6;16,80], "")
17 tests, 17 passed, 0 known failure, 0 skipped
[inst/vartest.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/vartest.m
***** error<vartest: too few input arguments.> vartest ();
***** error<vartest: invalid value for variance.> vartest ([1, 2, 3, 4], -0.5);
***** error<vartest: invalid value for alpha.> ...
 vartest ([1, 2, 3, 4], 1, "alpha", 0);
***** error<vartest: invalid value for alpha.> ...
 vartest ([1, 2, 3, 4], 1, "alpha", 1.2);
***** error<vartest: invalid value for alpha.> ...
 vartest ([1, 2, 3, 4], 1, "alpha", "val");
***** error<vartest: invalid value for tail.>  ...
 vartest ([1, 2, 3, 4], 1, "tail", "val");
***** error<vartest: invalid value for tail.>  ...
 vartest ([1, 2, 3, 4], 1, "alpha", 0.01, "tail", "val");
***** error<vartest: invalid value for operating dimension.> ...
 vartest ([1, 2, 3, 4], 1, "dim", 3);
***** error<vartest: invalid value for operating dimension.> ...
 vartest ([1, 2, 3, 4], 1, "alpha", 0.01, "tail", "both", "dim", 3);
***** error<vartest: invalid name for optional arguments.> ...
 vartest ([1, 2, 3, 4], 1, "alpha", 0.01, "tail", "both", "badoption", 3);
***** error<vartest: optional arguments must be in name/value pairs.> ...
 vartest ([1, 2, 3, 4], 1, "alpha", 0.01, "tail");
***** test
 load carsmall
 [h, pval, ci] = vartest (MPG, 7^2);
 assert (h, 1);
 assert (pval, 0.04335086742174443, 1e-14);
 assert (ci, [49.397; 88.039], 1e-3);
12 tests, 12 passed, 0 known failure, 0 skipped
[inst/crossval.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/crossval.m
***** test
 load fisheriris
 y = meas(:, 1);
 X = [ones(size(y)) meas(:, 2:4)];
 f = @(X1, y1, X2, y2) meansq (y2 - X2*regress(y1, X1));
 results0 = crossval (f, X, y);
 results1 = crossval (f, X, y, 'KFold', 10);
 folds = 5;
 results2 = crossval (f, X, y, 'KFold', folds);
 results3 = crossval (f, X, y, 'Partition', cvpartition (numel (y), 'KFold', folds));
 results4 = crossval (f, X, y, 'LeaveOut', 1);
 mcreps = 2; n_holdout = 20;
 results5 = crossval (f, X, y, 'HoldOut', n_holdout, 'mcreps', mcreps);

 ## ensure equal representation of iris species in the training set -- tends
 ## to slightly reduce cross-validation mean square error
 results6 = crossval (f, X, y, 'KFold', 5, 'stratify', grp2idx(species));

 assert (results0, results1, 2e-15);
 assert (results2, results3, 5e-17);
 assert (size(results4), [1 numel(y)]);
 assert (mean(results4), 0.1018, 1e-4);
 assert (size(results5), [mcreps 1]);
warning: strmatch is obsolete; use strncmp or strcmp instead
1 test, 1 passed, 0 known failure, 0 skipped
[inst/dist_stat/loglstat.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_stat/loglstat.m
***** error<loglstat: function called with too few input arguments.> loglstat ()
***** error<loglstat: function called with too few input arguments.> loglstat (1)
***** error<loglstat: MU and SIGMA must be numeric.> loglstat ({}, 2)
***** error<loglstat: MU and SIGMA must be numeric.> loglstat (1, "")
***** error<loglstat: MU and SIGMA must not be complex.> loglstat (i, 2)
***** error<loglstat: MU and SIGMA must not be complex.> loglstat (1, i)
***** error<loglstat: MU and SIGMA must be of common size or scalars.> ...
 loglstat (ones (3), ones (2))
***** error<loglstat: MU and SIGMA must be of common size or scalars.> ...
 loglstat (ones (2), ones (3))
***** test
 [m, v] = loglstat (0, 1);
 assert (m, Inf, 0.001);
 assert (v, Inf, 0.001);
***** test
 [m, v] = loglstat (0, 0.8);
 assert (m, 4.2758, 0.001);
 assert (v, Inf, 0.001);
***** test
 [m, v] = loglstat (0, 0.6);
 assert (m, 1.9820, 0.001);
 assert (v, Inf, 0.001);
***** test
 [m, v] = loglstat (0, 0.4);
 assert (m, 1.3213, 0.001);
 assert (v, 2.5300, 0.001);
***** test
 [m, v] = loglstat (0, 0.2);
 assert (m, 1.0690, 0.001);
 assert (v, 0.1786, 0.001);
13 tests, 13 passed, 0 known failure, 0 skipped
[inst/dist_stat/fstat.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_stat/fstat.m
***** error<fstat: function called with too few input arguments.> fstat ()
***** error<fstat: function called with too few input arguments.> fstat (1)
***** error<fstat: DF1 and DF2 must be numeric.> fstat ({}, 2)
***** error<fstat: DF1 and DF2 must be numeric.> fstat (1, "")
***** error<fstat: DF1 and DF2 must not be complex.> fstat (i, 2)
***** error<fstat: DF1 and DF2 must not be complex.> fstat (1, i)
***** error<fstat: DF1 and DF2 must be of common size or scalars.> ...
 fstat (ones (3), ones (2))
***** error<fstat: DF1 and DF2 must be of common size or scalars.> ...
 fstat (ones (2), ones (3))
***** test
 df1 = 1:6;
 df2 = 5:10;
 [m, v] = fstat (df1, df2);
 expected_mn = [1.6667, 1.5000, 1.4000, 1.3333, 1.2857, 1.2500];
 expected_v = [22.2222, 6.7500, 3.4844, 2.2222, 1.5869, 1.2153];
 assert (m, expected_mn, 0.001);
 assert (v, expected_v, 0.001);
***** test
 df1 = 1:6;
 [m, v] = fstat (df1, 5);
 expected_mn = [1.6667, 1.6667, 1.6667, 1.6667, 1.6667, 1.6667];
 expected_v = [22.2222, 13.8889, 11.1111, 9.7222, 8.8889, 8.3333];
 assert (m, expected_mn, 0.001);
 assert (v, expected_v, 0.001);
10 tests, 10 passed, 0 known failure, 0 skipped
[inst/dist_stat/gpstat.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_stat/gpstat.m
***** error<gpstat: function called with too few input arguments.> gpstat ()
***** error<gpstat: function called with too few input arguments.> gpstat (1)
***** error<gpstat: function called with too few input arguments.> gpstat (1, 2)
***** error<gpstat: K, SIGMA, and MU must be numeric.> gpstat ({}, 2, 3)
***** error<gpstat: K, SIGMA, and MU must be numeric.> gpstat (1, "", 3)
***** error<gpstat: K, SIGMA, and MU must be numeric.> gpstat (1, 2, "")
***** error<gpstat: K, SIGMA, and MU must not be complex.> gpstat (i, 2, 3)
***** error<gpstat: K, SIGMA, and MU must not be complex.> gpstat (1, i, 3)
***** error<gpstat: K, SIGMA, and MU must not be complex.> gpstat (1, 2, i)
***** error<gpstat: K, SIGMA, and MU must be of common size or scalars.> ...
 gpstat (ones (3), ones (2), 3)
***** error<gpstat: K, SIGMA, and MU must be of common size or scalars.> ...
 gpstat (ones (2), 2, ones (3))
***** error<gpstat: K, SIGMA, and MU must be of common size or scalars.> ...
 gpstat (1, ones (2), ones (3))
***** shared x, y
 x = [-Inf, -1, 0, 1/2, 1, Inf];
 y = [0, 0.5, 1, 2, Inf, Inf];
***** assert (gpstat (x, ones (1,6), zeros (1,6)), y, eps)
***** assert (gpstat (single (x), 1, 0), single (y), eps("single"))
***** assert (gpstat (x, single (1), 0), single (y), eps("single"))
***** assert (gpstat (x, 1, single (0)), single (y), eps("single"))
***** assert (gpstat (single ([x, NaN]), 1, 0), single ([y, NaN]), eps("single"))
***** assert (gpstat ([x, NaN], single (1), 0), single ([y, NaN]), eps("single"))
***** assert (gpstat ([x, NaN], 1, single (0)), single ([y, NaN]), eps("single"))
19 tests, 19 passed, 0 known failure, 0 skipped
[inst/dist_stat/nakastat.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_stat/nakastat.m
***** error<nakastat: function called with too few input arguments.> nakastat ()
***** error<nakastat: function called with too few input arguments.> nakastat (1)
***** error<nakastat: MU and OMEGA must be numeric.> nakastat ({}, 2)
***** error<nakastat: MU and OMEGA must be numeric.> nakastat (1, "")
***** error<nakastat: MU and OMEGA must not be complex.> nakastat (i, 2)
***** error<nakastat: MU and OMEGA must not be complex.> nakastat (1, i)
***** error<nakastat: MU and OMEGA must be of common size or scalars.> ...
 nakastat (ones (3), ones (2))
***** error<nakastat: MU and OMEGA must be of common size or scalars.> ...
 nakastat (ones (2), ones (3))
***** test
 [m, v] = nakastat (1, 1);
 assert (m, 0.8862269254, 1e-10);
 assert (v, 0.2146018366, 1e-10);
***** test
 [m, v] = nakastat (1, 2);
 assert (m, 1.25331413731, 1e-10);
 assert (v, 0.42920367321, 1e-10);
***** test
 [m, v] = nakastat (2, 1);
 assert (m, 0.93998560299, 1e-10);
 assert (v, 0.11642706618, 1e-10);
11 tests, 11 passed, 0 known failure, 0 skipped
[inst/dist_stat/ncfstat.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_stat/ncfstat.m
***** error<ncfstat: function called with too few input arguments.> ncfstat ()
***** error<ncfstat: function called with too few input arguments.> ncfstat (1)
***** error<ncfstat: function called with too few input arguments.> ncfstat (1, 2)
***** error<ncfstat: DF1, DF2, and LAMBDA must be numeric.> ncfstat ({}, 2, 3)
***** error<ncfstat: DF1, DF2, and LAMBDA must be numeric.> ncfstat (1, "", 3)
***** error<ncfstat: DF1, DF2, and LAMBDA must be numeric.> ncfstat (1, 2, "")
***** error<ncfstat: DF1, DF2, and LAMBDA must not be complex.> ncfstat (i, 2, 3)
***** error<ncfstat: DF1, DF2, and LAMBDA must not be complex.> ncfstat (1, i, 3)
***** error<ncfstat: DF1, DF2, and LAMBDA must not be complex.> ncfstat (1, 2, i)
***** error<ncfstat: DF1, DF2, and LAMBDA must be of common size or scalars.> ...
 ncfstat (ones (3), ones (2), 3)
***** error<ncfstat: DF1, DF2, and LAMBDA must be of common size or scalars.> ...
 ncfstat (ones (2), 2, ones (3))
***** error<ncfstat: DF1, DF2, and LAMBDA must be of common size or scalars.> ...
 ncfstat (1, ones (2), ones (3))
***** shared df1, df2, lambda
 df1 = [2, 0, -1, 1, 4, 5];
 df2 = [2, 4, -1, 5, 6, 7];
 lambda = [1, NaN, 3, 0, 2, -1];
***** assert (ncfstat (df1, df2, lambda), [NaN, NaN, NaN, 1.6667, 2.25, 1.12], 1e-4);
***** assert (ncfstat (df1(4:6), df2(4:6), 1), [3.3333, 1.8750, 1.6800], 1e-4);
***** assert (ncfstat (df1(4:6), df2(4:6), 2), [5.0000, 2.2500, 1.9600], 1e-4);
***** assert (ncfstat (df1(4:6), df2(4:6), 3), [6.6667, 2.6250, 2.2400], 1e-4);
***** assert (ncfstat (2, [df2(1), df2(4:6)], 5), [NaN,5.8333,5.2500,4.9000], 1e-4);
***** assert (ncfstat (0, [df2(1), df2(4:6)], 5), [NaN, Inf, Inf, Inf]);
***** assert (ncfstat (1, [df2(1), df2(4:6)], 5), [NaN, 10, 9, 8.4], 1e-14);
***** assert (ncfstat (4, [df2(1), df2(4:6)], 5), [NaN, 3.75, 3.375, 3.15], 1e-14);
20 tests, 20 passed, 0 known failure, 0 skipped
[inst/dist_stat/tristat.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_stat/tristat.m
***** error<tristat: function called with too few input arguments.> tristat ()
***** error<tristat: function called with too few input arguments.> tristat (1)
***** error<tristat: function called with too few input arguments.> tristat (1, 2)
***** error<tristat: A, B, and C must be numeric.> tristat ("i", 2, 1)
***** error<tristat: A, B, and C must be numeric.> tristat (0, "d", 1)
***** error<tristat: A, B, and C must be numeric.> tristat (0, 3, {})
***** error<tristat: A, B, and C must be real.> tristat (i, 2, 1)
***** error<tristat: A, B, and C must be real.> tristat (0, i, 1)
***** error<tristat: A, B, and C must be real.> tristat (0, 3, i)
***** test
 a = 1:5;
 b = 3:7;
 c = 5:9;
 [m, v] = tristat (a, b, c);
 expected_m = [3, 4, 5, 6, 7];
 assert (m, expected_m);
 assert (v, ones (1, 5) * (2/3));
10 tests, 10 passed, 0 known failure, 0 skipped
[inst/dist_stat/gevstat.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_stat/gevstat.m
***** error<gevstat: function called with too few input arguments.> gevstat ()
***** error<gevstat: function called with too few input arguments.> gevstat (1)
***** error<gevstat: function called with too few input arguments.> gevstat (1, 2)
***** error<gevstat: K, SIGMA, and MU must be numeric.> gevstat ({}, 2, 3)
***** error<gevstat: K, SIGMA, and MU must be numeric.> gevstat (1, "", 3)
***** error<gevstat: K, SIGMA, and MU must be numeric.> gevstat (1, 2, "")
***** error<gevstat: K, SIGMA, and MU must not be complex.> gevstat (i, 2, 3)
***** error<gevstat: K, SIGMA, and MU must not be complex.> gevstat (1, i, 3)
***** error<gevstat: K, SIGMA, and MU must not be complex.> gevstat (1, 2, i)
***** error<gevstat: K, SIGMA, and MU must be of common size or scalars.> ...
 gevstat (ones (3), ones (2), 3)
***** error<gevstat: K, SIGMA, and MU must be of common size or scalars.> ...
 gevstat (ones (2), 2, ones (3))
***** error<gevstat: K, SIGMA, and MU must be of common size or scalars.> ...
 gevstat (1, ones (2), ones (3))
***** test
 k = [-1, -0.5, 0, 0.2, 0.4, 0.5, 1];
 sigma = 2;
 mu = 1;
 [m, v] = gevstat (k, sigma, mu);
 expected_m = [1, 1.4551, 2.1544, 2.6423, 3.4460, 4.0898, Inf];
 expected_v = [4, 3.4336, 6.5797, 13.3761, 59.3288, Inf, Inf];
 assert (m, expected_m, -0.001);
 assert (v, expected_v, -0.001);
13 tests, 13 passed, 0 known failure, 0 skipped
[inst/dist_stat/unidstat.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_stat/unidstat.m
***** error<unidstat: function called with too few input arguments.> unidstat ()
***** error<unidstat: N must be numeric.> unidstat ({})
***** error<unidstat: N must be numeric.> unidstat ("")
***** error<unidstat: N must not be complex.> unidstat (i)
***** test
 N = 1:6;
 [m, v] = unidstat (N);
 expected_m = [1.0000, 1.5000, 2.0000, 2.5000, 3.0000, 3.5000];
 expected_v = [0.0000, 0.2500, 0.6667, 1.2500, 2.0000, 2.9167];
 assert (m, expected_m, 0.001);
 assert (v, expected_v, 0.001);
5 tests, 5 passed, 0 known failure, 0 skipped
[inst/dist_stat/plstat.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_stat/plstat.m
***** shared x, Fx
 x = [0, 1, 3, 4, 7, 10];
 Fx = [0, 0.2, 0.5, 0.6, 0.7, 1];
***** assert (plstat (x, Fx), 4.15)
***** test
 [m, v] = plstat (x, Fx);
 assert (v, 10.3775, 1e-14)
***** error<plstat: function called with too few input arguments.> plstat ()
***** error<plstat: function called with too few input arguments.> plstat (1)
***** error<plstat: X and FX must be vectors of equal size.> ...
 plstat ([0, 1, 2], [0, 1])
***** error<plstat: X and FX must be at least two-elements long.> ...
 plstat ([0], [1])
***** error<plstat: FX must be bounded in the range> ...
 plstat ([0, 1, 2], [0, 1, 1.5])
***** error<plstat: FX must be bounded in the range> ...
 plstat ([0, 1, 2], [0, i, 1])
***** error<plstat: X and FX must not be complex.> ...
 plstat ([0, i, 2], [0, 0.5, 1])
***** error<plstat: X and FX must not be complex.> ...
 plstat ([0, i, 2], [0, 0.5i, 1])
10 tests, 10 passed, 0 known failure, 0 skipped
[inst/dist_stat/nbinstat.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_stat/nbinstat.m
***** error<nbinstat: function called with too few input arguments.> nbinstat ()
***** error<nbinstat: function called with too few input arguments.> nbinstat (1)
***** error<nbinstat: R and PS must be numeric.> nbinstat ({}, 2)
***** error<nbinstat: R and PS must be numeric.> nbinstat (1, "")
***** error<nbinstat: R and PS must not be complex.> nbinstat (i, 2)
***** error<nbinstat: R and PS must not be complex.> nbinstat (1, i)
***** error<nbinstat: R and PS must be of common size or scalars.> ...
 nbinstat (ones (3), ones (2))
***** error<nbinstat: R and PS must be of common size or scalars.> ...
 nbinstat (ones (2), ones (3))
***** test
 r = 1:4;
 ps = 0.2:0.2:0.8;
 [m, v] = nbinstat (r, ps);
 expected_m = [ 4.0000, 3.0000, 2.0000, 1.0000];
 expected_v = [20.0000, 7.5000, 3.3333, 1.2500];
 assert (m, expected_m, 0.001);
 assert (v, expected_v, 0.001);
***** test
 r = 1:4;
 [m, v] = nbinstat (r, 0.5);
 expected_m = [1, 2, 3, 4];
 expected_v = [2, 4, 6, 8];
 assert (m, expected_m, 0.001);
 assert (v, expected_v, 0.001);
10 tests, 10 passed, 0 known failure, 0 skipped
[inst/dist_stat/betastat.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_stat/betastat.m
***** error<betastat: function called with too few input arguments.> betastat ()
***** error<betastat: function called with too few input arguments.> betastat (1)
***** error<betastat: A and B must be numeric.> betastat ({}, 2)
***** error<betastat: A and B must be numeric.> betastat (1, "")
***** error<betastat: A and B must not be complex.> betastat (i, 2)
***** error<betastat: A and B must not be complex.> betastat (1, i)
***** error<betastat: A and B must be of common size or scalars.> ...
 betastat (ones (3), ones (2))
***** error<betastat: A and B must be of common size or scalars.> ...
 betastat (ones (2), ones (3))
***** test
 a = -2:6;
 b = 0.4:0.2:2;
 [m, v] = betastat (a, b);
 expected_m = [NaN NaN NaN 1/2 2/3.2 3/4.4 4/5.6 5/6.8 6/8];
 expected_v = [NaN NaN NaN 0.0833, 0.0558, 0.0402, 0.0309, 0.0250, 0.0208];
 assert (m, expected_m, eps*100);
 assert (v, expected_v, 0.001);
***** test
 a = -2:1:6;
 [m, v] = betastat (a, 1.5);
 expected_m = [NaN NaN NaN 1/2.5 2/3.5 3/4.5 4/5.5 5/6.5 6/7.5];
 expected_v = [NaN NaN NaN 0.0686, 0.0544, 0.0404, 0.0305, 0.0237, 0.0188];
 assert (m, expected_m);
 assert (v, expected_v, 0.001);
***** test
 a = [14  Inf   10  NaN  10];
 b = [12    9  NaN  Inf  12];
 [m, v] = betastat (a, b);
 expected_m = [14/26 NaN NaN NaN 10/22];
 expected_v = [168/18252 NaN NaN NaN 120/11132];
 assert (m, expected_m);
 assert (v, expected_v);
***** assert (nthargout (1:2, @betastat, 5, []), {[], []})
***** assert (nthargout (1:2, @betastat, [], 5), {[], []})
***** assert (size (betastat (rand (10, 5, 4), rand (10, 5, 4))), [10 5 4])
***** assert (size (betastat (rand (10, 5, 4), 7)), [10 5 4])
15 tests, 15 passed, 0 known failure, 0 skipped
[inst/dist_stat/tlsstat.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_stat/tlsstat.m
***** error<tlsstat: function called with too few input arguments.> tlsstat ()
***** error<tlsstat: function called with too few input arguments.> tlsstat (1)
***** error<tlsstat: function called with too few input arguments.> tlsstat (1, 2)
***** error<tlsstat: MU, SIGMA, and NU must be numeric.> tlsstat ({}, 2, 3)
***** error<tlsstat: MU, SIGMA, and NU must be numeric.> tlsstat (1, "", 3)
***** error<tlsstat: MU, SIGMA, and NU must be numeric.> tlsstat (1, 2, ["d"])
***** error<tlsstat: MU, SIGMA, and NU must not be complex.> tlsstat (i, 2, 3)
***** error<tlsstat: MU, SIGMA, and NU must not be complex.> tlsstat (1, i, 3)
***** error<tlsstat: MU, SIGMA, and NU must not be complex.> tlsstat (1, 2, i)
***** error<tlsstat: MU, SIGMA, and NU must be of common size or scalars.> ...
 tlsstat (ones (3), ones (2), 1)
***** error<tlsstat: MU, SIGMA, and NU must be of common size or scalars.> ...
 tlsstat (ones (2), 1, ones (3))
***** error<tlsstat: MU, SIGMA, and NU must be of common size or scalars.> ...
 tlsstat (1, ones (2), ones (3))
***** test
 [m, v] = tlsstat (0, 1, 0);
 assert (m, NaN);
 assert (v, NaN);
***** test
 [m, v] = tlsstat (0, 1, 1);
 assert (m, NaN);
 assert (v, NaN);
***** test
 [m, v] = tlsstat (2, 1, 1);
 assert (m, NaN);
 assert (v, NaN);
***** test
 [m, v] = tlsstat (-2, 1, 1);
 assert (m, NaN);
 assert (v, NaN);
***** test
 [m, v] = tlsstat (0, 1, 2);
 assert (m, 0);
 assert (v, NaN);
***** test
 [m, v] = tlsstat (2, 1, 2);
 assert (m, 2);
 assert (v, NaN);
***** test
 [m, v] = tlsstat (-2, 1, 2);
 assert (m, -2);
 assert (v, NaN);
***** test
 [m, v] = tlsstat (0, 2, 2);
 assert (m, 0);
 assert (v, NaN);
***** test
 [m, v] = tlsstat (2, 2, 2);
 assert (m, 2);
 assert (v, NaN);
***** test
 [m, v] = tlsstat (-2, 2, 2);
 assert (m, -2);
 assert (v, NaN);
***** test
 [m, v] = tlsstat (0, 1, 3);
 assert (m, 0);
 assert (v, 3);
***** test
 [m, v] = tlsstat (0, 2, 3);
 assert (m, 0);
 assert (v, 6);
***** test
 [m, v] = tlsstat (2, 1, 3);
 assert (m, 2);
 assert (v, 3);
***** test
 [m, v] = tlsstat (2, 2, 3);
 assert (m, 2);
 assert (v, 6);
***** test
 [m, v] = tlsstat (-2, 1, 3);
 assert (m, -2);
 assert (v, 3);
***** test
 [m, v] = tlsstat (-2, 2, 3);
 assert (m, -2);
 assert (v, 6);
28 tests, 28 passed, 0 known failure, 0 skipped
[inst/dist_stat/chi2stat.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_stat/chi2stat.m
***** error<chi2stat: function called with too few input arguments.> chi2stat ()
***** error<chi2stat: DF must be numeric.> chi2stat ({})
***** error<chi2stat: DF must be numeric.> chi2stat ("")
***** error<chi2stat: DF must not be complex.> chi2stat (i)
***** test
 df = 1:6;
 [m, v] = chi2stat (df);
 assert (m, df);
 assert (v, [2, 4, 6, 8, 10, 12], 0.001);
5 tests, 5 passed, 0 known failure, 0 skipped
[inst/dist_stat/expstat.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_stat/expstat.m
***** error<expstat: function called with too few input arguments.> expstat ()
***** error<expstat: MU must be numeric.> expstat ({})
***** error<expstat: MU must be numeric.> expstat ("")
***** error<expstat: MU must not be complex.> expstat (i)
***** test
 mu = 1:6;
 [m, v] = expstat (mu);
 assert (m, [1, 2, 3, 4, 5, 6], 0.001);
 assert (v, [1, 4, 9, 16, 25, 36], 0.001);
5 tests, 5 passed, 0 known failure, 0 skipped
[inst/dist_stat/logistat.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_stat/logistat.m
***** error<logistat: function called with too few input arguments.> logistat ()
***** error<logistat: function called with too few input arguments.> logistat (1)
***** error<logistat: MU and SIGMA must be numeric.> logistat ({}, 2)
***** error<logistat: MU and SIGMA must be numeric.> logistat (1, "")
***** error<logistat: MU and SIGMA must not be complex.> logistat (i, 2)
***** error<logistat: MU and SIGMA must not be complex.> logistat (1, i)
***** error<logistat: MU and SIGMA must be of common size or scalars.> ...
 logistat (ones (3), ones (2))
***** error<logistat: MU and SIGMA must be of common size or scalars.> ...
 logistat (ones (2), ones (3))
***** test
 [m, v] = logistat (0, 1);
 assert (m, 0);
 assert (v, 3.2899, 0.001);
***** test
 [m, v] = logistat (0, 0.8);
 assert (m, 0);
 assert (v, 2.1055, 0.001);
***** test
 [m, v] = logistat (1, 0.6);
 assert (m, 1);
 assert (v, 1.1844, 0.001);
***** test
 [m, v] = logistat (0, 0.4);
 assert (m, 0);
 assert (v, 0.5264, 0.001);
***** test
 [m, v] = logistat (-1, 0.2);
 assert (m, -1);
 assert (v, 0.1316, 0.001);
13 tests, 13 passed, 0 known failure, 0 skipped
[inst/dist_stat/burrstat.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_stat/burrstat.m
***** error<burrstat: function called with too few input arguments.> burrstat ()
***** error<burrstat: function called with too few input arguments.> burrstat (1)
***** error<burrstat: function called with too few input arguments.> burrstat (1, 2)
***** error<burrstat: LAMBDA, C, and K must be numeric.> burrstat ({}, 2, 3)
***** error<burrstat: LAMBDA, C, and K must be numeric.> burrstat (1, "", 3)
***** error<burrstat: LAMBDA, C, and K must be numeric.> burrstat (1, 2, "")
***** error<burrstat: LAMBDA, C, and K must not be complex.> burrstat (i, 2, 3)
***** error<burrstat: LAMBDA, C, and K must not be complex.> burrstat (1, i, 3)
***** error<burrstat: LAMBDA, C, and K must not be complex.> burrstat (1, 2, i)
***** error<burrstat: LAMBDA, C, and K must be of common size or scalars.> ...
 burrstat (ones (3), ones (2), 3)
***** error<burrstat: LAMBDA, C, and K must be of common size or scalars.> ...
 burrstat (ones (2), 2, ones (3))
***** error<burrstat: LAMBDA, C, and K must be of common size or scalars.> ...
 burrstat (1, ones (2), ones (3))
***** test
 [m, v] = burrstat (1, 2, 5);
 assert (m, 0.4295, 1e-4);
 assert (v, 0.0655, 1e-4);
***** test
 [m, v] = burrstat (1, 1, 1);
 assert (m, Inf);
 assert (v, Inf);
***** test
 [m, v] = burrstat (2, 4, 1);
 assert (m, 2.2214, 1e-4);
 assert (v, 1.3484, 1e-4);
15 tests, 15 passed, 0 known failure, 0 skipped
[inst/dist_stat/normstat.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_stat/normstat.m
***** error<normstat: function called with too few input arguments.> normstat ()
***** error<normstat: function called with too few input arguments.> normstat (1)
***** error<normstat: MU and SIGMA must be numeric.> normstat ({}, 2)
***** error<normstat: MU and SIGMA must be numeric.> normstat (1, "")
***** error<normstat: MU and SIGMA must not be complex.> normstat (i, 2)
***** error<normstat: MU and SIGMA must not be complex.> normstat (1, i)
***** error<normstat: MU and SIGMA must be of common size or scalars.> ...
 normstat (ones (3), ones (2))
***** error<normstat: MU and SIGMA must be of common size or scalars.> ...
 normstat (ones (2), ones (3))
***** test
 mu = 1:6;
 sigma = 0.2:0.2:1.2;
 [m, v] = normstat (mu, sigma);
 expected_v = [0.0400, 0.1600, 0.3600, 0.6400, 1.0000, 1.4400];
 assert (m, mu);
 assert (v, expected_v, 0.001);
***** test
 sigma = 0.2:0.2:1.2;
 [m, v] = normstat (0, sigma);
 expected_mn = [0, 0, 0, 0, 0, 0];
 expected_v = [0.0400, 0.1600, 0.3600, 0.6400, 1.0000, 1.4400];
 assert (m, expected_mn, 0.001);
 assert (v, expected_v, 0.001);
10 tests, 10 passed, 0 known failure, 0 skipped
[inst/dist_stat/ricestat.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_stat/ricestat.m
***** error<ricestat: function called with too few input arguments.> ricestat ()
***** error<ricestat: function called with too few input arguments.> ricestat (1)
***** error<ricestat: S and SIGMA must be numeric.> ricestat ({}, 2)
***** error<ricestat: S and SIGMA must be numeric.> ricestat (1, "")
***** error<ricestat: S and SIGMA must not be complex.> ricestat (i, 2)
***** error<ricestat: S and SIGMA must not be complex.> ricestat (1, i)
***** error<ricestat: S and SIGMA must be of common size or scalars.> ...
 ricestat (ones (3), ones (2))
***** error<ricestat: S and SIGMA must be of common size or scalars.> ...
 ricestat (ones (2), ones (3))
***** shared s, sigma
 s = [2, 0, -1, 1, 4];
 sigma = [1, NaN, 3, -1, 2];
***** assert (ricestat (s, sigma), [2.2724, NaN, NaN, NaN, 4.5448], 1e-4);
***** assert (ricestat ([s(1:2), s(4:5)], 1), [2.2724, 1.2533, 1.5486, 4.1272], 1e-4);
***** assert (ricestat ([s(1:2), s(4:5)], 3), [4.1665, 3.7599, 3.8637, 5.2695], 1e-4);
***** assert (ricestat ([s(1:2), s(4:5)], 2), [3.0971, 2.5066, 2.6609, 4.5448], 1e-4);
***** assert (ricestat (2, [sigma(1), sigma(3:5)]), [2.2724, 4.1665, NaN, 3.0971], 1e-4);
***** assert (ricestat (0, [sigma(1), sigma(3:5)]), [1.2533, 3.7599, NaN, 2.5066], 1e-4);
***** assert (ricestat (1, [sigma(1), sigma(3:5)]), [1.5486, 3.8637, NaN, 2.6609], 1e-4);
***** assert (ricestat (4, [sigma(1), sigma(3:5)]), [4.1272, 5.2695, NaN, 4.5448], 1e-4);
16 tests, 16 passed, 0 known failure, 0 skipped
[inst/dist_stat/geostat.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_stat/geostat.m
***** error<geostat: function called with too few input arguments.> geostat ()
***** error<geostat: PS must be numeric.> geostat ({})
***** error<geostat: PS must be numeric.> geostat ("")
***** error<geostat: PS must not be complex.> geostat (i)
***** test
 ps = 1 ./ (1:6);
 [m, v] = geostat (ps);
 assert (m, [0, 1, 2, 3, 4, 5], 0.001);
 assert (v, [0, 2, 6, 12, 20, 30], 0.001);
5 tests, 5 passed, 0 known failure, 0 skipped
[inst/dist_stat/gamstat.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_stat/gamstat.m
***** error<gamstat: function called with too few input arguments.> gamstat ()
***** error<gamstat: function called with too few input arguments.> gamstat (1)
***** error<gamstat: A and B must be numeric.> gamstat ({}, 2)
***** error<gamstat: A and B must be numeric.> gamstat (1, "")
***** error<gamstat: A and B must not be complex.> gamstat (i, 2)
***** error<gamstat: A and B must not be complex.> gamstat (1, i)
***** error<gamstat: A and B must be of common size or scalars.> ...
 gamstat (ones (3), ones (2))
***** error<gamstat: A and B must be of common size or scalars.> ...
 gamstat (ones (2), ones (3))
***** test
 a = 1:6;
 b = 1:0.2:2;
 [m, v] = gamstat (a, b);
 expected_m = [1.00, 2.40, 4.20,  6.40,  9.00, 12.00];
 expected_v = [1.00, 2.88, 5.88, 10.24, 16.20, 24.00];
 assert (m, expected_m, 0.001);
 assert (v, expected_v, 0.001);
***** test
 a = 1:6;
 [m, v] = gamstat (a, 1.5);
 expected_m = [1.50, 3.00, 4.50, 6.00,  7.50,  9.00];
 expected_v = [2.25, 4.50, 6.75, 9.00, 11.25, 13.50];
 assert (m, expected_m, 0.001);
 assert (v, expected_v, 0.001);
10 tests, 10 passed, 0 known failure, 0 skipped
[inst/dist_stat/bisastat.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_stat/bisastat.m
***** error<bisastat: function called with too few input arguments.> bisastat ()
***** error<bisastat: function called with too few input arguments.> bisastat (1)
***** error<bisastat: BETA and GAMMA must be numeric.> bisastat ({}, 2)
***** error<bisastat: BETA and GAMMA must be numeric.> bisastat (1, "")
***** error<bisastat: BETA and GAMMA must not be complex.> bisastat (i, 2)
***** error<bisastat: BETA and GAMMA must not be complex.> bisastat (1, i)
***** error<bisastat: BETA and GAMMA must be of common size or scalars.> ...
 bisastat (ones (3), ones (2))
***** error<bisastat: BETA and GAMMA must be of common size or scalars.> ...
 bisastat (ones (2), ones (3))
***** test
 beta = 1:6;
 gamma = 1:0.2:2;
 [m, v] = bisastat (beta, gamma);
 expected_m = [1.50, 3.44, 5.94,  9.12,  13.10, 18];
 expected_v = [2.25, 16.128, 60.858, 172.032, 409.050, 864];
 assert (m, expected_m, 1e-2);
 assert (v, expected_v, 1e-3);
***** test
 beta = 1:6;
 [m, v] = bisastat (beta, 1.5);
 expected_m = [2.125, 4.25, 6.375, 8.5, 10.625, 12.75];
 expected_v = [8.5781, 34.3125, 77.2031, 137.2500, 214.4531, 308.8125];
 assert (m, expected_m, 1e-3);
 assert (v, expected_v, 1e-4);
10 tests, 10 passed, 0 known failure, 0 skipped
[inst/dist_stat/binostat.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_stat/binostat.m
***** error<binostat: function called with too few input arguments.> binostat ()
***** error<binostat: function called with too few input arguments.> binostat (1)
***** error<binostat: N and PS must be numeric.> binostat ({}, 2)
***** error<binostat: N and PS must be numeric.> binostat (1, "")
***** error<binostat: N and PS must not be complex.> binostat (i, 2)
***** error<binostat: N and PS must not be complex.> binostat (1, i)
***** error<binostat: N and PS must be of common size or scalars.> ...
 binostat (ones (3), ones (2))
***** error<binostat: N and PS must be of common size or scalars.> ...
 binostat (ones (2), ones (3))
***** test
 n = 1:6;
 ps = 0:0.2:1;
 [m, v] = binostat (n, ps);
 expected_m = [0.00, 0.40, 1.20, 2.40, 4.00, 6.00];
 expected_v = [0.00, 0.32, 0.72, 0.96, 0.80, 0.00];
 assert (m, expected_m, 0.001);
 assert (v, expected_v, 0.001);
***** test
 n = 1:6;
 [m, v] = binostat (n, 0.5);
 expected_m = [0.50, 1.00, 1.50, 2.00, 2.50, 3.00];
 expected_v = [0.25, 0.50, 0.75, 1.00, 1.25, 1.50];
 assert (m, expected_m, 0.001);
 assert (v, expected_v, 0.001);
***** test
 n = [-Inf -3 5 0.5 3 NaN 100, Inf];
 [m, v] = binostat (n, 0.5);
 assert (isnan (m), [true true false true false true false false])
 assert (isnan (v), [true true false true false true false false])
 assert (m(end), Inf);
 assert (v(end), Inf);
***** assert (nthargout (1:2, @binostat, 5, []), {[], []})
***** assert (nthargout (1:2, @binostat, [], 5), {[], []})
***** assert (size (binostat (randi (100, 10, 5, 4), rand (10, 5, 4))), [10 5 4])
***** assert (size (binostat (randi (100, 10, 5, 4), 7)), [10 5 4])
15 tests, 15 passed, 0 known failure, 0 skipped
[inst/dist_stat/wblstat.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_stat/wblstat.m
***** error<wblstat: function called with too few input arguments.> wblstat ()
***** error<wblstat: function called with too few input arguments.> wblstat (1)
***** error<wblstat: LAMBDA and K must be numeric.> wblstat ({}, 2)
***** error<wblstat: LAMBDA and K must be numeric.> wblstat (1, "")
***** error<wblstat: LAMBDA and K must not be complex.> wblstat (i, 2)
***** error<wblstat: LAMBDA and K must not be complex.> wblstat (1, i)
***** error<wblstat: LAMBDA and K must be of common size or scalars.> ...
 wblstat (ones (3), ones (2))
***** error<wblstat: LAMBDA and K must be of common size or scalars.> ...
 wblstat (ones (2), ones (3))
***** test
 lambda = 3:8;
 k = 1:6;
 [m, v] = wblstat (lambda, k);
 expected_m = [3.0000, 3.5449, 4.4649, 5.4384, 6.4272, 7.4218];
 expected_v = [9.0000, 3.4336, 2.6333, 2.3278, 2.1673, 2.0682];
 assert (m, expected_m, 0.001);
 assert (v, expected_v, 0.001);
***** test
 k = 1:6;
 [m, v] = wblstat (6, k);
 expected_m = [ 6.0000, 5.3174, 5.3579, 5.4384, 5.5090, 5.5663];
 expected_v = [36.0000, 7.7257, 3.7920, 2.3278, 1.5923, 1.1634];
 assert (m, expected_m, 0.001);
 assert (v, expected_v, 0.001);
10 tests, 10 passed, 0 known failure, 0 skipped
[inst/dist_stat/invgstat.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_stat/invgstat.m
***** error<invgstat: function called with too few input arguments.> invgstat ()
***** error<invgstat: function called with too few input arguments.> invgstat (1)
***** error<invgstat: MU and LAMBDA must be numeric.> invgstat ({}, 2)
***** error<invgstat: MU and LAMBDA must be numeric.> invgstat (1, "")
***** error<invgstat: MU and LAMBDA must not be complex.> invgstat (i, 2)
***** error<invgstat: MU and LAMBDA must not be complex.> invgstat (1, i)
***** error<invgstat: MU and LAMBDA must be of common size or scalars.> ...
 invgstat (ones (3), ones (2))
***** error<invgstat: MU and LAMBDA must be of common size or scalars.> ...
 invgstat (ones (2), ones (3))
***** test
 [m, v] = invgstat (1, 1);
 assert (m, 1);
 assert (v, 1);
***** test
 [m, v] = invgstat (2, 1);
 assert (m, 2);
 assert (v, 8);
***** test
 [m, v] = invgstat (2, 2);
 assert (m, 2);
 assert (v, 4);
***** test
 [m, v] = invgstat (2, 2.5);
 assert (m, 2);
 assert (v, 3.2);
***** test
 [m, v] = invgstat (1.5, 0.5);
 assert (m, 1.5);
 assert (v, 6.75);
13 tests, 13 passed, 0 known failure, 0 skipped
[inst/dist_stat/poisstat.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_stat/poisstat.m
***** error<poisstat: function called with too few input arguments.> poisstat ()
***** error<poisstat: SIGMA must be numeric.> poisstat ({})
***** error<poisstat: SIGMA must be numeric.> poisstat ("")
***** error<poisstat: SIGMA must not be complex.> poisstat (i)
***** test
 lambda = 1 ./ (1:6);
 [m, v] = poisstat (lambda);
 assert (m, lambda);
 assert (v, lambda);
5 tests, 5 passed, 0 known failure, 0 skipped
[inst/dist_stat/nctstat.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_stat/nctstat.m
***** error<nctstat: function called with too few input arguments.> nctstat ()
***** error<nctstat: function called with too few input arguments.> nctstat (1)
***** error<nctstat: DF and MU must be numeric.> nctstat ({}, 2)
***** error<nctstat: DF and MU must be numeric.> nctstat (1, "")
***** error<nctstat: DF and MU must not be complex.> nctstat (i, 2)
***** error<nctstat: DF and MU must not be complex.> nctstat (1, i)
***** error<nctstat: DF and MU must be of common size or scalars.> ...
 nctstat (ones (3), ones (2))
***** error<nctstat: DF and MU must be of common size or scalars.> ...
 nctstat (ones (2), ones (3))
***** shared df, mu
 df = [2, 0, -1, 1, 4];
 mu = [1, NaN, 3, -1, 2];
***** assert (nctstat (df, mu), [1.7725, NaN, NaN, NaN, 2.5066], 1e-4);
***** assert (nctstat ([df(1:2), df(4:5)], 1), [1.7725, NaN, NaN, 1.2533], 1e-4);
***** assert (nctstat ([df(1:2), df(4:5)], 3), [5.3174, NaN, NaN, 3.7599], 1e-4);
***** assert (nctstat ([df(1:2), df(4:5)], 2), [3.5449, NaN, NaN, 2.5066], 1e-4);
***** assert (nctstat (2, [mu(1), mu(3:5)]), [1.7725,5.3174,-1.7725,3.5449], 1e-4);
***** assert (nctstat (0, [mu(1), mu(3:5)]), [NaN, NaN, NaN, NaN]);
***** assert (nctstat (1, [mu(1), mu(3:5)]), [NaN, NaN, NaN, NaN]);
***** assert (nctstat (4, [mu(1), mu(3:5)]), [1.2533,3.7599,-1.2533,2.5066], 1e-4);
16 tests, 16 passed, 0 known failure, 0 skipped
[inst/dist_stat/raylstat.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_stat/raylstat.m
***** error<raylstat: function called with too few input arguments.> raylstat ()
***** error<raylstat: SIGMA must be numeric.> raylstat ({})
***** error<raylstat: SIGMA must be numeric.> raylstat ("")
***** error<raylstat: SIGMA must not be complex.> raylstat (i)
***** test
 sigma = 1:6;
 [m, v] = raylstat (sigma);
 expected_m = [1.2533, 2.5066, 3.7599, 5.0133, 6.2666, 7.5199];
 expected_v = [0.4292, 1.7168, 3.8628, 6.8673, 10.7301, 15.4513];
 assert (m, expected_m, 0.001);
 assert (v, expected_v, 0.001);
5 tests, 5 passed, 0 known failure, 0 skipped
[inst/dist_stat/ncx2stat.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_stat/ncx2stat.m
***** error<ncx2stat: function called with too few input arguments.> ncx2stat ()
***** error<ncx2stat: function called with too few input arguments.> ncx2stat (1)
***** error<ncx2stat: DF and LAMBDA must be numeric.> ncx2stat ({}, 2)
***** error<ncx2stat: DF and LAMBDA must be numeric.> ncx2stat (1, "")
***** error<ncx2stat: DF and LAMBDA must not be complex.> ncx2stat (i, 2)
***** error<ncx2stat: DF and LAMBDA must not be complex.> ncx2stat (1, i)
***** error<ncx2stat: DF and LAMBDA must be of common size or scalars.> ...
 ncx2stat (ones (3), ones (2))
***** error<ncx2stat: DF and LAMBDA must be of common size or scalars.> ...
 ncx2stat (ones (2), ones (3))
***** shared df, d1
 df = [2, 0, -1, 1, 4];
 d1 = [1, NaN, 3, -1, 2];
***** assert (ncx2stat (df, d1), [3, NaN, NaN, NaN, 6]);
***** assert (ncx2stat ([df(1:2), df(4:5)], 1), [3, NaN, 2, 5]);
***** assert (ncx2stat ([df(1:2), df(4:5)], 3), [5, NaN, 4, 7]);
***** assert (ncx2stat ([df(1:2), df(4:5)], 2), [4, NaN, 3, 6]);
***** assert (ncx2stat (2, [d1(1), d1(3:5)]), [3, 5, NaN, 4]);
***** assert (ncx2stat (0, [d1(1), d1(3:5)]), [NaN, NaN, NaN, NaN]);
***** assert (ncx2stat (1, [d1(1), d1(3:5)]), [2, 4, NaN, 3]);
***** assert (ncx2stat (4, [d1(1), d1(3:5)]), [5, 7, NaN, 6]);
16 tests, 16 passed, 0 known failure, 0 skipped
[inst/dist_stat/lognstat.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_stat/lognstat.m
***** error<lognstat: function called with too few input arguments.> lognstat ()
***** error<lognstat: function called with too few input arguments.> lognstat (1)
***** error<lognstat: MU and SIGMA must be numeric.> lognstat ({}, 2)
***** error<lognstat: MU and SIGMA must be numeric.> lognstat (1, "")
***** error<lognstat: MU and SIGMA must not be complex.> lognstat (i, 2)
***** error<lognstat: MU and SIGMA must not be complex.> lognstat (1, i)
***** error<lognstat: MU and SIGMA must be of common size or scalars.> ...
 lognstat (ones (3), ones (2))
***** error<lognstat: MU and SIGMA must be of common size or scalars.> ...
 lognstat (ones (2), ones (3))
***** test
 mu = 0:0.2:1;
 sigma = 0.2:0.2:1.2;
 [m, v] = lognstat (mu, sigma);
 expected_m = [1.0202, 1.3231, 1.7860, 2.5093,  3.6693,   5.5845];
 expected_v = [0.0425, 0.3038, 1.3823, 5.6447, 23.1345, 100.4437];
 assert (m, expected_m, 0.001);
 assert (v, expected_v, 0.001);
***** test
 sigma = 0.2:0.2:1.2;
 [m, v] = lognstat (0, sigma);
 expected_m = [1.0202, 1.0833, 1.1972, 1.3771, 1.6487,  2.0544];
 expected_v = [0.0425, 0.2036, 0.6211, 1.7002, 4.6708, 13.5936];
 assert (m, expected_m, 0.001);
 assert (v, expected_v, 0.001);
10 tests, 10 passed, 0 known failure, 0 skipped
[inst/dist_stat/unifstat.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_stat/unifstat.m
***** error<unifstat: function called with too few input arguments.> unifstat ()
***** error<unifstat: function called with too few input arguments.> unifstat (1)
***** error<unifstat: A and B must be numeric.> unifstat ({}, 2)
***** error<unifstat: A and B must be numeric.> unifstat (1, "")
***** error<unifstat: A and B must not be complex.> unifstat (i, 2)
***** error<unifstat: A and B must not be complex.> unifstat (1, i)
***** error<unifstat: A and B must be of common size or scalars.> ...
 unifstat (ones (3), ones (2))
***** error<unifstat: A and B must be of common size or scalars.> ...
 unifstat (ones (2), ones (3))
***** test
 a = 1:6;
 b = 2:2:12;
 [m, v] = unifstat (a, b);
 expected_m = [1.5000, 3.0000, 4.5000, 6.0000, 7.5000, 9.0000];
 expected_v = [0.0833, 0.3333, 0.7500, 1.3333, 2.0833, 3.0000];
 assert (m, expected_m, 0.001);
 assert (v, expected_v, 0.001);
***** test
 a = 1:6;
 [m, v] = unifstat (a, 10);
 expected_m = [5.5000, 6.0000, 6.5000, 7.0000, 7.5000, 8.0000];
 expected_v = [6.7500, 5.3333, 4.0833, 3.0000, 2.0833, 1.3333];
 assert (m, expected_m, 0.001);
 assert (v, expected_v, 0.001);
10 tests, 10 passed, 0 known failure, 0 skipped
[inst/dist_stat/hnstat.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_stat/hnstat.m
***** error<hnstat: function called with too few input arguments.> hnstat ()
***** error<hnstat: function called with too few input arguments.> hnstat (1)
***** error<hnstat: MU and SIGMA must be numeric.> hnstat ({}, 2)
***** error<hnstat: MU and SIGMA must be numeric.> hnstat (1, "")
***** error<hnstat: MU and SIGMA must not be complex.> hnstat (i, 2)
***** error<hnstat: MU and SIGMA must not be complex.> hnstat (1, i)
***** error<hnstat: MU and SIGMA must be of common size or scalars.> ...
 hnstat (ones (3), ones (2))
***** error<hnstat: MU and SIGMA must be of common size or scalars.> ...
 hnstat (ones (2), ones (3))
***** test
 [m, v] = hnstat (0, 1);
 assert (m, 0.7979, 1e-4);
 assert (v, 0.3634, 1e-4);
***** test
 [m, v] = hnstat (2, 1);
 assert (m, 2.7979, 1e-4);
 assert (v, 0.3634, 1e-4);
***** test
 [m, v] = hnstat (2, 2);
 assert (m, 3.5958, 1e-4);
 assert (v, 1.4535, 1e-4);
***** test
 [m, v] = hnstat (2, 2.5);
 assert (m, 3.9947, 1e-4);
 assert (v, 2.2711, 1e-4);
***** test
 [m, v] = hnstat (1.5, 0.5);
 assert (m, 1.8989, 1e-4);
 assert (v, 0.0908, 1e-4);
***** test
 [m, v] = hnstat (-1.5, 0.5);
 assert (m, -1.1011, 1e-4);
 assert (v, 0.0908, 1e-4);
14 tests, 14 passed, 0 known failure, 0 skipped
[inst/dist_stat/tstat.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_stat/tstat.m
***** error<tstat: function called with too few input arguments.> tstat ()
***** error<tstat: DF must be numeric.> tstat ({})
***** error<tstat: DF must be numeric.> tstat ("")
***** error<tstat: DF must not be complex.> tstat (i)
***** test
 df = 3:8;
 [m, v] = tstat (df);
 expected_m = [0, 0, 0, 0, 0, 0];
 expected_v = [3.0000, 2.0000, 1.6667, 1.5000, 1.4000, 1.3333];
 assert (m, expected_m);
 assert (v, expected_v, 0.001);
5 tests, 5 passed, 0 known failure, 0 skipped
[inst/dist_stat/hygestat.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_stat/hygestat.m
***** error<hygestat: function called with too few input arguments.> hygestat ()
***** error<hygestat: function called with too few input arguments.> hygestat (1)
***** error<hygestat: function called with too few input arguments.> hygestat (1, 2)
***** error<hygestat: M, K, and N must be numeric.> hygestat ({}, 2, 3)
***** error<hygestat: M, K, and N must be numeric.> hygestat (1, "", 3)
***** error<hygestat: M, K, and N must be numeric.> hygestat (1, 2, "")
***** error<hygestat: M, K, and N must not be complex.> hygestat (i, 2, 3)
***** error<hygestat: M, K, and N must not be complex.> hygestat (1, i, 3)
***** error<hygestat: M, K, and N must not be complex.> hygestat (1, 2, i)
***** error<hygestat: M, K, and N must be of common size or scalars.> ...
 hygestat (ones (3), ones (2), 3)
***** error<hygestat: M, K, and N must be of common size or scalars.> ...
 hygestat (ones (2), 2, ones (3))
***** error<hygestat: M, K, and N must be of common size or scalars.> ...
 hygestat (1, ones (2), ones (3))
***** test
 m = 4:9;
 k = 0:5;
 n = 1:6;
 [mn, v] = hygestat (m, k, n);
 expected_mn = [0.0000, 0.4000, 1.0000, 1.7143, 2.5000, 3.3333];
 expected_v = [0.0000, 0.2400, 0.4000, 0.4898, 0.5357, 0.5556];
 assert (mn, expected_mn, 0.001);
 assert (v, expected_v, 0.001);
***** test
 m = 4:9;
 k = 0:5;
 [mn, v] = hygestat (m, k, 2);
 expected_mn = [0.0000, 0.4000, 0.6667, 0.8571, 1.0000, 1.1111];
 expected_v = [0.0000, 0.2400, 0.3556, 0.4082, 0.4286, 0.4321];
 assert (mn, expected_mn, 0.001);
 assert (v, expected_v, 0.001);
14 tests, 14 passed, 0 known failure, 0 skipped
[inst/dist_stat/evstat.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_stat/evstat.m
***** error<evstat: function called with too few input arguments.> evstat ()
***** error<evstat: function called with too few input arguments.> evstat (1)
***** error<evstat: MU and SIGMA must be numeric.> evstat ({}, 2)
***** error<evstat: MU and SIGMA must be numeric.> evstat (1, "")
***** error<evstat: MU and SIGMA must not be complex.> evstat (i, 2)
***** error<evstat: MU and SIGMA must not be complex.> evstat (1, i)
***** error<evstat: MU and SIGMA must be of common size or scalars.> ...
 evstat (ones (3), ones (2))
***** error<evstat: MU and SIGMA must be of common size or scalars.> ...
 evstat (ones (2), ones (3))
***** shared x, y0, y1
 x = [-5, 0, 1, 2, 3];
 y0 = [NaN, NaN, 0.4228, 0.8456, 1.2684];
 y1 = [-5.5772, -3.4633, -3.0405, -2.6177, -2.1949];
***** assert (evstat (x, x), y0, 1e-4)
***** assert (evstat (x, x+6), y1, 1e-4)
***** assert (evstat (x, x-6), NaN (1,5))
11 tests, 11 passed, 0 known failure, 0 skipped
[inst/fitrgam.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/fitrgam.m
***** demo
 # Train a RegressionGAM Model for synthetic values

 f1 = @(x) cos (3 *x);
 f2 = @(x) x .^ 3;

 # generate x1 and x2 for f1 and f2
 x1 = 2 * rand (50, 1) - 1;
 x2 = 2 * rand (50, 1) - 1;

 # calculate y
 y = f1(x1) + f2(x2);

 # add noise
 y = y + y .* 0.2 .* rand (50,1);
 X = [x1, x2];

 # create an object
 a = fitrgam (X, y, "tol", 1e-3)
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = [1; 2; 3; 4];
 a = fitrgam (x, y);
 assert ({a.X, a.Y}, {x, y})
 assert ({a.BaseModel.Intercept}, {2.5000})
 assert ({a.Knots, a.Order, a.DoF}, {[5, 5, 5], [3, 3, 3], [8, 8, 8]})
 assert ({a.NumObservations, a.NumPredictors}, {4, 3})
 assert ({a.ResponseName, a.PredictorNames}, {"Y", {"x1", "x2", "x3"}})
 assert ({a.Formula}, {[]})
***** test
 x = [1, 2, 3, 4; 4, 5, 6, 7; 7, 8, 9, 1; 3, 2, 1, 2];
 y = [1; 2; 3; 4];
 pnames = {"A", "B", "C", "D"};
 formula = "Y ~ A + B + C + D + A:C";
 intMat = logical ([1,0,0,0;0,1,0,0;0,0,1,0;0,0,0,1;1,0,1,0]);
 a = fitrgam (x, y, "predictors", pnames, "formula", formula);
 assert ({a.IntMatrix}, {intMat})
 assert ({a.ResponseName, a.PredictorNames}, {"Y", pnames})
 assert ({a.Formula}, {formula})
***** error<fitrgam: too few arguments.> fitrgam ()
***** error<fitrgam: too few arguments.> fitrgam (ones(10,2))
***** error<fitrgam: Name-Value arguments must be in pairs.>
 fitrgam (ones (4,2), ones (4, 1), "K")
***** error<fitrgam: number of rows in X and Y must be equal.>
 fitrgam (ones (4,2), ones (3, 1))
***** error<fitrgam: number of rows in X and Y must be equal.>
 fitrgam (ones (4,2), ones (3, 1), "K", 2)
7 tests, 7 passed, 0 known failure, 0 skipped
[inst/crosstab.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/crosstab.m
***** error crosstab ()
***** error crosstab (1)
***** error <crosstab: x1, x2 ... xn must be vectors.> crosstab (ones (2), [1 1])
***** error <crosstab: x1, x2 ... xn must be vectors.> crosstab ([1 1], ones (2))
***** error <x1, x2 .* must be vectors of the same length> crosstab ([1], [1 2])
***** error <x1, x2 .* must be vectors of the same length> crosstab ([1 2], [1])
***** test
 load carbig
 [table, chisq, p, labels] = crosstab (cyl4, when, org);
 assert (table(2,3,1), 38);
 assert (labels{3,3}, "Japan");
***** test
 load carbig
 [table, chisq, p, labels] = crosstab (cyl4, when, org);
 assert (table(2,3,2), 17);
 assert (labels{1,3}, "USA");
8 tests, 8 passed, 0 known failure, 0 skipped
[inst/fishertest.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/fishertest.m
***** demo
 ## A Fisher's exact test example

 x = [3, 1; 1, 3]
 [h, p, stats] = fishertest(x)
***** assert (fishertest ([3, 4; 5, 7]), false);
***** assert (isa (fishertest ([3, 4; 5, 7]), "logical"), true);
***** test
 [h, pval, stats] = fishertest ([3, 4; 5, 7]);
 assert (pval, 1, 1e-14);
 assert (stats.OddsRatio, 1.05);
 CI = [0.159222057151289, 6.92429189601808];
 assert (stats.ConfidenceInterval, CI, 1e-14)
***** test
 [h, pval, stats] = fishertest ([3, 4; 5, 0]);
 assert (pval, 0.08080808080808080, 1e-14);
 assert (stats.OddsRatio, 0);
 assert (stats.ConfidenceInterval, [-Inf, Inf])
***** error fishertest ();
***** error fishertest (1, 2, 3, 4, 5, 6);
***** error<fishertest: X must be a 2-dimensional matrix.> ...
 fishertest (ones (2, 2, 2));
***** error<fishertest: X must contain only non-negative real integers.> ...
 fishertest ([1, 2; -3, 4]);
***** error<fishertest: X must contain only non-negative real integers.> ...
 fishertest ([1, 2; 3, 4+i]);
***** error<fishertest: X must contain only non-negative real integers.> ...
 fishertest ([1, 2; 3, 4.2]);
***** error<fishertest: X must contain only non-negative real integers.> ...
 fishertest ([NaN, 2; 3, 4]);
***** error<fishertest: X must contain only non-negative real integers.> ...
 fishertest ([1, Inf; 3, 4]);
***** error<fishertest: cannot handle large entries> ...
 fishertest (ones (2) * 1e8);
***** error<fishertest: invalid value for alpha.> ...
 fishertest ([1, 2; 3, 4], "alpha", 0);
***** error<fishertest: invalid value for alpha.> ...
 fishertest ([1, 2; 3, 4], "alpha", 1.2);
***** error<fishertest: invalid value for alpha.> ...
 fishertest ([1, 2; 3, 4], "alpha", "val");
***** error<fishertest: invalid value for tail.>  ...
 fishertest ([1, 2; 3, 4], "tail", "val");
***** error<fishertest: invalid value for tail.>  ...
 fishertest ([1, 2; 3, 4], "alpha", 0.01, "tail", "val");
***** error<fishertest: invalid name for optional arguments.> ...
 fishertest ([1, 2; 3, 4], "alpha", 0.01, "badoption", 3);
19 tests, 19 passed, 0 known failure, 0 skipped
[inst/squareform.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/squareform.m
***** shared v, m
 v = 1:6;
 m = [0 1 2 3;1 0 4 5;2 4 0 6;3 5 6 0];
***** assert (squareform (v), m)
***** assert (squareform (squareform (v)), v)
***** assert (squareform (m), v)
***** assert (squareform (v'), m)
***** assert (squareform (1), [0 1;1 0])
***** assert (squareform (1, "tomatrix"), [0 1; 1 0])
***** assert (squareform (0, "tovector"), zeros (1, 0))
***** warning <not a symmetric matrix> squareform ([0 1 2; 3 0 4; 5 6 0]);
***** test
 for c = {@single, @double, @uint8, @uint32, @uint64}
   f = c{1};
   assert (squareform (f (v)), f (m))
   assert (squareform (f (m)), f (v))
 endfor
9 tests, 9 passed, 0 known failure, 0 skipped
[inst/loadmodel.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/loadmodel.m
***** error<loadmodel: too few arguments.> loadmodel ()
***** error<loadmodel: 'fisheriris.mat' does not contain a Classification or Regression object.> ...
 loadmodel ("fisheriris.mat")
***** error<loadmodel: 'ClassificationModel' is not supported.> ...
 loadmodel ("fail_loadmodel.mdl")
***** error<ClassificationKNN.load_model: invalid model in 'fail_load_model.mdl'.> ...
 loadmodel ("fail_load_model.mdl")
4 tests, 4 passed, 0 known failure, 0 skipped
[inst/pcares.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/pcares.m
***** demo
 x = [ 7    26     6    60;
       1    29    15    52;
      11    56     8    20;
      11    31     8    47;
       7    52     6    33;
      11    55     9    22;
       3    71    17     6;
       1    31    22    44;
       2    54    18    22;
      21    47     4    26;
       1    40    23    34;
      11    66     9    12;
      10    68     8    12];

 ## As we increase the number of principal components, the norm
 ## of the residuals matrix will decrease
 r1 = pcares (x,1);
 n1 = norm (r1)
 r2 = pcares (x,2);
 n2 = norm (r2)
 r3 = pcares (x,3);
 n3 = norm (r3)
 r4 = pcares (x,4);
 n4 = norm (r4)
***** test
 load hald
 r1 = pcares (ingredients,1);
 r2 = pcares (ingredients,2);
 r3 = pcares (ingredients,3);
 assert (r1(1,:), [2.0350,  2.8304, -6.8378, 3.0879], 1e-4);
 assert (r2(1,:), [-2.4037, 2.6930, -1.6482, 2.3425], 1e-4);
 assert (r3(1,:), [ 0.2008, 0.1957,  0.2045, 0.1921], 1e-4);
***** error<pcares: too few input arguments.> pcares (ones (20, 3))
***** error<pcares: NDIM must be less than or equal to the column of X.> ...
 pcares (ones (30, 2), 3)
3 tests, 3 passed, 0 known failure, 0 skipped
[inst/kmeans.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/kmeans.m
***** demo
 ## Generate a two-cluster problem
 randn ("seed", 31)  # for reproducibility
 C1 = randn (100, 2) + 1;
 randn ("seed", 32)  # for reproducibility
 C2 = randn (100, 2) - 1;
 data = [C1; C2];

 ## Perform clustering
 rand ("seed", 1)  # for reproducibility
 [idx, centers] = kmeans (data, 2);

 ## Plot the result
 figure;
 plot (data (idx==1, 1), data (idx==1, 2), "ro");
 hold on;
 plot (data (idx==2, 1), data (idx==2, 2), "bs");
 plot (centers (:, 1), centers (:, 2), "kv", "markersize", 10);
 hold off;
***** demo
 ## Cluster data using k-means clustering, then plot the cluster regions
 ## Load Fisher's iris data set and use the petal lengths and widths as
 ## predictors

 load fisheriris
 X = meas(:,3:4);

 figure;
 plot (X(:,1), X(:,2), "k*", "MarkerSize", 5);
 title ("Fisher's Iris Data");
 xlabel ("Petal Lengths (cm)");
 ylabel ("Petal Widths (cm)");

 ## Cluster the data. Specify k = 3 clusters
 rand ("seed", 1)  # for reproducibility
 [idx, C] = kmeans (X, 3);
 x1 = min (X(:,1)):0.01:max (X(:,1));
 x2 = min (X(:,2)):0.01:max (X(:,2));
 [x1G, x2G] = meshgrid (x1, x2);
 XGrid = [x1G(:), x2G(:)];

 idx2Region = kmeans (XGrid, 3, "MaxIter", 1, "Start", C);
 figure;
 gscatter (XGrid(:,1), XGrid(:,2), idx2Region, ...
           [0, 0.75, 0.75; 0.75, 0, 0.75; 0.75, 0.75, 0], "..");
 hold on;
 plot (X(:,1), X(:,2), "k*", "MarkerSize", 5);
 title ("Fisher's Iris Data");
 xlabel ("Petal Lengths (cm)");
 ylabel ("Petal Widths (cm)");
 legend ("Region 1", "Region 2", "Region 3", "Data", "Location", "SouthEast");
 hold off
***** demo
 ## Partition Data into Two Clusters

 randn ("seed", 1)  # for reproducibility
 r1 = randn (100, 2) * 0.75 + ones (100, 2);
 randn ("seed", 2)  # for reproducibility
 r2 = randn (100, 2) * 0.5 - ones (100, 2);
 X = [r1; r2];

 figure;
 plot (X(:,1), X(:,2), ".");
 title ("Randomly Generated Data");
 rand ("seed", 1)  # for reproducibility
 [idx, C] = kmeans (X, 2, "Distance", "cityblock", ...
                          "Replicates", 5, "Display", "final");
 figure;
 plot (X(idx==1,1), X(idx==1,2), "r.", "MarkerSize", 12);
 hold on
 plot(X(idx==2,1), X(idx==2,2), "b.", "MarkerSize", 12);
 plot (C(:,1), C(:,2), "kx", "MarkerSize", 15, "LineWidth", 3);
 legend ("Cluster 1", "Cluster 2", "Centroids", "Location", "NorthWest");
 title ("Cluster Assignments and Centroids");
 hold off
***** demo
 ## Assign New Data to Existing Clusters

 ## Generate a training data set using three distributions
 randn ("seed", 5)  # for reproducibility
 r1 = randn (100, 2) * 0.75 + ones (100, 2);
 randn ("seed", 7)  # for reproducibility
 r2 = randn (100, 2) * 0.5 - ones (100, 2);
 randn ("seed", 9)  # for reproducibility
 r3 = randn (100, 2) * 0.75;
 X = [r1; r2; r3];

 ## Partition the training data into three clusters by using kmeans

 rand ("seed", 1)  # for reproducibility
 [idx, C] = kmeans (X, 3);

 ## Plot the clusters and the cluster centroids

 figure
 gscatter (X(:,1), X(:,2), idx, "bgm", "***");
 hold on
 plot (C(:,1), C(:,2), "kx");
 legend ("Cluster 1", "Cluster 2", "Cluster 3", "Cluster Centroid")

 ## Generate a test data set
 randn ("seed", 25)  # for reproducibility
 r1 = randn (100, 2) * 0.75 + ones (100, 2);
 randn ("seed", 27)  # for reproducibility
 r2 = randn (100, 2) * 0.5 - ones (100, 2);
 randn ("seed", 29)  # for reproducibility
 r3 = randn (100, 2) * 0.75;
 Xtest = [r1; r2; r3];

 ## Classify the test data set using the existing clusters
 ## Find the nearest centroid from each test data point by using pdist2

 D = pdist2 (C, Xtest, "euclidean");
 [group, ~] = find (D == min (D));

 ## Plot the test data and label the test data using idx_test with gscatter

 gscatter (Xtest(:,1), Xtest(:,2), group, "bgm", "ooo");
 legend ("Cluster 1", "Cluster 2", "Cluster 3", "Cluster Centroid", ...
         "Data classified to Cluster 1", "Data classified to Cluster 2", ...
         "Data classified to Cluster 3", "Location", "NorthWest");
 title ("Assign New Data to Existing Clusters");
***** test
 samples = 4;
 dims = 3;
 k = 2;
 [cls, c, d, z] = kmeans (rand (samples,dims), k, "start", rand (k,dims, 5),
                          "emptyAction", "singleton");
 assert (size (cls), [samples, 1]);
 assert (size (c), [k, dims]);
 assert (size (d), [k, 1]);
 assert (size (z), [samples, k]);
***** test
 samples = 4;
 dims = 3;
 k = 2;
 [cls, c, d, z] = kmeans (rand (samples,dims), [], "start", rand (k,dims, 5),
                          "emptyAction", "singleton");
 assert (size (cls), [samples, 1]);
 assert (size (c), [k, dims]);
 assert (size (d), [k, 1]);
 assert (size (z), [samples, k]);
***** test
 [cls, c] = kmeans ([1 0; 2 0], 2, "start", [8,0;0,8], "emptyaction", "drop");
 assert (cls, [1; 1]);
 assert (c, [1.5, 0; NA, NA]);
***** test
 kmeans (rand (4,3), 2, "start", rand (2,3, 5), "replicates", 5,
         "emptyAction", "singleton");
***** test
 kmeans (rand (3,4), 2, "start", "sample", "emptyAction", "singleton");
***** test
 kmeans (rand (3,4), 2, "start", "plus", "emptyAction", "singleton");
***** test
 kmeans (rand (3,4), 2, "start", "cluster", "emptyAction", "singleton");
***** test
 kmeans (rand (3,4), 2, "start", "uniform", "emptyAction", "singleton");
***** test
 kmeans (rand (4,3), 2, "distance", "sqeuclidean", "emptyAction", "singleton");
***** test
 kmeans (rand (4,3), 2, "distance", "cityblock", "emptyAction", "singleton");
***** test
 kmeans (rand (4,3), 2, "distance", "cosine", "emptyAction", "singleton");
***** test
 kmeans (rand (4,3), 2, "distance", "correlation", "emptyAction", "singleton");
***** test
 kmeans (rand (4,3), 2, "distance", "hamming", "emptyAction", "singleton");
***** test
 kmeans ([1 0; 1.1 0], 2, "start", eye(2), "emptyaction", "singleton");
***** error kmeans (rand (3,2), 4);
***** error kmeans ([1 0; 1.1 0], 2, "start", eye(2), "emptyaction", "panic");
***** error kmeans (rand (4,3), 2, "start", rand (2,3, 5), "replicates", 1);
***** error kmeans (rand (4,3), 2, "start", rand (2,2));
***** error kmeans (rand (4,3), 2, "distance", "manhattan");
***** error kmeans (rand (3,4), 2, "start", "normal");
***** error kmeans (rand (4,3), 2, "replicates", i);
***** error kmeans (rand (4,3), 2, "replicates", -1);
***** error kmeans (rand (4,3), 2, "replicates", []);
***** error kmeans (rand (4,3), 2, "replicates", [1 2]);
***** error kmeans (rand (4,3), 2, "replicates", "one");
***** error kmeans (rand (4,3), 2, "MAXITER", i);
***** error kmeans (rand (4,3), 2, "MaxIter", -1);
***** error kmeans (rand (4,3), 2, "maxiter", []);
***** error kmeans (rand (4,3), 2, "maxiter", [1 2]);
***** error kmeans (rand (4,3), 2, "maxiter", "one");
***** error <empty cluster created> kmeans ([1 0; 1.1 0], 2, "start", eye(2), "emptyaction", "error");
31 tests, 31 passed, 0 known failure, 0 skipped
[inst/wblplot.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/wblplot.m
***** demo
 x = [16 34 53 75 93 120];
 wblplot (x);
***** demo
 x = [2 3 5 7 11 13 17 19 23 29 31 37 41 43 47 53 59 61 67]';
 c = [0 1 0 1 0 1 1 1 0 0 1 0 1 0 1 1 0 1 1]';
 [h, p] = wblplot (x, c);
 p
***** demo
 x = [16, 34, 53, 75, 93, 120, 150, 191, 240 ,339];
 [h, p] = wblplot (x, [], [], 0.05);
 p
 ## Benchmark Reliasoft eta = 146.2545 beta 1.1973 rho = 0.9999
***** demo
 x = [46 64 83 105 123 150 150];
 c = [0 0 0 0 0 0 1];
 f = [1 1 1 1 1 1 4];
 wblplot (x, c, f, 0.05);
***** demo
 x = [46 64 83 105 123 150 150];
 c = [0 0 0 0 0 0 1];
 f = [1 1 1 1 1 1 4];
 ## Subtract 30.92 from x to simulate a 3 parameter wbl with gamma = 30.92
 wblplot (x - 30.92, c, f, 0.05);
***** test
 hf = figure ("visible", "off");
 unwind_protect
   x = [16, 34, 53, 75, 93, 120, 150, 191, 240 ,339];
   [h, p] = wblplot (x, [], [], 0.05);
   assert (numel (h), 4)
   assert (p(1), 146.2545, 1E-4)
   assert (p(2), 1.1973, 1E-4)
   assert (p(3), 0.9999, 5E-5)
 unwind_protect_cleanup
   close (hf);
 end_unwind_protect
1 test, 1 passed, 0 known failure, 0 skipped
[inst/signrank.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/signrank.m
***** test
 load gradespaired.mat
 [p, h, stats] = signrank (gradespaired(:,1), ...
                           gradespaired(:,2), 'tail', 'left');
 assert (p, 0.0047, 1e-4);
 assert (h, true);
 assert (stats.zval, -2.5982, 1e-4);
 assert (stats.signedrank, 2017.5);
***** test
 load ('gradespaired.mat');
 [p, h, stats] = signrank (gradespaired(:,1), gradespaired(:,2), ...
                           'tail', 'left', 'method', 'exact');
 assert (p, 0.0045, 1e-4);
 assert (h, true);
 assert (stats.zval, NaN);
 assert (stats.signedrank, 2017.5);
***** test
 load mileage
 [p, h, stats] = signrank (mileage(:,2), 33);
 assert (p, 0.0312, 1e-4);
 assert (h, true);
 assert (stats.zval, NaN);
 assert (stats.signedrank, 21);
***** test
 load mileage
 [p, h, stats] = signrank (mileage(:,2), 33, 'tail', 'right');
 assert (p, 0.0156, 1e-4);
 assert (h, true);
 assert (stats.zval, NaN);
 assert (stats.signedrank, 21);
***** test
 load mileage
 [p, h, stats] = signrank (mileage(:,2), 33, 'tail', 'right', ...
                           'alpha', 0.01, 'method', 'approximate');
 assert (p, 0.0180, 1e-4);
 assert (h, false);
 assert (stats.zval, 2.0966, 1e-4);
 assert (stats.signedrank, 21);
***** error <signrank: X must be a vector.> signrank (ones (2))
***** error <signrank: Y must be either a scalar of a vector.> ...
 signrank ([1, 2, 3, 4], ones (2))
***** error <signrank: X and Y vectors have different lengths.> ...
 signrank ([1, 2, 3, 4], [1, 2, 3])
***** error <signrank: optional arguments must be in pairs.> ...
 signrank ([1, 2, 3, 4], [], 'tail')
***** error <signrank: 'alpha' must be a numeric scalar in the range 0 to 1.> ...
 signrank ([1, 2, 3, 4], [], 'alpha', 1.2)
***** error <signrank: 'alpha' must be a numeric scalar in the range 0 to 1.> ...
 signrank ([1, 2, 3, 4], [], 'alpha', 0)
***** error <signrank: 'alpha' must be a numeric scalar in the range 0 to 1.> ...
 signrank ([1, 2, 3, 4], [], 'alpha', -0.05)
***** error <signrank: 'alpha' must be a numeric scalar in the range 0 to 1.> ...
 signrank ([1, 2, 3, 4], [], 'alpha', "a")
***** error <signrank: 'alpha' must be a numeric scalar in the range 0 to 1.> ...
 signrank ([1, 2, 3, 4], [], 'alpha', [0.01, 0.05])
***** error <signrank: 'tail' argument must be a character vector.> ...
 signrank ([1, 2, 3, 4], [], 'tail', 0.01)
***** error <signrank: 'tail' argument must be a character vector.> ...
 signrank ([1, 2, 3, 4], [], 'tail', {"both"})
***** error <signrank: 'tail' value must be either 'both', right' or 'left'.> ...
 signrank ([1, 2, 3, 4], [], 'tail', "some")
***** error <signrank: 'tail' value must be either 'both', right' or 'left'.> ...
 signrank ([1, 2, 3, 4], [], 'method', 'exact', 'tail', "some")
***** error <signrank: 'method' argument must be a character vector.> ...
 signrank ([1, 2, 3, 4], [], 'method', 0.01)
***** error <signrank: 'method' argument must be a character vector.> ...
 signrank ([1, 2, 3, 4], [], 'method', {"exact"})
***** error <signrank: 'method' value must be either 'exact' or 'approximate'.> ...
 signrank ([1, 2, 3, 4], [], 'method', "some")
***** error <signrank: 'method' value must be either 'exact' or 'approximate'.> ...
 signrank ([1, 2, 3, 4], [], 'tail', "both", 'method', "some")
22 tests, 22 passed, 0 known failure, 0 skipped
[inst/inconsistent.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/inconsistent.m
***** error inconsistent ()
***** error inconsistent ([1 2 1], 2, 3)
***** error <Z must be .* generated by the linkage .*> inconsistent (ones (2, 2))
***** error <d must be a positive integer scalar> inconsistent ([1 2 1], -1)
***** error <d must be a positive integer scalar> inconsistent ([1 2 1], 1.3)
***** error <d must be a positive integer scalar> inconsistent ([1 2 1], [1 1])
***** error <Z must be .* generated by the linkage .*> inconsistent (ones (2, 3))
***** test
 load fisheriris;
 Z = linkage(meas, 'average', 'chebychev');
 assert (cond (inconsistent (Z)), 39.9, 1e-3);
8 tests, 8 passed, 0 known failure, 0 skipped
[inst/datasample.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/datasample.m
***** error datasample();
***** error datasample(1);
***** error <data must be a vector or matrix> datasample({1, 2, 3}, 1);
***** error <k must be a positive integer scalar> datasample([1 2], -1);
***** error <k must be a positive integer scalar> datasample([1 2], 1.5);
***** error <k must be a positive integer scalar> datasample([1 2], [1 1]);
***** error <k must be a positive integer scalar> datasample([1 2], 'g', [1 1]);
***** error <DIM must be a positive integer scalar> datasample([1 2], 1, -1);
***** error <DIM must be a positive integer scalar> datasample([1 2], 1, 1.5);
***** error <DIM must be a positive integer scalar> datasample([1 2], 1, [1 1]);
***** error <Replace> datasample([1 2], 1, 1, "Replace", -2);
***** error <weights must be defined> datasample([1 2], 1, 1, "Weights", "abc");
***** error <weights must be defined> datasample([1 2], 1, 1, "Weights", [1 -2 3]);
***** error <weights must be defined> datasample([1 2], 1, 1, "Weights", ones (2));
***** error <weights must be equal> datasample([1 2], 1, 1, "Weights", [1 2 3]);
***** test
 dat = randn (10, 4);
 assert (size (datasample (dat, 3, 1)), [3 4]);
***** test
 dat = randn (10, 4);
 assert (size (datasample (dat, 3, 2)), [10 3]);
17 tests, 17 passed, 0 known failure, 0 skipped
[inst/clusterdata.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/clusterdata.m
***** demo
 randn ("seed", 1)  # for reproducibility
 r1 = randn (10, 2) * 0.25 + 1;
 randn ("seed", 5)  # for reproducibility
 r2 = randn (20, 2) * 0.5 - 1;
 X = [r1; r2];

 wnl = warning ("off", "Octave:linkage_savemem", "local");
 T = clusterdata (X, "linkage", "ward", "MaxClust", 2);
 scatter (X(:,1), X(:,2), 36, T, "filled");
***** error<clusterdata: function called with too few input arguments.> ...
 clusterdata ()
***** error<clusterdata: function called with too few input arguments.> ...
 clusterdata (1)
***** error <unknown property .*> clusterdata ([1 1], "Bogus", 1)
***** error <specify .* 'MaxClust' or 'Cutoff' .*> clusterdata ([1 1], "Depth", 1)
4 tests, 4 passed, 0 known failure, 0 skipped
[inst/hotelling_t2test.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/hotelling_t2test.m
***** error<Invalid call to hotelling_t2test.  Correct usage> hotelling_t2test ();
***** error<hotelling_t2test: X must be a vector or a 2D matrix.> ...
 hotelling_t2test (1);
***** error<hotelling_t2test: X must be a vector or a 2D matrix.> ...
 hotelling_t2test (ones(2,2,2));
***** error<hotelling_t2test: invalid value for alpha.> ...
 hotelling_t2test (ones(20,2), [0, 0], "alpha", 1);
***** error<hotelling_t2test: invalid value for alpha.> ...
 hotelling_t2test (ones(20,2), [0, 0], "alpha", -0.2);
***** error<hotelling_t2test: invalid value for alpha.> ...
 hotelling_t2test (ones(20,2), [0, 0], "alpha", "a");
***** error<hotelling_t2test: invalid value for alpha.> ...
 hotelling_t2test (ones(20,2), [0, 0], "alpha", [0.01, 0.05]);
***** error<hotelling_t2test: invalid Name argument.> ...
 hotelling_t2test (ones(20,2), [0, 0], "name", 0.01);
***** error<hotelling_t2test: if X is a vector, M must be a scalar.> ...
 hotelling_t2test (ones(20,1), [0, 0]);
***** error<hotelling_t2test: X must have more rows than columns.> ...
 hotelling_t2test (ones(4,5), [0, 0, 0, 0, 0]);
***** error<hotelling_t2test: if X is a matrix, M must be a vector of length> ...
 hotelling_t2test (ones(20,5), [0, 0, 0, 0]);
***** test
 randn ("seed", 1);
 x = randn (50000, 5);
 [h, pval, stats] = hotelling_t2test (x);
 assert (h, 0);
 assert (stats.df1, 5);
 assert (stats.df2, 49995);
***** test
 randn ("seed", 1);
 x = randn (50000, 5);
 [h, pval, stats] = hotelling_t2test (x, ones (1, 5) * 10);
 assert (h, 1);
 assert (stats.df1, 5);
 assert (stats.df2, 49995);
13 tests, 13 passed, 0 known failure, 0 skipped
[inst/anovan.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/anovan.m
***** demo

 # Two-sample unpaired test on independent samples (equivalent to Student's
 # t-test). Note that the absolute value of t-statistic can be obtained by
 # taking the square root of the reported F statistic. In this example,
 # t = sqrt (1.44) = 1.20.

 score = [54 23 45 54 45 43 34 65 77 46 65]';
 gender = {"male" "male" "male" "male" "male" "female" "female" "female" ...
           "female" "female" "female"}';

 [P, ATAB, STATS] = anovan (score, gender, "display", "on", "varnames", "gender");
***** demo

 # Two-sample paired test on dependent or matched samples equivalent to a
 # paired t-test. As for the first example, the t-statistic can be obtained by
 # taking the square root of the reported F statistic. Note that the interaction
 # between treatment x subject was dropped from the full model by assigning
 # subject as a random factor (').

 score = [4.5 5.6; 3.7 6.4; 5.3 6.4; 5.4 6.0; 3.9 5.7]';
 treatment = {"before" "after"; "before" "after"; "before" "after";
              "before" "after"; "before" "after"}';
 subject = {"GS" "GS"; "JM" "JM"; "HM" "HM"; "JW" "JW"; "PS" "PS"}';

 [P, ATAB, STATS] = anovan (score(:), {treatment(:), subject(:)}, ...
                            "model", "full", "random", 2, "sstype", 2, ...
                            "varnames", {"treatment", "subject"}, ...
                            "display", "on");
***** demo

 # One-way ANOVA on the data from a study on the strength of structural beams,
 # in Hogg and Ledolter (1987) Engineering Statistics. New York: MacMillan

 strength = [82 86 79 83 84 85 86 87 74 82 ...
            78 75 76 77 79 79 77 78 82 79]';
 alloy = {"st","st","st","st","st","st","st","st", ...
          "al1","al1","al1","al1","al1","al1", ...
          "al2","al2","al2","al2","al2","al2"}';

 [P, ATAB, STATS] = anovan (strength, alloy, "display", "on", ...
                            "varnames", "alloy");
***** demo

 # One-way repeated measures ANOVA on the data from a study on the number of
 # words recalled by 10 subjects for three time condtions, in Loftus & Masson
 # (1994) Psychon Bull Rev. 1(4):476-490, Table 2. Note that the interaction
 # between seconds x subject was dropped from the full model by assigning
 # subject as a random factor (').

 words = [10 13 13; 6 8 8; 11 14 14; 22 23 25; 16 18 20; ...
          15 17 17; 1 1 4; 12 15 17;  9 12 12;  8 9 12];
 seconds = [1 2 5; 1 2 5; 1 2 5; 1 2 5; 1 2 5; ...
            1 2 5; 1 2 5; 1 2 5; 1 2 5; 1 2 5;];
 subject = [ 1  1  1;  2  2  2;  3  3  3;  4  4  4;  5  5  5; ...
             6  6  6;  7  7  7;  8  8  8;  9  9  9; 10 10 10];

 [P, ATAB, STATS] = anovan (words(:), {seconds(:), subject(:)}, ...
                            "model", "full", "random", 2, "sstype", 2, ...
                            "display", "on", "varnames", {"seconds", "subject"});
***** demo

 # Balanced two-way ANOVA with interaction on the data from a study of popcorn
 # brands and popper types, in Hogg and Ledolter (1987) Engineering Statistics.
 # New York: MacMillan

 popcorn = [5.5, 4.5, 3.5; 5.5, 4.5, 4.0; 6.0, 4.0, 3.0; ...
            6.5, 5.0, 4.0; 7.0, 5.5, 5.0; 7.0, 5.0, 4.5];
 brands = {"Gourmet", "National", "Generic"; ...
           "Gourmet", "National", "Generic"; ...
           "Gourmet", "National", "Generic"; ...
           "Gourmet", "National", "Generic"; ...
           "Gourmet", "National", "Generic"; ...
           "Gourmet", "National", "Generic"};
 popper = {"oil", "oil", "oil"; "oil", "oil", "oil"; "oil", "oil", "oil"; ...
           "air", "air", "air"; "air", "air", "air"; "air", "air", "air"};

 [P, ATAB, STATS] = anovan (popcorn(:), {brands(:), popper(:)}, ...
                            "display", "on", "model", "full", ...
                            "varnames", {"brands", "popper"});
***** demo

 # Unbalanced two-way ANOVA (2x2) on the data from a study on the effects of
 # gender and having a college degree on salaries of company employees,
 # in Maxwell, Delaney and Kelly (2018): Chapter 7, Table 15

 salary = [24 26 25 24 27 24 27 23 15 17 20 16, ...
           25 29 27 19 18 21 20 21 22 19]';
 gender = {"f" "f" "f" "f" "f" "f" "f" "f" "f" "f" "f" "f"...
           "m" "m" "m" "m" "m" "m" "m" "m" "m" "m"}';
 degree = [1 1 1 1 1 1 1 1 0 0 0 0 1 1 1 0 0 0 0 0 0 0]';

 [P, ATAB, STATS] = anovan (salary, {gender, degree}, "model", "full", ...
                            "sstype", 3, "display", "on", "varnames", ...
                            {"gender", "degree"});
***** demo

 # Unbalanced two-way ANOVA (3x2) on the data from a study of the effect of
 # adding sugar and/or milk on the tendency of coffee to make people babble,
 # in from Navarro (2019): 16.10

 sugar = {"real" "fake" "fake" "real" "real" "real" "none" "none" "none" ...
          "fake" "fake" "fake" "real" "real" "real" "none" "none" "fake"}';
 milk = {"yes" "no" "no" "yes" "yes" "no" "yes" "yes" "yes" ...
         "no" "no" "yes" "no" "no" "no" "no" "no" "yes"}';
 babble = [4.6 4.4 3.9 5.6 5.1 5.5 3.9 3.5 3.7...
           5.6 4.7 5.9 6.0 5.4 6.6 5.8 5.3 5.7]';

 [P, ATAB, STATS] = anovan (babble, {sugar, milk}, "model", "full",  ...
                            "sstype", 3, "display", "on", ...
                            "varnames", {"sugar", "milk"});
***** demo

 # Unbalanced three-way ANOVA (3x2x2) on the data from a study of the effects
 # of three different drugs, biofeedback and diet on patient blood pressure,
 # adapted* from Maxwell, Delaney and Kelly (2018): Chapter 8, Table 12
 # * Missing values introduced to make the sample sizes unequal to test the
 #   calculation of different types of sums-of-squares

 drug = {"X" "X" "X" "X" "X" "X" "X" "X" "X" "X" "X" "X" ...
         "X" "X" "X" "X" "X" "X" "X" "X" "X" "X" "X" "X";
         "Y" "Y" "Y" "Y" "Y" "Y" "Y" "Y" "Y" "Y" "Y" "Y" ...
         "Y" "Y" "Y" "Y" "Y" "Y" "Y" "Y" "Y" "Y" "Y" "Y";
         "Z" "Z" "Z" "Z" "Z" "Z" "Z" "Z" "Z" "Z" "Z" "Z" ...
         "Z" "Z" "Z" "Z" "Z" "Z" "Z" "Z" "Z" "Z" "Z" "Z"};
 feedback = [1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0;
             1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0;
             1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0];
 diet = [0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 1 1 1;
         0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 1 1 1;
         0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 1 1 1];
 BP = [170 175 165 180 160 158 161 173 157 152 181 190 ...
       173 194 197 190 176 198 164 190 169 164 176 175;
       186 194 201 215 219 209 164 166 159 182 187 174 ...
       189 194 217 206 199 195 171 173 196 199 180 NaN;
       180 187 199 170 204 194 162 184 183 156 180 173 ...
       202 228 190 206 224 204 205 199 170 160 NaN NaN];

 [P, ATAB, STATS] = anovan (BP(:), {drug(:), feedback(:), diet(:)}, ...
                                    "model", "full", "sstype", 3, ...
                                    "display", "on", ...
                                    "varnames", {"drug", "feedback", "diet"});
***** demo

 # Balanced three-way ANOVA (2x2x2) with one of the factors being a blocking
 # factor. The data is from a randomized block design study on the effects
 # of antioxidant treatment on glutathione-S-transferase (GST) levels in
 # different mouse strains, from Festing (2014), ILAR Journal, 55(3):427-476.
 # Note that all interactions involving block were dropped from the full model
 # by assigning block as a random factor (').

 measurement = [444 614 423 625 408  856 447 719 ...
                764 831 586 782 609 1002 606 766]';
 strain= {"NIH","NIH","BALB/C","BALB/C","A/J","A/J","129/Ola","129/Ola", ...
          "NIH","NIH","BALB/C","BALB/C","A/J","A/J","129/Ola","129/Ola"}';
 treatment={"C" "T" "C" "T" "C" "T" "C" "T" "C" "T" "C" "T" "C" "T" "C" "T"}';
 block = [1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2]';

 [P, ATAB, STATS] = anovan (measurement/10, {strain, treatment, block}, ...
                            "sstype", 2, "model", "full", "random", 3, ...
                            "display", "on", ...
                            "varnames", {"strain", "treatment", "block"});
***** demo

 # One-way ANCOVA on data from a study of the additive effects of species
 # and temperature on chirpy pulses of crickets, from Stitch, The Worst Stats
 # Text eveR

 pulse = [67.9 65.1 77.3 78.7 79.4 80.4 85.8 86.6 87.5 89.1 ...
          98.6 100.8 99.3 101.7 44.3 47.2 47.6 49.6 50.3 51.8 ...
          60 58.5 58.9 60.7 69.8 70.9 76.2 76.1 77 77.7 84.7]';
 temp = [20.8 20.8 24 24 24 24 26.2 26.2 26.2 26.2 28.4 ...
         29 30.4 30.4 17.2 18.3 18.3 18.3 18.9 18.9 20.4 ...
         21 21 22.1 23.5 24.2 25.9 26.5 26.5 26.5 28.6]';
 species = {"ex" "ex" "ex" "ex" "ex" "ex" "ex" "ex" "ex" "ex" "ex" ...
            "ex" "ex" "ex" "niv" "niv" "niv" "niv" "niv" "niv" "niv" ...
            "niv" "niv" "niv" "niv" "niv" "niv" "niv" "niv" "niv" "niv"};

 [P, ATAB, STATS] = anovan (pulse, {species, temp}, "model", "linear", ...
                           "continuous", 2, "sstype", "h", "display", "on", ...
                           "varnames", {"species", "temp"});
***** demo

 # Factorial ANCOVA on data from a study of the effects of treatment and
 # exercise on stress reduction score after adjusting for age. Data from R
 # datarium package).

 score = [95.6 82.2 97.2 96.4 81.4 83.6 89.4 83.8 83.3 85.7 ...
          97.2 78.2 78.9 91.8 86.9 84.1 88.6 89.8 87.3 85.4 ...
          81.8 65.8 68.1 70.0 69.9 75.1 72.3 70.9 71.5 72.5 ...
          84.9 96.1 94.6 82.5 90.7 87.0 86.8 93.3 87.6 92.4 ...
          100. 80.5 92.9 84.0 88.4 91.1 85.7 91.3 92.3 87.9 ...
          91.7 88.6 75.8 75.7 75.3 82.4 80.1 86.0 81.8 82.5]';
 treatment = {"yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" ...
              "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" ...
              "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" ...
              "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  ...
              "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  ...
              "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"}';
 exercise = {"lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  ...
             "mid" "mid" "mid" "mid" "mid" "mid" "mid" "mid" "mid" "mid" ...
             "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  ...
             "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  ...
             "mid" "mid" "mid" "mid" "mid" "mid" "mid" "mid" "mid" "mid" ...
             "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"}';
 age = [59 65 70 66 61 65 57 61 58 55 62 61 60 59 55 57 60 63 62 57 ...
        58 56 57 59 59 60 55 53 55 58 68 62 61 54 59 63 60 67 60 67 ...
        75 54 57 62 65 60 58 61 65 57 56 58 58 58 52 53 60 62 61 61]';

 [P, ATAB, STATS] = anovan (score, {treatment, exercise, age}, ...
                            "model", [1 0 0; 0 1 0; 0 0 1; 1 1 0], ...
                            "continuous", 3, "sstype", "h", "display", "on", ...
                            "varnames", {"treatment", "exercise", "age"});
***** demo

 # Unbalanced one-way ANOVA with custom, orthogonal contrasts. The statistics
 # relating to the contrasts are shown in the table of model parameters, and
 # can be retrieved from the STATS.coeffs output.

 dv =  [ 8.706 10.362 11.552  6.941 10.983 10.092  6.421 14.943 15.931 ...
        22.968 18.590 16.567 15.944 21.637 14.492 17.965 18.851 22.891 ...
        22.028 16.884 17.252 18.325 25.435 19.141 21.238 22.196 18.038 ...
        22.628 31.163 26.053 24.419 32.145 28.966 30.207 29.142 33.212 ...
        25.694 ]';
 g = [1 1 1 1 1 1 1 1 2 2 2 2 2 3 3 3 3 3 3 3 3 ...
      4 4 4 4 4 4 4 5 5 5 5 5 5 5 5 5]';
 C = [ 0.4001601  0.3333333  0.5  0.0
       0.4001601  0.3333333 -0.5  0.0
       0.4001601 -0.6666667  0.0  0.0
      -0.6002401  0.0000000  0.0  0.5
      -0.6002401  0.0000000  0.0 -0.5];

 [P,ATAB, STATS] = anovan (dv, g, "contrasts", C, "varnames", "score", ...
                          "alpha", 0.05, "display", "on");
***** demo

 # One-way ANOVA with the linear model fit by weighted least squares to
 # account for heteroskedasticity. In this example, the variance appears
 # proportional to the outcome, so weights have been estimated by initially
 # fitting the model without weights and regressing the absolute residuals on
 # the fitted values. Although this data could have been analysed by Welch's
 # ANOVA test, the approach here can generalize to ANOVA models with more than
 # one factor.

 g = [1, 1, 1, 1, 1, 1, 1, 1, ...
      2, 2, 2, 2, 2, 2, 2, 2, ...
      3, 3, 3, 3, 3, 3, 3, 3]';
 y = [13, 16, 16,  7, 11,  5,  1,  9, ...
      10, 25, 66, 43, 47, 56,  6, 39, ...
      11, 39, 26, 35, 25, 14, 24, 17]';

 [P,ATAB,STATS] = anovan(y, g, "display", "off");
 fitted = STATS.X * STATS.coeffs(:,1); # fitted values
 b = polyfit (fitted, abs (STATS.resid), 1);
 v = polyval (b, fitted);  # Variance as a function of the fitted values
 figure("Name", "Regression of the absolute residuals on the fitted values");
 plot (fitted, abs (STATS.resid),'ob');hold on; plot(fitted,v,'-r'); hold off;
 xlabel("Fitted values"); ylabel("Absolute residuals");

 [P,ATAB,STATS] = anovan (y, g, "weights", v.^-1);
***** test
 score = [54 23 45 54 45 43 34 65 77 46 65]';
 gender = {'male' 'male' 'male' 'male' 'male' 'female' 'female' 'female' ...
           'female' 'female' 'female'}';

 [P, T, STATS] = anovan (score,gender,'display','off');
 assert (P(1), 0.2612876773271042, 1e-09);              # compared to p calculated by MATLAB anovan
 assert (sqrt(T{2,6}), abs(1.198608733288208), 1e-09);  # compared to abs(t) calculated from sqrt(F) by MATLAB anovan
 assert (P(1), 0.2612876773271047, 1e-09);              # compared to p calculated by MATLAB ttest2
 assert (sqrt(T{2,6}), abs(-1.198608733288208), 1e-09); # compared to abs(t) calculated by MATLAB ttest2
***** test
 score = [4.5 5.6; 3.7 6.4; 5.3 6.4; 5.4 6.0; 3.9 5.7]';
 treatment = {'before' 'after'; 'before' 'after'; 'before' 'after';
              'before' 'after'; 'before' 'after'}';
 subject = {'GS' 'GS'; 'JM' 'JM'; 'HM' 'HM'; 'JW' 'JW'; 'PS' 'PS'}';

 [P, ATAB, STATS] = anovan (score(:),{treatment(:),subject(:)},'display','off','sstype',2);
 assert (P(1), 0.016004356735364, 1e-09);              # compared to p calculated by MATLAB anovan
 assert (sqrt(ATAB{2,6}), abs(4.00941576558195), 1e-09);  # compared to abs(t) calculated from sqrt(F) by MATLAB anovan
 assert (P(1), 0.016004356735364, 1e-09);              # compared to p calculated by MATLAB ttest2
 assert (sqrt(ATAB{2,6}), abs(-4.00941576558195), 1e-09); # compared to abs(t) calculated by MATLAB ttest2
***** test
 strength = [82 86 79 83 84 85 86 87 74 82 ...
            78 75 76 77 79 79 77 78 82 79]';
 alloy = {'st','st','st','st','st','st','st','st', ...
          'al1','al1','al1','al1','al1','al1', ...
          'al2','al2','al2','al2','al2','al2'}';

 [P, ATAB, STATS] = anovan (strength,{alloy},'display','off');
 assert (P(1), 0.000152643638830491, 1e-09);
 assert (ATAB{2,6}, 15.4, 1e-09);
***** test
 words = [10 13 13; 6 8 8; 11 14 14; 22 23 25; 16 18 20; ...
          15 17 17; 1 1 4; 12 15 17;  9 12 12;  8 9 12];
 subject = [ 1  1  1;  2  2  2;  3  3  3;  4  4  4;  5  5  5; ...
             6  6  6;  7  7  7;  8  8  8;  9  9  9; 10 10 10];
 seconds = [1 2 5; 1 2 5; 1 2 5; 1 2 5; 1 2 5; ...
            1 2 5; 1 2 5; 1 2 5; 1 2 5; 1 2 5;];

 [P, ATAB, STATS] = anovan (words(:),{seconds(:),subject(:)},'model','full','random',2,'sstype',2,'display','off');
 assert (P(1), 1.51865926758752e-07, 1e-09);
 assert (ATAB{2,2}, 52.2666666666667, 1e-09);
 assert (ATAB{3,2}, 942.533333333333, 1e-09);
 assert (ATAB{4,2}, 11.0666666666667, 1e-09);
***** test
 popcorn = [5.5, 4.5, 3.5; 5.5, 4.5, 4.0; 6.0, 4.0, 3.0; ...
            6.5, 5.0, 4.0; 7.0, 5.5, 5.0; 7.0, 5.0, 4.5];
 brands = {'Gourmet', 'National', 'Generic'; ...
           'Gourmet', 'National', 'Generic'; ...
           'Gourmet', 'National', 'Generic'; ...
           'Gourmet', 'National', 'Generic'; ...
           'Gourmet', 'National', 'Generic'; ...
           'Gourmet', 'National', 'Generic'};
 popper = {'oil', 'oil', 'oil'; 'oil', 'oil', 'oil'; 'oil', 'oil', 'oil'; ...
           'air', 'air', 'air'; 'air', 'air', 'air'; 'air', 'air', 'air'};

 [P, ATAB, STATS] = anovan (popcorn(:),{brands(:),popper(:)},'display','off','model','full');
 assert (P(1), 7.67895738278171e-07, 1e-09);
 assert (P(2), 0.000100373896304998, 1e-09);
 assert (P(3), 0.746215396636649, 1e-09);
 assert (ATAB{2,6}, 56.7, 1e-09);
 assert (ATAB{3,6}, 32.4, 1e-09);
 assert (ATAB{4,6}, 0.29999999999997, 1e-09);
***** test
 salary = [24 26 25 24 27 24 27 23 15 17 20 16, ...
           25 29 27 19 18 21 20 21 22 19]';
 gender = {'f' 'f' 'f' 'f' 'f' 'f' 'f' 'f' 'f' 'f' 'f' 'f'...
           'm' 'm' 'm' 'm' 'm' 'm' 'm' 'm' 'm' 'm'}';
 degree = [1 1 1 1 1 1 1 1 0 0 0 0 1 1 1 0 0 0 0 0 0 0]';

 [P, ATAB, STATS] = anovan (salary,{gender,degree},'model','full','sstype',1,'display','off');
 assert (P(1), 0.747462549227232, 1e-09);
 assert (P(2), 1.03809316857694e-08, 1e-09);
 assert (P(3), 0.523689833702691, 1e-09);
 assert (ATAB{2,2}, 0.296969696969699, 1e-09);
 assert (ATAB{3,2}, 272.391841491841, 1e-09);
 assert (ATAB{4,2}, 1.17482517482512, 1e-09);
 assert (ATAB{5,2}, 50.0000000000001, 1e-09);
 [P, ATAB, STATS] = anovan (salary,{degree,gender},'model','full','sstype',1,'display','off');
 assert (P(1), 2.53445097305047e-08, 1e-09);
 assert (P(2), 0.00388133678528749, 1e-09);
 assert (P(3), 0.523689833702671, 1e-09);
 assert (ATAB{2,2}, 242.227272727273, 1e-09);
 assert (ATAB{3,2}, 30.4615384615384, 1e-09);
 assert (ATAB{4,2}, 1.17482517482523, 1e-09);
 assert (ATAB{5,2}, 50.0000000000001, 1e-09);
 [P, ATAB, STATS] = anovan (salary,{gender,degree},'model','full','sstype',2,'display','off');
 assert (P(1), 0.00388133678528743, 1e-09);
 assert (P(2), 1.03809316857694e-08, 1e-09);
 assert (P(3), 0.523689833702691, 1e-09);
 assert (ATAB{2,2}, 30.4615384615385, 1e-09);
 assert (ATAB{3,2}, 272.391841491841, 1e-09);
 assert (ATAB{4,2}, 1.17482517482512, 1e-09);
 assert (ATAB{5,2}, 50.0000000000001, 1e-09);
 [P, ATAB, STATS] = anovan (salary,{gender,degree},'model','full','sstype',3,'display','off');
 assert (P(1), 0.00442898146583742, 1e-09);
 assert (P(2), 1.30634252053587e-08, 1e-09);
 assert (P(3), 0.523689833702691, 1e-09);
 assert (ATAB{2,2}, 29.3706293706294, 1e-09);
 assert (ATAB{3,2}, 264.335664335664, 1e-09);
 assert (ATAB{4,2}, 1.17482517482512, 1e-09);
 assert (ATAB{5,2}, 50.0000000000001, 1e-09);
***** test
 sugar = {'real' 'fake' 'fake' 'real' 'real' 'real' 'none' 'none' 'none' ...
          'fake' 'fake' 'fake' 'real' 'real' 'real' 'none' 'none' 'fake'}';
 milk = {'yes' 'no' 'no' 'yes' 'yes' 'no' 'yes' 'yes' 'yes' ...
         'no' 'no' 'yes' 'no' 'no' 'no' 'no' 'no' 'yes'}';
 babble = [4.6 4.4 3.9 5.6 5.1 5.5 3.9 3.5 3.7...
           5.6 4.7 5.9 6.0 5.4 6.6 5.8 5.3 5.7]';

 [P, ATAB, STATS] = anovan (babble,{sugar,milk},'model','full','sstype',1,'display','off');
 assert (P(1), 0.0108632139833963, 1e-09);
 assert (P(2), 0.0810606976703546, 1e-09);
 assert (P(3), 0.00175433329935627, 1e-09);
 assert (ATAB{2,2}, 3.55752380952381, 1e-09);
 assert (ATAB{3,2}, 0.956108477471702, 1e-09);
 assert (ATAB{4,2}, 5.94386771300448, 1e-09);
 assert (ATAB{5,2}, 3.1625, 1e-09);
 [P, ATAB, STATS] = anovan (babble,{milk,sugar},'model','full','sstype',1,'display','off');
 assert (P(1), 0.0373333189297505, 1e-09);
 assert (P(2), 0.017075098787169, 1e-09);
 assert (P(3), 0.00175433329935627, 1e-09);
 assert (ATAB{2,2}, 1.444, 1e-09);
 assert (ATAB{3,2}, 3.06963228699552, 1e-09);
 assert (ATAB{4,2}, 5.94386771300448, 1e-09);
 assert (ATAB{5,2}, 3.1625, 1e-09);
 [P, ATAB, STATS] = anovan (babble,{sugar,milk},'model','full','sstype',2,'display','off');
 assert (P(1), 0.017075098787169, 1e-09);
 assert (P(2), 0.0810606976703546, 1e-09);
 assert (P(3), 0.00175433329935627, 1e-09);
 assert (ATAB{2,2}, 3.06963228699552, 1e-09);
 assert (ATAB{3,2}, 0.956108477471702, 1e-09);
 assert (ATAB{4,2}, 5.94386771300448, 1e-09);
 assert (ATAB{5,2}, 3.1625,  1e-09);
 [P, ATAB, STATS] = anovan (babble,{sugar,milk},'model','full','sstype',3,'display','off');
 assert (P(1), 0.0454263063473954, 1e-09);
 assert (P(2), 0.0746719907091438, 1e-09);
 assert (P(3), 0.00175433329935627, 1e-09);
 assert (ATAB{2,2}, 2.13184977578476, 1e-09);
 assert (ATAB{3,2}, 1.00413461538462, 1e-09);
 assert (ATAB{4,2}, 5.94386771300448, 1e-09);
 assert (ATAB{5,2}, 3.1625, 1e-09);
***** test
 drug = {'X' 'X' 'X' 'X' 'X' 'X' 'X' 'X' 'X' 'X' 'X' 'X' ...
         'X' 'X' 'X' 'X' 'X' 'X' 'X' 'X' 'X' 'X' 'X' 'X';
         'Y' 'Y' 'Y' 'Y' 'Y' 'Y' 'Y' 'Y' 'Y' 'Y' 'Y' 'Y' ...
         'Y' 'Y' 'Y' 'Y' 'Y' 'Y' 'Y' 'Y' 'Y' 'Y' 'Y' 'Y';
         'Z' 'Z' 'Z' 'Z' 'Z' 'Z' 'Z' 'Z' 'Z' 'Z' 'Z' 'Z' ...
         'Z' 'Z' 'Z' 'Z' 'Z' 'Z' 'Z' 'Z' 'Z' 'Z' 'Z' 'Z'};
 feedback = [1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0;
             1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0;
             1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0];
 diet = [0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 1 1 1;
         0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 1 1 1;
         0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 1 1 1];
 BP = [170 175 165 180 160 158 161 173 157 152 181 190 ...
       173 194 197 190 176 198 164 190 169 164 176 175;
       186 194 201 215 219 209 164 166 159 182 187 174 ...
       189 194 217 206 199 195 171 173 196 199 180 NaN;
       180 187 199 170 204 194 162 184 183 156 180 173 ...
       202 228 190 206 224 204 205 199 170 160 NaN NaN];

 [P, ATAB, STATS] = anovan (BP(:),{drug(:),feedback(:),diet(:)},'model','full','sstype', 1,'display','off');
 assert (P(1), 7.02561843825325e-05, 1e-09);
 assert (P(2), 0.000425806013389362, 1e-09);
 assert (P(3), 6.16780773446401e-07, 1e-09);
 assert (P(4), 0.261347622678438, 1e-09);
 assert (P(5), 0.0542278432357043, 1e-09);
 assert (P(6), 0.590353225626655, 1e-09);
 assert (P(7), 0.0861628249564267, 1e-09);
 assert (ATAB{2,2}, 3614.70355731226, 1e-09);
 assert (ATAB{3,2}, 2227.46639771024, 1e-09);
 assert (ATAB{4,2}, 5008.25614451819, 1e-09);
 assert (ATAB{5,2}, 437.066007908781, 1e-09);
 assert (ATAB{6,2}, 976.180770397332, 1e-09);
 assert (ATAB{7,2}, 46.616653365254, 1e-09);
 assert (ATAB{8,2}, 814.345251396648, 1e-09);
 assert (ATAB{9,2}, 9065.8,  1e-09);
 [P, ATAB, STATS] = anovan (BP(:),{drug(:),feedback(:),diet(:)},'model','full','sstype',2,'display','off');
 assert (P(1), 9.4879638470754e-05, 1e-09);
 assert (P(2), 0.00124177666315809, 1e-09);
 assert (P(3), 6.86162012732911e-07, 1e-09);
 assert (P(4), 0.260856132341256, 1e-09);
 assert (P(5), 0.0523758623892078, 1e-09);
 assert (P(6), 0.590353225626655, 1e-09);
 assert (P(7), 0.0861628249564267, 1e-09);
 assert (ATAB{2,2}, 3481.72176560122, 1e-09);
 assert (ATAB{3,2}, 1837.08812970469, 1e-09);
 assert (ATAB{4,2}, 4957.20277938622, 1e-09);
 assert (ATAB{5,2}, 437.693674777847, 1e-09);
 assert (ATAB{6,2}, 988.431929811402, 1e-09);
 assert (ATAB{7,2}, 46.616653365254, 1e-09);
 assert (ATAB{8,2}, 814.345251396648, 1e-09);
 assert (ATAB{9,2}, 9065.8,  1e-09);
 [P, ATAB, STATS] = anovan (BP(:),{drug(:),feedback(:),diet(:)},'model','full','sstype', 3,'display','off');
 assert (P(1), 0.000106518678028207, 1e-09);
 assert (P(2), 0.00125371366571508, 1e-09);
 assert (P(3), 5.30813260778464e-07, 1e-09);
 assert (P(4), 0.308353667232981, 1e-09);
 assert (P(5), 0.0562901327343161, 1e-09);
 assert (P(6), 0.599091042141092, 1e-09);
 assert (P(7), 0.0861628249564267, 1e-09);
 assert (ATAB{2,2}, 3430.88156424581, 1e-09);
 assert (ATAB{3,2}, 1833.68031496063, 1e-09);
 assert (ATAB{4,2}, 5080.48346456693, 1e-09);
 assert (ATAB{5,2}, 382.07709497207, 1e-09);
 assert (ATAB{6,2}, 963.037988826813, 1e-09);
 assert (ATAB{7,2}, 44.4519685039322, 1e-09);
 assert (ATAB{8,2}, 814.345251396648, 1e-09);
 assert (ATAB{9,2}, 9065.8, 1e-09);
***** test
 measurement = [444 614 423 625 408  856 447 719 ...
                764 831 586 782 609 1002 606 766]';
 strain= {'NIH','NIH','BALB/C','BALB/C','A/J','A/J','129/Ola','129/Ola', ...
          'NIH','NIH','BALB/C','BALB/C','A/J','A/J','129/Ola','129/Ola'}';
 treatment={'C' 'T' 'C' 'T' 'C' 'T' 'C' 'T' 'C' 'T' 'C' 'T' 'C' 'T' 'C' 'T'}';
 block = [1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2]';

 [P, ATAB, STATS] = anovan (measurement/10,{strain,treatment,block},'model','full','random',3,'display','off');
 assert (P(1), 0.0914352969909372, 1e-09);
 assert (P(2), 5.04077373924908e-05, 1e-09);
 assert (P(4), 0.0283196918836667, 1e-09);
 assert (ATAB{2,2}, 286.132500000002, 1e-09);
 assert (ATAB{3,2}, 2275.29, 1e-09);
 assert (ATAB{4,2}, 1242.5625, 1e-09);
 assert (ATAB{5,2}, 495.905000000001, 1e-09);
 assert (ATAB{6,2}, 207.007499999999, 1e-09);
***** test
 pulse = [67.9 65.1 77.3 78.7 79.4 80.4 85.8 86.6 87.5 89.1 ...
          98.6 100.8 99.3 101.7 44.3 47.2 47.6 49.6 50.3 51.8 ...
          60 58.5 58.9 60.7 69.8 70.9 76.2 76.1 77 77.7 84.7]';
 temp = [20.8 20.8 24 24 24 24 26.2 26.2 26.2 26.2 28.4 ...
         29 30.4 30.4 17.2 18.3 18.3 18.3 18.9 18.9 20.4 ...
         21 21 22.1 23.5 24.2 25.9 26.5 26.5 26.5 28.6]';
 species = {'ex' 'ex' 'ex' 'ex' 'ex' 'ex' 'ex' 'ex' 'ex' 'ex' 'ex' ...
            'ex' 'ex' 'ex' 'niv' 'niv' 'niv' 'niv' 'niv' 'niv' 'niv' ...
            'niv' 'niv' 'niv' 'niv' 'niv' 'niv' 'niv' 'niv' 'niv' 'niv'};

 [P, ATAB, STATS] = anovan (pulse,{species,temp},'model','linear','continuous',2,'sstype','h','display','off');
 assert (P(1), 6.27153318786007e-14, 1e-09);
 assert (P(2), 2.48773241196644e-25, 1e-09);
 assert (ATAB{2,2}, 598.003953318404, 1e-09);
 assert (ATAB{3,2}, 4376.08256843712, 1e-09);
 assert (ATAB{4,2}, 89.3498685376726, 1e-09);
 assert (ATAB{2,6}, 187.399388123951, 1e-09);
 assert (ATAB{3,6}, 1371.35413763454, 1e-09);
***** test
 score = [95.6 82.2 97.2 96.4 81.4 83.6 89.4 83.8 83.3 85.7 ...
          97.2 78.2 78.9 91.8 86.9 84.1 88.6 89.8 87.3 85.4 ...
          81.8 65.8 68.1 70.0 69.9 75.1 72.3 70.9 71.5 72.5 ...
          84.9 96.1 94.6 82.5 90.7 87.0 86.8 93.3 87.6 92.4 ...
          100. 80.5 92.9 84.0 88.4 91.1 85.7 91.3 92.3 87.9 ...
          91.7 88.6 75.8 75.7 75.3 82.4 80.1 86.0 81.8 82.5]';
 treatment = {'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' ...
              'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' ...
              'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' ...
              'no'  'no'  'no'  'no'  'no'  'no'  'no'  'no'  'no'  'no'  ...
              'no'  'no'  'no'  'no'  'no'  'no'  'no'  'no'  'no'  'no'  ...
              'no'  'no'  'no'  'no'  'no'  'no'  'no'  'no'  'no'  'no'}';
 exercise = {'lo'  'lo'  'lo'  'lo'  'lo'  'lo'  'lo'  'lo'  'lo'  'lo'  ...
             'mid' 'mid' 'mid' 'mid' 'mid' 'mid' 'mid' 'mid' 'mid' 'mid' ...
             'hi'  'hi'  'hi'  'hi'  'hi'  'hi'  'hi'  'hi'  'hi'  'hi'  ...
             'lo'  'lo'  'lo'  'lo'  'lo'  'lo'  'lo'  'lo'  'lo'  'lo'  ...
             'mid' 'mid' 'mid' 'mid' 'mid' 'mid' 'mid' 'mid' 'mid' 'mid' ...
             'hi'  'hi'  'hi'  'hi'  'hi'  'hi'  'hi'  'hi'  'hi'  'hi'}';
 age = [59 65 70 66 61 65 57 61 58 55 62 61 60 59 55 57 60 63 62 57 ...
        58 56 57 59 59 60 55 53 55 58 68 62 61 54 59 63 60 67 60 67 ...
        75 54 57 62 65 60 58 61 65 57 56 58 58 58 52 53 60 62 61 61]';

 [P, ATAB, STATS] = anovan (score,{treatment,exercise,age},'model','full','continuous',3,'sstype','h','display','off');
 assert (P(5), 0.9245630968248468, 1e-09);
 assert (P(6), 0.791115159521822, 1e-09);
 assert (P(7), 0.9296668751457956, 1e-09);
 [P, ATAB, STATS] = anovan (score,{treatment,exercise,age},'model',[1 0 0; 0 1 0; 0 0 1; 1 1 0],'continuous',3,'sstype','h','display','off');
 assert (P(1), 0.00158132928938933, 1e-09);
 assert (P(2), 2.12537505039986e-07, 1e-09);
 assert (P(3), 0.00390292555160047, 1e-09);
 assert (P(4), 0.0164086580775543, 1e-09);
 assert (ATAB{2,6}, 11.0956027650549, 1e-09);
 assert (ATAB{3,6}, 20.8195665467178, 1e-09);
 assert (ATAB{4,6}, 9.10966630720186, 1e-09);
 assert (ATAB{5,6}, 4.4457923698584, 1e-09);
***** test
 dv =  [ 8.706 10.362 11.552  6.941 10.983 10.092  6.421 14.943 15.931 ...
        22.968 18.590 16.567 15.944 21.637 14.492 17.965 18.851 22.891 ...
        22.028 16.884 17.252 18.325 25.435 19.141 21.238 22.196 18.038 ...
        22.628 31.163 26.053 24.419 32.145 28.966 30.207 29.142 33.212 ...
        25.694 ]';
 g = [1 1 1 1 1 1 1 1 2 2 2 2 2 3 3 3 3 3 3 3 3 4 4 4 4 4 4 4 5 5 5 5 5 5 5 5 5]';
 C = [ 0.4001601  0.3333333  0.5  0.0
       0.4001601  0.3333333 -0.5  0.0
       0.4001601 -0.6666667  0.0  0.0
      -0.6002401  0.0000000  0.0  0.5
      -0.6002401  0.0000000  0.0 -0.5];

 [P,ATAB,STATS] = anovan (dv,g,'contrasts',{C},'display','off');
 assert (STATS.coeffs(1,1), 19.4001, 1e-04);
 assert (STATS.coeffs(2,1), -9.3297, 1e-04);
 assert (STATS.coeffs(3,1), -5.0000, 1e-04);
 assert (STATS.coeffs(4,1), -8.0000, 1e-04);
 assert (STATS.coeffs(5,1), -8.0000, 1e-04);
 assert (STATS.coeffs(1,2), 0.4831, 1e-04);
 assert (STATS.coeffs(2,2), 0.9694, 1e-04);
 assert (STATS.coeffs(3,2), 1.3073, 1e-04);
 assert (STATS.coeffs(4,2), 1.6411, 1e-04);
 assert (STATS.coeffs(5,2), 1.4507, 1e-04);
 assert (STATS.coeffs(1,5), 40.161, 1e-03);
 assert (STATS.coeffs(2,5), -9.624, 1e-03);
 assert (STATS.coeffs(3,5), -3.825, 1e-03);
 assert (STATS.coeffs(4,5), -4.875, 1e-03);
 assert (STATS.coeffs(5,5), -5.515, 1e-03);
 assert (STATS.coeffs(2,6), 5.74e-11, 1e-12);
 assert (STATS.coeffs(3,6), 0.000572, 1e-06);
 assert (STATS.coeffs(4,6), 2.86e-05, 1e-07);
 assert (STATS.coeffs(5,6), 4.44e-06, 1e-08);
12 tests, 12 passed, 0 known failure, 0 skipped
[inst/trimmean.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/trimmean.m
***** test
 x = reshape (1:40, [5, 4, 2]);
 x([3, 37]) = -100;
 assert (trimmean (x, 10, "all"), 19.4722, 1e-4);
***** test
 x = reshape (1:40, [5, 4, 2]);
 x([3, 37]) = -100;
 out = trimmean (x, 10, [1, 2]);
 assert (out(1,1,1), 10.3889, 1e-4);
 assert (out(1,1,2), 29.6111, 1e-4);
***** test
 x = reshape (1:40, [5, 4, 2]);
 x([3, 37]) = -100;
 x([4, 38]) = NaN;
 assert (trimmean (x, 10, "all"), 19.3824, 1e-4);
***** test
 x = reshape (1:40, [5, 4, 2]);
 x([3, 37]) = -100;
 out = trimmean (x, 10, 1);
 assert (out(:,:,1), [-17.6, 8, 13, 18]);
 assert (out(:,:,2), [23, 28, 33, 10.6]);
***** test
 x = reshape (1:40, [5, 4, 2]);
 x([3, 37]) = -100;
 x([4, 38]) = NaN;
 out = trimmean (x, 10, 1);
 assert (out(:,:,1), [-23, 8, 13, 18]);
 assert (out(:,:,2), [23, 28, 33, 3.75]);
***** test
 x = reshape (1:40, [5, 4, 2]);
 x([3, 37]) = -100;
 out = trimmean (x, 10, 2);
 assert (out(:,:,1), [8.5; 9.5; -15.25; 11.5; 12.5]);
 assert (out(:,:,2), [28.5; -4.75; 30.5; 31.5; 32.5]);
***** test
 x = reshape (1:40, [5, 4, 2]);
 x([3, 37]) = -100;
 x([4, 38]) = NaN;
 out = trimmean (x, 10, 2);
 assert (out(:,:,1), [8.5; 9.5; -15.25; 14; 12.5]);
 assert (out(:,:,2), [28.5; -4.75; 28; 31.5; 32.5]);
***** test
 x = reshape (1:40, [5, 4, 2]);
 x([3, 37]) = -100;
 out = trimmean (x, 10, [1, 2, 3]);
 assert (out, trimmean (x, 10, "all"));
***** test
 x = reshape (1:40, [5, 4, 2]);
 x([3, 37]) = -100;
 x([4, 38]) = NaN;
 out = trimmean (x, 10, [1, 2]);
 assert (out(1,1,1), 10.7647, 1e-4);
 assert (out(1,1,2), 29.1176, 1e-4);
***** test
 x = reshape (1:40, [5, 4, 2]);
 x([3, 37]) = -100;
 x([4, 38]) = NaN;
 out = trimmean (x, 10, [1, 3]);
 assert (out, [2.5556, 18, 23, 11.6667], 1e-4);
***** test
 x = reshape (1:40, [5, 4, 2]);
 x([3, 37]) = -100;
 x([4, 38]) = NaN;
 out = trimmean (x, 10, [2, 3]);
 assert (out, [18.5; 2.3750; 3.2857; 24; 22.5], 1e-4);
***** test
 x = reshape (1:40, [5, 4, 2]);
 x([3, 37]) = -100;
 x([4, 38]) = NaN;
 out = trimmean (x, 10, [1, 2, 3]);
 assert (out, trimmean (x, 10, "all"));
***** test
 x = reshape (1:40, [5, 4, 2]);
 x([3, 37]) = -100;
 x([4, 38]) = NaN;
 out = trimmean (x, 10, [2, 3, 5]);
 assert (out, [18.5; 2.3750; 3.2857; 24; 22.5], 1e-4);
***** assert (trimmean (reshape (1:40, [5, 4, 2]), 10, 4), reshape(1:40, [5, 4, 2]))
***** assert (trimmean ([], 10), NaN)
***** assert (trimmean ([1;2;3;4;5], 10, 2), [1;2;3;4;5])
***** error<Invalid call to trimmean.  Correct usage is:> trimmean (1)
***** error<Invalid call to trimmean.  Correct usage is:> trimmean (1,2,3,4,5)
***** error<trimmean: invalid percent.> trimmean ([1 2 3 4], -10)
***** error<trimmean: invalid percent.> trimmean ([1 2 3 4], 100)
***** error<trimmean: invalid FLAG argument.> trimmean ([1 2 3 4], 10, "flag")
***** error<trimmean: invalid FLAG argument.> trimmean ([1 2 3 4], 10, "flag", 1)
***** error<trimmean: DIM must be a positive integer scalar or vector.> ...
 trimmean ([1 2 3 4], 10, -1)
***** error<trimmean: DIM must be a positive integer scalar or vector.> ...
 trimmean ([1 2 3 4], 10, "floor", -1)
***** error<trimmean: DIM must be a positive integer scalar or vector.> ...
 trimmean (reshape (1:40, [5, 4, 2]), 10, [-1, 2])
***** error<trimmean: VECDIM must contain non-repeating positive integers.> ...
 trimmean (reshape (1:40, [5, 4, 2]), 10, [1, 2, 2])
26 tests, 26 passed, 0 known failure, 0 skipped
[inst/ff2n.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/ff2n.m
***** error ff2n ();
***** error ff2n (2, 5);
***** error ff2n (2.5);
***** error ff2n (0);
***** error ff2n (-3);
***** error ff2n (3+2i);
***** error ff2n (Inf);
***** error ff2n (NaN);
***** test
 A = ff2n (3);
 assert (A, fullfact (3));
***** test
 A = ff2n (8);
 assert (A, fullfact (8));
10 tests, 10 passed, 0 known failure, 0 skipped
[inst/violin.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/violin.m
***** demo
 clf
 x = zeros (9e2, 10);
 for i=1:10
   x(:,i) = (0.1 * randn (3e2, 3) * (randn (3,1) + 1) + 2 * randn (1,3))(:);
 endfor
 h = violin (x, "color", "c");
 axis tight
 set (h.violin, "linewidth", 2);
 set (gca, "xgrid", "on");
 xlabel ("Variables")
 ylabel ("Values")
***** demo
 clf
 data = {randn(100,1)*5+140, randn(130,1)*8+135};
 subplot (1,2,1)
 title ("Grade 3 heights - vertical");
 set (gca, "xtick", 1:2, "xticklabel", {"girls"; "boys"});
 violin (data, "Nbins", 10);
 axis tight

 subplot(1,2,2)
 title ("Grade 3 heights - horizontal");
 set (gca, "ytick", 1:2, "yticklabel", {"girls"; "boys"});
 violin (data, "horizontal", "Nbins", 10);
 axis tight
***** demo
 clf
 data = exprnd (0.1, 500,4);
 violin (data, "nbins", {5,10,50,100});
 axis ([0 5 0 max(data(:))])
***** demo
 clf
 data = exprnd (0.1, 500,4);
 violin (data, "color", jet(4));
 axis ([0 5 0 max(data(:))])
***** demo
 clf
 data = repmat(exprnd (0.1, 500,1), 1, 4);
 violin (data, "width", linspace (0.1,0.5,4));
 axis ([0 5 0 max(data(:))])
***** demo
 clf
 data = repmat(exprnd (0.1, 500,1), 1, 4);
 violin (data, "nbins", [5,10,50,100], "smoothfactor", [4 4 8 10]);
 axis ([0 5 0 max(data(:))])
***** test
 hf = figure ("visible", "off");
 unwind_protect
   data = exprnd (0.1, 500,4);
   violin (data, "color", jet(4));
   axis ([0 5 0 max(data(:))])
 unwind_protect_cleanup
   close (hf);
 end_unwind_protect
***** test
 hf = figure ("visible", "off");
 unwind_protect
   data = {randn(100,1)*5+140, randn(130,1)*8+135};
   subplot (1,2,1)
   title ("Grade 3 heights - vertical");
   set (gca, "xtick", 1:2, "xticklabel", {"girls"; "boys"});
   violin (data, "Nbins", 10);
   axis tight
 unwind_protect_cleanup
   close (hf);
 end_unwind_protect
***** test
 hf = figure ("visible", "off");
 unwind_protect
   data = {randn(100,1)*5+140, randn(130,1)*8+135};
   subplot (1,2,1)
   title ("Grade 3 heights - vertical");
   set (gca, "xtick", 1:2, "xticklabel", {"girls"; "boys"});
   violin (data, "Nbins", 10);
   axis tight
   subplot(1,2,2)
   title ("Grade 3 heights - horizontal");
   set (gca, "ytick", 1:2, "yticklabel", {"girls"; "boys"});
   violin (data, "horizontal", "Nbins", 10);
   axis tight
 unwind_protect_cleanup
   close (hf);
 end_unwind_protect
***** test
 hf = figure ("visible", "off");
 unwind_protect
   data = repmat(exprnd (0.1, 500,1), 1, 4);
   violin (data, "nbins", [5,10,50,100], "smoothfactor", [4 4 8 10]);
   axis ([0 5 0 max(data(:))])
 unwind_protect_cleanup
   close (hf);
 end_unwind_protect
***** test
 hf = figure ("visible", "off");
 unwind_protect
   data = repmat(exprnd (0.1, 500,1), 1, 4);
   violin (data, "width", linspace (0.1,0.5,4));
   axis ([0 5 0 max(data(:))])
 unwind_protect_cleanup
   close (hf);
 end_unwind_protect
5 tests, 5 passed, 0 known failure, 0 skipped
[inst/x2fx.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/x2fx.m
***** test
 X = [1, 10; 2, 20; 3, 10; 4, 20; 5, 15; 6, 15];
 D = x2fx(X,'quadratic');
 assert (D(1,:) , [1, 1, 10, 10, 1, 100]);
 assert (D(2,:) , [1, 2, 20, 40, 4, 400]);
***** test
 X = [1, 10; 2, 20; 3, 10; 4, 20; 5, 15; 6, 15];
 model = [0, 0; 1, 0; 0, 1; 1, 1; 2, 0];
 D = x2fx(X,model);
 assert (D(1,:) , [1, 1, 10, 10, 1]);
 assert (D(2,:) , [1, 2, 20, 40, 4]);
 assert (D(4,:) , [1, 4, 20, 80, 16]);
***** error x2fx ([1, 10; 2, 20; 3, 10], [0; 1]);
***** error x2fx ([1, 10, 15; 2, 20, 40; 3, 10, 25], [0, 0; 1, 0; 0, 1; 1, 1; 2, 0]);
***** error x2fx ([1, 10; 2, 20; 3, 10], "whatever");
5 tests, 5 passed, 0 known failure, 0 skipped
[inst/@cvpartition/set.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/@cvpartition/set.m
***** shared C
 C = cvpartition (ones (10, 1), "KFold", 5);
***** test
 Cnew = set (C, "inds", [1 2 2 2 3 4 3 4 5 5]');
 assert (get (Cnew, "inds"), [1 2 2 2 3 4 3 4 5 5]');
***** error<set: expecting field/value pairs.> set (C)
***** error<set: expecting field/value pairs.> set (C, "NumObservations")
***** error<set: invalid field some.> set (C, "some", 15)
***** error<set: expecting the field to be a string.> set (C, 15, 15)
5 tests, 5 passed, 0 known failure, 0 skipped
[inst/@cvpartition/display.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/@cvpartition/display.m
***** test
 C = cvpartition (ones (10, 1), "KFold", 5);
 s = evalc ("display (C)");
 sout = "K-fold cross validation partition";
 assert (strcmpi (s(1:length (sout)), sout), true);
***** test
 C = cvpartition (ones (10, 1), "HoldOut", 5);
 s = evalc ("display (C)");
 sout = "HoldOut cross validation partition";
 assert (strcmpi (s(1:length (sout)), sout), true);
***** test
 C = cvpartition (ones (10, 1), "LeaveOut", 5);
 s = evalc ("display (C)");
 sout = "Leave-One-Out cross validation partition";
 assert (strcmpi (s(1:length (sout)), sout), true);
***** test
 C = cvpartition (ones (10, 1), "resubstitution", 5);
 s = evalc ("display (C)");
 sout = "Resubstitution cross validation partition";
 assert (strcmpi (s(1:length (sout)), sout), true);
***** test
 C = cvpartition (ones (10, 1), "Given", 5);
 s = evalc ("display (C)");
 sout = "Given cross validation partition";
 assert (strcmpi (s(1:length (sout)), sout), true);
***** error<Invalid call to display.  Correct usage is> display ()
6 tests, 6 passed, 0 known failure, 0 skipped
[inst/@cvpartition/repartition.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/@cvpartition/repartition.m
***** test
 C = cvpartition (ones (10, 1), "KFold", 5);
 Cnew = repartition (C);
 assert (isa (Cnew, "cvpartition"), true);
***** test
 C = cvpartition (ones (100, 1), "HoldOut", 5);
 Cnew = repartition (C);
 indC = get (C, "inds");
 indCnew = get (Cnew, "inds");
 assert (isequal (indC, indCnew), false);
2 tests, 2 passed, 0 known failure, 0 skipped
[inst/@cvpartition/cvpartition.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/@cvpartition/cvpartition.m
***** demo
 ## Partition with Fisher iris dataset (n = 150)
 ## Stratified by species
 load fisheriris
 y = species;
 ## 10-fold cross-validation partition
 c = cvpartition (species, 'KFold', 10)
 ## leave-10-out partition
 c1 = cvpartition (species, 'HoldOut', 10)
 idx1 = test (c, 2);
 idx2 = training (c, 2);
 ## another leave-10-out partition
 c2 = repartition (c1)
***** test
 C = cvpartition (ones (10, 1));
 assert (isa (C, "cvpartition"), true);
***** test
 C = cvpartition (ones (10, 1), "KFold", 5);
 assert (get (C, "NumObservations"), 10);
 assert (get (C, "NumTestSets"), 5);
 assert (get (C, "TrainSize"), ones(5,1) * 8);
 assert (get (C, "TestSize"), ones (5,1) * 2);
 assert (get (C, "inds"), [1 1 2 2 3 3 4 4 5 5]');
 assert (get (C, "Type"), "kfold");
***** test
 C = cvpartition (ones (10, 1), "KFold", 2);
 assert (get (C, "NumObservations"), 10);
 assert (get (C, "NumTestSets"), 2);
 assert (get (C, "TrainSize"), [5; 5]);
 assert (get (C, "TestSize"), [5; 5]);
 assert (get (C, "inds"), [1 1 1 1 1 2 2 2 2 2]');
 assert (get (C, "Type"), "kfold");
***** test
 C = cvpartition (ones (10, 1), "HoldOut", 5);
 assert (get (C, "NumObservations"), 10);
 assert (get (C, "NumTestSets"), 1);
 assert (get (C, "TrainSize"), 5);
 assert (get (C, "TestSize"), 5);
 assert (class (get (C, "inds")), "logical");
 assert (length (get (C, "inds")), 10);
 assert (get (C, "Type"), "holdout");
***** test
 C = cvpartition ([1 2 3 4 5 6 7 8 9 10], "LeaveOut", 5);
 assert (get (C, "NumObservations"), 10);
 assert (get (C, "NumTestSets"), 10);
 assert (get (C, "TrainSize"), ones (10, 1));
 assert (get (C, "TestSize"), ones (10, 1) * 9);
 assert (get (C, "inds"), []);
 assert (get (C, "Type"), "leaveout");
***** test
 C = cvpartition ([1 2 3 4 5 6 7 8 9 10], "resubstitution", 5);
 assert (get (C, "NumObservations"), 10);
 assert (get (C, "NumTestSets"), 1);
 assert (get (C, "TrainSize"), 10);
 assert (get (C, "TestSize"), 10);
 assert (get (C, "inds"), []);
 assert (get (C, "Type"), "resubstitution");
***** test
 C = cvpartition ([1 2 3 4 5 6 7 8 9 10], "Given", 2);
 assert (get (C, "NumObservations"), 10);
 assert (get (C, "NumTestSets"), 10);
 assert (get (C, "TrainSize"), ones (10, 1) * 9);
 assert (get (C, "TestSize"), ones (10, 1));
 assert (get (C, "inds"), [1:10]');
 assert (get (C, "Type"), "given");
***** warning<cvpartition: unrecognized type, using KFold.> ...
 C = cvpartition ([1 2 3 4 5 6 7 8 9 10], "some", 2);
8 tests, 8 passed, 0 known failure, 0 skipped
[inst/@cvpartition/test.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/@cvpartition/test.m
***** shared C
 C = cvpartition (ones (10, 1), "KFold", 5);
***** assert (test (C, 1), logical ([1 1 0 0 0 0 0 0 0 0]'))
***** assert (test (C, 2), logical ([0 0 1 1 0 0 0 0 0 0]'))
***** assert (test (C, 3), logical ([0 0 0 0 1 1 0 0 0 0]'))
***** assert (test (C, 4), logical ([0 0 0 0 0 0 1 1 0 0]'))
***** assert (test (C, 5), logical ([0 0 0 0 0 0 0 0 1 1]'))
***** test
 C = set (C, "inds", [1 2 2 2 3 4 3 4 5 5]');
***** assert (test (C), logical ([1 0 0 0 0 0 0 0 0 0]'))
***** assert (test (C, 2), logical ([0 1 1 1 0 0 0 0 0 0]'))
***** assert (test (C, 3), logical ([0 0 0 0 1 0 1 0 0 0]'))
9 tests, 9 passed, 0 known failure, 0 skipped
[inst/@cvpartition/training.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/@cvpartition/training.m
***** shared C
 C = cvpartition (ones (10, 1), "KFold", 5);
***** assert (training (C, 1), logical ([0 0 1 1 1 1 1 1 1 1]'))
***** assert (training (C, 2), logical ([1 1 0 0 1 1 1 1 1 1]'))
***** assert (training (C, 3), logical ([1 1 1 1 0 0 1 1 1 1]'))
***** assert (training (C, 4), logical ([1 1 1 1 1 1 0 0 1 1]'))
***** assert (training (C, 5), logical ([1 1 1 1 1 1 1 1 0 0]'))
***** test
 C = set (C, "inds", [1 2 2 2 3 4 3 4 5 5]');
***** assert (training (C), logical ([0 1 1 1 1 1 1 1 1 1]'))
***** assert (training (C, 2), logical ([1 0 0 0 1 1 1 1 1 1]'))
***** assert (training (C, 3), logical ([1 1 1 1 0 1 0 1 1 1]'))
9 tests, 9 passed, 0 known failure, 0 skipped
[inst/@cvpartition/get.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/@cvpartition/get.m
***** shared C
 C = cvpartition (ones (10, 1), "KFold", 5);
***** assert (get (C, "NumObservations"), 10);
***** assert (get (C, "NumTestSets"), 5);
***** assert (get (C, "TrainSize"), ones(5,1) * 8);
***** assert (get (C, "TestSize"), ones (5,1) * 2);
***** assert (get (C, "inds"), [1 1 2 2 3 3 4 4 5 5]');
***** assert (get (C, "Type"), "kfold");
***** error<get: invalid property some.> get (C, "some")
***** error<get: expecting the property to be a string.> get (C, 25)
***** error<get: expecting the property to be a string.> get (C, {25})
9 tests, 9 passed, 0 known failure, 0 skipped
[inst/grpstats.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/grpstats.m
***** demo
 load carsmall;
 [m,p,g] = grpstats (Weight, Model_Year, {"mean", "predci", "gname"})
 n = length(m);
 errorbar((1:n)',m,p(:,2)-m);
 set (gca, "xtick", 1:n, "xticklabel", g);
 title ("95% prediction intervals for mean weight by year");
***** demo
 load carsmall;
 [m,p,g] = grpstats ([Acceleration,Weight/1000],Cylinders, ...
                     {"mean", "meanci", "gname"}, 0.05)
 [c,r] = size (m);
 errorbar((1:c)'.*ones(c,r),m,p(:,[(1:r)])-m);
 set (gca, "xtick", 1:c, "xticklabel", g);
 title ("95% prediction intervals for mean weight by year");
***** test
 load carsmall
 means = grpstats (Acceleration, Origin);
 assert (means, [14.4377; 18.0500; 15.8867; 16.3778; 16.6000; 15.5000], 0.001);
***** test
 load carsmall
 [grpMin,grpMax,grp] = grpstats (Acceleration, Origin, {"min","max","gname"});
 assert (grpMin, [8.0; 15.3; 13.9; 12.2; 15.7; 15.5]);
 assert (grpMax, [22.2; 21.9; 18.2; 24.6; 17.5; 15.5]);
***** test
 load carsmall
 [grpMin,grpMax,grp] = grpstats (Acceleration, Origin, {"min","max","gname"});
 assert (grp', {"USA", "France", "Japan", "Germany", "Sweden", "Italy"});
***** test
 load carsmall
 [m,p,g] = grpstats ([Acceleration,Weight/1000], Cylinders, ...
                     {"mean", "meanci", "gname"}, 0.05);
 assert (p(:,1), [11.17621760075134, 16.13845847655224, 16.16222663683362]', ...
                 [1e-14, 2e-14, 1e-14]');
4 tests, 4 passed, 0 known failure, 0 skipped
[inst/friedman.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/friedman.m
***** demo
 load popcorn;
 friedman (popcorn, 3);
***** demo
 load popcorn;
 [p, atab] = friedman (popcorn, 3, "off");
 disp (p);
***** test
 popcorn = [5.5, 4.5, 3.5; 5.5, 4.5, 4.0; 6.0, 4.0, 3.0; ...
            6.5, 5.0, 4.0; 7.0, 5.5, 5.0; 7.0, 5.0, 4.5];
 [p, atab] = friedman (popcorn, 3, "off");
 assert (p, 0.001028853354594794, 1e-14);
 assert (atab{2,2}, 99.75, 1e-14);
 assert (atab{2,3}, 2, 0);
 assert (atab{2,4}, 49.875, 1e-14);
 assert (atab{2,5}, 13.75862068965517, 1e-14);
 assert (atab{2,6}, 0.001028853354594794, 1e-14);
 assert (atab{3,2}, 0.08333333333333215, 1e-14);
 assert (atab{3,4}, 0.04166666666666607, 1e-14);
 assert (atab{4,3}, 12, 0);
***** test
 popcorn = [5.5, 4.5, 3.5; 5.5, 4.5, 4.0; 6.0, 4.0, 3.0; ...
            6.5, 5.0, 4.0; 7.0, 5.5, 5.0; 7.0, 5.0, 4.5];
 [p, atab, stats] = friedman (popcorn, 3, "off");
 assert (atab{5,2}, 116, 0);
 assert (atab{5,3}, 17, 0);
 assert (stats.source, "friedman");
 assert (stats.n, 2);
 assert (stats.meanranks, [8, 4.75, 2.25], 0);
 assert (stats.sigma, 2.692582403567252, 1e-14);
2 tests, 2 passed, 0 known failure, 0 skipped
[inst/pdist2.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/pdist2.m
***** shared x, y, xx
 x = [1, 1, 1; 2, 2, 2; 3, 3, 3];
 y = [0, 0, 0; 1, 2, 3; 0, 2, 4; 4, 7, 1];
 xx = [1 2 3; 4 5 6; 7 8 9; 3 2 1];
***** test
 d = sqrt([3, 5, 11, 45; 12, 2, 8, 30; 27, 5, 11, 21]);
 assert (pdist2 (x, y), d);
***** test
 d = [5.1962, 2.2361, 3.3166, 6.7082; ...
      3.4641, 2.2361, 3.3166, 5.4772];
 i = [3, 1, 1, 1; 2, 3, 3, 2];
 [D, I] = pdist2 (x, y, "euclidean", "largest", 2);
 assert ({D, I}, {d, i}, 1e-4);
***** test
 d = [1.7321, 1.4142, 2.8284, 4.5826; ...
      3.4641, 2.2361, 3.3166, 5.4772];
 i = [1, 2, 2, 3;2, 1, 1, 2];
 [D, I] = pdist2 (x, y, "euclidean", "smallest", 2);
 assert ({D, I}, {d, i}, 1e-4);
***** test
 yy = [1 2 3;5 6 7;9 5 1];
 d = [0, 6.1644, 5.3852; 1.4142, 6.9282, 8.7750; ...
      3.7417, 7.0711, 9.9499; 6.1644, 10.4881, 10.3441];
 i = [2, 4, 4; 3, 2, 2; 1, 3, 3; 4, 1, 1];
 [D, I] = pdist2 (y, yy, "euclidean", "smallest", 4);
 assert ({D, I}, {d, i}, 1e-4);
***** test
 yy = [1 2 3;5 6 7;9 5 1];
 d = [0, 38, 29; 2, 48, 77; 14, 50, 99; 38, 110, 107];
 i = [2, 4, 4; 3, 2, 2; 1, 3, 3; 4, 1, 1];
 [D, I] = pdist2 (y, yy, "squaredeuclidean", "smallest", 4);
 assert ({D, I}, {d, i}, 1e-4);
***** test
 yy = [1 2 3;5 6 7;9 5 1];
 d = [0, 3.3256, 2.7249; 0.7610, 3.3453, 4.4799; ...
      1.8514, 3.3869, 5.0703; 2.5525, 5.0709, 5.1297];
 i = [2, 2, 4; 3, 4, 2; 1, 3, 1; 4, 1, 3];
 [D, I] = pdist2 (y, yy, "seuclidean", "smallest", 4);
 assert ({D, I}, {d, i}, 1e-4);
***** test
 d = [2.1213, 4.2426, 6.3640; 1.2247, 2.4495, 4.4159; ...
      3.2404, 4.8990, 6.8191; 2.7386, 4.2426, 6.1237];
 assert (pdist2 (y, x, "mahalanobis"), d, 1e-4);
***** test
 xx = [1, 3, 4; 3, 5, 4; 8, 7, 6];
 d = [1.3053, 1.8257, 15.0499; 1.3053, 3.3665, 16.5680];
 i = [2, 2, 2; 3, 4, 4];
 [D, I] = pdist2 (y, xx, "mahalanobis", "smallest", 2);
 assert ({D, I}, {d, i}, 1e-4);
***** test
 d = [2.5240, 4.1633, 17.3638; 2.0905, 3.9158, 17.0147];
 i = [1, 1, 3; 4, 3, 1];
 [D, I] = pdist2 (y, xx, "mahalanobis", "largest", 2);
 assert ({D, I}, {d, i}, 1e-4);
***** test
 d = [3, 3, 5, 9; 6, 2, 4, 8; 9, 3, 5, 7];
 assert (pdist2 (x, y, "cityblock"), d);
***** test
 d = [1, 2, 3, 6; 2, 1, 2, 5; 3, 2, 3, 4];
 assert (pdist2 (x, y, "chebychev"), d);
***** test
 d = repmat ([NaN, 0.0742, 0.2254, 0.1472], [3, 1]);
 assert (pdist2 (x, y, "cosine"), d, 1e-4);
***** test
 yy = [1 2 3;5 6 7;9 5 1];
 d = [0, 0, 0.5; 0, 0, 2; 1.5, 1.5, 2; NaN, NaN, NaN];
 i = [2, 2, 4; 3, 3, 2; 4, 4, 3; 1, 1, 1];
 [D, I] = pdist2 (y, yy, "correlation", "smallest", 4);
 assert ({D, I}, {d, i}, eps);
 [D, I] = pdist2 (y, yy, "spearman", "smallest", 4);
 assert ({D, I}, {d, i}, eps);
***** test
 d = [1, 2/3, 1, 1; 1, 2/3, 1, 1; 1, 2/3, 2/3, 2/3];
 i = [1, 1, 1, 2; 2, 2, 3, 3; 3, 3, 2, 1];
 [D, I] = pdist2 (x, y, "hamming", "largest", 4);
 assert ({D, I}, {d, i}, eps);
 [D, I] = pdist2 (x, y, "jaccard", "largest", 4);
 assert ({D, I}, {d, i}, eps);
***** test
 xx = [1, 2, 3, 4; 2, 3, 4, 5; 3, 4, 5, 6];
 yy = [1, 2, 2, 3; 2, 3, 3, 4];
 [D, I] = pdist2 (x, y, "euclidean", "Smallest", 4);
 eucldist = @(v,m) sqrt(sumsq(repmat(v,rows(m),1)-m,2));
 [d, i] = pdist2 (x, y, eucldist, "Smallest", 4);
 assert ({D, I}, {d, i});
***** warning<pdist2: matrix is close to singular> ...
 pdist2 (xx, xx, "mahalanobis");
***** error<pdist2: too few input arguments.> pdist2 (1)
***** error<pdist2: X and Y must have equal number of columns.> ...
 pdist2 (ones (4, 5), ones (4))
***** error<pdist2: X and Y must be 2 dimensional matrices.> ...
 pdist2 (ones (4, 2, 3), ones (3, 2))
***** error<pdist2: missing value in optional name/value paired arguments.> ...
 pdist2 (ones (3), ones (3), "euclidean", "Largest")
***** error<pdist2: missing value in optional name/value paired arguments.> ...
 pdist2 (ones (3), ones (3), "minkowski", 3, "Largest")
***** error<pdist2: invalid NAME in optional pairs of arguments.> ...
 pdist2 (ones (3), ones (3), "minkowski", 3, "large", 4)
***** error<pdist2: you can only use either Smallest or Largest.> ...
 pdist2 (ones (3), ones (3), "minkowski", 3, "Largest", 4, "smallest", 5)
***** error<pdist2: Smallest or Largest must be specified to compute second output.> ...
 [d, i] = pdist2(ones (3), ones (3), "minkowski", 3)
***** error<pdist2: DistParameter for standardized euclidean must be a vector of> ...
 pdist2 (ones (3), ones (3), "seuclidean", 3)
***** error<pdist2: DistParameter for standardized euclidean must be a nonnegative> ...
 pdist2 (ones (3), ones (3), "seuclidean", [1, -1, 3])
***** error<pdist2: DistParameter for mahalanobis distance must be a covariance> ...
 pdist2 (ones (3), eye (3), "mahalanobis", eye(2))
***** error<pdist2: covariance matrix for mahalanobis distance must be symmetric> ...
 pdist2 (ones (3), eye (3), "mahalanobis", ones(3))
***** error<pdist2: DistParameter for minkowski distance must be a positive scalar.> ...
 pdist2 (ones (3), eye (3), "minkowski", 0)
***** error<pdist2: DistParameter for minkowski distance must be a positive scalar.> ...
 pdist2 (ones (3), eye (3), "minkowski", -5)
***** error<pdist2: DistParameter for minkowski distance must be a positive scalar.> ...
 pdist2 (ones (3), eye (3), "minkowski", [1, 2])
***** error<pdist2: invalid function handle for distance metric.> ...
 pdist2 (ones (3), ones (3), @(v,m) sqrt(repmat(v,rows(m),1)-m,2))
***** error<pdist2: custom distance function produces wrong output size.> ...
 pdist2 (ones (3), ones (3), @(v,m) sqrt(sum(sumsq(repmat(v,rows(m),1)-m,2))))
33 tests, 33 passed, 0 known failure, 0 skipped
[inst/hist3.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/hist3.m
***** demo
 X = [
    1    1
    1    1
    1   10
    1   10
    5    5
    5    5
    5    5
    5    5
    5    5
    7    3
    7    3
    7    3
   10   10
   10   10];
 hist3 (X)
***** test
 N_exp = [ 0  0  0  5 20
           0  0 10 15  0
           0 15 10  0  0
          20  5  0  0  0];

 n = 100;
 x = [1:n]';
 y = [n:-1:1]';
 D = [x y];
 N = hist3 (D, [4 5]);
 assert (N, N_exp);
***** test
 N_exp = [0  0  0  0  1
          0  0  0  0  1
          0  0  0  0  1
          1  1  1  1 93];

 n = 100;
 x = [1:n]';
 y = [n:-1:1]';
 D = [x y];
 C{1} = [1 1.7 3 4];
 C{2} = [1:5];
 N = hist3 (D, C);
 assert (N, N_exp);
***** test
 D = [1 1; 3 1; 3 3; 3 1];
 [c, nn] = hist3 (D, {0:4, 0:4});
 exp_c = zeros (5);
 exp_c([7 9 19]) = [1 2 1];
 assert (c, exp_c);
 assert (nn, {0:4, 0:4});
***** test
 for i = 10
   assert (size (hist3 (rand (9, 2), "Edges", {[0:.2:1]; [0:.2:1]})), [6 6])
 endfor
***** test
 edge_1 = linspace (0, 10, 10);
 edge_2 = linspace (0, 50, 10);
 [c, nn] = hist3 ([1:10; 1:5:50]', "Edges", {edge_1, edge_2});
 exp_c = zeros (10, 10);
 exp_c([1 12 13 24 35 46 57 68 79 90]) = 1;
 assert (c, exp_c);

 assert (nn{1}, edge_1 + edge_1(2)/2, eps*10^4)
 assert (nn{2}, edge_2 + edge_2(2)/2, eps*10^4)
***** shared X
 X = [
  5  2
  5  3
  1  4
  5  3
  4  4
  1  2
  2  3
  3  3
  5  4
  5  3];
***** test
 N = zeros (10);
 N([1 10 53 56 60 91 98 100]) = [1 1 1 1 3 1 1 1];
 C = {(1.2:0.4:4.8), (2.1:0.2:3.9)};
 assert (nthargout ([1 2], @hist3, X), {N C}, eps*10^3)
***** test
 N = zeros (5, 7);
 N([1 5 17 18 20 31 34 35]) = [1 1 1 1 3 1 1 1];
 C = {(1.4:0.8:4.6), ((2+(1/7)):(2/7):(4-(1/7)))};
 assert (nthargout ([1 2], @hist3, X, [5 7]), {N C}, eps*10^3)
 assert (nthargout ([1 2], @hist3, X, "Nbins", [5 7]), {N C}, eps*10^3)
***** test
 N = [0 1 0; 0 1 0; 0 0 1; 0 0 0];
 C = {(2:5), (2.5:1:4.5)};
 assert (nthargout ([1 2], @hist3, X, "Edges", {(1.5:4.5), (2:4)}), {N C})
***** test
 N = [0 0 1 0 1 0; 0 0 0 1 0 0; 0 0 1 4 2 0];
 C = {(1.2:3.2), (0:5)};
 assert (nthargout ([1 2], @hist3, X, "Ctrs", C), {N C})
 assert (nthargout ([1 2], @hist3, X, C), {N C})
***** test
 [~, C] = hist3 (rand (10, 2), "Edges", {[0 .05 .15 .35 .55 .95],
                                         [-1 .05 .07 .2 .3 .5 .89 1.2]});
 C_exp = {[ 0.025  0.1   0.25   0.45  0.75  1.15], ...
          [-0.475  0.06  0.135  0.25  0.4   0.695  1.045  1.355]};
 assert (C, C_exp, eps*10^2)
***** test
 Xv = repmat ([1:10]', [1 2]);

 ## Test Centers
 assert (hist3 (Xv, "Ctrs", {1:10, 1:10}), eye (10))

 N_exp = eye (6);
 N_exp([1 end]) = 3;
 assert (hist3 (Xv, "Ctrs", {3:8, 3:8}), N_exp)

 N_exp = zeros (8, 6);
 N_exp([1 2 11 20 29 38 47 48]) = [2 1 1 1 1 1 1 2];
 assert (hist3 (Xv, "Ctrs", {2:9, 3:8}), N_exp)

 ## Test Edges
 assert (hist3 (Xv, "Edges", {1:10, 1:10}), eye (10))
 assert (hist3 (Xv, "Edges", {3:8, 3:8}), eye (6))
 assert (hist3 (Xv, "Edges", {2:9, 3:8}), [zeros(1, 6); eye(6); zeros(1, 6)])

 N_exp = zeros (14);
 N_exp(3:12, 3:12) = eye (10);
 assert (hist3 (Xv, "Edges", {-1:12, -1:12}), N_exp)

 ## Test for Nbins
 assert (hist3 (Xv), eye (10))
 assert (hist3 (Xv, [10 10]), eye (10))
 assert (hist3 (Xv, "nbins", [10 10]), eye (10))
 assert (hist3 (Xv, [5 5]), eye (5) * 2)

 N_exp = zeros (7, 5);
 N_exp([1 9 10 18 26 27 35]) = [2 1 1 2 1 1 2];
 assert (hist3 (Xv, [7 5]), N_exp)
***** test # bug #51059
 D = [1 1; NaN 2; 3 1; 3 3; 1 NaN; 3 1];
 [c, nn] = hist3 (D, {0:4, 0:4});
 exp_c = zeros (5);
 exp_c([7 9 19]) = [1 2 1];
 assert (c, exp_c)
 assert (nn, {0:4, 0:4})
***** test
 [c, nn] = hist3 ([1 8]);
 exp_c = zeros (10, 10);
 exp_c(6, 6) = 1;
 exp_nn = {-4:5, 3:12};
 assert (c, exp_c)
 assert (nn, exp_nn, eps)

 [c, nn] = hist3 ([1 8], [10 11]);
 exp_c = zeros (10, 11);
 exp_c(6, 6) = 1;
 exp_nn = {-4:5, 3:13};
 assert (c, exp_c)
 assert (nn, exp_nn, eps)
***** test
 [c, nn] = hist3 ([1 NaN; 2 3; 6 9; 8 NaN]);
 exp_c = zeros (10, 10);
 exp_c(2, 1) = 1;
 exp_c(8, 10) = 1;
 exp_nn = {linspace(1.35, 7.65, 10) linspace(3.3, 8.7, 10)};
 assert (c, exp_c)
 assert (nn, exp_nn, eps*100)
***** test
 [c, nn] = hist3 ([1 NaN; 2 NaN; 6 NaN; 8 NaN]);
 exp_c = zeros (10, 10);
 exp_nn = {linspace(1.35, 7.65, 10) NaN(1, 10)};
 assert (c, exp_c)
 assert (nn, exp_nn, eps*100)
***** test
 [c, nn] = hist3 ([1 NaN; NaN 3; NaN 9; 8 NaN]);
 exp_c = zeros (10, 10);
 exp_nn = {linspace(1.35, 7.65, 10) linspace(3.3, 8.7, 10)};
 assert (c, exp_c)
 assert (nn, exp_nn, eps*100)
16 tests, 16 passed, 0 known failure, 0 skipped
[inst/mnrfit.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/mnrfit.m
***** error<mnrfit: too few input arguments.> mnrfit (ones (50,1))
***** error<mnrfit: Predictors must be numeric.> ...
 mnrfit ({1 ;2 ;3 ;4 ;5}, ones (5,1))
***** error<mnrfit: Predictors must be a vector or a 2D matrix.> ...
 mnrfit (ones (50, 4, 2), ones (50, 1))
***** error<mnrfit: Response must be a vector or a 2D matrix.> ...
 mnrfit (ones (50, 4), ones (50, 1, 3))
***** error<mnrfit: Y must have the same number of rows as X.> ...
 mnrfit (ones (50, 4), ones (45,1))
***** error<mnrfit: Response labels must be a character array, a cell vector> ...
 mnrfit (ones (5, 4), {1 ;2 ;3 ;4 ;5})
***** error<mnrfit: optional arguments must be in pairs.> ...
 mnrfit (ones (5, 4), ones (5, 1), "model")
***** error<mnrfit: Y must be a column vector when given as cellstr.> ...
 mnrfit (ones (5, 4), {"q","q";"w","w";"q","q";"w","w";"q","q"})
***** error<mnrfit: Y must contain only 1 and 0 when given as a 2D matrix.> ...
 mnrfit (ones (5, 4), [1, 2; 1, 2; 1, 2; 1, 2; 1, 2])
***** error<mnrfit: Y must contain positive integer category numbers.> ...
 mnrfit (ones (5, 4), [1; -1; 1; 2; 1])
***** error<mnrfit: fitting more than 2 nominal responses not supported.> ...
 mnrfit (ones (5, 4), [1; 2; 3; 2; 1], "model", "nominal")
***** error<mnrfit: fitting hierarchical responses not supported.> ...
 mnrfit (ones (5, 4), [1; 2; 3; 2; 1], "model", "hierarchical")
***** error<mnrfit: model type not recognised.> ...
 mnrfit (ones (5, 4), [1; 2; 3; 2; 1], "model", "whatever")
13 tests, 13 passed, 0 known failure, 0 skipped
[inst/hotelling_t2test2.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/hotelling_t2test2.m
***** error<Invalid call to hotelling_t2test2.  Correct usage> hotelling_t2test2 ();
***** error<Invalid call to hotelling_t2test2.  Correct usage> ...
 hotelling_t2test2 ([2, 3, 4, 5, 6]);
***** error<hotelling_t2test2: X must be a vector or a 2D matrix.> ...
 hotelling_t2test2 (1, [2, 3, 4, 5, 6]);
***** error<hotelling_t2test2: X must be a vector or a 2D matrix.> ...
 hotelling_t2test2 (ones (2,2,2), [2, 3, 4, 5, 6]);
***** error<hotelling_t2test2: Y must be a vector or a 2D matrix.> ...
 hotelling_t2test2 ([2, 3, 4, 5, 6], 2);
***** error<hotelling_t2test2: Y must be a vector or a 2D matrix.> ...
 hotelling_t2test2 ([2, 3, 4, 5, 6], ones (2,2,2));
***** error<hotelling_t2test2: invalid value for alpha.> ...
 hotelling_t2test2 (ones (20,2), ones (20,2), "alpha", 1);
***** error<hotelling_t2test2: invalid value for alpha.> ...
 hotelling_t2test2 (ones (20,2), ones (20,2), "alpha", -0.2);
***** error<hotelling_t2test2: invalid value for alpha.> ...
 hotelling_t2test2 (ones (20,2), ones (20,2), "alpha", "a");
***** error<hotelling_t2test2: invalid value for alpha.> ...
 hotelling_t2test2 (ones (20,2), ones (20,2), "alpha", [0.01, 0.05]);
***** error<hotelling_t2test2: invalid Name argument.> ...
 hotelling_t2test2 (ones (20,2), ones (20,2), "name", 0.01);
***** error<hotelling_t2test2: if X is a vector, Y must also be a vector.> ...
 hotelling_t2test2 (ones (20,1), ones (20,2));
***** error<hotelling_t2test2: X and Y must have the same number of columns.> ...
 hotelling_t2test2 (ones (20,2), ones (25,3));
***** test
 randn ("seed", 1);
 x1 = randn (60000, 5);
 randn ("seed", 5);
 x2 = randn (30000, 5);
 [h, pval, stats] = hotelling_t2test2 (x1, x2);
 assert (h, 0);
 assert (stats.df1, 5);
 assert (stats.df2, 89994);
14 tests, 14 passed, 0 known failure, 0 skipped
[inst/combnk.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/combnk.m
***** demo
 c = combnk (1:5, 2);
 disp ("All pairs of integers between 1 and 5:");
 disp (c);
***** test
 c = combnk (1:3, 2);
 assert (c, [1, 2; 1, 3; 2, 3]);
***** test
 c = combnk (1:3, 6);
 assert (isempty (c));
***** test
 c = combnk ({1, 2, 3}, 2);
 assert (c, {1, 2; 1, 3; 2, 3});
***** test
 c = combnk ("hello", 2);
 assert (c, ["lo"; "lo"; "ll"; "eo"; "el"; "el"; "ho"; "hl"; "hl"; "he"]);
4 tests, 4 passed, 0 known failure, 0 skipped
[inst/regression_ttest.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/regression_ttest.m
***** error<Invalid call to regression_ttest.  Correct usage> regression_ttest ();
***** error<Invalid call to regression_ttest.  Correct usage> regression_ttest (1);
***** error<regression_ttest: Y must contain finite real numbers.> ...
 regression_ttest ([1 2 NaN]', [2 3 4]');
***** error<regression_ttest: Y must contain finite real numbers.> ...
 regression_ttest ([1 2 Inf]', [2 3 4]');
***** error<regression_ttest: Y must contain finite real numbers.> ...
 regression_ttest ([1 2 3+i]', [2 3 4]');
***** error<regression_ttest: X must contain finite real numbers.> ...
 regression_ttest ([1 2 3]', [2 3 NaN]');
***** error<regression_ttest: X must contain finite real numbers.> ...
 regression_ttest ([1 2 3]', [2 3 Inf]');
***** error<regression_ttest: X must contain finite real numbers.> ...
 regression_ttest ([1 2 3]', [3 4 3+i]');
***** error<regression_ttest: Y and X must be vectors of equal length.> ...
 regression_ttest ([1 2 3]', [3 4 4 5]');
***** error<regression_ttest: invalid value for alpha.> ...
 regression_ttest ([1 2 3]', [2 3 4]', "alpha", 0);
***** error<regression_ttest: invalid value for alpha.> ...
 regression_ttest ([1 2 3]', [2 3 4]', "alpha", 1.2);
***** error<regression_ttest: invalid value for alpha.> ...
 regression_ttest ([1 2 3]', [2 3 4]', "alpha", [.02 .1]);
***** error<regression_ttest: invalid value for alpha.> ...
 regression_ttest ([1 2 3]', [2 3 4]', "alpha", "a");
***** error<regression_ttest: invalid Name argument.> ...
 regression_ttest ([1 2 3]', [2 3 4]', "some", 0.05);
***** error<regression_ttest: invalid value for tail.>  ...
 regression_ttest ([1 2 3]', [2 3 4]', "tail", "val");
***** error<regression_ttest: invalid value for tail.>  ...
 regression_ttest ([1 2 3]', [2 3 4]', "alpha", 0.01, "tail", "val");
16 tests, 16 passed, 0 known failure, 0 skipped
[inst/plsregress.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/plsregress.m
***** demo
 ## Perform Partial Least-Squares Regression

 ## Load the spectra data set and use the near infrared (NIR) spectral
 ## intensities (NIR) as the predictor and the corresponding octave
 ## ratings (octave) as the response.
 load spectra

 ## Perform PLS regression with 10 components
 [xload, yload, xscore, yscore, coef, ptcVar] = plsregress (NIR, octane, 10);

 ## Plot the percentage of explained variance in the response variable
 ## (PCTVAR) as a function of the number of components.
 plot (1:10, cumsum (100 * ptcVar(2,:)), "-ro");
 xlim ([1, 10]);
 xlabel ("Number of PLS components");
 ylabel ("Percentage of Explained Variance in octane");
 title ("Explained Variance per PLS components");

 ## Compute the fitted response and display the residuals.
 octane_fitted = [ones(size(NIR,1),1), NIR] * coef;
 residuals = octane - octane_fitted;
 figure
 stem (residuals, "color", "r", "markersize", 4, "markeredgecolor", "r")
 xlabel ("Observations");
 ylabel ("Residuals");
 title ("Residuals in octane's fitted responce");
***** demo
 ## Calculate Variable Importance in Projection (VIP) for PLS Regression

 ## Load the spectra data set and use the near infrared (NIR) spectral
 ## intensities (NIR) as the predictor and the corresponding octave
 ## ratings (octave) as the response.  Variables with a VIP score greater than
 ## 1 are considered important for the projection of the PLS regression model.
 load spectra

 ## Perform PLS regression with 10 components
 [xload, yload, xscore, yscore, coef, pctVar, mse, stats] = ...
                                                 plsregress (NIR, octane, 10);

 ## Calculate the normalized PLS weights
 W0 = stats.W ./ sqrt(sum(stats.W.^2,1));

 ## Calculate the VIP scores for 10 components
 nobs = size (xload, 1);
 SS = sum (xscore .^ 2, 1) .* sum (yload .^ 2, 1);
 VIPscore = sqrt (nobs * sum (SS .* (W0 .^ 2), 2) ./ sum (SS, 2));

 ## Find variables with a VIP score greater than or equal to 1
 VIPidx = find (VIPscore >= 1);

 ## Plot the VIP scores
 scatter (1:length (VIPscore), VIPscore, "xb");
 hold on
 scatter (VIPidx, VIPscore (VIPidx), "xr");
 plot ([1, length(VIPscore)], [1, 1], "--k");
 hold off
 axis ("tight");
 xlabel ("Predictor Variables");
 ylabel ("VIP scores");
 title ("VIP scores for each predictror variable with 10 components");
***** test
 load spectra
 [xload, yload, xscore, yscore, coef, pctVar] = plsregress (NIR, octane, 10);
 xload1_out = [-0.0170, 0.0039, 0.0095,  0.0258, 0.0025, ...
               -0.0075, 0.0000, 0.0018, -0.0027, 0.0020];
 yload_out = [6.6384, 9.3106, 2.0505, 0.6471, 0.9625, ...
              0.5905, 0.4244, 0.2437, 0.3516, 0.2548];
 xscore1_out = [-0.0401, -0.1764, -0.0340, 0.1669,  0.1041, ...
                -0.2067,  0.0457,  0.1565, 0.0706, -0.1471];
 yscore1_out = [-12.4635, -15.0003,  0.0638,  0.0652, -0.0070, ...
                 -0.0634,   0.0062, -0.0012, -0.0151, -0.0173];
 assert (xload(1,:), xload1_out, 1e-4);
 assert (yload, yload_out, 1e-4);
 assert (xscore(1,:), xscore1_out, 1e-4);
 assert (yscore(1,:), yscore1_out, 1e-4);
***** test
 load spectra
 [xload, yload, xscore, yscore, coef, pctVar] = plsregress (NIR, octane, 5);
 xload1_out = [-0.0170, 0.0039, 0.0095, 0.0258, 0.0025];
 yload_out = [6.6384, 9.3106, 2.0505, 0.6471, 0.9625];
 xscore1_out = [-0.0401, -0.1764, -0.0340, 0.1669, 0.1041];
 yscore1_out = [-12.4635, -15.0003, 0.0638, 0.0652, -0.0070];
 assert (xload(1,:), xload1_out, 1e-4);
 assert (yload, yload_out, 1e-4);
 assert (xscore(1,:), xscore1_out, 1e-4);
 assert (yscore(1,:), yscore1_out, 1e-4);
***** error<plsregress: function called with too few input arguments.>
 plsregress (1)
***** error<plsregress: X and Y must be real matrices.> plsregress (1, "asd")
***** error<plsregress: X and Y must be real matrices.> plsregress (1, {1,2,3})
***** error<plsregress: X and Y must be real matrices.> plsregress ("asd", 1)
***** error<plsregress: X and Y must be real matrices.> plsregress ({1,2,3}, 1)
***** error<plsregress: X and Y observations mismatch.> ...
 plsregress (ones (20,3), ones (15,1))
***** error<plsregress: invalid value for NCOMP.> ...
 plsregress (ones (20,3), ones (20,1), 0)
***** error<plsregress: invalid value for NCOMP.> ...
 plsregress (ones (20,3), ones (20,1), -5)
***** error<plsregress: invalid value for NCOMP.> ...
 plsregress (ones (20,3), ones (20,1), 3.2)
***** error<plsregress: invalid value for NCOMP.> ...
 plsregress (ones (20,3), ones (20,1), [2, 3])
***** error<plsregress: NCOMP exceeds maximum components for X.> ...
 plsregress (ones (20,3), ones (20,1), 4)
***** error<plsregress: invalid VALUE for 'cv' optional argument.> ...
 plsregress (ones (20,3), ones (20,1), 3, "cv", 4.5)
***** error<plsregress: invalid VALUE for 'cv' optional argument.> ...
 plsregress (ones (20,3), ones (20,1), 3, "cv", -1)
***** error<plsregress: invalid VALUE for 'cv' optional argument.> ...
 plsregress (ones (20,3), ones (20,1), 3, "cv", "somestring")
***** error<plsregress: invalid VALUE for 'mcreps' optional argument.> ...
 plsregress (ones (20,3), ones (20,1), 3, "cv", 3, "mcreps", 2.2)
***** error<plsregress: invalid VALUE for 'mcreps' optional argument.> ...
 plsregress (ones (20,3), ones (20,1), 3, "cv", 3, "mcreps", -2)
***** error<plsregress: invalid VALUE for 'mcreps' optional argument.> ...
 plsregress (ones (20,3), ones (20,1), 3, "cv", 3, "mcreps", [1, 2])
***** error<plsregress: invalid NAME argument.> ...
 plsregress (ones (20,3), ones (20,1), 3, "Name", 3, "mcreps", 1)
***** error<plsregress: invalid NAME argument.> ...
 plsregress (ones (20,3), ones (20,1), 3, "cv", 3, "Name", 1)
***** error<plsregress: 'mcreps' must be 1 when 'resubstitution' is specified> ...
 plsregress (ones (20,3), ones (20,1), 3, "mcreps", 2)
***** error<plsregress: 'mcreps' must be 1 when 'resubstitution' is specified> ...
 plsregress (ones (20,3), ones (20,1), 3, "cv", "resubstitution", "mcreps", 2)
***** error<Invalid call to plsregress.  Correct usage is:> plsregress (1, 2)
24 tests, 24 passed, 0 known failure, 0 skipped
[inst/randsample.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/randsample.m
***** test
 n = 20;
 k = 5;
 x = randsample(n, k);
 assert (size(x), [1 k]);
 x = randsample(n, k, true);
 assert (size(x), [1 k]);
 x = randsample(n, k, false);
 assert (size(x), [1 k]);
 x = randsample(n, k, true, ones(n, 1));
 assert (size(x), [1 k]);
 x = randsample(1:n, k);
 assert (size(x), [1 k]);
 x = randsample(1:n, k, true);
 assert (size(x), [1 k]);
 x = randsample(1:n, k, false);
 assert (size(x), [1 k]);
 x = randsample(1:n, k, true, ones(n, 1));
 assert (size(x), [1 k]);
 x = randsample((1:n)', k);
 assert (size(x), [k 1]);
 x = randsample((1:n)', k, true);
 assert (size(x), [k 1]);
 x = randsample((1:n)', k, false);
 assert (size(x), [k 1]);
 x = randsample((1:n)', k, true, ones(n, 1));
 assert (size(x), [k 1]);
 n = 10;
 k = 100;
 x = randsample(n, k, true, 1:n);
 assert (size(x), [1 k]);
 x = randsample((1:n)', k, true);
 assert (size(x), [k 1]);
 x = randsample(k, k, false, 1:k);
 assert (size(x), [1 k]);
1 test, 1 passed, 0 known failure, 0 skipped
[inst/princomp.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/princomp.m
***** shared COEFF,SCORE,latent,tsquare,m,x,R,V,lambda,i,S,F
***** test
 x=[7 4 3
    4 1 8
    6 3 5
    8 6 1
    8 5 7
    7 2 9
    5 3 3
    9 5 8
    7 4 5
    8 2 2];
 R = corrcoef (x);
 [V, lambda] = eig (R);
 [~, i] = sort(diag(lambda), "descend"); #arrange largest PC first
 S = V(:, i) * diag(sqrt(diag(lambda)(i)));
 ## contribution of first 2 PCs to each original variable
***** assert(diag(S(:, 1:2)*S(:, 1:2)'), [0.8662; 0.8420; 0.9876], 1E-4);
 B = V(:, i) * diag( 1./ sqrt(diag(lambda)(i)));
 F = zscore(x)*B;
 [COEFF,SCORE,latent,tsquare] = princomp(zscore(x, 1));
***** assert(tsquare,sumsq(F, 2),1E4*eps);
***** test
 x=[1,2,3;2,1,3]';
 [COEFF,SCORE,latent,tsquare] = princomp(x);
 m=[sqrt(2),sqrt(2);sqrt(2),-sqrt(2);-2*sqrt(2),0]/2;
 m(:,1) = m(:,1)*sign(COEFF(1,1));
 m(:,2) = m(:,2)*sign(COEFF(1,2));
***** assert(COEFF,m(1:2,:),10*eps);
***** assert(SCORE,-m,10*eps);
***** assert(latent,[1.5;.5],10*eps);
***** assert(tsquare,[4;4;4]/3,10*eps);
***** test
 x=x';
 [COEFF,SCORE,latent,tsquare] = princomp(x);
 m=[sqrt(2),sqrt(2),0;-sqrt(2),sqrt(2),0;0,0,2]/2;
 m(:,1) = m(:,1)*sign(COEFF(1,1));
 m(:,2) = m(:,2)*sign(COEFF(1,2));
 m(:,3) = m(:,3)*sign(COEFF(3,3));
***** assert(COEFF,m,10*eps);
***** assert(SCORE(:,1),-m(1:2,1),10*eps);
***** assert(SCORE(:,2:3),zeros(2),10*eps);
***** assert(latent,[1;0;0],10*eps);
***** assert(tsquare,[0.5;0.5],10*eps)
***** test
 [COEFF,SCORE,latent,tsquare] = princomp(x, "econ");
***** assert(COEFF,m(:, 1),10*eps);
***** assert(SCORE,-m(1:2,1),10*eps);
***** assert(latent,[1],10*eps);
***** assert(tsquare,[0.5;0.5],10*eps)
19 tests, 19 passed, 0 known failure, 0 skipped
[inst/qrandn.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/qrandn.m
***** demo
 z = qrandn (-5, 5e6);
 [c x] = hist (z,linspace(-1.5,1.5,200),1);
 figure(1)
 plot(x,c,"r."); axis tight; axis([-1.5,1.5]);

 z = qrandn (-0.14286, 5e6);
 [c x] = hist (z,linspace(-2,2,200),1);
 figure(2)
 plot(x,c,"r."); axis tight; axis([-2,2]);

 z = qrandn (2.75, 5e6);
 [c x] = hist (z,linspace(-1e3,1e3,1e3),1);
 figure(3)
 semilogy(x,c,"r."); axis tight; axis([-100,100]);

 # ---------
 # Figures from the reference paper.
***** error<qrandn: the parameter q must be a scalar.> qrandn ([1 2], 1)
***** error<qrandn: the parameter q must be lower than 3.> qrandn (4, 1)
***** error<qrandn: the parameter q must be lower than 3.> qrandn (3, 1)
***** error qrandn (2.5, 1, 2, 3)
***** error qrandn (2.5)
***** test
 q = 1.5;
 s = [2, 3];
 z = qrandn (q, s);
 assert (isnumeric (z) && isequal (size (z), s));
6 tests, 6 passed, 0 known failure, 0 skipped
[inst/binotest.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/binotest.m
***** demo
 % flip a coin 1000 times, showing 475 heads
 % Hypothesis: coin is fair, i.e. p=1/2
 [h,p_val,ci] = binotest(475,1000,0.5)
 % Result: h = 0 : null hypothesis not rejected, coin could be fair
 %         P value 0.12, i.e. hypothesis not rejected for alpha up to 12%
 %         0.444 <= p <= 0.506 with 95% confidence
***** demo
 % flip a coin 100 times, showing 65 heads
 % Hypothesis: coin shows less than 50% heads, i.e. p<=1/2
 [h,p_val,ci] = binotest(65,100,0.5,'tail','left','alpha',0.01)
 % Result: h = 1 : null hypothesis is rejected, i.e. coin shows more heads than tails
 %         P value 0.0018, i.e. hypothesis not rejected for alpha up to 0.18%
 %         0 <= p <= 0.76 with 99% confidence
***** test #example from https://en.wikipedia.org/wiki/Binomial_test
 [h,p_val,ci] = binotest (51,235,1/6);
 assert (p_val, 0.0437, 0.00005)
 [h,p_val,ci] = binotest (51,235,1/6,'tail','left');
 assert (p_val, 0.027, 0.0005)
1 test, 1 passed, 0 known failure, 0 skipped
[inst/mahal.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/mahal.m
***** error mahal ()
***** error mahal (1, 2, 3)
***** error mahal ("A", "B")
***** error <must be numeric> mahal ([1, 2], ["A", "B"])
***** error mahal (ones (2, 2, 2))
***** error <must be 2-D matrices> mahal (ones (2, 2), ones (2, 2, 2))
***** error <same number of columns> mahal (ones (2, 2), ones (2, 3))
***** test
 X = [1 0; 0 1; 1 1; 0 0];
 assert (mahal (X, X), [1.5; 1.5; 1.5; 1.5], 10*eps)
 assert (mahal (X, X+1), [7.5; 7.5; 1.5; 13.5], 10*eps)
***** assert (mahal ([true; true], [false; true]), [0.5; 0.5], eps)
9 tests, 9 passed, 0 known failure, 0 skipped
[inst/silhouette.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/silhouette.m
***** demo
 load fisheriris;
 X = meas(:,3:4);
 cidcs = kmeans (X, 3, "Replicates", 5);
 silhouette (X, cidcs);
 y_labels(cidcs([1 51 101])) = unique (species);
 set (gca, "yticklabel", y_labels);
 title ("Fisher's iris data");
***** error silhouette ();
***** error silhouette ([1 2; 1 1]);
***** error <X .* doesn't match .* clust> silhouette ([1 2; 1 1], [1 2 3]');
***** error <invalid metric> silhouette ([1 2; 1 1], [1 2]', "xxx");
4 tests, 4 passed, 0 known failure, 0 skipped
[inst/adtest.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/adtest.m
***** error<Invalid call to adtest.  Correct usage is:> adtest ();
***** error<adtest: X must be a vector of real numbers.> adtest (ones (20,2));
***** error<adtest: X must be a vector of real numbers.> adtest ([1+i,0-3i]);
***** error<adtest: invalid distribution family in char string.> ...
 adtest (ones (20,1), "Distribution", "normal");
***** error<adtest: invalid distribution family in cell array.> ...
 adtest (rand (20,1), "Distribution", {"normal", 5, 3});
***** error<adtest: invalid distribution parameters in cell array.> ...
 adtest (rand (20,1), "Distribution", {"norm", 5});
***** error<adtest: invalid distribution parameters in cell array.> ...
 adtest (rand (20,1), "Distribution", {"exp", 5, 4});
***** error<adtest: invalid distribution parameters in cell array.> ...
 adtest (rand (20,1), "Distribution", {"ev", 5});
***** error<adtest: invalid distribution parameters in cell array.> ...
 adtest (rand (20,1), "Distribution", {"logn", 5, 3, 2});
***** error<adtest: invalid distribution parameters in cell array.> ...
 adtest (rand (20,1), "Distribution", {"Weibull", 5});
***** error<adtest: invalid distribution option.> ...
 adtest (rand (20,1), "Distribution", 35);
***** error<adtest: invalid Name argument.> ...
 adtest (rand (20,1), "Name", "norm");
***** error<adtest: invalid Name argument.> ...
 adtest (rand (20,1), "Name", {"norm", 75, 10});
***** error<adtest: asymptotic option is not valid for the composite> ...
 adtest (rand (20,1), "Distribution", "norm", "Asymptotic", true);
***** error<adtest: asymptotic option is not valid for the composite> ...
 adtest (rand (20,1), "MCTol", 0.001, "Asymptotic", true);
***** error<adtest: asymptotic option is not valid for the Monte Carlo> ...
 adtest (rand (20,1), "Distribution", {"norm", 5, 3}, "MCTol", 0.001, ...
         "Asymptotic", true);
***** error<adtest: out of range invalid alpha - lower limit: 0.0005 upper> ...
 [h, pval, ADstat, CV] = adtest (ones (20,1), "Distribution", {"norm",5,3},...
                                 "Alpha", 0.000000001);
***** error<adtest: out of range invalid alpha - lower limit: 0.0005 upper> ...
 [h, pval, ADstat, CV] = adtest (ones (20,1), "Distribution", {"norm",5,3},...
                                 "Alpha", 0.999999999);
***** error<adtest: not enough data for composite testing.> ...
 adtest (10);
***** warning<adtest: out of range min p-value:> ...
 randn ("seed", 34);
 adtest (ones (20,1), "Alpha", 0.000001);
***** warning<adtest: alpha not within the lookup table.> ...
 randn ("seed", 34);
 adtest (normrnd(0,1,100,1), "Alpha", 0.99999);
***** warning<adtest: alpha not within the lookup table.> ...
 randn ("seed", 34);
 adtest (normrnd(0,1,100,1), "Alpha", 0.00001);
***** test
 load examgrades
 x = grades(:,1);
 [h, pval, adstat, cv] = adtest (x);
 assert (h, false);
 assert (pval, 0.1854, 1e-4);
 assert (adstat, 0.5194, 1e-4);
 assert (cv, 0.7470, 1e-4);
***** test
 load examgrades
 x = grades(:,1);
 [h, pval, adstat, cv] = adtest (x, "Distribution", "ev");
 assert (h, false);
 assert (pval, 0.071363, 1e-6);
***** test
 load examgrades
 x = grades(:,1);
 [h, pval, adstat, cv] = adtest (x, "Distribution", {"norm", 75, 10});
 assert (h, false);
 assert (pval, 0.4687, 1e-4);
25 tests, 25 passed, 0 known failure, 0 skipped
[inst/confusionmat.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/confusionmat.m
***** test
 Yt = [8 5 6 8 5 3 1 6 4 2 5 3 1 4]';
 Yp = [8 5 6 8 5 2 3 4 4 5 5 7 2 6]';
 C  = [0 1 1 0 0 0 0 0; 0 0 0 0 1 0 0 0; 0 1 0 0 0 0 1 0; 0 0 0 1 0 1 0 0; ...
       0 0 0 0 3 0 0 0; 0 0 0 1 0 1 0 0; 0 0 0 0 0 0 0 0; 0 0 0 0 0 0 0 2];
 assert (confusionmat (Yt, Yp), C)
1 test, 1 passed, 0 known failure, 0 skipped
[inst/signtest.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/signtest.m
***** test
 [pval, h, stats] = signtest ([-ones(1, 1000) 1], 0, "tail", "left");
 assert (pval, 1.091701889420221e-218, 1e-14);
 assert (h, 1);
 assert (stats.zval, -31.5437631079266, 1e-14);
***** test
 [pval, h, stats] = signtest ([-2 -1 0 2 1 3 1], 0);
 assert (pval, 0.6875000000000006, 1e-14);
 assert (h, 0);
 assert (stats.zval, NaN);
 assert (stats.sign, 4);
***** test
 [pval, h, stats] = signtest ([-2 -1 0 2 1 3 1], 0, "method", "approximate");
 assert (pval, 0.6830913983096086, 1e-14);
 assert (h, 0);
 assert (stats.zval, 0.4082482904638631, 1e-14);
 assert (stats.sign, 4);
***** error <signtest: X must be a vector.> signtest (ones (2))
***** error <signtest: Y must be either a scalar of a vector.> ...
 signtest ([1, 2, 3, 4], ones (2))
***** error <signtest: X and Y vectors have different lengths.> ...
 signtest ([1, 2, 3, 4], [1, 2, 3])
***** error <signtest: optional arguments must be in pairs.> ...
 signtest ([1, 2, 3, 4], [], 'tail')
***** error <signtest: 'alpha' must be a numeric scalar in the range 0 to 1.> ...
 signtest ([1, 2, 3, 4], [], 'alpha', 1.2)
***** error <signtest: 'alpha' must be a numeric scalar in the range 0 to 1.> ...
 signtest ([1, 2, 3, 4], [], 'alpha', 0)
***** error <signtest: 'alpha' must be a numeric scalar in the range 0 to 1.> ...
 signtest ([1, 2, 3, 4], [], 'alpha', -0.05)
***** error <signtest: 'alpha' must be a numeric scalar in the range 0 to 1.> ...
 signtest ([1, 2, 3, 4], [], 'alpha', "a")
***** error <signtest: 'alpha' must be a numeric scalar in the range 0 to 1.> ...
 signtest ([1, 2, 3, 4], [], 'alpha', [0.01, 0.05])
***** error <signtest: 'tail' argument must be a character vector.> ...
 signtest ([1, 2, 3, 4], [], 'tail', 0.01)
***** error <signtest: 'tail' argument must be a character vector.> ...
 signtest ([1, 2, 3, 4], [], 'tail', {"both"})
***** error <signtest: 'tail' value must be either 'both', right' or 'left'.> ...
 signtest ([1, 2, 3, 4], [], 'tail', "some")
***** error <signtest: 'tail' value must be either 'both', right' or 'left'.> ...
 signtest ([1, 2, 3, 4], [], 'method', 'exact', 'tail', "some")
***** error <signtest: 'method' argument must be a character vector.> ...
 signtest ([1, 2, 3, 4], [], 'method', 0.01)
***** error <signtest: 'method' argument must be a character vector.> ...
 signtest ([1, 2, 3, 4], [], 'method', {"exact"})
***** error <signtest: 'method' value must be either 'exact' or 'approximate'.> ...
 signtest ([1, 2, 3, 4], [], 'method', "some")
***** error <signtest: 'method' value must be either 'exact' or 'approximate'.> ...
 signtest ([1, 2, 3, 4], [], 'tail', "both", 'method', "some")
20 tests, 20 passed, 0 known failure, 0 skipped
[inst/regress.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/regress.m
***** test
 % Longley data from the NIST Statistical Reference Dataset
 Z = [  60323    83.0   234289   2356     1590    107608  1947
        61122    88.5   259426   2325     1456    108632  1948
        60171    88.2   258054   3682     1616    109773  1949
        61187    89.5   284599   3351     1650    110929  1950
        63221    96.2   328975   2099     3099    112075  1951
        63639    98.1   346999   1932     3594    113270  1952
        64989    99.0   365385   1870     3547    115094  1953
        63761   100.0   363112   3578     3350    116219  1954
        66019   101.2   397469   2904     3048    117388  1955
        67857   104.6   419180   2822     2857    118734  1956
        68169   108.4   442769   2936     2798    120445  1957
        66513   110.8   444546   4681     2637    121950  1958
        68655   112.6   482704   3813     2552    123366  1959
        69564   114.2   502601   3931     2514    125368  1960
        69331   115.7   518173   4806     2572    127852  1961
        70551   116.9   554894   4007     2827    130081  1962 ];
 % Results certified by NIST using 500 digit arithmetic
 % b and standard error in b
 V = [  -3482258.63459582         890420.383607373
         15.0618722713733         84.9149257747669
        -0.358191792925910E-01    0.334910077722432E-01
        -2.02022980381683         0.488399681651699
        -1.03322686717359         0.214274163161675
        -0.511041056535807E-01    0.226073200069370
         1829.15146461355         455.478499142212 ];
 Rsq = 0.995479004577296;
 F = 330.285339234588;
 y = Z(:,1); X = [ones(rows(Z),1), Z(:,2:end)];
 alpha = 0.05;
 [b, bint, r, rint, stats] = regress (y, X, alpha);
 assert(b,V(:,1),4e-6);
 assert(stats(1),Rsq,1e-12);
 assert(stats(2),F,3e-8);
 assert(((bint(:,1)-bint(:,2))/2)/tinv(alpha/2,9),V(:,2),-1.e-5);
warning: matrix singular to machine precision, rcond = 3.50566e-20
warning: called from
    regress at line 131 column 7
    __test__ at line 33 column 28
    test at line 682 column 11
    /tmp/tmp.knTBKLi3ba at line 1150 column 31

1 test, 1 passed, 0 known failure, 0 skipped
[inst/gscatter.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/gscatter.m
***** demo
 load fisheriris;
 X = meas(:,3:4);
 cidcs = kmeans (X, 3, "Replicates", 5);
 gscatter (X(:,1), X(:,2), cidcs, [.75 .75 0; 0 .75 .75; .75 0 .75], "os^");
 title ("Fisher's iris data");
***** shared visibility_setting
 visibility_setting = get (0, "DefaultFigureVisible");
***** test
 hf = figure ("visible", "off");
 unwind_protect
   load fisheriris;
   X = meas(:,3:4);
   cidcs = kmeans (X, 3, "Replicates", 5);
   gscatter (X(:,1), X(:,2), cidcs, [.75 .75 0; 0 .75 .75; .75 0 .75], "os^");
   title ("Fisher's iris data");
 unwind_protect_cleanup
   close (hf);
 end_unwind_protect
warning: legend: 'best' not yet implemented for location specifier, using 'northeast' instead
***** error gscatter ();
***** error gscatter ([1]);
***** error gscatter ([1], [2]);
***** error <x must be a numeric vector> gscatter ('abc', [1 2 3], [1]);
***** error <x and y must have the same size> gscatter ([1 2 3], [1 2], [1]);
***** error <y must be a numeric vector> gscatter ([1 2 3], 'abc', [1]);
***** error <g must have the same size as x and y> gscatter ([1 2], [1 2], [1]);
***** error <invalid dolegend> gscatter ([1 2], [1 2], [1 2], 'rb', 'so', 12, 'xxx');
9 tests, 9 passed, 0 known failure, 0 skipped
[inst/evalclusters.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/evalclusters.m
***** demo
 load fisheriris;
 eva = evalclusters (meas, "kmeans", "calinskiharabasz", "KList", [1:6])
 plot (eva)
***** error evalclusters ()
***** error evalclusters ([1 1;0 1])
***** error evalclusters ([1 1;0 1], "kmeans")
***** error <'x' must be a numeric*> evalclusters ("abc", "kmeans", "gap")
***** error <unknown clustering*> evalclusters ([1 1;0 1], "xxx", "gap")
***** error <invalid matrix*> evalclusters ([1 1;0 1], [1 2], "gap")
***** error <invalid argument*> evalclusters ([1 1;0 1], 1.2, "gap")
***** error <invalid criterion*> evalclusters ([1 1;0 1], [1; 2], 123)
***** error <unknown criterion*> evalclusters ([1 1;0 1], [1; 2], "xxx")
***** error <'KList' can be empty*> evalclusters ([1 1;0 1], "kmeans", "gap")
***** error <invalid parameter*> evalclusters ([1 1;0 1], [1; 2], "gap", 1)
***** error <invalid property*> evalclusters ([1 1;0 1], [1; 2], "gap", 1, 1)
***** error <unknown property*> evalclusters ([1 1;0 1], [1; 2], "gap", "xxx", 1)
***** error <'KList'*> evalclusters ([1 1;0 1], [1; 2], "gap", "KList", [-1 0])
***** error <'KList'*> evalclusters ([1 1;0 1], [1; 2], "gap", "KList", [1 .5])
***** error <'KList'*> evalclusters ([1 1;0 1], [1; 2], "gap", "KList", [1 1; 1 1])
***** error <unknown distance*> evalclusters ([1 1;0 1], [1; 2], "gap", ...
                                        "distance", "a")
***** error <distance metric*> evalclusters ([1 1;0 1], [1; 2], "daviesbouldin", ...
                                       "distance", "a")
***** error <cluster prior*> evalclusters ([1 1;0 1], [1; 2], "gap", ...
                                     "clusterpriors", "equal")
***** error <invalid cluster prior*> evalclusters ([1 1;0 1], [1; 2], ...
                                         "silhouette", "clusterpriors", "xxx")
***** error <'clust' must be a clustering*> evalclusters ([1 1;0 1], [1; 2], "gap")
***** test
 load fisheriris;
 eva = evalclusters (meas, "kmeans", "calinskiharabasz", "KList", [1:6]);
 assert (isa (eva, "CalinskiHarabaszEvaluation"));
 assert (eva.NumObservations, 150);
 assert (eva.OptimalK, 3);
 assert (eva.InspectedK, [1 2 3 4 5 6]);
22 tests, 22 passed, 0 known failure, 0 skipped
[inst/normalise_distribution.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/normalise_distribution.m
***** test
 v = normalise_distribution ([1 2 3], [], 1);
 assert (v, [0 0 0])
***** test
 v = normalise_distribution ([1 2 3], [], 2);
 assert (v, norminv ([1 3 5] / 6), 3 * eps)
***** test
 v = normalise_distribution ([1 2 3]', [], 2);
 assert (v, [0 0 0]')
***** test
 v = normalise_distribution ([1 2 3]', [], 1);
 assert (v, norminv ([1 3 5]' / 6), 3 * eps)
***** test
 v = normalise_distribution ([1 1 2 2 3 3], [], 2);
 assert (v, norminv ([3 3 7 7 11 11] / 12), 3 * eps)
***** test
 v = normalise_distribution ([1 1 2 2 3 3]', [], 1);
 assert (v, norminv ([3 3 7 7 11 11]' / 12), 3 * eps)
***** test
 A = randn ( 10 );
 N = normalise_distribution (A, @normcdf);
 assert (A, N, 10000 * eps)
***** test
 A = exprnd (1, 100);
 N = normalise_distribution (A, @(x)(expcdf (x, 1)));
 assert (mean (vec (N)), 0, 0.1)
 assert (std (vec (N)), 1, 0.1)
***** test
 A = rand (1000,1);
 N = normalise_distribution (A, {@(x)(unifcdf (x, 0, 1))});
 assert (mean (vec (N)), 0, 0.2)
 assert (std (vec (N)), 1, 0.1)
***** test
 A = [rand(1000,1), randn(1000, 1)];
 N = normalise_distribution (A, {@(x)(unifcdf (x, 0, 1)), @normcdf});
 assert (mean (N), [0, 0], 0.2)
 assert (std (N), [1, 1], 0.1)
***** test
 A = [rand(1000,1), randn(1000, 1), exprnd(1, 1000, 1)]';
 N = normalise_distribution  (A, {@(x)(unifcdf (x, 0, 1)); @normcdf; @(x)(expcdf (x, 1))}, 2);
 assert (mean (N, 2), [0, 0, 0]', 0.2);
 assert (std (N, [], 2), [1, 1, 1]', 0.1);
***** xtest
 A = exprnd (1, 1000, 9); A (300:500, 4:6) = 17;
 N = normalise_distribution (A);
 assert (mean (N), [0 0 0 0.38 0.38 0.38 0 0 0], 0.1);
 assert (var (N), [1 1 1 2.59 2.59 2.59 1 1 1], 0.1);
***** test
***** error normalise_distribution (zeros (3, 4), ...
 {@(x)(unifcdf (x, 0, 1)); @normcdf; @(x)(expcdf (x,1))});
14 tests, 14 passed, 0 known failure, 0 skipped
[inst/chi2gof.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/chi2gof.m
***** demo
 x = normrnd (50, 5, 100, 1);
 [h, p, stats] = chi2gof (x)
 [h, p, stats] = chi2gof (x, "cdf", @(x)normcdf (x, mean(x), std(x)))
 [h, p, stats] = chi2gof (x, "cdf", {@normcdf, mean(x), std(x)})
***** demo
 x = rand (100,1 );
 n = length (x);
 binedges = linspace (0, 1, 11);
 expectedCounts = n * diff (binedges);
 [h, p, stats] = chi2gof (x, "binedges", binedges, "expected", expectedCounts)
***** demo
 bins = 0:5;
 obsCounts = [6 16 10 12 4 2];
 n = sum(obsCounts);
 lambdaHat = sum(bins.*obsCounts) / n;
 expCounts = n * poisspdf(bins,lambdaHat);
 [h, p, stats] = chi2gof (bins, "binctrs", bins, "frequency", obsCounts, ...
                          "expected", expCounts, "nparams",1)
***** error chi2gof ()
***** error chi2gof ([2,3;3,4])
***** error chi2gof ([1,2,3,4], "nbins", 3, "ctrs", [2,3,4])
***** error chi2gof ([1,2,3,4], "frequency", [2,3,2])
***** error chi2gof ([1,2,3,4], "frequency", [2,3,2,-2])
***** error chi2gof ([1,2,3,4], "frequency", [2,3,2,2], "nparams", i)
***** error chi2gof ([1,2,3,4], "frequency", [2,3,2,2], "alpha", 1.3)
***** error chi2gof ([1,2,3,4], "expected", [-3,2,2])
***** error chi2gof ([1,2,3,4], "expected", [3,2,2], "nbins", 5)
***** error chi2gof ([1,2,3,4], "cdf", @normcdff)
***** test
 x = [1 2 1 3 2 4 3 2 4 3 2 2];
 [h, p, stats] = chi2gof (x);
 assert (h, 0);
 assert (p, NaN);
 assert (stats.chi2stat, 0.1205375022748029, 1e-14);
 assert (stats.df, 0);
 assert (stats.edges, [1, 2.5, 4], 1e-14);
 assert (stats.O, [7, 5], 1e-14);
 assert (stats.E, [6.399995519909668, 5.600004480090332], 1e-14);
11 tests, 11 passed, 0 known failure, 0 skipped
[inst/gmdistribution.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/gmdistribution.m
***** test
 mu = eye(2);
 Sigma = eye(2);
 GM = gmdistribution (mu, Sigma);
 density = GM.pdf ([0 0; 1 1]);
 assert (density(1) - density(2), 0, 1e-6);

 [idx, nlogl, P, logpdf,M] = cluster (GM, eye(2));
 assert (idx, [1; 2]);
 [idx2,nlogl2,P2,logpdf2] = GM.cluster (eye(2));
 assert (nlogl - nlogl2, 0, 1e-6);
 [idx3,nlogl3,P3] = cluster (GM, eye(2));
 assert (P - P3, zeros (2), 1e-6);
 [idx4,nlogl4] = cluster (GM, eye(2));
 assert (size (nlogl4), [1 1]);
 idx5 = cluster (GM, eye(2));
 assert (idx - idx5, zeros (2,1));

 D = GM.mahal ([1;0]);
 assert (D - M(1,:), zeros (1,2), 1e-6);

 P = GM.posterior ([0 1]);
 assert (P - P2(2,:), zeros (1,2), 1e-6);

 R = GM.random(20);
 assert (size(R), [20, 2]);

 R = GM.random();
 assert (size(R), [1, 2]);
1 test, 1 passed, 0 known failure, 0 skipped
[inst/nansum.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/nansum.m
***** assert (nansum ([2 4 NaN 7]), 13)
***** assert (nansum ([2 4 NaN Inf]), Inf)
***** assert (nansum ([1 NaN 3; NaN 5 6; 7 8 NaN]), [8 13 9])
***** assert (nansum ([1 NaN 3; NaN 5 6; 7 8 NaN], 2), [4; 11; 15])
***** assert (nansum (single ([1 NaN 3; NaN 5 6; 7 8 NaN])), single ([8 13 9]))
***** assert (nansum (single ([1 NaN 3; NaN 5 6; 7 8 NaN]), "double"), [8 13 9])
***** assert (nansum (uint8 ([2 4 1 7])), 14)
***** assert (nansum (uint8 ([2 4 1 7]), "native"), uint8 (14))
***** assert (nansum (uint8 ([2 4 1 7])), 14)
9 tests, 9 passed, 0 known failure, 0 skipped
[inst/ztest.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/ztest.m
***** error<ztest: too few input arguments.> ztest ();
***** error<ztest: invalid value for standard deviation.> ...
 ztest ([1, 2, 3, 4], 2, -0.5);
***** error<ztest: invalid VALUE for alpha.> ...
 ztest ([1, 2, 3, 4], 1, 2, "alpha", 0);
***** error<ztest: invalid VALUE for alpha.> ...
 ztest ([1, 2, 3, 4], 1, 2, "alpha", 1.2);
***** error<ztest: invalid VALUE for alpha.> ...
 ztest ([1, 2, 3, 4], 1, 2, "alpha", "val");
***** error<ztest: invalid VALUE for tail.>  ...
 ztest ([1, 2, 3, 4], 1, 2, "tail", "val");
***** error<ztest: invalid VALUE for tail.>  ...
 ztest ([1, 2, 3, 4], 1, 2, "alpha", 0.01, "tail", "val");
***** error<ztest: invalid VALUE for operating dimension.> ...
 ztest ([1, 2, 3, 4], 1, 2, "dim", 3);
***** error<ztest: invalid VALUE for operating dimension.> ...
 ztest ([1, 2, 3, 4], 1, 2, "alpha", 0.01, "tail", "both", "dim", 3);
***** error<ztest: invalid NAME for optional arguments.> ...
 ztest ([1, 2, 3, 4], 1, 2, "alpha", 0.01, "tail", "both", "badoption", 3);
***** test
 load carsmall
 [h, pval, ci] = ztest (MPG, mean (MPG, "omitnan"), std (MPG, "omitnan"));
 assert (h, 0);
 assert (pval, 1, 1e-14);
 assert (ci, [22.094; 25.343], 1e-3);
***** test
 load carsmall
 [h, pval, ci] = ztest (MPG, 26, 8);
 assert (h, 1);
 assert (pval, 0.00568359158544743, 1e-14);
 assert (ci, [22.101; 25.335], 1e-3);
***** test
 load carsmall
 [h, pval, ci] = ztest (MPG, 26, 4);
 assert (h, 1);
 assert (pval, 3.184168011941316e-08, 1e-14);
 assert (ci, [22.909; 24.527], 1e-3);
13 tests, 13 passed, 0 known failure, 0 skipped
[inst/regression_ftest.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/regression_ftest.m
***** error<Invalid call to regression_ftest.  Correct usage> regression_ftest ();
***** error<Invalid call to regression_ftest.  Correct usage> ...
 regression_ftest ([1 2 3]', [2 3 4; 3 4 5]');
***** error<regression_ftest: Y must contain finite real numbers.> ...
 regression_ftest ([1 2 NaN]', [2 3 4; 3 4 5]', [1 0.5]);
***** error<regression_ftest: Y must contain finite real numbers.> ...
 regression_ftest ([1 2 Inf]', [2 3 4; 3 4 5]', [1 0.5]);
***** error<regression_ftest: Y must contain finite real numbers.> ...
 regression_ftest ([1 2 3+i]', [2 3 4; 3 4 5]', [1 0.5]);
***** error<regression_ftest: X must contain finite real numbers.> ...
 regression_ftest ([1 2 3]', [2 3 NaN; 3 4 5]', [1 0.5]);
***** error<regression_ftest: X must contain finite real numbers.> ...
 regression_ftest ([1 2 3]', [2 3 Inf; 3 4 5]', [1 0.5]);
***** error<regression_ftest: X must contain finite real numbers.> ...
 regression_ftest ([1 2 3]', [2 3 4; 3 4 3+i]', [1 0.5]);
***** error<regression_ftest: invalid value for alpha.> ...
 regression_ftest ([1 2 3]', [2 3 4; 3 4 5]', [1 0.5], [], "alpha", 0);
***** error<regression_ftest: invalid value for alpha.> ...
 regression_ftest ([1 2 3]', [2 3 4; 3 4 5]', [1 0.5], [], "alpha", 1.2);
***** error<regression_ftest: invalid value for alpha.> ...
 regression_ftest ([1 2 3]', [2 3 4; 3 4 5]', [1 0.5], [], "alpha", [.02 .1]);
***** error<regression_ftest: invalid value for alpha.> ...
 regression_ftest ([1 2 3]', [2 3 4; 3 4 5]', [1 0.5], [], "alpha", "a");
***** error<regression_ftest: invalid Name argument.> ...
 regression_ftest ([1 2 3]', [2 3 4; 3 4 5]', [1 0.5], [], "some", 0.05);
***** error<regression_ftest: Y must be a vector of length> ...
 regression_ftest ([1 2 3]', [2 3; 3 4]', [1 0.5]);
***** error<regression_ftest: Y must be a vector of length> ...
 regression_ftest ([1 2; 3 4]', [2 3; 3 4]', [1 0.5]);
***** error<regression_ftest: reduced model, RM, must be a numeric vector or> ...
 regression_ftest ([1 2 3]', [2 3 4; 3 4 5]', [1 0.5], ones (2));
***** error<regression_ftest: reduced model, RM, must be a numeric vector or> ...
 regression_ftest ([1 2 3]', [2 3 4; 3 4 5]', [1 0.5], "alpha");
***** error<regression_ftest: reduced model, RM, must have smaller length than> ...
 regression_ftest ([1 2 3]', [2 3 4; 3 4 5]', [1 0.5], [1 2]);
18 tests, 18 passed, 0 known failure, 0 skipped
[inst/anova1.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/anova1.m
***** demo
 x = meshgrid (1:6);
 randn ("seed", 15);    # for reproducibility
 x = x + normrnd (0, 1, 6, 6);
 anova1 (x, [], 'off');
***** demo
 x = meshgrid (1:6);
 randn ("seed", 15);    # for reproducibility
 x = x + normrnd (0, 1, 6, 6);
 [p, atab] = anova1(x);
***** demo
 x = ones (50, 4) .* [-2, 0, 1, 5];
 randn ("seed", 13);    # for reproducibility
 x = x + normrnd (0, 2, 50, 4);
 groups = {"A", "B", "C", "D"};
 anova1 (x, groups);
***** demo
 y = [54 87 45; 23 98 39; 45 64 51; 54 77 49; 45 89 50; 47 NaN 55];
 g = [1  2  3 ; 1  2  3 ; 1  2  3 ; 1  2  3 ; 1  2  3 ; 1  2  3 ];
 anova1 (y(:), g(:), "on", "unequal");
***** test
 data = [1.006, 0.996, 0.998, 1.000, 0.992, 0.993, 1.002, 0.999, 0.994, 1.000, ...
         0.998, 1.006, 1.000, 1.002, 0.997, 0.998, 0.996, 1.000, 1.006, 0.988, ...
         0.991, 0.987, 0.997, 0.999, 0.995, 0.994, 1.000, 0.999, 0.996, 0.996, ...
         1.005, 1.002, 0.994, 1.000, 0.995, 0.994, 0.998, 0.996, 1.002, 0.996, ...
         0.998, 0.998, 0.982, 0.990, 1.002, 0.984, 0.996, 0.993, 0.980, 0.996, ...
         1.009, 1.013, 1.009, 0.997, 0.988, 1.002, 0.995, 0.998, 0.981, 0.996, ...
         0.990, 1.004, 0.996, 1.001, 0.998, 1.000, 1.018, 1.010, 0.996, 1.002, ...
         0.998, 1.000, 1.006, 1.000, 1.002, 0.996, 0.998, 0.996, 1.002, 1.006, ...
         1.002, 0.998, 0.996, 0.995, 0.996, 1.004, 1.004, 0.998, 0.999, 0.991, ...
         0.991, 0.995, 0.984, 0.994, 0.997, 0.997, 0.991, 0.998, 1.004, 0.997];
 group = [1:10] .* ones (10,10);
 group = group(:);
 [p, tbl] = anova1 (data, group, "off");
 assert (p, 0.022661, 1e-6);
 assert (tbl{2,5}, 2.2969, 1e-4);
 assert (tbl{2,3}, 9, 0);
 assert (tbl{4,2}, 0.003903, 1e-6);
 data = reshape (data, 10, 10);
 [p, tbl, stats] = anova1 (data, [], "off");
 assert (p, 0.022661, 1e-6);
 assert (tbl{2,5}, 2.2969, 1e-4);
 assert (tbl{2,3}, 9, 0);
 assert (tbl{4,2}, 0.003903, 1e-6);
 means = [0.998, 0.9991, 0.9954, 0.9982, 0.9919, 0.9988, 1.0015, 1.0004, 0.9983, 0.9948];
 N = 10 * ones (1, 10);
 assert (stats.means, means, 1e-6);
 assert (length (stats.gnames), 10, 0);
 assert (stats.n, N, 0);
***** test
 y = [54 87 45; 23 98 39; 45 64 51; 54 77 49; 45 89 50; 47 NaN 55];
 g = [1  2  3 ; 1  2  3 ; 1  2  3 ; 1  2  3 ; 1  2  3 ; 1  2  3 ];
 [p, tbl] = anova1 (y(:), g(:), "off", "equal");
 assert (p, 0.00004163, 1e-6);
 assert (tbl{2,5}, 22.573418, 1e-6);
 assert (tbl{2,3}, 2, 0);
 assert (tbl{3,3}, 14, 0);
 [p, tbl] = anova1 (y(:), g(:), "off", "unequal");
 assert (p, 0.00208877, 1e-8);
 assert (tbl{2,5}, 15.523192, 1e-6);
 assert (tbl{2,3}, 2, 0);
 assert (tbl{2,4}, 7.5786897, 1e-6);
2 tests, 2 passed, 0 known failure, 0 skipped
[inst/Regression/RegressionGAM.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/Regression/RegressionGAM.m
***** demo
 ## Train a RegressionGAM Model for synthetic values
 f1 = @(x) cos (3 * x);
 f2 = @(x) x .^ 3;
 x1 = 2 * rand (50, 1) - 1;
 x2 = 2 * rand (50, 1) - 1;
 y = f1(x1) + f2(x2);
 y = y + y .* 0.2 .* rand (50,1);
 X = [x1, x2];
 a = fitrgam (X, y, "tol", 1e-3)
***** demo
 ## Declare two different functions
 f1 = @(x) cos (3 * x);
 f2 = @(x) x .^ 3;

 ## Generate 80 samples for f1 and f2
 x = [-4*pi:0.1*pi:4*pi-0.1*pi]';
 X1 = f1 (x);
 X2 = f2 (x);

 ## Create a synthetic response by adding noise
 rand ("seed", 3);
 Ytrue = X1 + X2;
 Y = Ytrue + Ytrue .* 0.2 .* rand (80,1);

 ## Assemble predictor data
 X = [X1, X2];

 ## Train the GAM and test on the same data
 a = fitrgam (X, Y, "order", [5, 5]);
 [ypred, ySDsd, yInt] = predict (a, X);

 ## Plot the results
 figure
 [sortedY, indY] = sort (Ytrue);
 plot (sortedY, "r-");
 xlim ([0, 80]);
 hold on
 plot (ypred(indY), "g+")
 plot (yInt(indY,1), "k:")
 plot (yInt(indY,2), "k:")
 xlabel ("Predictor samples");
 ylabel ("Response");
 title ("actual vs predicted values for function f1(x) = cos (3x) ");
 legend ({"Theoretical Response", "Predicted Response", "Prediction Intervals"});

 ## Use 30% Holdout partitioning for training and testing data
 C = cvpartition (80, "HoldOut", 0.3);
 [ypred, ySDsd, yInt] = predict (a, X(test(C),:));

 ## Plot the results
 figure
 [sortedY, indY] = sort (Ytrue(test(C)));
 plot (sortedY, 'r-');
 xlim ([0, sum(test(C))]);
 hold on
 plot (ypred(indY), "g+")
 plot (yInt(indY,1),'k:')
 plot (yInt(indY,2),'k:')
 xlabel ("Predictor samples");
 ylabel ("Response");
 title ("actual vs predicted values for function f1(x) = cos (3x) ");
 legend ({"Theoretical Response", "Predicted Response", "Prediction Intervals"});
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = [1; 2; 3; 4];
 a = RegressionGAM (x, y);
 assert ({a.X, a.Y}, {x, y})
 assert ({a.BaseModel.Intercept}, {2.5000})
 assert ({a.Knots, a.Order, a.DoF}, {[5, 5, 5], [3, 3, 3], [8, 8, 8]})
 assert ({a.NumObservations, a.NumPredictors}, {4, 3})
 assert ({a.ResponseName, a.PredictorNames}, {"Y", {"x1", "x2", "x3"}})
 assert ({a.Formula}, {[]})
***** test
 x = [1, 2, 3, 4; 4, 5, 6, 7; 7, 8, 9, 1; 3, 2, 1, 2];
 y = [1; 2; 3; 4];
 pnames = {"A", "B", "C", "D"};
 formula = "Y ~ A + B + C + D + A:C";
 intMat = logical ([1,0,0,0;0,1,0,0;0,0,1,0;0,0,0,1;1,0,1,0]);
 a = RegressionGAM (x, y, "predictors", pnames, "formula", formula);
 assert ({a.IntMatrix}, {intMat})
 assert ({a.ResponseName, a.PredictorNames}, {"Y", pnames})
 assert ({a.Formula}, {formula})
***** error<RegressionGAM: too few input arguments.> RegressionGAM ()
***** error<RegressionGAM: too few input arguments.> RegressionGAM (ones(10,2))
***** error<RegressionGAM: number of rows in X and Y must be equal.> ...
 RegressionGAM (ones(10,2), ones (5,1))
***** error<RegressionGAM: invalid values in X.> ...
 RegressionGAM ([1;2;3;"a";4], ones (5,1))
***** error<RegressionGAM: invalid parameter name in optional pair arguments.> ...
 RegressionGAM (ones(10,2), ones (10,1), "some", "some")
***** error<RegressionGAM: Formula must be a string.>
 RegressionGAM (ones(10,2), ones (10,1), "formula", {"y~x1+x2"})
***** error<RegressionGAM: Formula must be a string.>
 RegressionGAM (ones(10,2), ones (10,1), "formula", [0, 1, 0])
***** error<RegressionGAM: invalid syntax in Formula.> ...
 RegressionGAM (ones(10,2), ones (10,1), "formula", "something")
***** error<RegressionGAM: no predictor terms in Formula.> ...
 RegressionGAM (ones(10,2), ones (10,1), "formula", "something~")
***** error<RegressionGAM: no predictor terms in Formula.> ...
 RegressionGAM (ones(10,2), ones (10,1), "formula", "something~")
***** error<RegressionGAM: some predictors have not been identified> ...
 RegressionGAM (ones(10,2), ones (10,1), "formula", "something~x1:")
***** error<RegressionGAM: invalid Interactions parameter.> ...
 RegressionGAM (ones(10,2), ones (10,1), "interactions", "some")
***** error<RegressionGAM: invalid Interactions parameter.> ...
 RegressionGAM (ones(10,2), ones (10,1), "interactions", -1)
***** error<RegressionGAM: invalid Interactions parameter.> ...
 RegressionGAM (ones(10,2), ones (10,1), "interactions", [1 2 3 4])
***** error<RegressionGAM: number of interaction terms requested is larger than> ...
 RegressionGAM (ones(10,2), ones (10,1), "interactions", 3)
***** error<RegressionGAM: Formula has been already defined.> ...
 RegressionGAM (ones(10,2), ones (10,1), "formula", "y ~ x1 + x2", "interactions", 1)
***** error<RegressionGAM: Interactions have been already defined.> ...
 RegressionGAM (ones(10,2), ones (10,1), "interactions", 1, "formula", "y ~ x1 + x2")
***** error<RegressionGAM: invalid value for Knots.> ...
 RegressionGAM (ones(10,2), ones (10,1), "knots", "a")
***** error<RegressionGAM: DoF and Order have been set already.> ...
 RegressionGAM (ones(10,2), ones (10,1), "order", 3, "dof", 2, "knots", 5)
***** error<RegressionGAM: invalid value for DoF.> ...
 RegressionGAM (ones(10,2), ones (10,1), "dof", 'a')
***** error<RegressionGAM: Knots and Order have been set already.> ...
 RegressionGAM (ones(10,2), ones (10,1), "knots", 5, "order", 3, "dof", 2)
***** error<RegressionGAM: invalid value for Order.> ...
 RegressionGAM (ones(10,2), ones (10,1), "order", 'a')
***** error<RegressionGAM: DoF and Knots have been set already.> ...
 RegressionGAM (ones(10,2), ones (10,1), "knots", 5, "dof", 2, "order", 2)
***** error<RegressionGAM: Tolerance must be a Positive scalar.> ...
 RegressionGAM (ones(10,2), ones (10,1), "tol", -1)
***** error<RegressionGAM: ResponseName must be a char string.> ...
 RegressionGAM (ones(10,2), ones (10,1), "responsename", -1)
***** error<RegressionGAM: PredictorNames must be a cellstring array.> ...
 RegressionGAM (ones(10,2), ones (10,1), "predictors", -1)
***** error<RegressionGAM: PredictorNames must be a cellstring array.> ...
 RegressionGAM (ones(10,2), ones (10,1), "predictors", ['a','b','c'])
***** error<RegressionGAM: PredictorNames must have same number of columns as X.> ...
 RegressionGAM (ones(10,2), ones (10,1), "predictors", {'a','b','c'})
***** error<RegressionGAM.predict: too few arguments.> ...
 predict (RegressionGAM (ones(10,1), ones(10,1)))
***** error<RegressionGAM.predict: Xfit is empty.> ...
 predict (RegressionGAM (ones(10,1), ones(10,1)), [])
***** error<RegressionGAM.predict: Xfit must have the same number of features> ...
 predict (RegressionGAM(ones(10,2), ones(10,1)), 2)
***** error<RegressionGAM.predict: invalid NAME in optional pairs of arguments.> ...
 predict (RegressionGAM(ones(10,2), ones(10,1)), ones (10,2), "some", "some")
***** error<RegressionGAM.predict: includeinteractions must be a logical value.> ...
 predict (RegressionGAM(ones(10,2), ones(10,1)), ones (10,2), "includeinteractions", "some")
***** error<RegressionGAM.predict: includeinteractions must be a logical value.> ...
 predict (RegressionGAM(ones(10,2), ones(10,1)), ones (10,2), "includeinteractions", 5)
***** error<RegressionGAM.predict: alpha must be a scalar value between 0 and 1.> ...
 predict (RegressionGAM(ones(10,2), ones(10,1)), ones (10,2), "alpha", 5)
***** error<RegressionGAM.predict: alpha must be a scalar value between 0 and 1.> ...
 predict (RegressionGAM(ones(10,2), ones(10,1)), ones (10,2), "alpha", -1)
***** error<RegressionGAM.predict: alpha must be a scalar value between 0 and 1.> ...
 predict (RegressionGAM(ones(10,2), ones(10,1)), ones (10,2), "alpha", 'a')
39 tests, 39 passed, 0 known failure, 0 skipped
[inst/fitlm.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/fitlm.m
***** demo
 y =  [ 8.706 10.362 11.552  6.941 10.983 10.092  6.421 14.943 15.931 ...
        22.968 18.590 16.567 15.944 21.637 14.492 17.965 18.851 22.891 ...
        22.028 16.884 17.252 18.325 25.435 19.141 21.238 22.196 18.038 ...
        22.628 31.163 26.053 24.419 32.145 28.966 30.207 29.142 33.212 ...
        25.694 ]';
 X = [1 1 1 1 1 1 1 1 2 2 2 2 2 3 3 3 3 3 3 3 3 4 4 4 4 4 4 4 5 5 5 5 5 5 5 5 5]';

 [TAB,STATS] = fitlm (X,y,"linear","CategoricalVars",1,"display","on");
***** demo
 popcorn = [5.5, 4.5, 3.5; 5.5, 4.5, 4.0; 6.0, 4.0, 3.0; ...
            6.5, 5.0, 4.0; 7.0, 5.5, 5.0; 7.0, 5.0, 4.5];
 brands = {'Gourmet', 'National', 'Generic'; ...
           'Gourmet', 'National', 'Generic'; ...
           'Gourmet', 'National', 'Generic'; ...
           'Gourmet', 'National', 'Generic'; ...
           'Gourmet', 'National', 'Generic'; ...
           'Gourmet', 'National', 'Generic'};
 popper = {'oil', 'oil', 'oil'; 'oil', 'oil', 'oil'; 'oil', 'oil', 'oil'; ...
           'air', 'air', 'air'; 'air', 'air', 'air'; 'air', 'air', 'air'};

 [TAB, STATS] = fitlm ({brands(:),popper(:)},popcorn(:),"interactions",...
                          "CategoricalVars",[1,2],"display","on");
***** test
 y =  [ 8.706 10.362 11.552  6.941 10.983 10.092  6.421 14.943 15.931 ...
        22.968 18.590 16.567 15.944 21.637 14.492 17.965 18.851 22.891 ...
        22.028 16.884 17.252 18.325 25.435 19.141 21.238 22.196 18.038 ...
        22.628 31.163 26.053 24.419 32.145 28.966 30.207 29.142 33.212 ...
        25.694 ]';
 X = [1 1 1 1 1 1 1 1 2 2 2 2 2 3 3 3 3 3 3 3 3 4 4 4 4 4 4 4 5 5 5 5 5 5 5 5 5]';
 [TAB,STATS] = fitlm (X,y,"continuous",[],"display","off");
 [TAB,STATS] = fitlm (X,y,"CategoricalVars",1,"display","off");
 [TAB,STATS] = fitlm (X,y,"constant","categorical",1,"display","off");
 [TAB,STATS] = fitlm (X,y,"linear","categorical",1,"display","off");
 [TAB,STATS] = fitlm (X,y,[0,0;1,0],"categorical",1,"display","off");
 assert (TAB{2,2}, 10, 1e-04);
 assert (TAB{3,2}, 7.99999999999999, 1e-09);
 assert (TAB{4,2}, 8.99999999999999, 1e-09);
 assert (TAB{5,2}, 11.0001428571429, 1e-09);
 assert (TAB{6,2}, 19.0001111111111, 1e-09);
 assert (TAB{2,3}, 1.01775379540949, 1e-09);
 assert (TAB{3,3}, 1.64107868458008, 1e-09);
 assert (TAB{4,3}, 1.43932122062479, 1e-09);
 assert (TAB{5,3}, 1.48983900477565, 1e-09);
 assert (TAB{6,3}, 1.3987687997822, 1e-09);
 assert (TAB{2,6}, 9.82555903510687, 1e-09);
 assert (TAB{3,6}, 4.87484242844031, 1e-09);
 assert (TAB{4,6}, 6.25294748040552, 1e-09);
 assert (TAB{5,6}, 7.38344399756088, 1e-09);
 assert (TAB{6,6}, 13.5834536158296, 1e-09);
 assert (TAB{3,7}, 2.85812420217862e-05, 1e-12);
 assert (TAB{4,7}, 5.22936741204002e-07, 1e-06);
 assert (TAB{5,7}, 2.12794763209106e-08, 1e-07);
 assert (TAB{6,7}, 7.82091664406755e-15, 1e-08);
***** test
 popcorn = [5.5, 4.5, 3.5; 5.5, 4.5, 4.0; 6.0, 4.0, 3.0; ...
            6.5, 5.0, 4.0; 7.0, 5.5, 5.0; 7.0, 5.0, 4.5];
 brands = bsxfun (@times, ones(6,1), [1,2,3]);
 popper = bsxfun (@times, [1;1;1;2;2;2], ones(1,3));

 [TAB, STATS] = fitlm ({brands(:),popper(:)},popcorn(:),"interactions",...
                          "categoricalvars",[1,2],"display","off");
 assert (TAB{2,2}, 5.66666666666667, 1e-09);
 assert (TAB{3,2}, -1.33333333333333, 1e-09);
 assert (TAB{4,2}, -2.16666666666667, 1e-09);
 assert (TAB{5,2}, 1.16666666666667, 1e-09);
 assert (TAB{6,2}, -0.333333333333334, 1e-09);
 assert (TAB{7,2}, -0.166666666666667, 1e-09);
 assert (TAB{2,3}, 0.215165741455965, 1e-09);
 assert (TAB{3,3}, 0.304290309725089, 1e-09);
 assert (TAB{4,3}, 0.304290309725089, 1e-09);
 assert (TAB{5,3}, 0.304290309725089, 1e-09);
 assert (TAB{6,3}, 0.43033148291193, 1e-09);
 assert (TAB{7,3}, 0.43033148291193, 1e-09);
 assert (TAB{2,6}, 26.3362867542108, 1e-09);
 assert (TAB{3,6}, -4.38178046004138, 1e-09);
 assert (TAB{4,6}, -7.12039324756724, 1e-09);
 assert (TAB{5,6}, 3.83405790253621, 1e-09);
 assert (TAB{6,6}, -0.774596669241495, 1e-09);
 assert (TAB{7,6}, -0.387298334620748, 1e-09);
 assert (TAB{2,7}, 5.49841502258254e-12, 1e-09);
 assert (TAB{3,7}, 0.000893505495903642, 1e-09);
 assert (TAB{4,7}, 1.21291454302428e-05, 1e-09);
 assert (TAB{5,7}, 0.00237798044119407, 1e-09);
 assert (TAB{6,7}, 0.453570536021938, 1e-09);
 assert (TAB{7,7}, 0.705316781644046, 1e-09);
 ## Test with string ids for categorical variables
 brands = {'Gourmet', 'National', 'Generic'; ...
           'Gourmet', 'National', 'Generic'; ...
           'Gourmet', 'National', 'Generic'; ...
           'Gourmet', 'National', 'Generic'; ...
           'Gourmet', 'National', 'Generic'; ...
           'Gourmet', 'National', 'Generic'};
 popper = {'oil', 'oil', 'oil'; 'oil', 'oil', 'oil'; 'oil', 'oil', 'oil'; ...
           'air', 'air', 'air'; 'air', 'air', 'air'; 'air', 'air', 'air'};
 [TAB, STATS] = fitlm ({brands(:),popper(:)},popcorn(:),"interactions",...
                          "categoricalvars",[1,2],"display","off");
***** test
 load carsmall
 X = [Weight,Horsepower,Acceleration];
 [TAB, STATS] = fitlm (X, MPG,"constant","display","off");
 [TAB, STATS] = fitlm (X, MPG,"linear","display","off");
 assert (TAB{2,2}, 47.9767628118615, 1e-09);
 assert (TAB{3,2}, -0.00654155878851796, 1e-09);
 assert (TAB{4,2}, -0.0429433065881864, 1e-09);
 assert (TAB{5,2}, -0.0115826516894871, 1e-09);
 assert (TAB{2,3}, 3.87851641748551, 1e-09);
 assert (TAB{3,3}, 0.00112741016370336, 1e-09);
 assert (TAB{4,3}, 0.0243130608813806, 1e-09);
 assert (TAB{5,3}, 0.193325043113178, 1e-09);
 assert (TAB{2,6}, 12.369874881944, 1e-09);
 assert (TAB{3,6}, -5.80228828790225, 1e-09);
 assert (TAB{4,6}, -1.76626492228599, 1e-09);
 assert (TAB{5,6}, -0.0599128364487485, 1e-09);
 assert (TAB{2,7}, 4.89570341688996e-21, 1e-09);
 assert (TAB{3,7}, 9.87424814144e-08, 1e-09);
 assert (TAB{4,7}, 0.0807803098213114, 1e-09);
 assert (TAB{5,7}, 0.952359384151778, 1e-09);
3 tests, 3 passed, 0 known failure, 0 skipped
[inst/knnsearch.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/knnsearch.m
***** demo
 ## find 10 nearest neighbour of a point using different distance metrics
 ## and compare the results by plotting
 load fisheriris
 X = meas(:,3:4);
 Y = species;
 point = [5, 1.45];

 ## calculate 10 nearest-neighbours by minkowski distance
 [id, d] = knnsearch (X, point, "K", 10);

 ## calculate 10 nearest-neighbours by minkowski distance
 [idm, dm] = knnsearch (X, point, "K", 10, "distance", "minkowski", "p", 5);

 ## calculate 10 nearest-neighbours by chebychev distance
 [idc, dc] = knnsearch (X, point, "K", 10, "distance", "chebychev");

 ## plotting the results
 gscatter (X(:,1), X(:,2), species, [.75 .75 0; 0 .75 .75; .75 0 .75], ".", 20);
 title ("Fisher's Iris Data - Nearest Neighbors with different types of distance metrics");
 xlabel("Petal length (cm)");
 ylabel("Petal width (cm)");

 line (point(1), point(2), "marker", "X", "color", "k", ...
       "linewidth", 2, "displayname", "query point")
 line (X(id,1), X(id,2), "color", [0.5 0.5 0.5], "marker", "o", ...
       "linestyle", "none", "markersize", 10, "displayname", "eulcidean")
 line (X(idm,1), X(idm,2), "color", [0.5 0.5 0.5], "marker", "d", ...
       "linestyle", "none", "markersize", 10, "displayname", "Minkowski")
 line (X(idc,1), X(idc,2), "color", [0.5 0.5 0.5], "marker", "p", ...
       "linestyle", "none", "markersize", 10, "displayname", "chebychev")
 xlim ([4.5 5.5]);
 ylim ([1 2]);
 axis square;
***** demo
 ## knnsearch on iris dataset using kdtree method
 load fisheriris
 X = meas(:,3:4);
 gscatter (X(:,1), X(:,2), species, [.75 .75 0; 0 .75 .75; .75 0 .75], ".", 20);
 title ("Fisher's iris dataset : Nearest Neighbors with kdtree search");

 ## new point to be predicted
 point = [5 1.45];

 line (point(1), point(2), "marker", "X", "color", "k", ...
       "linewidth", 2, "displayname", "query point")

 ## knnsearch using kdtree method
 [idx, d] = knnsearch (X, point, "K", 10, "NSMethod", "kdtree");

 ## plotting predicted neighbours
 line (X(idx,1), X(idx,2), "color", [0.5 0.5 0.5], "marker", "o", ...
       "linestyle", "none", "markersize", 10, ...
       "displayname", "nearest neighbour")
 xlim ([4 6])
 ylim ([1 3])
 axis square
 ## details of predicted labels
 tabulate (species(idx))

 ctr = point - d(end);
 diameter = 2 * d(end);
 ##  Draw a circle around the 10 nearest neighbors.
 h = rectangle ("position", [ctr, diameter, diameter], "curvature", [1 1]);

 ## here only 8 neighbours are plotted instead of 10 since the dataset
 ## contains duplicate values
***** shared X, Y
 X = [1, 2, 3, 4; 2, 3, 4, 5; 3, 4, 5, 6];
 Y = [1, 2, 2, 3; 2, 3, 3, 4];
***** test
 [idx, D] = knnsearch (X, Y, "Distance", "euclidean");
 assert (idx, [1; 1]);
 assert (D, ones (2, 1) * sqrt (2));
***** test
 eucldist = @(v,m) sqrt(sumsq(repmat(v,rows(m),1)-m,2));
 [idx, D] = knnsearch (X, Y, "Distance", eucldist);
 assert (idx, [1; 1]);
 assert (D, ones (2, 1) * sqrt (2));
***** test
 [idx, D] = knnsearch (X, Y, "Distance", "euclidean", "includeties", true);
 assert (iscell (idx), true);
 assert (iscell (D), true)
 assert (idx {1}, [1]);
 assert (idx {2}, [1, 2]);
 assert (D{1}, ones (1, 1) * sqrt (2));
 assert (D{2}, ones (1, 2) * sqrt (2));
***** test
 [idx, D] = knnsearch (X, Y, "Distance", "euclidean", "k", 2);
 assert (idx, [1, 2; 1, 2]);
 assert (D, [sqrt(2), 3.162277660168380; sqrt(2), sqrt(2)], 1e-14);
***** test
 [idx, D] = knnsearch (X, Y, "Distance", "seuclidean");
 assert (idx, [1; 1]);
 assert (D, ones (2, 1) * sqrt (2));
***** test
 [idx, D] = knnsearch (X, Y, "Distance", "seuclidean", "k", 2);
 assert (idx, [1, 2; 1, 2]);
 assert (D, [sqrt(2), 3.162277660168380; sqrt(2), sqrt(2)], 1e-14);
***** test
 xx = [1, 2; 1, 3; 2, 4; 3, 6];
 yy = [2, 4; 2, 6];
 [idx, D] = knnsearch (xx, yy, "Distance", "mahalanobis");
 assert (idx, [3; 2]);
 assert (D, [0; 3.162277660168377], 1e-14);
***** test
 [idx, D] = knnsearch (X, Y, "Distance", "minkowski");
 assert (idx, [1; 1]);
 assert (D, ones (2, 1) * sqrt (2));
***** test
 [idx, D] = knnsearch (X, Y, "Distance", "minkowski", "p", 3);
 assert (idx, [1; 1]);
 assert (D, ones (2, 1) * 1.259921049894873, 1e-14);
***** test
 [idx, D] = knnsearch (X, Y, "Distance", "cityblock");
 assert (idx, [1; 1]);
 assert (D, [2; 2]);
***** test
 [idx, D] = knnsearch (X, Y, "Distance", "chebychev");
 assert (idx, [1; 1]);
 assert (D, [1; 1]);
***** test
 [idx, D] = knnsearch (X, Y, "Distance", "cosine");
 assert (idx, [2; 3]);
 assert (D, [0.005674536395645; 0.002911214328620], 1e-14);
***** test
 [idx, D] = knnsearch (X, Y, "Distance", "correlation");
 assert (idx, [1; 1]);
 assert (D, ones (2, 1) * 0.051316701949486, 1e-14);
***** test
 [idx, D] = knnsearch (X, Y, "Distance", "spearman");
 assert (idx, [1; 1]);
 assert (D, ones (2, 1) * 0.051316701949486, 1e-14);
***** test
 [idx, D] = knnsearch (X, Y, "Distance", "hamming");
 assert (idx, [1; 1]);
 assert (D, [0.5; 0.5]);
***** test
 [idx, D] = knnsearch (X, Y, "Distance", "jaccard");
 assert (idx, [1; 1]);
 assert (D, [0.5; 0.5]);
***** test
 [idx, D] = knnsearch (X, Y, "Distance", "jaccard", "k", 2);
 assert (idx, [1, 2; 1, 2]);
 assert (D, [0.5, 1; 0.5, 0.5]);
***** test
 a = [1, 5; 1, 2; 2, 2; 1.5, 1.5; 5, 1; 2 -1.34; 1, -3; 4, -4; -3, 1; 8, 9];
 b = [1, 1];
 [idx, D] = knnsearch (a, b, "K", 5, "NSMethod", "kdtree", "includeties", true);
 assert (iscell (idx), true);
 assert (iscell (D), true)
 assert (cell2mat (idx), [4, 2, 3, 6, 1, 5, 7, 9]);
 assert (cell2mat (D), [0.7071, 1.0000, 1.4142, 2.5447, 4.0000, 4.0000, 4.0000, 4.0000],1e-4);
***** test
 a = [1, 5; 1, 2; 2, 2; 1.5, 1.5; 5, 1; 2 -1.34; 1, -3; 4, -4; -3, 1; 8, 9];
 b = [1, 1];
 [idx, D] = knnsearch (a, b, "K", 5, "NSMethod", "exhaustive", "includeties", true);
 assert (iscell (idx), true);
 assert (iscell (D), true)
 assert (cell2mat (idx), [4, 2, 3, 6, 1, 5, 7, 9]);
 assert (cell2mat (D), [0.7071, 1.0000, 1.4142, 2.5447, 4.0000, 4.0000, 4.0000, 4.0000],1e-4);
***** test
 a = [1, 5; 1, 2; 2, 2; 1.5, 1.5; 5, 1; 2 -1.34; 1, -3; 4, -4; -3, 1; 8, 9];
 b = [1, 1];
 [idx, D] = knnsearch (a, b, "K", 5, "NSMethod", "kdtree", "includeties", false);
 assert (iscell (idx), false);
 assert (iscell (D), false)
 assert (idx, [4, 2, 3, 6, 1]);
 assert (D, [0.7071, 1.0000, 1.4142, 2.5447, 4.0000],1e-4);
***** test
 a = [1, 5; 1, 2; 2, 2; 1.5, 1.5; 5, 1; 2 -1.34; 1, -3; 4, -4; -3, 1; 8, 9];
 b = [1, 1];
 [idx, D] = knnsearch (a, b, "K", 5, "NSMethod", "exhaustive", "includeties", false);
 assert (iscell (idx), false);
 assert (iscell (D), false)
 assert (idx, [4, 2, 3, 6, 1]);
 assert (D, [0.7071, 1.0000, 1.4142, 2.5447, 4.0000],1e-4);
***** test
 load fisheriris
 a = meas;
 b = min(meas);
 [idx, D] = knnsearch (a, b, "K", 5, "NSMethod", "kdtree");
 assert (idx, [42, 9, 14, 39, 13]);
 assert (D, [0.5099, 0.9950, 1.0050, 1.0536, 1.1874],1e-4);
***** test
 load fisheriris
 a = meas;
 b = mean(meas);
 [idx, D] = knnsearch (a, b, "K", 5, "NSMethod", "kdtree");
 assert (idx, [65, 83, 89, 72, 100]);
 assert (D, [0.3451, 0.3869, 0.4354, 0.4481, 0.4625],1e-4);
***** test
 load fisheriris
 a = meas;
 b = max(meas);
 [idx, D] = knnsearch (a, b, "K", 5, "NSMethod", "kdtree");
 assert (idx, [118, 132, 110, 106, 136]);
 assert (D, [0.7280, 0.9274, 1.3304, 1.5166, 1.6371],1e-4);

***** test
 load fisheriris
 a = meas;
 b = max(meas);
 [idx, D] = knnsearch (a, b, "K", 5, "includeties", true);
 assert ( iscell (idx), true);
 assert ( iscell (D), true);
 assert (cell2mat (idx), [118, 132, 110, 106, 136]);
 assert (cell2mat (D), [0.7280, 0.9274, 1.3304, 1.5166, 1.6371],1e-4);
***** error<knnsearch: too few input arguments.> knnsearch (1)
***** error<knnsearch: number of columns in X and Y must match.> ...
 knnsearch (ones (4, 5), ones (4))
***** error<knnsearch: invalid NAME in optional pairs of arguments.> ...
 knnsearch (ones (4, 2), ones (3, 2), "Distance", "euclidean", "some", "some")
***** error<knnsearch: only a single distance parameter can be defined.> ...
 knnsearch (ones (4, 5), ones (1, 5), "scale", ones (1, 5), "P", 3)
***** error<knnsearch: invalid value of K.> ...
 knnsearch (ones (4, 5), ones (1, 5), "K", 0)
***** error<knnsearch: invalid value of Minkowski Exponent.> ...
 knnsearch (ones (4, 5), ones (1, 5), "P",-2)
***** error<knnsearch: invalid value in Scale or the size of Scale.> ...
 knnsearch (ones (4, 5), ones (1, 5), "scale", ones(4,5), "distance", "euclidean")
***** error<knnsearch: invalid value in Cov, Cov can only be given for mahalanobis distance.> ...
 knnsearch (ones (4, 5), ones (1, 5), "cov", ["some" "some"])
***** error<knnsearch: invalid value in Cov, Cov can only be given for mahalanobis distance.> ...
 knnsearch (ones (4, 5), ones (1, 5), "cov", ones(4,5), "distance", "euclidean")
***** error<knnsearch: invalid value of bucketsize.> ...
 knnsearch (ones (4, 5), ones (1, 5), "bucketsize", -1)
***** error<knnsearch: 'kdtree' cannot be used with the given distance metric.> ...
 knnsearch (ones (4, 5), ones (1, 5), "NSmethod", "kdtree", "distance", "cosine")
***** error<knnsearch: 'kdtree' cannot be used with the given distance metric.> ...
 knnsearch (ones (4, 5), ones (1, 5), "NSmethod", "kdtree", "distance", "mahalanobis")
***** error<knnsearch: 'kdtree' cannot be used with the given distance metric.> ...
 knnsearch (ones (4, 5), ones (1, 5), "NSmethod", "kdtree", "distance", "correlation")
***** error<knnsearch: 'kdtree' cannot be used with the given distance metric.> ...
 knnsearch (ones (4, 5), ones (1, 5), "NSmethod", "kdtree", "distance", "seuclidean")
***** error<knnsearch: 'kdtree' cannot be used with the given distance metric.> ...
 knnsearch (ones (4, 5), ones (1, 5), "NSmethod", "kdtree", "distance", "spearman")
***** error<knnsearch: 'kdtree' cannot be used with the given distance metric.> ...
 knnsearch (ones (4, 5), ones (1, 5), "NSmethod", "kdtree", "distance", "hamming")
***** error<knnsearch: 'kdtree' cannot be used with the given distance metric.> ...
 knnsearch (ones (4, 5), ones (1, 5), "NSmethod", "kdtree", "distance", "jaccard")
42 tests, 42 passed, 0 known failure, 0 skipped
[inst/slicesample.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/slicesample.m
***** demo
 ## Define function to sample
 d = 2;
 mu = [-1; 2];
 rand ("seed", 5)  # for reproducibility
 Sigma = rand (d);
 Sigma = (Sigma + Sigma');
 Sigma += eye (d)*abs (eigs (Sigma, 1, "sa")) * 1.1;
 pdf = @(x)(2*pi)^(-d/2)*det(Sigma)^-.5*exp(-.5*sum((x.'-mu).*(Sigma\(x.'-mu)),1));

 ## Inputs
 start = ones (1,2);
 nsamples = 500;
 K = 500;
 m = 10;
 rande ("seed", 4);  rand ("seed", 5)  # for reproducibility
 [smpl, accept] = slicesample (start, nsamples, "pdf", pdf, "burnin", K, "thin", m, "width", [20, 30]);
 figure;
 hold on;
 plot (smpl(:,1), smpl(:,2), 'x');
 [x, y] = meshgrid (linspace (-6,4), linspace(-3,7));
 z = reshape (pdf ([x(:), y(:)]), size(x));
 mesh (x, y, z, "facecolor", "None");

 ## Using sample points to find the volume of half a sphere with radius of .5
 f = @(x) ((.25-(x(:,1)+1).^2-(x(:,2)-2).^2).^.5.*(((x(:,1)+1).^2+(x(:,2)-2).^2)<.25)).';
 int = mean (f (smpl) ./ pdf (smpl));
 errest = std (f (smpl) ./ pdf (smpl)) / nsamples^.5;
 trueerr = abs (2/3*pi*.25^(3/2)-int);
 fprintf ("Monte Carlo integral estimate int f(x) dx = %f\n", int);
 fprintf ("Monte Carlo integral error estimate %f\n", errest);
 fprintf ("The actual error %f\n", trueerr);
 mesh (x,y,reshape (f([x(:), y(:)]), size(x)), "facecolor", "None");
***** demo
 ## Integrate truncated normal distribution to find normilization constant
 pdf = @(x) exp (-.5*x.^2)/(pi^.5*2^.5);
 nsamples = 1e3;
 rande ("seed", 4);  rand ("seed", 5)  # for reproducibility
 [smpl, accept] = slicesample (1, nsamples, "pdf", pdf, "thin", 4);
 f = @(x) exp (-.5 * x .^ 2) .* (x >= -2 & x <= 2);
 x = linspace (-3, 3, 1000);
 area (x, f(x));
 xlabel ("x");
 ylabel ("f(x)");
 int = mean (f (smpl) ./ pdf (smpl));
 errest = std (f (smpl) ./ pdf (smpl)) / nsamples ^ 0.5;
 trueerr = abs (erf (2 ^ 0.5) * 2 ^ 0.5 * pi ^ 0.5 - int);
 fprintf("Monte Carlo integral estimate int f(x) dx = %f\n", int);
 fprintf("Monte Carlo integral error estimate %f\n", errest);
 fprintf("The actual error %f\n", trueerr);
***** test
 start = 0.5;
 nsamples = 1e3;
 pdf = @(x) exp (-.5*(x-1).^2)/(2*pi)^.5;
 [smpl, accept] = slicesample (start, nsamples, "pdf", pdf, "thin", 2, "burnin", 0, "width", 5);
 assert (mean (smpl, 1), 1, .15);
 assert (var (smpl, 1), 1, .25);
***** error slicesample ();
***** error slicesample (1);
***** error slicesample (1, 1);
4 tests, 4 passed, 0 known failure, 0 skipped
[inst/mhsample.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/mhsample.m
***** demo
 ## Define function to sample
 d = 2;
 mu = [-1; 2];
 rand ("seed", 5)  # for reproducibility
 Sigma = rand (d);
 Sigma = (Sigma + Sigma');
 Sigma += eye (d) * abs (eigs (Sigma, 1, "sa")) * 1.1;
 pdf = @(x)(2*pi)^(-d/2)*det(Sigma)^-.5*exp(-.5*sum((x.'-mu).*(Sigma\(x.'-mu)),1));
 ## Inputs
 start = ones (1, 2);
 nsamples = 500;
 sym = true;
 K = 500;
 m = 10;
 rand ("seed", 8)  # for reproducibility
 proprnd = @(x) (rand (size (x)) - .5) * 3 + x;
 [smpl, accept] = mhsample (start, nsamples, "pdf", pdf, "proprnd", proprnd, ...
                            "symmetric", sym, "burnin", K, "thin", m);
 figure;
 hold on;
 plot (smpl(:, 1), smpl(:, 2), 'x');
 [x, y] = meshgrid (linspace (-6, 4), linspace(-3, 7));
 z = reshape (pdf ([x(:), y(:)]), size(x));
 mesh (x, y, z, "facecolor", "None");
 ## Using sample points to find the volume of half a sphere with radius of .5
 f = @(x) ((.25-(x(:,1)+1).^2-(x(:,2)-2).^2).^.5.*(((x(:,1)+1).^2+(x(:,2)-2).^2)<.25)).';
 int = mean (f (smpl) ./ pdf (smpl));
 errest = std (f (smpl) ./ pdf (smpl)) / nsamples ^ .5;
 trueerr = abs (2 / 3 * pi * .25 ^ (3 / 2) - int);
 printf ("Monte Carlo integral estimate int f(x) dx = %f\n", int);
 printf ("Monte Carlo integral error estimate %f\n", errest);
 printf ("The actual error %f\n", trueerr);
 mesh (x, y, reshape (f([x(:), y(:)]), size(x)), "facecolor", "None");
***** demo
 ## Integrate truncated normal distribution to find normilization constant
 pdf = @(x) exp (-.5*x.^2)/(pi^.5*2^.5);
 nsamples = 1e3;
 rand ("seed", 5)  # for reproducibility
 proprnd = @(x) (rand (size (x)) - .5) * 3 + x;
 [smpl, accept] = mhsample (1, nsamples, "pdf", pdf, "proprnd", proprnd, ...
                            "symmetric", true, "thin", 4);
 f = @(x) exp(-.5 * x .^ 2) .* (x >= -2 & x <= 2);
 x = linspace (-3, 3, 1000);
 area(x, f(x));
 xlabel ('x');
 ylabel ('f(x)');
 int = mean (f (smpl) ./ pdf (smpl));
 errest = std (f (smpl) ./ pdf (smpl)) / nsamples^ .5;
 trueerr = abs (erf (2 ^ .5) * 2 ^ .5 * pi ^ .5 - int);
 printf ("Monte Carlo integral estimate int f(x) dx = %f\n", int);
 printf ("Monte Carlo integral error estimate %f\n", errest);
 printf ("The actual error %f\n", trueerr);
***** test
 nchain = 1e4;
 start = rand (nchain, 1);
 nsamples = 1e3;
 pdf = @(x) exp (-.5*(x-1).^2)/(2*pi)^.5;
 proppdf = @(x, y) 1/3;
 proprnd = @(x) 3 * (rand (size (x)) - .5) + x;
 [smpl, accept] = mhsample (start, nsamples, "pdf", pdf, "proppdf", proppdf, ...
                            "proprnd", proprnd, "thin", 2, "nchain", nchain, ...
                            "burnin", 0);
 assert (mean (mean (smpl, 1), 3), 1, .01);
 assert (mean (var (smpl, 1), 3), 1, .01)
***** error mhsample ();
***** error mhsample (1);
***** error mhsample (1, 1);
***** error mhsample (1, 1, "pdf", @(x)x);
***** error mhsample (1, 1, "pdf", @(x)x, "proprnd", @(x)x+rand(size(x)));
6 tests, 6 passed, 0 known failure, 0 skipped
[inst/rmmissing.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/rmmissing.m
***** assert (rmmissing ([1,NaN,3]), [1,3])
***** assert (rmmissing ('abcd f'), 'abcdf')
***** assert (rmmissing ({'xxx','','xyz'}), {'xxx','xyz'})
***** assert (rmmissing ({'xxx','';'xyz','yyy'}), {'xyz','yyy'})
***** assert (rmmissing ({'xxx','';'xyz','yyy'}, 2), {'xxx';'xyz'})
***** assert (rmmissing ([1,2;NaN,2]), [1,2])
***** assert (rmmissing ([1,2;NaN,2], 2), [2,2]')
***** assert (rmmissing ([1,2;NaN,4;NaN,NaN],"MinNumMissing", 2), [1,2;NaN,4])
***** test
 x = [1:6];
 x([2,4]) = NaN;
 [~, idx] = rmmissing (x);
 assert (idx, logical ([0, 1, 0, 1, 0, 0]));
 assert (class(idx), 'logical');
 x = reshape (x, [2, 3]);
 [~, idx] = rmmissing (x);
 assert (idx, logical ([0; 1]));
 assert (class(idx), 'logical');
 [~, idx] = rmmissing (x, 2);
 assert (idx, logical ([1, 1, 0]));
 assert (class(idx), 'logical');
 [~, idx] = rmmissing (x, 1, "MinNumMissing", 2);
 assert (idx, logical ([0; 1]));
 assert (class(idx), 'logical');
 [~, idx] = rmmissing (x, 2, "MinNumMissing", 2);
 assert (idx, logical ([0, 0, 0]));
 assert (class(idx), 'logical');
***** assert (rmmissing (single ([1 2 NaN; 3 4 5])), single ([3 4 5]))
***** assert (rmmissing (logical (ones (3))), logical (ones (3)))
***** assert (rmmissing (int32 (ones (3))), int32 (ones (3)))
***** assert (rmmissing (uint32 (ones (3))), uint32 (ones (3)))
***** assert (rmmissing ({1, 2, 3}), {1, 2, 3})
***** assert (rmmissing ([struct, struct, struct]), [struct, struct, struct])
***** assert (rmmissing ([]), [])
***** assert (rmmissing (ones (1,0)), ones (1,0))
***** assert (rmmissing (ones (1,0), 1), ones (1,0))
***** assert (rmmissing (ones (1,0), 2), ones (1,0))
***** assert (rmmissing (ones (0,1)), ones (0,1))
***** assert (rmmissing (ones (0,1), 1), ones (0,1))
***** assert (rmmissing (ones (0,1), 2), ones (0,1))
***** error <input dimension> rmmissing (ones (0,1,2))
***** error rmmissing ()
***** error <input dimension> rmmissing (ones(2,2,2))
***** error <must be either 1 or 2> rmmissing ([1 2; 3 4], 5)
***** error <unknown parameter name> rmmissing ([1 2; 3 4], "XXX", 1)
***** error <'MinNumMissing'> rmmissing ([1 2; 3 4], 2, "MinNumMissing", -2)
***** error <'MinNumMissing'> rmmissing ([1 2; 3 4], "MinNumMissing", 3.8)
***** error <'MinNumMissing'> rmmissing ([1 2; 3 4], "MinNumMissing", [1 2 3])
***** error <'MinNumMissing'> rmmissing ([1 2; 3 4], "MinNumMissing", 'xxx')
31 tests, 31 passed, 0 known failure, 0 skipped
[inst/ttest.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/ttest.m
***** test
 x = 8:0.1:12;
 [h, pval, ci] = ttest (x, 10);
 assert (h, 0)
 assert (pval, 1, 10*eps)
 assert (ci, [9.6219 10.3781], 1E-5)
 [h, pval, ci0] = ttest (x, 0);
 assert (h, 1)
 assert (pval, 0)
 assert (ci0, ci, 2e-15)
 [h, pval, ci] = ttest (x, 10, "tail", "right", "dim", 2, "alpha", 0.05);
 assert (h, 0)
 assert (pval, 0.5, 10*eps)
 assert (ci, [9.68498 Inf], 1E-5)
***** error ttest ([8:0.1:12], 10, "tail", "invalid");
***** error ttest ([8:0.1:12], 10, "tail", 25);
3 tests, 3 passed, 0 known failure, 0 skipped
[inst/canoncorr.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/canoncorr.m
***** shared X,Y,A,B,r,U,V,k
 k = 10;
 X = [1:k; sin(1:k); cos(1:k)]'; Y = [tan(1:k); tanh((1:k)/k)]';
 [A,B,r,U,V,stats] = canoncorr (X,Y);
***** assert (A, [-0.329229   0.072908; 0.074870   1.389318; -0.069302  -0.024109], 1E-6);
***** assert (B, [-0.017086  -0.398402; -4.475049  -0.824538], 1E-6);
***** assert (r, [0.99590   0.26754], 1E-5);
***** assert (U, center(X) * A, 10*eps);
***** assert (V, center(Y) * B, 10*eps);
***** assert (cov(U), eye(size(U, 2)), 10*eps);
***** assert (cov(V), eye(size(V, 2)), 10*eps);
 rand ("state", 1); [A,B,r] = canoncorr (rand(5, 10),rand(5, 20));
***** assert (r, ones(1, 5), 10*eps);
8 tests, 8 passed, 0 known failure, 0 skipped
[inst/nanmax.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/nanmax.m
***** demo
 ## Find the column maximum values and their indices
 ## for matrix data with missing values.

 x = magic (3);
 x([1, 6:9]) = NaN
 [y, ind] = nanmax (x)
***** demo
 ## Find the maximum of all the values in an array, ignoring missing values.
 ## Create a 2-by-5-by-3 array x with some missing values.

 x = reshape (1:30, [2, 5, 3]);
 x([10:12, 25]) = NaN

 ## Find the maximum of the elements of x.

 y = nanmax (x, [], 'all')
***** assert (nanmax ([2, 4, NaN, 7]), 7)
***** assert (nanmax ([2, 4, NaN, Inf]), Inf)
***** assert (nanmax ([1, NaN, 3; NaN, 5, 6; 7, 8, NaN]), [7, 8, 6])
***** assert (nanmax ([1, NaN, 3; NaN, 5, 6; 7, 8, NaN]'), [3, 6, 8])
***** assert (nanmax (single ([1, NaN, 3; NaN, 5, 6; 7, 8, NaN])), single ([7, 8, 6]))
***** shared x, y
 x(:,:,1) = [1.77, -0.005, NaN, -2.95; NaN, 0.34, NaN, 0.19];
 x(:,:,2) = [1.77, -0.005, NaN, -2.95; NaN, 0.34, NaN, 0.19] + 5;
 y = x;
 y(2,3,1) = 0.51;
***** assert (nanmax (x, [], [1, 2])(:), [1.77;6.77])
***** assert (nanmax (x, [], [1, 3])(:), [6.77;5.34;NaN;5.19])
***** assert (nanmax (x, [], [2, 3])(:), [6.77;5.34])
***** assert (nanmax (x, [], [1, 2, 3]), 6.77)
***** assert (nanmax (x, [], 'all'), 6.77)
***** assert (nanmax (y, [], [1, 3])(:), [6.77;5.34;0.51;5.19])
***** assert (nanmax (x(1,:,1), x(2,:,1)), [1.77, 0.34, NaN, 0.19])
***** assert (nanmax (x(1,:,2), x(2,:,2)), [6.77, 5.34, NaN, 5.19])
***** assert (nanmax (y(1,:,1), y(2,:,1)), [1.77, 0.34, 0.51, 0.19])
***** assert (nanmax (y(1,:,2), y(2,:,2)), [6.77, 5.34, NaN, 5.19])
***** test
 xx = repmat ([1:20;6:25], [5 2 6 3]);
 assert (size (nanmax (xx, [], [3, 2])), [10, 1, 1, 3]);
 assert (size (nanmax (xx, [], [1, 2])), [1, 1, 6, 3]);
 assert (size (nanmax (xx, [], [1, 2, 4])), [1, 1, 6]);
 assert (size (nanmax (xx, [], [1, 4, 3])), [1, 40]);
 assert (size (nanmax (xx, [], [1, 2, 3, 4])), [1, 1]);
***** assert (nanmax (ones (2), [], 3), ones (2, 2))
***** assert (nanmax (ones (2, 2, 2), [], 99), ones (2, 2, 2))
***** assert (nanmax (magic (3), [], 3), magic (3))
***** assert (nanmax (magic (3), [], [1, 3]), [8, 9, 7])
***** assert (nanmax (magic (3), [], [1, 99]), [8, 9, 7])
***** assert (nanmax (ones (2), 3), 3 * ones (2,2))
***** error <nanmax: VECDIM must contain non-repeating positive integers.> ...
 nanmax (y, [], [1, 1, 2])
***** error <nanmax: a second output is not supported with this syntax.> ...
 [v, idx] = nanmax(x, y, [1 2])
24 tests, 24 passed, 0 known failure, 0 skipped
[inst/pca.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/pca.m
***** shared COEFF,SCORE,latent,tsquare,m,x,R,V,lambda,i,S,F
***** test
 x=[7 4 3
    4 1 8
    6 3 5
    8 6 1
    8 5 7
    7 2 9
    5 3 3
    9 5 8
    7 4 5
    8 2 2];
 R = corrcoef (x);
 [V, lambda] = eig (R);
 [~, i] = sort(diag(lambda), "descend"); #arrange largest PC first
 S = V(:, i) * diag(sqrt(diag(lambda)(i)));
***** assert(diag(S(:, 1:2)*S(:, 1:2)'), [0.8662; 0.8420; 0.9876], 1E-4); #contribution of first 2 PCs to each original variable
 B = V(:, i) * diag( 1./ sqrt(diag(lambda)(i)));
 F = zscore(x)*B;
 [COEFF,SCORE,latent,tsquare] = pca(zscore(x, 1));
***** assert(tsquare,sumsq(F, 2),1E4*eps);
***** test
 x=[1,2,3;2,1,3]';
 [COEFF,SCORE,latent,tsquare] = pca(x, "Economy", false);
 m=[sqrt(2),sqrt(2);sqrt(2),-sqrt(2);-2*sqrt(2),0]/2;
 m(:,1) = m(:,1)*sign(COEFF(1,1));
 m(:,2) = m(:,2)*sign(COEFF(1,2));
***** assert(COEFF,m(1:2,:),10*eps);
***** assert(SCORE,-m,10*eps);
***** assert(latent,[1.5;.5],10*eps);
***** assert(tsquare,[4;4;4]/3,10*eps);
 [COEFF,SCORE,latent,tsquare] = pca(x, "Economy", false, "weights", [1 2 1], "variableweights", "variance");
***** assert(COEFF, [0.632455532033676 -0.632455532033676; 0.741619848709566 0.741619848709566], 10*eps);
***** assert(SCORE, [-0.622019449426284 0.959119380657905; -0.505649896847432 -0.505649896847431; 1.633319243121148 0.052180413036957], 10*eps);
***** assert(latent, [1.783001790889027; 0.716998209110974], 10*eps);
***** xtest assert(tsquare, [1.5; 0.5; 1.5], 10*eps);  #currently, [4; 2; 4]/3 is actually returned; see comments above
!!!!! known failure
ASSERT errors for:  assert (tsquare,[1.5; 0.5; 1.5],10 * eps)

  Location  |  Observed  |  Expected  |  Reason
    (1)         1.3333        1.5        Abs err 0.16667 exceeds tol 2.2204e-15 by 0.2
    (2)        0.66667        0.5        Abs err 0.16667 exceeds tol 2.2204e-15 by 0.2
    (3)         1.3333        1.5        Abs err 0.16667 exceeds tol 2.2204e-15 by 0.2
***** test
 x=x';
 [COEFF,SCORE,latent,tsquare] = pca(x, "Economy", false);
 m=[sqrt(2),sqrt(2),0;-sqrt(2),sqrt(2),0;0,0,2]/2;
 m(:,1) = m(:,1)*sign(COEFF(1,1));
 m(:,2) = m(:,2)*sign(COEFF(1,2));
 m(:,3) = m(:,3)*sign(COEFF(3,3));
***** assert(COEFF,m,10*eps);
***** assert(SCORE(:,1),-m(1:2,1),10*eps);
***** assert(SCORE(:,2:3),zeros(2),10*eps);
***** assert(latent,[1;0;0],10*eps);
***** assert(tsquare,[0.5;0.5],10*eps)
***** test
 [COEFF,SCORE,latent,tsquare] = pca(x);
***** assert(COEFF,m(:, 1),10*eps);
***** assert(SCORE,-m(1:2,1),10*eps);
***** assert(latent,[1],10*eps);
***** assert(tsquare,[0.5;0.5],10*eps)
***** error <invalid algorithm> pca([1 2; 3 4], "Algorithm", "xxx")
***** error <'centered' requires a boolean value> pca([1 2; 3 4], "Centered", "xxx")
***** error <must be a positive integer> pca([1 2; 3 4], "NumComponents", -4)
***** error <invalid value for rows> pca([1 2; 3 4], "Rows", 1)
***** error <weights must be> pca([1 2; 3 4], "Weights", [1 2 3])
***** error <weights must be> pca([1 2; 3 4], "Weights", [-1 2])
***** error <variable weights must be> pca([1 2; 3 4], "VariableWeights", [-1 2])
***** error <variable weights must be> pca([1 2; 3 4], "VariableWeights", "xxx")
***** error <unknown property> pca([1 2; 3 4], "XXX", 1)
32 tests, 31 passed, 1 known failure, 0 skipped
[inst/Clustering/ClusterCriterion.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/Clustering/ClusterCriterion.m
***** error <ClusterCriterion: 'x' must be a numeric matrix> ...
 ClusterCriterion ("1", "kmeans", [1:6])
***** error <ClusterCriterion: unknown clustering algorithm 'k'> ...
 ClusterCriterion ([1, 2, 1, 3, 2, 4, 3], "k", [1:6])
***** error <ClusterCriterion: invalid matrix of clustering solutions> ...
 ClusterCriterion ([1, 2, 1; 3, 2, 4], 1, [1:6])
***** error <ClusterCriterion: invalid argument> ...
 ClusterCriterion ([1, 2, 1; 3, 2, 4], ones (2, 2, 2), [1:6])
4 tests, 4 passed, 0 known failure, 0 skipped
[inst/Clustering/DaviesBouldinEvaluation.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/Clustering/DaviesBouldinEvaluation.m
***** test
 load fisheriris
 eva = evalclusters (meas, "kmeans", "DaviesBouldin", "KList", [1:6]);
 assert (class (eva), "DaviesBouldinEvaluation");
1 test, 1 passed, 0 known failure, 0 skipped
[inst/Clustering/GapEvaluation.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/Clustering/GapEvaluation.m
***** test
 load fisheriris
 eva = evalclusters (meas([1:50],:), "kmeans", "gap", "KList", [1:3], ...
                     "referencedistribution", "uniform");
 assert (class (eva), "GapEvaluation");
1 test, 1 passed, 0 known failure, 0 skipped
[inst/Clustering/SilhouetteEvaluation.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/Clustering/SilhouetteEvaluation.m
***** test
 load fisheriris
 eva = evalclusters (meas, "kmeans", "silhouette", "KList", [1:6]);
 assert (class (eva), "SilhouetteEvaluation");
1 test, 1 passed, 0 known failure, 0 skipped
[inst/Clustering/CalinskiHarabaszEvaluation.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/Clustering/CalinskiHarabaszEvaluation.m
***** test
 load fisheriris
 eva = evalclusters (meas, "kmeans", "calinskiharabasz", "KList", [1:6]);
 assert (class (eva), "CalinskiHarabaszEvaluation");
1 test, 1 passed, 0 known failure, 0 skipped
[inst/dist_fit/nbinfit.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fit/nbinfit.m
***** demo
 ## Sample 2 populations from different negative binomial distibutions
 randp ("seed", 5); randg ("seed", 5);    # for reproducibility
 r1 = nbinrnd (2, 0.15, 5000, 1);
 randp ("seed", 8); randg ("seed", 8);    # for reproducibility
 r2 = nbinrnd (5, 0.2, 5000, 1);
 r = [r1, r2];

 ## Plot them normalized and fix their colors
 hist (r, [0:51], 1);
 h = findobj (gca, "Type", "patch");
 set (h(1), "facecolor", "c");
 set (h(2), "facecolor", "g");
 hold on

 ## Estimate their probability of success
 r_psA = nbinfit (r(:,1));
 r_psB = nbinfit (r(:,2));

 ## Plot their estimated PDFs
 x = [0:40];
 y = nbinpdf (x, r_psA(1), r_psA(2));
 plot (x, y, "-pg");
 x = [min(r(:,2)):max(r(:,2))];
 y = nbinpdf (x, r_psB(1), r_psB(2));
 plot (x, y, "-sc");
 ylim ([0, 0.1])
 xlim ([0, 50])
 legend ({"Normalized HIST of sample 1 with r=2 and ps=0.15", ...
          "Normalized HIST of sample 2 with r=5 and ps=0.2", ...
          sprintf("PDF for sample 1 with estimated r=%0.2f and ps=%0.2f", ...
                  r_psA(1), r_psA(2)), ...
          sprintf("PDF for sample 2 with estimated r=%0.2f and ps=%0.2f", ...
                  r_psB(1), r_psB(2))})
 title ("Two population samples from negative different binomial distibutions")
 hold off
***** test
 [paramhat, paramci] = nbinfit ([1:50]);
 assert (paramhat, [2.420857, 0.086704], 1e-6);
 assert (paramci(:,1), [1.382702; 3.459012], 1e-6);
 assert (paramci(:,2), [0.049676; 0.123732], 1e-6);
***** test
 [paramhat, paramci] = nbinfit ([1:20]);
 assert (paramhat, [3.588233, 0.254697], 1e-6);
 assert (paramci(:,1), [0.451693; 6.724774], 1e-6);
 assert (paramci(:,2), [0.081143; 0.428251], 1e-6);
***** test
 [paramhat, paramci] = nbinfit ([1:10]);
 assert (paramhat, [8.8067, 0.6156], 1e-4);
 assert (paramci(:,1), [0; 30.7068], 1e-4);
 assert (paramci(:,2), [0.0217; 1], 1e-4);
***** test
 [paramhat, paramci] = nbinfit ([1:10], 0.05, ones (1, 10));
 assert (paramhat, [8.8067, 0.6156], 1e-4);
 assert (paramci(:,1), [0; 30.7068], 1e-4);
 assert (paramci(:,2), [0.0217; 1], 1e-4);
***** test
 [paramhat, paramci] = nbinfit ([1:11], 0.05, [ones(1, 10), 0]);
 assert (paramhat, [8.8067, 0.6156], 1e-4);
 assert (paramci(:,1), [0; 30.7068], 1e-4);
 assert (paramci(:,2), [0.0217; 1], 1e-4);
***** error<nbinfit: X cannot have negative values.> nbinfit ([-1 2 3 3])
***** error<nbinfit: X must be a vector.> nbinfit (ones (2))
***** error<nbinfit: X must be a non-negative integer.> nbinfit ([1 2 1.2 3])
***** error<nbinfit: wrong value for ALPHA.> nbinfit ([1 2 3], 0)
***** error<nbinfit: wrong value for ALPHA.> nbinfit ([1 2 3], 1.2)
***** error<nbinfit: wrong value for ALPHA.> nbinfit ([1 2 3], [0.02 0.05])
***** error<nbinfit: X and FREQ vectors mismatch.> ...
 nbinfit ([1, 2, 3, 4, 5], 0.05, [1, 2, 3, 2]);
***** error<nbinfit: FREQ must not contain negative values.> ...
 nbinfit ([1, 2, 3, 4, 5], 0.05, [1, 2, 3, 2, -1]);
***** error<nbinfit: FREQ must contain integer values.> ...
 nbinfit ([1, 2, 3, 4, 5], 0.05, [1, 2, 3, 2, 1.5]);
***** error<nbinfit: 'options' argument must be a structure> ...
 nbinfit ([1, 2, 3, 4, 5], 0.05, struct ("option", 234));
***** error<nbinfit: 'options' argument must be a structure> ...
 nbinfit ([1, 2, 3, 4, 5], 0.05, ones (1,5), struct ("option", 234));
16 tests, 16 passed, 0 known failure, 0 skipped
[inst/dist_fit/geofit.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fit/geofit.m
***** demo
 ## Sample 2 populations from different geometric distibutions
 rande ("seed", 1);    # for reproducibility
 r1 = geornd (0.15, 1000, 1);
 rande ("seed", 2);    # for reproducibility
 r2 = geornd (0.5, 1000, 1);
 r = [r1, r2];

 ## Plot them normalized and fix their colors
 hist (r, 0:0.5:20.5, 1);
 h = findobj (gca, "Type", "patch");
 set (h(1), "facecolor", "c");
 set (h(2), "facecolor", "g");
 hold on

 ## Estimate their probability of success
 pshatA = geofit (r(:,1));
 pshatB = geofit (r(:,2));

 ## Plot their estimated PDFs
 x = [0:15];
 y = geopdf (x, pshatA);
 plot (x, y, "-pg");
 y = geopdf (x, pshatB);
 plot (x, y, "-sc");
 xlim ([0, 15])
 ylim ([0, 0.6])
 legend ({"Normalized HIST of sample 1 with ps=0.15", ...
          "Normalized HIST of sample 2 with ps=0.50", ...
          sprintf("PDF for sample 1 with estimated ps=%0.2f", ...
                  mean (pshatA)), ...
          sprintf("PDF for sample 2 with estimated ps=%0.2f", ...
                  mean (pshatB))})
 title ("Two population samples from different geometric distibutions")
 hold off
***** test
 x = 0:5;
 [pshat, psci] = geofit (x);
 assert (pshat, 0.2857, 1e-4);
 assert (psci, [0.092499; 0.478929], 1e-5);
***** test
 x = 0:5;
 [pshat, psci] = geofit (x, [], [1 1 1 1 1 1]);
 assert (pshat, 0.2857, 1e-4);
 assert (psci, [0.092499; 0.478929], 1e-5);
***** assert (geofit ([1 1 2 3]), geofit ([1 2 3], [] ,[2 1 1]))
***** error<geofit: function called with too few input arguments.> geofit ()
***** error<geofit: X cannot have negative values.> geofit (-1, [1 2 3 3])
***** error<geofit: wrong value for ALPHA.> geofit (1, 0)
***** error<geofit: wrong value for ALPHA.> geofit (1, 1.2)
***** error<geofit: wrong value for ALPHA.> geofit (1, [0.02 0.05])
***** error<geofit: X and FREQ vector mismatch.> ...
 geofit ([1.5, 0.2], [], [0, 0, 0, 0, 0])
***** error<geofit: X and FREQ vector mismatch.> ...
 geofit ([1.5, 0.2], [], [1, 1, 1])
10 tests, 10 passed, 0 known failure, 0 skipped
[inst/dist_fit/gumbelfit.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fit/gumbelfit.m
***** demo
 ## Sample 3 populations from different Gumbel distibutions
 rand ("seed", 1);    # for reproducibility
 r1 = gumbelrnd (2, 5, 400, 1);
 rand ("seed", 11);    # for reproducibility
 r2 = gumbelrnd (-5, 3, 400, 1);
 rand ("seed", 16);    # for reproducibility
 r3 = gumbelrnd (14, 8, 400, 1);
 r = [r1, r2, r3];

 ## Plot them normalized and fix their colors
 hist (r, 25, 0.32);
 h = findobj (gca, "Type", "patch");
 set (h(1), "facecolor", "c");
 set (h(2), "facecolor", "g");
 set (h(3), "facecolor", "r");
 ylim ([0, 0.28])
 xlim ([-11, 50]);
 hold on

 ## Estimate their MU and BETA parameters
 mu_betaA = gumbelfit (r(:,1));
 mu_betaB = gumbelfit (r(:,2));
 mu_betaC = gumbelfit (r(:,3));

 ## Plot their estimated PDFs
 x = [min(r(:)):max(r(:))];
 y = gumbelpdf (x, mu_betaA(1), mu_betaA(2));
 plot (x, y, "-pr");
 y = gumbelpdf (x, mu_betaB(1), mu_betaB(2));
 plot (x, y, "-sg");
 y = gumbelpdf (x, mu_betaC(1), mu_betaC(2));
 plot (x, y, "-^c");
 legend ({"Normalized HIST of sample 1 with μ=2 and β=5", ...
          "Normalized HIST of sample 2 with μ=-5 and β=3", ...
          "Normalized HIST of sample 3 with μ=14 and β=8", ...
          sprintf("PDF for sample 1 with estimated μ=%0.2f and β=%0.2f", ...
                  mu_betaA(1), mu_betaA(2)), ...
          sprintf("PDF for sample 2 with estimated μ=%0.2f and β=%0.2f", ...
                  mu_betaB(1), mu_betaB(2)), ...
          sprintf("PDF for sample 3 with estimated μ=%0.2f and β=%0.2f", ...
                  mu_betaC(1), mu_betaC(2))})
 title ("Three population samples from different Gumbel distibutions")
 hold off
***** test
 x = 1:50;
 [paramhat, paramci] = gumbelfit (x);
 paramhat_out = [18.3188, 13.0509];
 paramci_out = [14.4882, 10.5294; 22.1495, 16.1763];
 assert (paramhat, paramhat_out, 1e-4);
 assert (paramci, paramci_out, 1e-4);
***** test
 x = 1:50;
 [paramhat, paramci] = gumbelfit (x, 0.01);
 paramci_out = [13.2845, 9.8426; 23.3532, 17.3051];
 assert (paramci, paramci_out, 1e-4);
***** error<gumbelfit: X must be a double-precision vector.> gumbelfit (ones (2,5));
***** error<gumbelfit: X must be a double-precision vector.> ...
 gumbelfit (single (ones (1,5)));
***** error<gumbelfit: X must NOT contain missing values> ...
 gumbelfit ([1, 2, 3, 4, NaN]);
***** error<gumbelfit: wrong value for ALPHA.> gumbelfit ([1, 2, 3, 4, 5], 1.2);
***** error<gumbelfit: X and CENSOR vectors mismatch.> ...
 gumbelfit ([1, 2, 3, 4, 5], 0.05, [1 1 0]);
***** error<gumbelfit: X and FREQ vectors mismatch.> ...
 gumbelfit ([1, 2, 3, 4, 5], 0.05, [], [1 1 0]);
***** error<gamfit: FREQ must not contain negative values.>
 gamfit ([1, 2, 3], 0.05, [], [1 5 -1])
***** error<gumbelfit: 'options' 5th argument> ...
 gumbelfit ([1, 2, 3, 4, 5], 0.05, [], [], 2);
10 tests, 10 passed, 0 known failure, 0 skipped
[inst/dist_fit/ricefit.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fit/ricefit.m
***** demo
 ## Sample 3 populations from different Gamma distibutions
 randg ("seed", 5);    # for reproducibility
 randp ("seed", 6);
 r1 = ricernd (1, 2, 3000, 1);
 randg ("seed", 2);    # for reproducibility
 randp ("seed", 8);
 r2 = ricernd (2, 4, 3000, 1);
 randg ("seed", 7);    # for reproducibility
 randp ("seed", 9);
 r3 = ricernd (7.5, 1, 3000, 1);
 r = [r1, r2, r3];

 ## Plot them normalized and fix their colors
 hist (r, 75, 4);
 h = findobj (gca, "Type", "patch");
 set (h(1), "facecolor", "c");
 set (h(2), "facecolor", "g");
 set (h(3), "facecolor", "r");
 ylim ([0, 0.7]);
 xlim ([0, 12]);
 hold on

 ## Estimate their α and β parameters
 s_sigmaA = ricefit (r(:,1));
 s_sigmaB = ricefit (r(:,2));
 s_sigmaC = ricefit (r(:,3));

 ## Plot their estimated PDFs
 x = [0.01,0.1:0.2:18];
 y = ricepdf (x, s_sigmaA(1), s_sigmaA(2));
 plot (x, y, "-pr");
 y = ricepdf (x, s_sigmaB(1), s_sigmaB(2));
 plot (x, y, "-sg");
 y = ricepdf (x, s_sigmaC(1), s_sigmaC(2));
 plot (x, y, "-^c");
 hold off
 legend ({"Normalized HIST of sample 1 with s=1 and σ=2", ...
          "Normalized HIST of sample 2 with s=2 and σ=4", ...
          "Normalized HIST of sample 3 with s=7.5 and σ=1", ...
          sprintf("PDF for sample 1 with estimated s=%0.2f and σ=%0.2f", ...
                  s_sigmaA(1), s_sigmaA(2)), ...
          sprintf("PDF for sample 2 with estimated s=%0.2f and σ=%0.2f", ...
                  s_sigmaB(1), s_sigmaB(2)), ...
          sprintf("PDF for sample 3 with estimated s=%0.2f and σ=%0.2f", ...
                  s_sigmaC(1), s_sigmaC(2))})
 title ("Three population samples from different Rician distibutions")
 hold off
***** test
 [paramhat, paramci] = ricefit ([1:50]);
 assert (paramhat, [15.3057, 17.6668], 1e-4);
 assert (paramci, [9.5468, 11.7802; 24.5383, 26.4952], 1e-4);
***** test
 [paramhat, paramci] = ricefit ([1:50], 0.01);
 assert (paramhat, [15.3057, 17.6668], 1e-4);
 assert (paramci, [8.2309, 10.3717; 28.4615, 30.0934], 1e-4);
***** test
 [paramhat, paramci] = ricefit ([1:5]);
 assert (paramhat, [2.3123, 1.6812], 1e-4);
 assert (paramci, [1.0819, 0.6376; 4.9424, 4.4331], 1e-4);
***** test
 [paramhat, paramci] = ricefit ([1:5], 0.01);
 assert (paramhat, [2.3123, 1.6812], 1e-4);
 assert (paramci, [0.8521, 0.4702; 6.2747, 6.0120], 1e-4);
***** test
 freq = [1 1 1 1 5];
 [paramhat, paramci] = ricefit ([1:5], [], [], freq);
 assert (paramhat, [3.5181, 1.5565], 1e-4);
 assert (paramci, [2.5893, 0.9049; 4.7801, 2.6772], 1e-4);
***** test
 censor = [1 0 0 0 0];
 [paramhat, paramci] = ricefit ([1:5], [], censor);
 assert (paramhat, [3.2978, 1.1527], 1e-4);
 assert (paramci, [2.3192, 0.5476; 4.6895, 2.4261], 1e-4);
***** assert (class (ricefit (single ([1:50]))), "single")
***** error<ricefit: X must be a vector.> ricefit (ones (2))
***** error<ricefit: wrong value for ALPHA.> ricefit ([1:50], 1)
***** error<ricefit: wrong value for ALPHA.> ricefit ([1:50], -1)
***** error<ricefit: wrong value for ALPHA.> ricefit ([1:50], {0.05})
***** error<ricefit: wrong value for ALPHA.> ricefit ([1:50], "k")
***** error<ricefit: wrong value for ALPHA.> ricefit ([1:50], i)
***** error<ricefit: wrong value for ALPHA.> ricefit ([1:50], [0.01 0.02])
***** error<ricefit: X and CENSOR vectors mismatch.> ricefit ([1:50], [], [1 1])
***** error<ricefit: X and FREQ vectors mismatch.> ricefit ([1:50], [], [], [1 1])
***** error<ricefit: FREQ cannot have negative values.> ...
 ricefit ([1:5], [], [], [1, 1, 2, 1, -1])
***** error<ricefit: X must contain positive values.> ricefit ([1 2 3 -4])
***** error<ricefit: X must contain positive values.> ricefit ([1 2 0], [], [1 0 0])
19 tests, 19 passed, 0 known failure, 0 skipped
[inst/dist_fit/poissfit.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fit/poissfit.m
***** demo
 ## Sample 3 populations from 3 different Poisson distibutions
 randp ("seed", 2);    # for reproducibility
 r1 = poissrnd (1, 1000, 1);
 randp ("seed", 2);    # for reproducibility
 r2 = poissrnd (4, 1000, 1);
 randp ("seed", 3);    # for reproducibility
 r3 = poissrnd (10, 1000, 1);
 r = [r1, r2, r3];

 ## Plot them normalized and fix their colors
 hist (r, [0:20], 1);
 h = findobj (gca, "Type", "patch");
 set (h(1), "facecolor", "c");
 set (h(2), "facecolor", "g");
 set (h(3), "facecolor", "r");
 hold on

 ## Estimate their lambda parameter
 lambdahat = poissfit (r);

 ## Plot their estimated PDFs
 x = [0:20];
 y = poisspdf (x, lambdahat(1));
 plot (x, y, "-pr");
 y = poisspdf (x, lambdahat(2));
 plot (x, y, "-sg");
 y = poisspdf (x, lambdahat(3));
 plot (x, y, "-^c");
 xlim ([0, 20])
 ylim ([0, 0.4])
 legend ({"Normalized HIST of sample 1 with λ=1", ...
          "Normalized HIST of sample 2 with λ=4", ...
          "Normalized HIST of sample 3 with λ=10", ...
          sprintf("PDF for sample 1 with estimated λ=%0.2f", ...
                  lambdahat(1)), ...
          sprintf("PDF for sample 2 with estimated λ=%0.2f", ...
                  lambdahat(2)), ...
          sprintf("PDF for sample 3 with estimated λ=%0.2f", ...
                  lambdahat(3))})
 title ("Three population samples from different Poisson distibutions")
 hold off
***** test
 x = [1 3 2 4 5 4 3 4];
 [lhat, lci] = poissfit (x);
 assert (lhat, 3.25)
 assert (lci, [2.123007901949543; 4.762003010390628], 1e-14)
***** test
 x = [1 3 2 4 5 4 3 4];
 [lhat, lci] = poissfit (x, 0.01);
 assert (lhat, 3.25)
 assert (lci, [1.842572740234582; 5.281369033298528], 1e-14)
***** test
 x = [1 2 3 4 5];
 f = [1 1 2 3 1];
 [lhat, lci] = poissfit (x, [], f);
 assert (lhat, 3.25)
 assert (lci, [2.123007901949543; 4.762003010390628], 1e-14)
***** test
 x = [1 2 3 4 5];
 f = [1 1 2 3 1];
 [lhat, lci] = poissfit (x, 0.01, f);
 assert (lhat, 3.25)
 assert (lci, [1.842572740234582; 5.281369033298528], 1e-14)
***** error<poissfit: X cannot have negative values.> poissfit ([1 2 -1 3])
***** error<poissfit: wrong value for ALPHA.> poissfit ([1 2 3], 0)
***** error<poissfit: wrong value for ALPHA.> poissfit ([1 2 3], 1.2)
***** error<poissfit: wrong value for ALPHA.> poissfit ([1 2 3], [0.02 0.05])
***** error<poissfit: X and FREQ vectors mismatch.>
 poissfit ([1 2 3], [], [1 5])
***** error<poissfit: FREQ must not contain negative values.>
 poissfit ([1 2 3], [], [1 5 -1])
10 tests, 10 passed, 0 known failure, 0 skipped
[inst/dist_fit/burrfit.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fit/burrfit.m
***** demo
 ## Sample 3 populations from different Burr type XII distibutions
 rand ("seed", 4);    # for reproducibility
 r1 = burrrnd (3.5, 2, 2.5, 10000, 1);
 rand ("seed", 2);    # for reproducibility
 r2 = burrrnd (1, 3, 1, 10000, 1);
 rand ("seed", 9);    # for reproducibility
 r3 = burrrnd (0.5, 2, 3, 10000, 1);
 r = [r1, r2, r3];

 ## Plot them normalized and fix their colors
 hist (r, [0.1:0.2:20], [18, 5, 3]);
 h = findobj (gca, "Type", "patch");
 set (h(1), "facecolor", "c");
 set (h(2), "facecolor", "g");
 set (h(3), "facecolor", "r");
 ylim ([0, 3]);
 xlim ([0, 5]);
 hold on

 ## Estimate their α and β parameters
 lambda_c_kA = burrfit (r(:,1));
 lambda_c_kB = burrfit (r(:,2));
 lambda_c_kC = burrfit (r(:,3));

 ## Plot their estimated PDFs
 x = [0.01:0.15:15];
 y = burrpdf (x, lambda_c_kA(1), lambda_c_kA(2), lambda_c_kA(3));
 plot (x, y, "-pr");
 y = burrpdf (x, lambda_c_kB(1), lambda_c_kB(2), lambda_c_kB(3));
 plot (x, y, "-sg");
 y = burrpdf (x, lambda_c_kC(1), lambda_c_kC(2), lambda_c_kC(3));
 plot (x, y, "-^c");
 hold off
 legend ({"Normalized HIST of sample 1 with λ=3.5, c=2, and k=2.5", ...
          "Normalized HIST of sample 2 with λ=1, c=3, and k=1", ...
          "Normalized HIST of sample 3 with λ=0.5, c=2, and k=3", ...
  sprintf("PDF for sample 1 with estimated λ=%0.2f, c=%0.2f, and k=%0.2f", ...
          lambda_c_kA(1), lambda_c_kA(2), lambda_c_kA(3)), ...
  sprintf("PDF for sample 2 with estimated λ=%0.2f, c=%0.2f, and k=%0.2f", ...
          lambda_c_kB(1), lambda_c_kB(2), lambda_c_kB(3)), ...
  sprintf("PDF for sample 3 with estimated λ=%0.2f, c=%0.2f, and k=%0.2f", ...
          lambda_c_kC(1), lambda_c_kC(2), lambda_c_kC(3))})
 title ("Three population samples from different Burr type XII distibutions")
 hold off
***** test
 l = 1; c = 2; k = 3;
 r = burrrnd (l, c, k, 100000, 1);
 lambda_c_kA = burrfit (r);
 assert (lambda_c_kA(1), l, 0.2);
 assert (lambda_c_kA(2), c, 0.2);
 assert (lambda_c_kA(3), k, 0.3);
***** test
 l = 0.5; c = 1; k = 3;
 r = burrrnd (l, c, k, 100000, 1);
 lambda_c_kA = burrfit (r);
 assert (lambda_c_kA(1), l, 0.2);
 assert (lambda_c_kA(2), c, 0.2);
 assert (lambda_c_kA(3), k, 0.3);
***** test
 l = 1; c = 3; k = 1;
 r = burrrnd (l, c, k, 100000, 1);
 lambda_c_kA = burrfit (r);
 assert (lambda_c_kA(1), l, 0.2);
 assert (lambda_c_kA(2), c, 0.2);
 assert (lambda_c_kA(3), k, 0.3);
***** test
 l = 3; c = 2; k = 1;
 r = burrrnd (l, c, k, 100000, 1);
 lambda_c_kA = burrfit (r);
 assert (lambda_c_kA(1), l, 0.2);
 assert (lambda_c_kA(2), c, 0.2);
 assert (lambda_c_kA(3), k, 0.3);
***** test
 l = 4; c = 2; k = 4;
 r = burrrnd (l, c, k, 100000, 1);
 lambda_c_kA = burrfit (r);
 assert (lambda_c_kA(1), l, 0.2);
 assert (lambda_c_kA(2), c, 0.2);
 assert (lambda_c_kA(3), k, 0.3);
***** error<burrfit: X must be a vector.> burrfit (ones (2,5));
***** error<burrfit: X must contain only positive values.> burrfit ([-1 2 3 4]);
***** error<burrfit: wrong value for ALPHA.> burrfit ([1, 2, 3, 4, 5], 1.2);
***** error<burrfit: wrong value for ALPHA.> burrfit ([1, 2, 3, 4, 5], 0);
***** error<burrfit: wrong value for ALPHA.> burrfit ([1, 2, 3, 4, 5], "alpha");
***** error<burrfit: X and CENSOR vectors mismatch.> ...
 burrfit ([1, 2, 3, 4, 5], 0.05, [1 1 0]);
***** error<burrfit: X and CENSOR vectors mismatch.> ...
 burrfit ([1, 2, 3, 4, 5], [], [1 1 0 1 1]');
***** error<burrfit: X and FREQ vectors mismatch.>
 burrfit ([1, 2, 3, 4, 5], 0.05, [], [1, 1, 5])
***** error<burrfit: FREQ must not contain negative values.>
 burrfit ([1, 2, 3, 4, 5], 0.05, [], [1, 5, 1, 1, -1])
***** error<burrfit: 'options' 5th argument must be a structure> ...
 burrfit ([1:10], 0.05, [], [], 5)
15 tests, 15 passed, 0 known failure, 0 skipped
[inst/dist_fit/explike.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fit/explike.m
***** test
 x = 12;
 beta = 5;
 [L, V] = explike (beta, x);
 expected_L = 4.0094;
 expected_V = 6.5789;
 assert (L, expected_L, 0.001);
 assert (V, expected_V, 0.001);
***** test
 x = 1:5;
 beta = 2;
 [L, V] = explike (beta, x);
 expected_L = 10.9657;
 expected_V = 0.4;
 assert (L, expected_L, 0.001);
 assert (V, expected_V, 0.001);
***** error<explike: function called with too few input arguments.> explike ()
***** error<explike: function called with too few input arguments.> explike (2)
***** error<explike: MU must be a scalar.> explike ([12, 3], [1:50])
***** error<explike: X must be a vector.> explike (3, ones (10, 2))
***** error<explike: X and CENSOR vectors mismatch.> ...
 explike (3, [1:50], [1, 2, 3])
***** error<explike: X and FREQ vectors mismatch.> ...
 explike (3, [1:50], [], [1, 2, 3])
8 tests, 8 passed, 0 known failure, 0 skipped
[inst/dist_fit/bisalike.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fit/bisalike.m
***** test
 nlogL = bisalike ([16.2649, 1.0156], [1:50]);
 assert (nlogL, 215.5905, 1e-4);
***** test
 nlogL = bisalike ([2.5585, 0.5839], [1:5]);
 assert (nlogL, 8.9950, 1e-4);
***** error<bisalike: function called with too few input arguments.> bisalike (3.25)
***** error<bisalike: X must be a vector.> bisalike ([5, 0.2], ones (2))
***** error<bisalike: X cannot have negative values.> bisalike ([5, 0.2], [-1, 3])
***** error<bisalike: PARAMS must be a two-element vector.> ...
 bisalike ([1, 0.2, 3], [1, 3, 5, 7])
***** error<bisalike: X and CENSOR vector mismatch.> ...
 bisalike ([1.5, 0.2], [1:5], [0, 0, 0])
***** error<bisalike: X and FREQ vector mismatch.> ...
 bisalike ([1.5, 0.2], [1:5], [0, 0, 0, 0, 0], [1, 1, 1])
***** error<bisalike: X and FREQ vector mismatch.> ...
 bisalike ([1.5, 0.2], [1:5], [], [1, 1, 1])
9 tests, 9 passed, 0 known failure, 0 skipped
[inst/dist_fit/loglfit.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fit/loglfit.m
***** demo
 ## Sample 3 populations from different log-logistic distibutions
 rand ("seed", 5)  # for reproducibility
 r1 = loglrnd (0, 1, 2000, 1);
 rand ("seed", 2)   # for reproducibility
 r2 = loglrnd (0, 0.5, 2000, 1);
 rand ("seed", 7)   # for reproducibility
 r3 = loglrnd (0, 0.125, 2000, 1);
 r = [r1, r2, r3];

 ## Plot them normalized and fix their colors
 hist (r, [0.05:0.1:2.5], 10);
 h = findobj (gca, "Type", "patch");
 set (h(1), "facecolor", "c");
 set (h(2), "facecolor", "g");
 set (h(3), "facecolor", "r");
 ylim ([0, 3.5]);
 xlim ([0, 2.0]);
 hold on

 ## Estimate their MU and LAMBDA parameters
 a_bA = loglfit (r(:,1));
 a_bB = loglfit (r(:,2));
 a_bC = loglfit (r(:,3));

 ## Plot their estimated PDFs
 x = [0.01:0.1:2.01];
 y = loglpdf (x, a_bA(1), a_bA(2));
 plot (x, y, "-pr");
 y = loglpdf (x, a_bB(1), a_bB(2));
 plot (x, y, "-sg");
 y = loglpdf (x, a_bC(1), a_bC(2));
 plot (x, y, "-^c");
 legend ({"Normalized HIST of sample 1 with α=1 and β=1", ...
          "Normalized HIST of sample 2 with α=1 and β=2", ...
          "Normalized HIST of sample 3 with α=1 and β=8", ...
          sprintf("PDF for sample 1 with estimated α=%0.2f and β=%0.2f", ...
                  a_bA(1), a_bA(2)), ...
          sprintf("PDF for sample 2 with estimated α=%0.2f and β=%0.2f", ...
                  a_bB(1), a_bB(2)), ...
          sprintf("PDF for sample 3 with estimated α=%0.2f and β=%0.2f", ...
                  a_bC(1), a_bC(2))})
 title ("Three population samples from different log-logistic distibutions")
 hold off
***** test
 [paramhat, paramci] = loglfit ([1:50]);
 paramhat_out = [3.09717, 0.468525];
 paramci_out = [2.87261, 0.370616; 3.32174, 0.5923];
 assert (paramhat, paramhat_out, 1e-5);
 assert (paramci, paramci_out, 1e-5);
***** test
 paramhat = loglfit ([1:5]);
 paramhat_out = [1.01124, 0.336449];
 assert (paramhat, paramhat_out, 1e-5);
***** test
 paramhat = loglfit ([1:6], [], [], [1 1 1 1 1 0]);
 paramhat_out = [1.01124, 0.336449];
 assert (paramhat, paramhat_out, 1e-4);
***** test
 paramhat = loglfit ([1:5], [], [], [1 1 1 1 2]);
 paramhat_out = loglfit ([1:5, 5]);
 assert (paramhat, paramhat_out, 1e-4);
***** error<loglfit: X must be a vector.> loglfit (ones (2,5));
***** error<loglfit: wrong value for ALPHA.> loglfit ([1, 2, 3, 4, 5], 1.2);
***** error<loglfit: wrong value for ALPHA.> loglfit ([1, 2, 3, 4, 5], 0);
***** error<loglfit: wrong value for ALPHA.> loglfit ([1, 2, 3, 4, 5], "alpha");
***** error<loglfit: X and CENSOR vectors mismatch.> ...
 loglfit ([1, 2, 3, 4, 5], 0.05, [1 1 0]);
***** error<loglfit: X and CENSOR vectors mismatch.> ...
 loglfit ([1, 2, 3, 4, 5], [], [1 1 0 1 1]');
***** error<loglfit: X and FREQ vectors mismatch.> ...
 loglfit ([1, 2, 3, 4, 5], 0.05, zeros (1,5), [1 1 0]);
***** error<loglfit: X and FREQ vectors mismatch.> ...
 loglfit ([1, 2, 3, 4, 5], [], [], [1 1 0 1 1]');
***** error<loglfit: 'options' 5th argument must be a structure> ...
 loglfit ([1, 2, 3, 4, 5], 0.05, [], [], 2);
13 tests, 13 passed, 0 known failure, 0 skipped
[inst/dist_fit/gevfit_lmom.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fit/gevfit_lmom.m
***** xtest <31070>
 data = 1:50;
 [pfit, pci] = gevfit_lmom (data);
 expected_p = [-0.28 15.01 20.22]';
 assert (pfit, expected_p, 0.1);
1 test, 1 passed, 0 known failure, 0 skipped
[inst/dist_fit/nakafit.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fit/nakafit.m
***** demo
 ## Sample 3 populations from different Nakagami distibutions
 randg ("seed", 5)  # for reproducibility
 r1 = nakarnd (0.5, 1, 2000, 1);
 randg ("seed", 2)   # for reproducibility
 r2 = nakarnd (5, 1, 2000, 1);
 randg ("seed", 7)   # for reproducibility
 r3 = nakarnd (2, 2, 2000, 1);
 r = [r1, r2, r3];

 ## Plot them normalized and fix their colors
 hist (r, [0.05:0.1:3.5], 10);
 h = findobj (gca, "Type", "patch");
 set (h(1), "facecolor", "c");
 set (h(2), "facecolor", "g");
 set (h(3), "facecolor", "r");
 ylim ([0, 2.5]);
 xlim ([0, 3.0]);
 hold on

 ## Estimate their MU and LAMBDA parameters
 mu_omegaA = nakafit (r(:,1));
 mu_omegaB = nakafit (r(:,2));
 mu_omegaC = nakafit (r(:,3));

 ## Plot their estimated PDFs
 x = [0.01:0.1:3.01];
 y = nakapdf (x, mu_omegaA(1), mu_omegaA(2));
 plot (x, y, "-pr");
 y = nakapdf (x, mu_omegaB(1), mu_omegaB(2));
 plot (x, y, "-sg");
 y = nakapdf (x, mu_omegaC(1), mu_omegaC(2));
 plot (x, y, "-^c");
 legend ({"Normalized HIST of sample 1 with μ=0.5 and ω=1", ...
          "Normalized HIST of sample 2 with μ=5 and ω=1", ...
          "Normalized HIST of sample 3 with μ=2 and ω=2", ...
          sprintf("PDF for sample 1 with estimated μ=%0.2f and ω=%0.2f", ...
                  mu_omegaA(1), mu_omegaA(2)), ...
          sprintf("PDF for sample 2 with estimated μ=%0.2f and ω=%0.2f", ...
                  mu_omegaB(1), mu_omegaB(2)), ...
          sprintf("PDF for sample 3 with estimated μ=%0.2f and ω=%0.2f", ...
                  mu_omegaC(1), mu_omegaC(2))})
 title ("Three population samples from different Nakagami distibutions")
 hold off
***** test
 paramhat = nakafit ([1:50]);
 paramhat_out = [0.7355, 858.5];
 assert (paramhat, paramhat_out, 1e-4);
***** test
 paramhat = nakafit ([1:5]);
 paramhat_out = [1.1740, 11];
 assert (paramhat, paramhat_out, 1e-4);
***** test
 paramhat = nakafit ([1:6], [], [], [1 1 1 1 1 0]);
 paramhat_out = [1.1740, 11];
 assert (paramhat, paramhat_out, 1e-4);
***** test
 paramhat = nakafit ([1:5], [], [], [1 1 1 1 2]);
 paramhat_out = nakafit ([1:5, 5]);
 assert (paramhat, paramhat_out, 1e-4);
***** error<nakafit: X must be a vector.> nakafit (ones (2,5));
***** error<nakafit: wrong value for ALPHA.> nakafit ([1, 2, 3, 4, 5], 1.2);
***** error<nakafit: wrong value for ALPHA.> nakafit ([1, 2, 3, 4, 5], 0);
***** error<nakafit: wrong value for ALPHA.> nakafit ([1, 2, 3, 4, 5], "alpha");
***** error<nakafit: X and CENSOR vectors mismatch.> ...
 nakafit ([1, 2, 3, 4, 5], 0.05, [1 1 0]);
***** error<nakafit: X and CENSOR vectors mismatch.> ...
 nakafit ([1, 2, 3, 4, 5], [], [1 1 0 1 1]');
***** error<nakafit: X and FREQ vectors mismatch.> ...
 nakafit ([1, 2, 3, 4, 5], 0.05, zeros (1,5), [1 1 0]);
***** error<nakafit: X and FREQ vectors mismatch.> ...
 nakafit ([1, 2, 3, 4, 5], [], [], [1 1 0 1 1]');
***** error<nakafit: FREQ must not contain negative values.> ...
 nakafit ([1, 2, 3, 4, 5], [], [], [1 1 -1 1 1]);
***** error<nakafit: FREQ must contain integer values.> ...
 nakafit ([1, 2, 3, 4, 5], [], [], [1 1 1.5 1 1]);
***** error<nakafit: 'options' 5th argument must be a structure> ...
 nakafit ([1, 2, 3, 4, 5], 0.05, [], [], 2);
15 tests, 15 passed, 0 known failure, 0 skipped
[inst/dist_fit/unifit.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fit/unifit.m
***** demo
 ## Sample 2 populations from different continuous uniform distibutions
 rand ("seed", 5);    # for reproducibility
 r1 = unifrnd (2, 5, 2000, 1);
 rand ("seed", 6);    # for reproducibility
 r2 = unifrnd (3, 9, 2000, 1);
 r = [r1, r2];

 ## Plot them normalized and fix their colors
 hist (r, 0:0.5:10, 2);
 h = findobj (gca, "Type", "patch");
 set (h(1), "facecolor", "c");
 set (h(2), "facecolor", "g");
 hold on

 ## Estimate their probability of success
 a_bA = unifit (r(:,1));
 a_bB = unifit (r(:,2));

 ## Plot their estimated PDFs
 x = [0:10];
 y = unifpdf (x, a_bA(1), a_bA(2));
 plot (x, y, "-pg");
 y = unifpdf (x, a_bB(1), a_bB(2));
 plot (x, y, "-sc");
 xlim ([1, 10])
 ylim ([0, 0.5])
 legend ({"Normalized HIST of sample 1 with a=2 and b=5", ...
          "Normalized HIST of sample 2 with a=3 and b=9", ...
          sprintf("PDF for sample 1 with estimated a=%0.2f and b=%0.2f", ...
                  a_bA(1), a_bA(2)), ...
          sprintf("PDF for sample 2 with estimated a=%0.2f and b=%0.2f", ...
                  a_bB(1), a_bB(2))})
 title ("Two population samples from different continuous uniform distibutions")
 hold off
***** test
 x = 0:5;
 [paramhat, paramci] = unifit (x);
 assert (paramhat, [0, 5]);
 assert (paramci, [-3.2377, 8.2377; 0, 5], 1e-4);
***** test
 x = 0:5;
 [paramhat, paramci] = unifit (x, [], [1 1 1 1 1 1]);
 assert (paramhat, [0, 5]);
 assert (paramci, [-3.2377, 8.2377; 0, 5], 1e-4);
***** assert (unifit ([1 1 2 3]), unifit ([1 2 3], [] ,[2 1 1]))
***** error<unifit: function called with too few input arguments.> unifit ()
***** error<unifit: X cannot have negative values.> unifit (-1, [1 2 3 3])
***** error<unifit: wrong value for ALPHA.> unifit (1, 0)
***** error<unifit: wrong value for ALPHA.> unifit (1, 1.2)
***** error<unifit: wrong value for ALPHA.> unifit (1, [0.02 0.05])
***** error<unifit: X and FREQ vector mismatch.> ...
 unifit ([1.5, 0.2], [], [0, 0, 0, 0, 0])
***** error<unifit: FREQ cannot have negative values.> ...
 unifit ([1.5, 0.2], [], [1, -1])
***** error<unifit: X and FREQ vector mismatch.> ...
 unifit ([1.5, 0.2], [], [1, 1, 1])
11 tests, 11 passed, 0 known failure, 0 skipped
[inst/dist_fit/invglike.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fit/invglike.m
***** test
 nlogL = invglike ([25.5, 19.6973], [1:50]);
 assert (nlogL, 219.1516, 1e-4);
***** test
 nlogL = invglike ([3, 8.1081], [1:5]);
 assert (nlogL, 9.0438, 1e-4);
***** error<invglike: function called with too few input arguments.> invglike (3.25)
***** error<invglike: X must be a vector.> invglike ([5, 0.2], ones (2))
***** error<invglike: X must have positive values.> invglike ([5, 0.2], [-1, 3])
***** error<invglike: PARAMS must be a two-element vector.> ...
 invglike ([1, 0.2, 3], [1, 3, 5, 7])
***** error<invglike: X and CENSOR vector mismatch.> ...
 invglike ([1.5, 0.2], [1:5], [0, 0, 0])
***** error<invglike: X and FREQ vector mismatch.> ...
 invglike ([1.5, 0.2], [1:5], [0, 0, 0, 0, 0], [1, 1, 1])
***** error<invglike: X and FREQ vector mismatch.> ...
 invglike ([1.5, 0.2], [1:5], [], [1, 1, 1])
9 tests, 9 passed, 0 known failure, 0 skipped
[inst/dist_fit/wblfit.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fit/wblfit.m
***** demo
 ## Sample 3 populations from 3 different Weibull distibutions
 rande ("seed", 1);    # for reproducibility
 r1 = wblrnd(2, 4, 2000, 1);
 rande ("seed", 2);    # for reproducibility
 r2 = wblrnd(5, 2, 2000, 1);
 rande ("seed", 5);    # for reproducibility
 r3 = wblrnd(1, 5, 2000, 1);
 r = [r1, r2, r3];

 ## Plot them normalized and fix their colors
 hist (r, 30, [2.5 2.1 3.2]);
 h = findobj (gca, "Type", "patch");
 set (h(1), "facecolor", "c");
 set (h(2), "facecolor", "g");
 set (h(3), "facecolor", "r");
 ylim ([0, 2]);
 xlim ([0, 10]);
 hold on

 ## Estimate their lambda parameter
 lambda_kA = wblfit (r(:,1));
 lambda_kB = wblfit (r(:,2));
 lambda_kC = wblfit (r(:,3));

 ## Plot their estimated PDFs
 x = [0:0.1:15];
 y = wblpdf (x, lambda_kA(1), lambda_kA(2));
 plot (x, y, "-pr");
 y = wblpdf (x, lambda_kB(1), lambda_kB(2));
 plot (x, y, "-sg");
 y = wblpdf (x, lambda_kC(1), lambda_kC(2));
 plot (x, y, "-^c");
 hold off
 legend ({"Normalized HIST of sample 1 with λ=2 and k=4", ...
          "Normalized HIST of sample 2 with λ=5 and k=2", ...
          "Normalized HIST of sample 3 with λ=1 and k=5", ...
          sprintf("PDF for sample 1 with estimated λ=%0.2f and k=%0.2f", ...
                  lambda_kA(1), lambda_kA(2)), ...
          sprintf("PDF for sample 2 with estimated λ=%0.2f and k=%0.2f", ...
                  lambda_kB(1), lambda_kB(2)), ...
          sprintf("PDF for sample 3 with estimated λ=%0.2f and k=%0.2f", ...
                  lambda_kC(1), lambda_kC(2))})
 title ("Three population samples from different Weibull distibutions")
 hold off
***** test
 x = 1:50;
 [paramhat, paramci] = wblfit (x);
 paramhat_out = [28.3636, 1.7130];
 paramci_out = [23.9531, 1.3551; 33.5861, 2.1655];
 assert (paramhat, paramhat_out, 1e-4);
 assert (paramci, paramci_out, 1e-4);
***** test
 x = 1:50;
 [paramhat, paramci] = wblfit (x, 0.01);
 paramci_out = [22.7143, 1.2589; 35.4179, 2.3310];
 assert (paramci, paramci_out, 1e-4);
***** error<wblfit: X must be a vector.> wblfit (ones (2,5));
***** error<wblfit: X must contain only positive values.> wblfit ([-1 2 3 4]);
***** error<wblfit: wrong value for ALPHA.> wblfit ([1, 2, 3, 4, 5], 1.2);
***** error<wblfit: wrong value for ALPHA.> wblfit ([1, 2, 3, 4, 5], 0);
***** error<wblfit: wrong value for ALPHA.> wblfit ([1, 2, 3, 4, 5], "alpha");
***** error<wblfit: X and CENSOR vectors mismatch.> ...
 wblfit ([1, 2, 3, 4, 5], 0.05, [1 1 0]);
***** error<wblfit: X and CENSOR vectors mismatch.> ...
 wblfit ([1, 2, 3, 4, 5], [], [1 1 0 1 1]');
***** error<wblfit: X and FREQ vectors mismatch.> ...
 wblfit ([1, 2, 3, 4, 5], 0.05, zeros (1,5), [1 1 0]);
***** error<wblfit: FREQ cannot have negative values.> ...
 wblfit ([1, 2, 3, 4, 5], [], [], [1 1 0 -1 1]);
***** error<wblfit: X and FREQ vectors mismatch.> ...
 wblfit ([1, 2, 3, 4, 5], [], [], [1 1 0 1 1]');
***** error<wblfit: 'options' 5th argument must be a structure> ...
 wblfit ([1, 2, 3, 4, 5], 0.05, [], [], 2);
13 tests, 13 passed, 0 known failure, 0 skipped
[inst/dist_fit/raylfit.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fit/raylfit.m
***** demo
 ## Sample 3 populations from 3 different Rayleigh distibutions
 rand ("seed", 2);    # for reproducibility
 r1 = raylrnd (1, 1000, 1);
 rand ("seed", 2);    # for reproducibility
 r2 = raylrnd (2, 1000, 1);
 rand ("seed", 3);    # for reproducibility
 r3 = raylrnd (4, 1000, 1);
 r = [r1, r2, r3];

 ## Plot them normalized and fix their colors
 hist (r, [0.5:0.5:10.5], 2);
 h = findobj (gca, "Type", "patch");
 set (h(1), "facecolor", "c");
 set (h(2), "facecolor", "g");
 set (h(3), "facecolor", "r");
 hold on

 ## Estimate their lambda parameter
 sigmaA = raylfit (r(:,1));
 sigmaB = raylfit (r(:,2));
 sigmaC = raylfit (r(:,3));

 ## Plot their estimated PDFs
 x = [0:0.1:10];
 y = raylpdf (x, sigmaA);
 plot (x, y, "-pr");
 y = raylpdf (x, sigmaB);
 plot (x, y, "-sg");
 y = raylpdf (x, sigmaC);
 plot (x, y, "-^c");
 xlim ([0, 10])
 ylim ([0, 0.7])
 legend ({"Normalized HIST of sample 1 with σ=1", ...
          "Normalized HIST of sample 2 with σ=2", ...
          "Normalized HIST of sample 3 with σ=4", ...
          sprintf("PDF for sample 1 with estimated σ=%0.2f", ...
                  sigmaA), ...
          sprintf("PDF for sample 2 with estimated σ=%0.2f", ...
                  sigmaB), ...
          sprintf("PDF for sample 3 with estimated σ=%0.2f", ...
                  sigmaC)})
 title ("Three population samples from different Rayleigh distibutions")
 hold off
***** test
 x = [1 3 2 4 5 4 3 4];
 [shat, sci] = raylfit (x);
 assert (shat, 2.4495, 1e-4)
 assert (sci, [1.8243; 3.7279], 1e-4)
***** test
 x = [1 3 2 4 5 4 3 4];
 [shat, sci] = raylfit (x, 0.01);
 assert (shat, 2.4495, 1e-4)
 assert (sci, [1.6738; 4.3208], 1e-4)
***** test
 x = [1 2 3 4 5];
 f = [1 1 2 3 1];
 [shat, sci] = raylfit (x, [], [], f);
 assert (shat, 2.4495, 1e-4)
 assert (sci, [1.8243; 3.7279], 1e-4)
***** test
 x = [1 2 3 4 5];
 f = [1 1 2 3 1];
 [shat, sci] = raylfit (x, 0.01, [], f);
 assert (shat, 2.4495, 1e-4)
 assert (sci, [1.6738; 4.3208], 1e-4)
***** test
 x = [1 2 3 4 5 6];
 c = [0 0 0 0 0 1];
 f = [1 1 2 3 1 1];
 [shat, sci] = raylfit (x, 0.01, c, f);
 assert (shat, 2.4495, 1e-4)
 assert (sci, [1.6738; 4.3208], 1e-4)
***** error<raylfit: X must be a vector.> raylfit (ones (2,5));
***** error<raylfit: X cannot have negative values.> raylfit ([1 2 -1 3])
***** error<raylfit: wrong value for ALPHA.> raylfit ([1 2 3], 0)
***** error<raylfit: wrong value for ALPHA.> raylfit ([1 2 3], 1.2)
***** error<raylfit: wrong value for ALPHA.> raylfit ([1 2 3], [0.02 0.05])
***** error<raylfit: X and CENSOR vectors mismatch.> ...
 raylfit ([1, 2, 3, 4, 5], 0.05, [1 1 0]);
***** error<raylfit: X and CENSOR vectors mismatch.> ...
 raylfit ([1, 2, 3, 4, 5], [], [1 1 0 1 1]');
***** error<raylfit: X and FREQ vectors mismatch.> ...
 raylfit ([1, 2, 3, 4, 5], 0.05, zeros (1,5), [1 1 0]);
***** error<raylfit: X and FREQ vectors mismatch.> ...
 raylfit ([1, 2, 3, 4, 5], [], [], [1 1 0 1 1]');
***** error<raylfit: X and FREQ vectors mismatch.>
 raylfit ([1 2 3], [], [], [1 5])
***** error<raylfit: FREQ must not contain negative values.>
 raylfit ([1 2 3], [], [], [1 5 -1])
16 tests, 16 passed, 0 known failure, 0 skipped
[inst/dist_fit/tlsfit.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fit/tlsfit.m
***** demo
 ## Sample 3 populations from 3 different location-scale T distibutions
 randn ("seed", 1);    # for reproducibility
 randg ("seed", 2);    # for reproducibility
 r1 = tlsrnd (-4, 3, 1, 2000, 1);
 randn ("seed", 3);    # for reproducibility
 randg ("seed", 4);    # for reproducibility
 r2 = tlsrnd (0, 3, 1, 2000, 1);
 randn ("seed", 5);    # for reproducibility
 randg ("seed", 6);    # for reproducibility
 r3 = tlsrnd (5, 5, 4, 2000, 1);
 r = [r1, r2, r3];

 ## Plot them normalized and fix their colors
 hist (r, [-21:21], [1, 1, 1]);
 h = findobj (gca, "Type", "patch");
 set (h(1), "facecolor", "c");
 set (h(2), "facecolor", "g");
 set (h(3), "facecolor", "r");
 ylim ([0, 0.25]);
 xlim ([-20, 20]);
 hold on

 ## Estimate their lambda parameter
 mu_sigma_nuA = tlsfit (r(:,1));
 mu_sigma_nuB = tlsfit (r(:,2));
 mu_sigma_nuC = tlsfit (r(:,3));

 ## Plot their estimated PDFs
 x = [-20:0.1:20];
 y = tlspdf (x, mu_sigma_nuA(1), mu_sigma_nuA(2), mu_sigma_nuA(3));
 plot (x, y, "-pr");
 y = tlspdf (x, mu_sigma_nuB(1), mu_sigma_nuB(2), mu_sigma_nuB(3));
 plot (x, y, "-sg");
 y = tlspdf (x, mu_sigma_nuC(1), mu_sigma_nuC(2), mu_sigma_nuC(3));
 plot (x, y, "-^c");
 hold off
 legend ({"Normalized HIST of sample 1 with μ=0, σ=2 and nu=1", ...
          "Normalized HIST of sample 2 with μ=5, σ=2 and nu=1", ...
          "Normalized HIST of sample 3 with μ=3, σ=4 and nu=3", ...
          sprintf("PDF for sample 1 with estimated μ=%0.2f, σ=%0.2f, and ν=%0.2f", ...
                  mu_sigma_nuA(1), mu_sigma_nuA(2), mu_sigma_nuA(3)), ...
          sprintf("PDF for sample 2 with estimated μ=%0.2f, σ=%0.2f, and ν=%0.2f", ...
                  mu_sigma_nuB(1), mu_sigma_nuB(2), mu_sigma_nuB(3)), ...
          sprintf("PDF for sample 3 with estimated μ=%0.2f, σ=%0.2f, and ν=%0.2f", ...
                  mu_sigma_nuC(1), mu_sigma_nuC(2), mu_sigma_nuC(3))})
 title ("Three population samples from different location-scale T distibutions")
 hold off
***** test
 x = [-1.2352, -0.2741, 0.1726, 7.4356, 1.0392, 16.4165];
 [paramhat, paramci] = tlsfit (x);
 paramhat_out = [0.035893, 0.862711, 0.649261];
 paramci_out = [-0.949034, 0.154655, 0.181080; 1.02082, 4.812444, 2.327914];
 assert (paramhat, paramhat_out, 1e-6);
 assert (paramci, paramci_out, 1e-5);
***** test
 x = [-1.2352, -0.2741, 0.1726, 7.4356, 1.0392, 16.4165];
 [paramhat, paramci] = tlsfit (x, 0.01);
 paramci_out = [-1.2585, 0.0901, 0.1212; 1.3303, 8.2591, 3.4771];
 assert (paramci, paramci_out, 1e-4);
***** error<tlsfit: X must be a vector.> tlsfit (ones (2,5));
***** error<tlsfit: wrong value for ALPHA.> tlsfit ([1, 2, 3, 4, 5], 1.2);
***** error<tlsfit: wrong value for ALPHA.> tlsfit ([1, 2, 3, 4, 5], 0);
***** error<tlsfit: wrong value for ALPHA.> tlsfit ([1, 2, 3, 4, 5], "alpha");
***** error<tlsfit: X and CENSOR vectors mismatch.> ...
 tlsfit ([1, 2, 3, 4, 5], 0.05, [1 1 0]);
***** error<tlsfit: X and CENSOR vectors mismatch.> ...
 tlsfit ([1, 2, 3, 4, 5], [], [1 1 0 1 1]');
***** error<tlsfit: X and FREQ vectors mismatch.> ...
 tlsfit ([1, 2, 3, 4, 5], 0.05, zeros (1,5), [1 1 0]);
***** error<tlsfit: X and FREQ vectors mismatch.> ...
 tlsfit ([1, 2, 3, 4, 5], [], [], [1 1 0 1 1]');
***** error<tlsfit: FREQ cannot have negative values.> ...
 tlsfit ([1, 2, 3, 4, 5], [], [], [1 1 0 1 -1]);
***** error<tlsfit: 'options' 5th argument must be a structure> ...
 tlsfit ([1, 2, 3, 4, 5], 0.05, [], [], 2);
12 tests, 12 passed, 0 known failure, 0 skipped
[inst/dist_fit/lognlike.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fit/lognlike.m
***** test
 x = 1:50;
 [nlogL, avar] = lognlike ([0, 0.25], x);
 avar_out = [-5.4749e-03, 2.8308e-04; 2.8308e-04, -1.1916e-05];
 assert (nlogL, 3962.330333301793, 1e-10);
 assert (avar, avar_out, 1e-7);
***** test
 x = 1:50;
 [nlogL, avar] = lognlike ([0, 0.25], x * 0.5);
 avar_out = [-7.6229e-03, 4.8722e-04; 4.8722e-04, -2.6754e-05];
 assert (nlogL, 2473.183051225747, 1e-10);
 assert (avar, avar_out, 1e-7);
***** test
 x = 1:50;
 [nlogL, avar] = lognlike ([0, 0.5], x);
 avar_out = [-2.1152e-02, 2.2017e-03; 2.2017e-03, -1.8535e-04];
 assert (nlogL, 1119.072424020455, 1e-12);
 assert (avar, avar_out, 1e-6);
***** test
 x = 1:50;
 censor = ones (1, 50);
 censor([2, 4, 6, 8, 12, 14]) = 0;
 [nlogL, avar] = lognlike ([0, 0.5], x, censor);
 avar_out = [-1.9823e-02, 2.0370e-03; 2.0370e-03, -1.6618e-04];
 assert (nlogL, 1091.746371145497, 1e-12);
 assert (avar, avar_out, 1e-6);
***** test
 x = 1:50;
 censor = ones (1, 50);
 censor([2, 4, 6, 8, 12, 14]) = 0;
 [nlogL, avar] = lognlike ([0, 1], x, censor);
 avar_out = [-6.8634e-02, 1.3968e-02; 1.3968e-02, -2.1664e-03];
 assert (nlogL, 349.3969104144271, 1e-12);
 assert (avar, avar_out, 1e-6);
***** error<lognlike: function called with too few input arguments.> ...
 lognlike ([12, 15]);
***** error<lognlike: X must be a vector.> lognlike ([12, 15], ones (2));
***** error<lognlike: PARAMS must be a two-element vector.> ...
 lognlike ([12, 15, 3], [1:50]);
***** error<lognlike: X and CENSOR vectors mismatch.> ...
 lognlike ([12, 15], [1:50], [1, 2, 3]);
***** error<lognlike: X and FREQ vectors mismatch.> ...
 lognlike ([12, 15], [1:50], [], [1, 2, 3]);
10 tests, 10 passed, 0 known failure, 0 skipped
[inst/dist_fit/normlike.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fit/normlike.m
***** error<normlike: too few input arguments.> normlike ([12, 15]);
***** error<normlike: X must be a vector.> normlike ([12, 15], ones (2));
***** error<normlike: PARAMS must be a two-element vector.> ...
 normlike ([12, 15, 3], [1:50]);
***** error<normlike: X and CENSOR vectors mismatch.> ...
 normlike ([12, 15], [1:50], [1, 2, 3]);
***** error<normlike: X and FREQ vectors mismatch.> ...
 normlike ([12, 15], [1:50], [], [1, 2, 3]);
***** error<normlike: FREQ must not contain negative values.> ...
 normlike ([12, 15], [1:5], [], [1, 2, 3, 2, -1]);
***** test
 x = 1:50;
 [nlogL, avar] = normlike ([2.3, 1.2], x);
 avar_out = [7.5767e-01, -1.8850e-02; -1.8850e-02, 4.8750e-04];
 assert (nlogL, 13014.95883783327, 1e-10);
 assert (avar, avar_out, 1e-4);
***** test
 x = 1:50;
 [nlogL, avar] = normlike ([2.3, 1.2], x * 0.5);
 avar_out = [3.0501e-01, -1.5859e-02; -1.5859e-02, 9.1057e-04];
 assert (nlogL, 2854.802587833265, 1e-10);
 assert (avar, avar_out, 1e-4);
***** test
 x = 1:50;
 [nlogL, avar] = normlike ([21, 15], x);
 avar_out = [5.460474308300396, -1.600790513833993; ...
             -1.600790513833993, 2.667984189723321];
 assert (nlogL, 206.738325604233, 1e-12);
 assert (avar, avar_out, 1e-14);
***** test
 x = 1:50;
 censor = ones (1, 50);
 censor([2, 4, 6, 8, 12, 14]) = 0;
 [nlogL, avar] = normlike ([2.3, 1.2], x, censor);
 avar_out = [3.0501e-01, -1.5859e-02; -1.5859e-02, 9.1057e-04];
 assert (nlogL, Inf);
 assert (avar, [NaN, NaN; NaN, NaN]);
***** test
 x = 1:50;
 censor = ones (1, 50);
 censor([2, 4, 6, 8, 12, 14]) = 0;
 [nlogL, avar] = normlike ([21, 15], x, censor);
 avar_out = [24.4824488866131, -10.6649544179636; ...
             -10.6649544179636, 6.22827849965737];
 assert (nlogL, 86.9254371829733, 1e-12);
 assert (avar, avar_out, 8e-14);
11 tests, 11 passed, 0 known failure, 0 skipped
[inst/dist_fit/hnfit.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fit/hnfit.m
***** demo
 ## Sample 2 populations from different half-normal distibutions
 rand ("seed", 1);   # for reproducibility
 r1 = hnrnd (0, 5, 5000, 1);
 rand ("seed", 2);   # for reproducibility
 r2 = hnrnd (0, 2, 5000, 1);
 r = [r1, r2];

 ## Plot them normalized and fix their colors
 hist (r, [0.5:20], 1);
 h = findobj (gca, "Type", "patch");
 set (h(1), "facecolor", "c");
 set (h(2), "facecolor", "g");
 hold on

 ## Estimate their shape parameters
 mu_sigmaA = hnfit (r(:,1), 0);
 mu_sigmaB = hnfit (r(:,2), 0);

 ## Plot their estimated PDFs
 x = [0:0.2:10];
 y = hnpdf (x, mu_sigmaA(1), mu_sigmaA(2));
 plot (x, y, "-pr");
 y = hnpdf (x, mu_sigmaB(1), mu_sigmaB(2));
 plot (x, y, "-sg");
 xlim ([0, 10])
 ylim ([0, 0.5])
 legend ({"Normalized HIST of sample 1 with μ=0 and σ=5", ...
          "Normalized HIST of sample 2 with μ=0 and σ=2", ...
          sprintf("PDF for sample 1 with estimated μ=%0.2f and σ=%0.2f", ...
                  mu_sigmaA(1), mu_sigmaA(2)), ...
          sprintf("PDF for sample 2 with estimated μ=%0.2f and σ=%0.2f", ...
                  mu_sigmaB(1), mu_sigmaB(2))})
 title ("Two population samples from different half-normal distibutions")
 hold off
***** test
 x = 1:20;
 [paramhat, paramci] = hnfit (x, 0);
 assert (paramhat, [0, 11.9791], 1e-4);
 assert (paramci, [0, 9.1648; 0, 17.2987], 1e-4);
***** test
 x = 1:20;
 [paramhat, paramci] = hnfit (x, 0, 0.01);
 assert (paramci, [0, 8.4709; 0, 19.6487], 1e-4);
***** error<hnfit: function called with too few input arguments.> hnfit ()
***** error<hnfit: function called with too few input arguments.> hnfit (1)
***** error<hnfit: X must be a vector of real values.> hnfit ([0.2, 0.5+i], 0);
***** error<hnfit: X must be a vector of real values.> hnfit (ones (2,2) * 0.5, 0);
***** error<hnfit: MU must be a real scalar value.> ...
 hnfit ([0.5, 1.2], [0, 1]);
***** error<hnfit: MU must be a real scalar value.> ...
 hnfit ([0.5, 1.2], 5+i);
***** error<hnfit: X cannot contain values less than MU.> ...
 hnfit ([1:5], 2);
***** error<hnfit: wrong value for ALPHA.> hnfit ([0.01:0.1:0.99], 0, 1.2);
***** error<hnfit: wrong value for ALPHA.> hnfit ([0.01:0.1:0.99], 0, i);
***** error<hnfit: wrong value for ALPHA.> hnfit ([0.01:0.1:0.99], 0, -1);
***** error<hnfit: wrong value for ALPHA.> hnfit ([0.01:0.1:0.99], 0, [0.05, 0.01]);
***** error<hnfit: X and FREQ vectors mismatch.>
 hnfit ([1 2 3], 0, [], [1 5])
***** error<hnfit: FREQ must not contain negative values.>
 hnfit ([1 2 3], 0, [], [1 5 -1])
15 tests, 15 passed, 0 known failure, 0 skipped
[inst/dist_fit/gamlike.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fit/gamlike.m
***** test
 [nlogL, acov] = gamlike([2, 3], [2, 3, 4, 5, 6, 7, 8, 9]);
 assert (nlogL, 19.4426, 1e-4);
 assert (acov, [2.7819, -5.0073; -5.0073, 9.6882], 1e-4);
***** test
 [nlogL, acov] = gamlike([2, 3], [5:45]);
 assert (nlogL, 305.8070, 1e-4);
 assert (acov, [0.0423, -0.0087; -0.0087, 0.0167], 1e-4);
***** test
 [nlogL, acov] = gamlike([2, 13], [5:45]);
 assert (nlogL, 163.2261, 1e-4);
 assert (acov, [0.2362, -1.6631; -1.6631, 13.9440], 1e-4);
***** error<gamlike: function called with too few input arguments.> ...
 gamlike ([12, 15])
***** error<gamlike: wrong parameters length.> gamlike ([12, 15, 3], [1:50])
***** error<gamlike: X must be a vector.> gamlike ([12, 3], ones (10, 2))
***** error<gamlike: X and CENSOR vectors mismatch.> ...
 gamlike ([12, 15], [1:50], [1, 2, 3])
***** error<gamlike: X and FREQ vectors mismatch.> ...
 gamlike ([12, 15], [1:50], [], [1, 2, 3])
8 tests, 8 passed, 0 known failure, 0 skipped
[inst/dist_fit/nakalike.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fit/nakalike.m
***** test
 nlogL = nakalike ([0.735504, 858.5], [1:50]);
 assert (nlogL, 202.8689, 1e-4);
***** test
 nlogL = nakalike ([1.17404, 11], [1:5]);
 assert (nlogL, 8.6976, 1e-4);
***** test
 nlogL = nakalike ([1.17404, 11], [1:5], [], [1, 1, 1, 1, 1]);
 assert (nlogL, 8.6976, 1e-4);
***** test
 nlogL = nakalike ([1.17404, 11], [1:6], [], [1, 1, 1, 1, 1, 0]);
 assert (nlogL, 8.6976, 1e-4);
***** error<nakalike: function called with too few input arguments.> nakalike (3.25)
***** error<nakalike: X must be a vector.> nakalike ([5, 0.2], ones (2))
***** error<nakalike: PARAMS must be a two-element vector.> ...
 nakalike ([1, 0.2, 3], [1, 3, 5, 7])
***** error<nakalike: X and CENSOR vector mismatch.> ...
 nakalike ([1.5, 0.2], [1:5], [0, 0, 0])
***** error<nakalike: X and FREQ vector mismatch.> ...
 nakalike ([1.5, 0.2], [1:5], [0, 0, 0, 0, 0], [1, 1, 1])
***** error<nakalike: X and FREQ vector mismatch.> ...
 nakalike ([1.5, 0.2], [1:5], [], [1, 1, 1])
***** error<nakalike: FREQ must not contain negative values.> ...
 nakalike ([1.5, 0.2], [1:5], [], [1, 1, 1, 1, -1])
11 tests, 11 passed, 0 known failure, 0 skipped
[inst/dist_fit/unidfit.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fit/unidfit.m
***** demo
 ## Sample 2 populations from different discrete uniform distibutions
 rand ("seed", 1);    # for reproducibility
 r1 = unidrnd (5, 1000, 1);
 rand ("seed", 2);    # for reproducibility
 r2 = unidrnd (9, 1000, 1);
 r = [r1, r2];

 ## Plot them normalized and fix their colors
 hist (r, 0:0.5:20.5, 1);
 h = findobj (gca, "Type", "patch");
 set (h(1), "facecolor", "c");
 set (h(2), "facecolor", "g");
 hold on

 ## Estimate their probability of success
 NhatA = unidfit (r(:,1));
 NhatB = unidfit (r(:,2));

 ## Plot their estimated PDFs
 x = [0:10];
 y = unidpdf (x, NhatA);
 plot (x, y, "-pg");
 y = unidpdf (x, NhatB);
 plot (x, y, "-sc");
 xlim ([0, 10])
 ylim ([0, 0.4])
 legend ({"Normalized HIST of sample 1 with N=5", ...
          "Normalized HIST of sample 2 with N=9", ...
          sprintf("PDF for sample 1 with estimated N=%0.2f", NhatA), ...
          sprintf("PDF for sample 2 with estimated N=%0.2f", NhatB)})
 title ("Two population samples from different discrete uniform distibutions")
 hold off
***** test
 x = 0:5;
 [Nhat, Nci] = unidfit (x);
 assert (Nhat, 5);
 assert (Nci, [5; 9]);
***** test
 x = 0:5;
 [Nhat, Nci] = unidfit (x, [], [1 1 1 1 1 1]);
 assert (Nhat, 5);
 assert (Nci, [5; 9]);
***** assert (unidfit ([1 1 2 3]), unidfit ([1 2 3], [] ,[2 1 1]))
***** error<unidfit: function called with too few input arguments.> unidfit ()
***** error<unidfit: X cannot have negative values.> unidfit (-1, [1 2 3 3])
***** error<unidfit: wrong value for ALPHA.> unidfit (1, 0)
***** error<unidfit: wrong value for ALPHA.> unidfit (1, 1.2)
***** error<unidfit: wrong value for ALPHA.> unidfit (1, [0.02 0.05])
***** error<unidfit: X and FREQ vector mismatch.> ...
 unidfit ([1.5, 0.2], [], [0, 0, 0, 0, 0])
***** error<unidfit: X and FREQ vector mismatch.> ...
 unidfit ([1.5, 0.2], [], [1, 1, 1])
***** error<unidfit: FREQ cannot have negative values.> ...
 unidfit ([1.5, 0.2], [], [1, -1])
11 tests, 11 passed, 0 known failure, 0 skipped
[inst/dist_fit/wbllike.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fit/wbllike.m
***** test
 x = 1:50;
 [nlogL, acov] = wbllike ([2.3, 1.2], x);
 avar_out = [0.0250, 0.0062; 0.0062, 0.0017];
 assert (nlogL, 945.9589180651594, 1e-12);
 assert (acov, avar_out, 1e-4);
***** test
 x = 1:50;
 [nlogL, acov] = wbllike ([2.3, 1.2], x * 0.5);
 avar_out = [-0.3238, -0.1112; -0.1112, -0.0376];
 assert (nlogL, 424.9879809704742, 6e-14);
 assert (acov, avar_out, 1e-4);
***** test
 x = 1:50;
 [nlogL, acov] = wbllike ([21, 15], x);
 avar_out = [-0.00001236, -0.00001166; -0.00001166, -0.00001009];
 assert (nlogL, 1635190.328991511, 1e-8);
 assert (acov, avar_out, 1e-8);
***** error<wbllike: too few input arguments.> wbllike ([12, 15]);
***** error<wbllike: wrong parameters length.> wbllike ([12, 15, 3], [1:50]);
***** error<wbllike: X must be a vector.> wbllike ([12, 3], ones (10, 2));
***** error<wbllike: X and CENSOR> wbllike ([12, 15], [1:50], [1, 2, 3]);
***** error<wbllike: X and FREQ> wbllike ([12, 15], [1:50], [], [1, 2, 3]);
***** error<wbllike: FREQ cannot have negative values.> ...
 wbllike ([12, 15], [1:5], [], [1, 2, 3, -1, 0]);
9 tests, 9 passed, 0 known failure, 0 skipped
[inst/dist_fit/ricelike.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fit/ricelike.m
***** test
 nlogL = ricelike ([15.3057344, 17.6668458], [1:50]);
 assert (nlogL, 204.5230311010569, 1e-12);
***** test
 nlogL = ricelike ([2.312346885, 1.681228265], [1:5]);
 assert (nlogL, 8.65562164930058, 1e-12);
***** error<ricelike: function called with too few input arguments.> ricelike (3.25)
***** error<ricelike: X must be a vector.> ricelike ([5, 0.2], ones (2))
***** error<ricelike: PARAMS must be a two-element vector.> ...
 ricelike ([1, 0.2, 3], [1, 3, 5, 7])
***** error<ricelike: X and CENSOR vector mismatch.> ...
 ricelike ([1.5, 0.2], [1:5], [0, 0, 0])
***** error<ricelike: X and FREQ vector mismatch.> ...
 ricelike ([1.5, 0.2], [1:5], [0, 0, 0, 0, 0], [1, 1, 1])
***** error<ricelike: X and FREQ vector mismatch.> ...
 ricelike ([1.5, 0.2], [1:5], [], [1, 1, 1])
***** error<ricelike: FREQ must not contain negative values.> ...
 ricelike ([1.5, 0.2], [1:5], [], [1, 1, 1, 0, -1])
9 tests, 9 passed, 0 known failure, 0 skipped
[inst/dist_fit/tlslike.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fit/tlslike.m
***** test
 x = [-1.2352, -0.2741, 0.1726, 7.4356, 1.0392, 16.4165];
 [nlogL, acov] = tlslike ([0.035893, 0.862711, 0.649261], x);
 acov_out = [0.2525, 0.0670, 0.0288; ...
             0.0670, 0.5724, 0.1786; ...
             0.0288, 0.1786, 0.1789];
 assert (nlogL, 17.9979636579, 1e-10);
 assert (acov, acov_out, 1e-4);
***** error<tlslike: too few input arguments.> tlslike ([12, 15, 1]);
***** error<tlslike: wrong parameters length.> tlslike ([12, 15], [1:50]);
***** error<tlslike: X must be a vector.> tlslike ([12, 3, 1], ones (10, 2));
***** error<tlslike: X and CENSOR> tlslike ([12, 15, 1], [1:50], [1, 2, 3]);
***** error<tlslike: X and FREQ> tlslike ([12, 15, 1], [1:50], [], [1, 2, 3]);
***** error<tlslike: FREQ cannot> tlslike ([12, 15, 1], [1:3], [], [1, 2, -3]);
7 tests, 7 passed, 0 known failure, 0 skipped
[inst/dist_fit/rayllike.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fit/rayllike.m
***** test
 x = [1 3 2 4 5 4 3 4];
 [nlogL, acov] = rayllike (3.25, x);
 assert (nlogL, 14.7442, 1e-4)
***** test
 x = [1 2 3 4 5];
 f = [1 1 2 3 1];
 [nlogL, acov] = rayllike (3.25, x, [], f);
 assert (nlogL, 14.7442, 1e-4)
***** test
 x = [1 2 3 4 5 6];
 f = [1 1 2 3 1 0];
 [nlogL, acov] = rayllike (3.25, x, [], f);
 assert (nlogL, 14.7442, 1e-4)
***** test
 x = [1 2 3 4 5 6];
 c = [0 0 0 0 0 1];
 f = [1 1 2 3 1 0];
 [nlogL, acov] = rayllike (3.25, x, c, f);
 assert (nlogL, 14.7442, 1e-4)
***** error<rayllike: function called with too few input arguments.> rayllike (1)
***** error<rayllike: SIGMA must be a positive scalar.> rayllike ([1 2 3], [1 2])
***** error<rayllike: X must be a vector of non-negative values.> ...
 rayllike (3.25, ones (10, 2))
***** error<rayllike: X must be a vector of non-negative values.> ...
 rayllike (3.25, [1 2 3 -4 5])
***** error<rayllike: X and CENSOR vectors mismatch.> ...
 rayllike (3.25, [1, 2, 3, 4, 5], [1 1 0]);
***** error<rayllike: X and CENSOR vectors mismatch.> ...
 rayllike (3.25, [1, 2, 3, 4, 5], [1 1 0 1 1]');
***** error<rayllike: X and FREQ vectors mismatch.> ...
 rayllike (3.25, [1, 2, 3, 4, 5], zeros (1,5), [1 1 0]);
***** error<rayllike: X and FREQ vectors mismatch.> ...
 rayllike (3.25, [1, 2, 3, 4, 5], [], [1 1 0 1 1]');
***** error<rayllike: FREQ must not contain negative values.> ...
 rayllike (3.25, ones (1, 8), [], [1 1 1 1 1 1 1 -1])
13 tests, 13 passed, 0 known failure, 0 skipped
[inst/dist_fit/invgfit.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fit/invgfit.m
***** demo
 ## Sample 3 populations from different inverse Gaussian distibutions
 rand ("seed", 5); randn ("seed", 5);   # for reproducibility
 r1 = invgrnd (1, 0.2, 2000, 1);
 rand ("seed", 2); randn ("seed", 2);   # for reproducibility
 r2 = invgrnd (1, 3, 2000, 1);
 rand ("seed", 7); randn ("seed", 7);   # for reproducibility
 r3 = invgrnd (3, 1, 2000, 1);
 r = [r1, r2, r3];

 ## Plot them normalized and fix their colors
 hist (r, [0.1:0.1:3.2], 9);
 h = findobj (gca, "Type", "patch");
 set (h(1), "facecolor", "c");
 set (h(2), "facecolor", "g");
 set (h(3), "facecolor", "r");
 ylim ([0, 3]);
 xlim ([0, 3]);
 hold on

 ## Estimate their MU and LAMBDA parameters
 mu_lambdaA = invgfit (r(:,1));
 mu_lambdaB = invgfit (r(:,2));
 mu_lambdaC = invgfit (r(:,3));

 ## Plot their estimated PDFs
 x = [0:0.1:3];
 y = invgpdf (x, mu_lambdaA(1), mu_lambdaA(2));
 plot (x, y, "-pr");
 y = invgpdf (x, mu_lambdaB(1), mu_lambdaB(2));
 plot (x, y, "-sg");
 y = invgpdf (x, mu_lambdaC(1), mu_lambdaC(2));
 plot (x, y, "-^c");
 hold off
 legend ({"Normalized HIST of sample 1 with μ=1 and λ=0.5", ...
          "Normalized HIST of sample 2 with μ=2 and λ=0.3", ...
          "Normalized HIST of sample 3 with μ=4 and λ=0.5", ...
          sprintf("PDF for sample 1 with estimated μ=%0.2f and λ=%0.2f", ...
                  mu_lambdaA(1), mu_lambdaA(2)), ...
          sprintf("PDF for sample 2 with estimated μ=%0.2f and λ=%0.2f", ...
                  mu_lambdaB(1), mu_lambdaB(2)), ...
          sprintf("PDF for sample 3 with estimated μ=%0.2f and λ=%0.2f", ...
                  mu_lambdaC(1), mu_lambdaC(2))})
 title ("Three population samples from different inverse Gaussian distibutions")
 hold off
***** test
 paramhat = invgfit ([1:50]);
 paramhat_out = [25.5, 19.6973];
 assert (paramhat, paramhat_out, 1e-4);
***** test
 paramhat = invgfit ([1:5]);
 paramhat_out = [3, 8.1081];
 assert (paramhat, paramhat_out, 1e-4);
***** error<invgfit: X must be a vector.> invgfit (ones (2,5));
***** error<invgfit: X must contain only positive values.> invgfit ([-1 2 3 4]);
***** error<invgfit: wrong value for ALPHA.> invgfit ([1, 2, 3, 4, 5], 1.2);
***** error<invgfit: wrong value for ALPHA.> invgfit ([1, 2, 3, 4, 5], 0);
***** error<invgfit: wrong value for ALPHA.> invgfit ([1, 2, 3, 4, 5], "alpha");
***** error<invgfit: X and CENSOR vectors mismatch.> ...
 invgfit ([1, 2, 3, 4, 5], 0.05, [1 1 0]);
***** error<invgfit: X and CENSOR vectors mismatch.> ...
 invgfit ([1, 2, 3, 4, 5], [], [1 1 0 1 1]');
***** error<invgfit: X and FREQ vectors mismatch.> ...
 invgfit ([1, 2, 3, 4, 5], 0.05, zeros (1,5), [1 1 0]);
***** error<invgfit: X and FREQ vectors mismatch.> ...
 invgfit ([1, 2, 3, 4, 5], [], [], [1 1 0 1 1]');
***** error<invgfit: 'options' 5th argument must be a structure> ...
 invgfit ([1, 2, 3, 4, 5], 0.05, [], [], 2);
12 tests, 12 passed, 0 known failure, 0 skipped
[inst/dist_fit/betalike.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fit/betalike.m
***** test
 x = 0.01:0.02:0.99;
 [nlogL, avar] = betalike ([2.3, 1.2], x);
 avar_out = [0.03691678, 0.02803056; 0.02803056, 0.03965629];
 assert (nlogL, 17.873477715879040, 3e-14);
 assert (avar, avar_out, 1e-7);
***** test
 x = 0.01:0.02:0.99;
 [nlogL, avar] = betalike ([1, 4], x);
 avar_out = [0.02793282, 0.02717274; 0.02717274, 0.03993361];
 assert (nlogL, 79.648061114839550, 1e-13);
 assert (avar, avar_out, 1e-7);
***** test
 x = 0.00:0.02:1;
 [nlogL, avar] = betalike ([1, 4], x);
 avar_out = [0.00000801564765, 0.00000131397245; ...
             0.00000131397245, 0.00070827639442];
 assert (nlogL, 573.2008434477486, 1e-10);
 assert (avar, avar_out, 1e-14);
***** error<betalike: function called with too few input arguments.> ...
 betalike ([12, 15]);
***** error<betalike: wrong parameters length.> betalike ([12, 15, 3], [1:50]);
***** error<betalike: X and FREQ vectors mismatch.> ...
 betalike ([12, 15], ones (10, 1), ones (8,1))
***** error<betalike: FREQ must not contain negative values.> ...
 betalike ([12, 15], ones (1, 8), [1 1 1 1 1 1 1 -1])
***** error<betalike: FREQ must contain integer values.> ...
 betalike ([12, 15], ones (1, 8), [1 1 1 1 1 1 1 1.5])
8 tests, 8 passed, 0 known failure, 0 skipped
[inst/dist_fit/logllike.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fit/logllike.m
***** test
 [nlogL, acov] = logllike ([3.09717, 0.468525], [1:50]);
 assert (nlogL, 211.2965, 1e-4);
 assert (acov, [0.0131, -0.0007; -0.0007, 0.0031], 1e-4);
***** test
 [nlogL, acov] = logllike ([1.01124, 0.336449], [1:5]);
 assert (nlogL, 9.2206, 1e-4);
 assert (acov, [0.0712, -0.0032; -0.0032, 0.0153], 1e-4);
***** error<logllike: function called with too few input arguments.> logllike (3.25)
***** error<logllike: X must be a vector.> logllike ([5, 0.2], ones (2))
***** error<logllike: PARAMS must be a two-element vector.> ...
 logllike ([1, 0.2, 3], [1, 3, 5, 7])
***** error<logllike: X and CENSOR vector mismatch.> ...
 logllike ([1.5, 0.2], [1:5], [0, 0, 0])
***** error<logllike: X and FREQ vector mismatch.> ...
 logllike ([1.5, 0.2], [1:5], [0, 0, 0, 0, 0], [1, 1, 1])
***** error<logllike: X and FREQ vector mismatch.> ...
 logllike ([1.5, 0.2], [1:5], [], [1, 1, 1])
8 tests, 8 passed, 0 known failure, 0 skipped
[inst/dist_fit/logilike.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fit/logilike.m
***** test
 nlogL = logilike ([25.5, 8.7725], [1:50]);
 assert (nlogL, 206.6769, 1e-4);
***** test
 nlogL = logilike ([3, 0.8645], [1:5]);
 assert (nlogL, 9.0699, 1e-4);
***** error<logilike: function called with too few input arguments.> logilike (3.25)
***** error<logilike: X must be a vector.> logilike ([5, 0.2], ones (2))
***** error<logilike: PARAMS must be a two-element vector.> ...
 logilike ([1, 0.2, 3], [1, 3, 5, 7])
***** error<logilike: X and CENSOR vector mismatch.> ...
 logilike ([1.5, 0.2], [1:5], [0, 0, 0])
***** error<logilike: X and FREQ vector mismatch.> ...
 logilike ([1.5, 0.2], [1:5], [0, 0, 0, 0, 0], [1, 1, 1])
***** error<logilike: X and FREQ vector mismatch.> ...
 logilike ([1.5, 0.2], [1:5], [], [1, 1, 1])
8 tests, 8 passed, 0 known failure, 0 skipped
[inst/dist_fit/hnlike.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fit/hnlike.m
***** test
 x = 1:20;
 paramhat = hnfit (x, 0);
 [nlogL, acov] = hnlike (paramhat, x);
 assert (nlogL, 64.179177404891300, 1e-14);
***** test
 x = 1:20;
 paramhat = hnfit (x, 0);
 [nlogL, acov] = hnlike (paramhat, x, ones (1, 20));
 assert (nlogL, 64.179177404891300, 1e-14);
***** error<hnlike: function called with too few input arguments.> ...
 hnlike ([12, 15]);
***** error<hnlike: wrong parameters length.> hnlike ([12, 15, 3], [1:50]);
***** error<hnlike: wrong parameters length.> hnlike ([3], [1:50]);
***** error<hnlike: X must be a vector of real values.> ...
 hnlike ([0, 3], ones (2));
***** error<hnlike: X must be a vector of real values.> ...
 hnlike ([0, 3], [1, 2, 3, 4, 5+i]);
***** error<hnlike: X and FREQ vectors mismatch.> ...
 hnlike ([1, 2], ones (10, 1), ones (8,1))
***** error<hnlike: FREQ must not contain negative values.> ...
 hnlike ([1, 2], ones (1, 8), [1 1 1 1 1 1 1 -1])
9 tests, 9 passed, 0 known failure, 0 skipped
[inst/dist_fit/evlike.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fit/evlike.m
***** test
 x = 1:50;
 [nlogL, acov] = evlike ([2.3, 1.2], x);
 avar_out = [-1.2778e-13, 3.1859e-15; 3.1859e-15, -7.9430e-17];
 assert (nlogL, 3.242264755689906e+17, 1e-14);
 assert (acov, avar_out, 1e-3);
***** test
 x = 1:50;
 [nlogL, acov] = evlike ([2.3, 1.2], x * 0.5);
 avar_out = [-7.6094e-05, 3.9819e-06; 3.9819e-06, -2.0836e-07];
 assert (nlogL, 481898704.0472211, 1e-6);
 assert (acov, avar_out, 1e-3);
***** test
 x = 1:50;
 [nlogL, acov] = evlike ([21, 15], x);
 avar_out = [11.73913876598908, -5.9546128523121216; ...
             -5.954612852312121, 3.708060045170236];
 assert (nlogL, 223.7612479380652, 1e-13);
 assert (acov, avar_out, 1e-14);
***** error<evlike: function called with too few input arguments.> evlike ([12, 15])
***** error<evlike: wrong parameters length.> evlike ([12, 15, 3], [1:50])
***** error<evlike: X must be a vector.> evlike ([12, 3], ones (10, 2))
***** error<evlike: X and CENSOR vectors mismatch.> ...
 evlike ([12, 15], [1:50], [1, 2, 3])
***** error<evlike: X and FREQ vectors mismatch.> ...
 evlike ([12, 15], [1:50], [], [1, 2, 3])
8 tests, 8 passed, 0 known failure, 0 skipped
[inst/dist_fit/binofit.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fit/binofit.m
***** demo
 ## Sample 2 populations from different binomial distibutions
 rand ("seed", 1);    # for reproducibility
 r1 = binornd (50, 0.15, 1000, 1);
 rand ("seed", 2);    # for reproducibility
 r2 = binornd (100, 0.5, 1000, 1);
 r = [r1, r2];

 ## Plot them normalized and fix their colors
 hist (r, 23, 0.35);
 h = findobj (gca, "Type", "patch");
 set (h(1), "facecolor", "c");
 set (h(2), "facecolor", "g");
 hold on

 ## Estimate their probability of success
 pshatA = binofit (r(:,1), 50);
 pshatB = binofit (r(:,2), 100);

 ## Plot their estimated PDFs
 x = [min(r(:,1)):max(r(:,1))];
 y = binopdf (x, 50, mean (pshatA));
 plot (x, y, "-pg");
 x = [min(r(:,2)):max(r(:,2))];
 y = binopdf (x, 100, mean (pshatB));
 plot (x, y, "-sc");
 ylim ([0, 0.2])
 legend ({"Normalized HIST of sample 1 with ps=0.15", ...
          "Normalized HIST of sample 2 with ps=0.50", ...
          sprintf("PDF for sample 1 with estimated ps=%0.2f", ...
                  mean (pshatA)), ...
          sprintf("PDF for sample 2 with estimated ps=%0.2f", ...
                  mean (pshatB))})
 title ("Two population samples from different binomial distibutions")
 hold off
***** test
 x = 0:3;
 [pshat, psci] = binofit (x, 3);
 assert (pshat, [0, 0.3333, 0.6667, 1], 1e-4);
 assert (psci(1,:), [0, 0.7076], 1e-4);
 assert (psci(2,:), [0.0084, 0.9057], 1e-4);
 assert (psci(3,:), [0.0943, 0.9916], 1e-4);
 assert (psci(4,:), [0.2924, 1.0000], 1e-4);
***** error<binofit: function called with too few input arguments.> ...
 binofit ([1 2 3 4])
***** error<binofit: X cannot have negative values.> ...
 binofit ([-1, 4, 3, 2], [1, 2, 3, 3])
***** error<binofit: X must be a vector.> binofit (ones(2), [1, 2, 3, 3])
***** error<binofit: N must be a non-negative integer.> ...
 binofit ([1, 4, 3, 2], [1, 2, -1, 3])
***** error<binofit: N must be a scalar or the same size as X.> ...
 binofit ([1, 4, 3, 2], [5, 5, 5])
***** error<binofit: N must be at least as large as X.> ...
 binofit ([1, 4, 3, 2], [5, 3, 5, 5])
***** error<binofit: wrong value for ALPHA.> binofit ([1, 2, 1], 3, 1.2);
***** error<binofit: wrong value for ALPHA.> binofit ([1, 2, 1], 3, 0);
***** error<binofit: wrong value for ALPHA.> binofit ([1, 2, 1], 3, "alpha");
10 tests, 10 passed, 0 known failure, 0 skipped
[inst/dist_fit/binolike.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fit/binolike.m
***** assert (binolike ([3, 0.333], [0:3]), 6.8302, 1e-4)
***** assert (binolike ([3, 0.333], 0), 1.2149, 1e-4)
***** assert (binolike ([3, 0.333], 1), 0.8109, 1e-4)
***** assert (binolike ([3, 0.333], 2), 1.5056, 1e-4)
***** assert (binolike ([3, 0.333], 3), 3.2988, 1e-4)
***** test
 [nlogL, acov] = binolike ([3, 0.333], 3);
 assert (acov(4), 0.0740, 1e-4)
***** error<binolike: function called with too few input arguments.> binolike (3.25)
***** error<binolike: X must be a vector.> binolike ([5, 0.2], ones (2))
***** error<binolike: PARAMS must be a two-element vector.> ...
 binolike ([1, 0.2, 3], [1, 3, 5, 7])
***** error<binolike: number of trials,> binolike ([1.5, 0.2], 1)
***** error<binolike: number of trials,> binolike ([-1, 0.2], 1)
***** error<binolike: number of trials,> binolike ([Inf, 0.2], 1)
***** error<binolike: probability of success,> binolike ([5, 1.2], [3, 5])
***** error<binolike: probability of success,> binolike ([5, -0.2], [3, 5])
***** error<binolike: X and FREQ vectors mismatch.> ...
 binolike ([5, 0.5], ones (10, 1), ones (8,1))
***** error<binolike: FREQ must not contain negative values.> ...
 binolike ([5, 0.5], ones (1, 8), [1 1 1 1 1 1 1 -1])
***** error<binolike: X cannot have negative values.> binolike ([5, 0.2], [-1, 3])
***** error<binolike: number of successes,> binolike ([5, 0.2], [3, 5, 7])
18 tests, 18 passed, 0 known failure, 0 skipped
[inst/dist_fit/poisslike.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fit/poisslike.m
***** test
 x = [1 3 2 4 5 4 3 4];
 [nlogL, avar] = poisslike (3.25, x);
 assert (nlogL, 13.9533, 1e-4)
***** test
 x = [1 2 3 4 5];
 f = [1 1 2 3 1];
 [nlogL, avar] = poisslike (3.25, x, f);
 assert (nlogL, 13.9533, 1e-4)
***** error<poisslike: function called with too few input arguments.> poisslike (1)
***** error<poisslike: LAMBDA must be a positive scalar.> poisslike ([1 2 3], [1 2])
***** error<poisslike: X must be a vector of non-negative values.> ...
 poisslike (3.25, ones (10, 2))
***** error<poisslike: X must be a vector of non-negative values.> ...
 poisslike (3.25, [1 2 3 -4 5])
***** error<poisslike: X and FREQ vectors mismatch.> ...
 poisslike (3.25, ones (10, 1), ones (8,1))
***** error<poisslike: FREQ must not contain negative values.> ...
 poisslike (3.25, ones (1, 8), [1 1 1 1 1 1 1 -1])
8 tests, 8 passed, 0 known failure, 0 skipped
[inst/dist_fit/gevfit.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fit/gevfit.m
***** demo
 ## Sample 2 populations from 2 different exponential distibutions
 rand ("seed", 1);   # for reproducibility
 r1 = gevrnd (-0.5, 1, 2, 5000, 1);
 rand ("seed", 2);   # for reproducibility
 r2 = gevrnd (0, 1, -4, 5000, 1);
 r = [r1, r2];

 ## Plot them normalized and fix their colors
 hist (r, 50, 5);
 h = findobj (gca, "Type", "patch");
 set (h(1), "facecolor", "c");
 set (h(2), "facecolor", "g");
 hold on

 ## Estimate their k, sigma, and mu parameters
 k_sigma_muA = gevfit (r(:,1));
 k_sigma_muB = gevfit (r(:,2));

 ## Plot their estimated PDFs
 x = [-10:0.5:20];
 y = gevpdf (x, k_sigma_muA(1), k_sigma_muA(2), k_sigma_muA(3));
 plot (x, y, "-pr");
 y = gevpdf (x, k_sigma_muB(1), k_sigma_muB(2), k_sigma_muB(3));
 plot (x, y, "-sg");
 ylim ([0, 0.7])
 xlim ([-7, 5])
 legend ({"Normalized HIST of sample 1 with k=-0.5, σ=1, μ=2", ...
          "Normalized HIST of sample 2 with k=0, σ=1, μ=-4",
     sprintf("PDF for sample 1 with estimated k=%0.2f, σ=%0.2f, μ=%0.2f", ...
                 k_sigma_muA(1), k_sigma_muA(2), k_sigma_muA(3)), ...
     sprintf("PDF for sample 3 with estimated k=%0.2f, σ=%0.2f, μ=%0.2f", ...
                 k_sigma_muB(1), k_sigma_muB(2), k_sigma_muB(3))})
 title ("Two population samples from different exponential distibutions")
 hold off
***** test
 x = 1:50;
 [pfit, pci] = gevfit (x);
 pfit_out = [-0.4407, 15.1923, 21.5309];
 pci_out = [-0.7532, 11.5878, 16.5686; -0.1282, 19.9183, 26.4926];
 assert (pfit, pfit_out, 1e-3);
 assert (pci, pci_out, 1e-3);
***** test
 x = 1:2:50;
 [pfit, pci] = gevfit (x);
 pfit_out = [-0.4434, 15.2024, 21.0532];
 pci_out = [-0.8904, 10.3439, 14.0168; 0.0035, 22.3429, 28.0896];
 assert (pfit, pfit_out, 1e-3);
 assert (pci, pci_out, 1e-3);
***** error<gevfit: X must be a vector.> gevfit (ones (2,5));
***** error<gevfit: wrong value for ALPHA.> gevfit ([1, 2, 3, 4, 5], 1.2);
***** error<gevfit: wrong value for ALPHA.> gevfit ([1, 2, 3, 4, 5], 0);
***** error<gevfit: wrong value for ALPHA.> gevfit ([1, 2, 3, 4, 5], "alpha");
***** error<gevfit: X and FREQ vectors mismatch.> ...
 gevfit ([1, 2, 3, 4, 5], 0.05, [1, 2, 3, 2]);
***** error<gevfit: FREQ must not contain negative values.> ...
 gevfit ([1, 2, 3, 4, 5], 0.05, [1, 2, 3, 2, -1]);
***** error<gevfit: FREQ must contain integer values.> ...
 gevfit ([1, 2, 3, 4, 5], 0.05, [1, 2, 3, 2, 1.5]);
***** error<gevfit: 'options' argument must be a structure> ...
 gevfit ([1, 2, 3, 4, 5], 0.05, struct ("option", 234));
***** error<gevfit: 'options' argument must be a structure> ...
 gevfit ([1, 2, 3, 4, 5], 0.05, ones (1,5), struct ("option", 234));
11 tests, 11 passed, 0 known failure, 0 skipped
[inst/dist_fit/burrlike.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fit/burrlike.m
***** error<burrlike: function called with too few input arguments.> burrlike (3.25)
***** error<burrlike: X must be a vector.> burrlike ([1, 2, 3], ones (2))
***** error<burrlike: X cannot have negative values.> burrlike ([1, 2, 3], [-1, 3])
***** error<burrlike: PARAMS must be a three-element vector.> ...
 burrlike ([1, 2], [1, 3, 5, 7])
***** error<burrlike: PARAMS must be a three-element vector.> ...
 burrlike ([1, 2, 3, 4], [1, 3, 5, 7])
***** error<burrlike: X and CENSOR vector mismatch.> ...
 burrlike ([1, 2, 3], [1:5], [0, 0, 0])
***** error<burrlike: X and FREQ vector mismatch.> ...
 burrlike ([1, 2, 3], [1:5], [0, 0, 0, 0, 0], [1, 1, 1])
***** error<burrlike: X and FREQ vector mismatch.> ...
 burrlike ([1, 2, 3], [1:5], [], [1, 1, 1])
8 tests, 8 passed, 0 known failure, 0 skipped
[inst/dist_fit/expfit.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fit/expfit.m
***** demo
 ## Sample 3 populations from 3 different exponential distibutions
 rande ("seed", 1);   # for reproducibility
 r1 = exprnd (2, 4000, 1);
 rande ("seed", 2);   # for reproducibility
 r2 = exprnd (5, 4000, 1);
 rande ("seed", 3);   # for reproducibility
 r3 = exprnd (12, 4000, 1);
 r = [r1, r2, r3];

 ## Plot them normalized and fix their colors
 hist (r, 48, 0.52);
 h = findobj (gca, "Type", "patch");
 set (h(1), "facecolor", "c");
 set (h(2), "facecolor", "g");
 set (h(3), "facecolor", "r");
 hold on

 ## Estimate their mu parameter
 muhat = expfit (r);

 ## Plot their estimated PDFs
 x = [0:max(r(:))];
 y = exppdf (x, muhat(1));
 plot (x, y, "-pr");
 y = exppdf (x, muhat(2));
 plot (x, y, "-sg");
 y = exppdf (x, muhat(3));
 plot (x, y, "-^c");
 ylim ([0, 0.6])
 xlim ([0, 40])
 legend ({"Normalized HIST of sample 1 with μ=2", ...
          "Normalized HIST of sample 2 with μ=5", ...
          "Normalized HIST of sample 3 with μ=12", ...
          sprintf("PDF for sample 1 with estimated μ=%0.2f", muhat(1)), ...
          sprintf("PDF for sample 2 with estimated μ=%0.2f", muhat(2)), ...
          sprintf("PDF for sample 3 with estimated μ=%0.2f", muhat(3))})
 title ("Three population samples from different exponential distibutions")
 hold off
***** assert (expfit (1), 1)
***** assert (expfit (1:3), 2)
***** assert (expfit ([1:3]'), 2)
***** assert (expfit (1:3, []), 2)
***** assert (expfit (1:3, [], [], []), 2)
***** assert (expfit (magic (3)), [5 5 5])
***** assert (expfit (cat (3, magic (3), 2*magic (3))), cat (3,[5 5 5], [10 10 10]))
***** assert (expfit (1:3, 0.1, [0 0 0], [1 1 1]), 2)
***** assert (expfit ([1:3]', 0.1, [0 0 0]', [1 1 1]'), 2)
***** assert (expfit (1:3, 0.1, [0 0 0]', [1 1 1]'), 2)
***** assert (expfit (1:3, 0.1, [1 0 0], [1 1 1]), 3)
***** assert (expfit (1:3, 0.1, [0 0 0], [4 1 1]), 1.5)
***** assert (expfit (1:3, 0.1, [1 0 0], [4 1 1]), 4.5)
***** assert (expfit (1:3, 0.1, [1 0 1], [4 1 1]), 9)
***** assert (expfit (1:3, 0.1, [], [-1 1 1]), 4)
***** assert (expfit (1:3, 0.1, [], [0.5 1 1]), 2.2)
***** assert (expfit (1:3, 0.1, [1 1 1]), NaN)
***** assert (expfit (1:3, 0.1, [], [0 0 0]), NaN)
***** assert (expfit (reshape (1:9, [3 3])), [2 5 8])
***** assert (expfit (reshape (1:9, [3 3]), [], eye(3)), [3 7.5 12])
***** assert (expfit (reshape (1:9, [3 3]), [], 2*eye(3)), [3 7.5 12])
***** assert (expfit (reshape (1:9, [3 3]), [], [], [2 2 2; 1 1 1; 1 1 1]), ...
 [1.75 4.75 7.75])
***** assert (expfit (reshape (1:9, [3 3]), [], [], [2 2 2; 1 1 1; 1 1 1]), ...
 [1.75 4.75 7.75])
***** assert (expfit (reshape (1:9, [3 3]), [], eye(3), [2 2 2; 1 1 1; 1 1 1]), ...
 [3.5 19/3 31/3])
***** assert ([~,muci] = expfit (1:3, 0), [0; Inf])
***** assert ([~,muci] = expfit (1:3, 2), [Inf; 0])
***** assert ([~,muci] = expfit (1:3, 0.1, [1 1 1]), [NaN; NaN])
***** assert ([~,muci] = expfit (1:3, 0.1, [], [0 0 0]), [NaN; NaN])
***** assert ([~,muci] = expfit (1:3, -1), [NaN; NaN])
***** assert ([~,muci] = expfit (1:3, 5), [NaN; NaN])
***** assert ([~,muci] = expfit (1:3), [0.830485728373393; 9.698190330474096], ...
             1000*eps)
***** assert ([~,muci] = expfit (1:3, 0.1), ...
                          [0.953017262058213; 7.337731146400207], 1000*eps)
***** assert ([~,muci] = expfit ([1:3;2:4]), ...
             [0.538440777613095, 0.897401296021825, 1.256361814430554; ...
             12.385982973214016, 20.643304955356694, 28.900626937499371], ...
             1000*eps)
***** assert ([~,muci] = expfit ([1:3;2:4], [], [1 1 1; 0 0 0]), ...
             100*[0.008132550920455, 0.013554251534091, 0.018975952147727; ...
             1.184936706156216, 1.974894510260360, 2.764852314364504], ...
             1000*eps)
***** assert ([~,muci] = expfit ([1:3;2:4], [], [], [3 3 3; 1 1 1]), ...
             [0.570302756652583, 1.026544961974649, 1.482787167296715; ...
             4.587722594914109, 8.257900670845396, 11.928078746776684], ...
             1000*eps)
***** assert ([~,muci] = expfit ([1:3;2:4], [], [0 0 0; 1 1 1], [3 3 3; 1 1 1]), ...
             [0.692071440311161, 1.245728592560089, 1.799385744809018; ...
             8.081825275395081, 14.547285495711145, 21.012745716027212], ...
             1000*eps)
***** test
 x = reshape (1:8, [4 2]);
 x(4) = NaN;
 [muhat,muci] = expfit (x);
 assert ({muhat, muci}, {[NaN, 6.5], ...
         [NaN, 2.965574334593430;NaN, 23.856157493553368]}, 1000*eps);
***** test
 x = magic (3);
 censor = [0 1 0; 0 1 0; 0 1 0];
 freq = [1 1 0; 1 1 0; 1 1 0];
 [muhat,muci] = expfit (x, [], censor, freq);
 assert ({muhat, muci}, {[5 NaN NaN], ...
                 [[2.076214320933482; 24.245475826185242],NaN(2)]}, 1000*eps);
***** error expfit ()
***** error expfit (1,2,3,4,5)
***** error [a b censor] = expfit (1)
***** error <ALPHA must be a scalar quantity> expfit (1, [1 2])
***** error <X cannot be negative> expfit ([-1 2 3 4 5])
***** error <CENSOR must be a numeric or logical array> expfit ([1:5], [], "test")
***** error <FREQ must be a numeric or logical array> expfit ([1:5], [], [], "test")
***** error <X and CENSOR vectors mismatch.> expfit ([1:5], [], [0 0 0 0])
***** error <X and FREQ vectors mismatch.> expfit ([1:5], [], [], [1 1 1 1])
47 tests, 47 passed, 0 known failure, 0 skipped
[inst/dist_fit/gamfit.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fit/gamfit.m
***** demo
 ## Sample 3 populations from different Gamma distibutions
 randg ("seed", 5);    # for reproducibility
 r1 = gamrnd (1, 2, 2000, 1);
 randg ("seed", 2);    # for reproducibility
 r2 = gamrnd (2, 2, 2000, 1);
 randg ("seed", 7);    # for reproducibility
 r3 = gamrnd (7.5, 1, 2000, 1);
 r = [r1, r2, r3];

 ## Plot them normalized and fix their colors
 hist (r, 75, 4);
 h = findobj (gca, "Type", "patch");
 set (h(1), "facecolor", "c");
 set (h(2), "facecolor", "g");
 set (h(3), "facecolor", "r");
 ylim ([0, 0.62]);
 xlim ([0, 12]);
 hold on

 ## Estimate their α and β parameters
 a_bA = gamfit (r(:,1));
 a_bB = gamfit (r(:,2));
 a_bC = gamfit (r(:,3));

 ## Plot their estimated PDFs
 x = [0.01,0.1:0.2:18];
 y = gampdf (x, a_bA(1), a_bA(2));
 plot (x, y, "-pr");
 y = gampdf (x, a_bB(1), a_bB(2));
 plot (x, y, "-sg");
 y = gampdf (x, a_bC(1), a_bC(2));
 plot (x, y, "-^c");
 hold off
 legend ({"Normalized HIST of sample 1 with α=1 and β=2", ...
          "Normalized HIST of sample 2 with α=2 and β=2", ...
          "Normalized HIST of sample 3 with α=7.5 and β=1", ...
          sprintf("PDF for sample 1 with estimated α=%0.2f and β=%0.2f", ...
                  a_bA(1), a_bA(2)), ...
          sprintf("PDF for sample 2 with estimated α=%0.2f and β=%0.2f", ...
                  a_bB(1), a_bB(2)), ...
          sprintf("PDF for sample 3 with estimated α=%0.2f and β=%0.2f", ...
                  a_bC(1), a_bC(2))})
 title ("Three population samples from different Gamma distibutions")
 hold off
***** shared x
 x = [1.2 1.6 1.7 1.8 1.9 2.0 2.2 2.6 3.0 3.5 4.0 4.8 5.6 6.6 7.6];
***** test
 [paramhat, paramci] = gamfit (x);
 assert (paramhat, [3.4248, 0.9752], 1e-4);
 assert (paramci, [1.7287, 0.4670; 6.7852, 2.0366], 1e-4);
***** test
 [paramhat, paramci] = gamfit (x, 0.01);
 assert (paramhat, [3.4248, 0.9752], 1e-4);
 assert (paramci, [1.3945, 0.3705; 8.4113, 2.5668], 1e-4);
***** test
 freq = [1 1 1 1 2 1 1 1 1 2 1 1 1 1 2];
 [paramhat, paramci] = gamfit (x, [], [], freq);
 assert (paramhat, [3.3025, 1.0615], 1e-4);
 assert (paramci, [1.7710, 0.5415; 6.1584, 2.0806], 1e-4);
***** test
 [paramhat, paramci] = gamfit (x, [], [], [1:15]);
 assert (paramhat, [4.4484, 0.9689], 1e-4);
 assert (paramci, [3.4848, 0.7482; 5.6785, 1.2546], 1e-4);
***** test
 [paramhat, paramci] = gamfit (x, 0.01, [], [1:15]);
 assert (paramhat, [4.4484, 0.9689], 1e-4);
 assert (paramci, [3.2275, 0.6899; 6.1312, 1.3608], 1e-4);
***** test
 cens = [0 0 0 0 1 0 0 0 0 0 0 0 0 0 0];
 [paramhat, paramci] = gamfit (x, [], cens, [1:15]);
 assert (paramhat, [4.7537, 0.9308], 1e-4);
 assert (paramci, [3.7123, 0.7162; 6.0872, 1.2097], 1e-4);
***** test
 cens = [0 0 0 0 1 0 0 0 0 0 0 0 0 0 0];
 freq = [1 1 1 1 2 1 1 1 1 2 1 1 1 1 2];
 [paramhat, paramci] = gamfit (x, [], cens, freq);
 assert (paramhat, [3.4736, 1.0847], 1e-4);
 assert (paramci, [1.8286, 0.5359; 6.5982, 2.1956], 1e-4);
***** test
 [paramhat, paramci] = gamfit ([1 1 1 1 1 1]);
 assert (paramhat, [Inf, 0]);
 assert (paramci, [Inf, 0; Inf, 0]);
***** test
 [paramhat, paramci] = gamfit ([1 1 1 1 1 1], [], [1 1 1 1 1 1]);
 assert (paramhat, [NaN, NaN]);
 assert (paramci, [NaN, NaN; NaN, NaN]);
***** test
 [paramhat, paramci] = gamfit ([1 1 1 1 1 1], [], [], [1 1 1 1 1 1]);
 assert (paramhat, [Inf, 0]);
 assert (paramci, [Inf, 0; Inf, 0]);
***** assert (class (gamfit (single (x))), "single")
***** error<gamfit: X must be a vector.> gamfit (ones (2))
***** error<gamfit: wrong value for ALPHA.> gamfit (x, 1)
***** error<gamfit: wrong value for ALPHA.> gamfit (x, -1)
***** error<gamfit: wrong value for ALPHA.> gamfit (x, {0.05})
***** error<gamfit: wrong value for ALPHA.> gamfit (x, "a")
***** error<gamfit: wrong value for ALPHA.> gamfit (x, i)
***** error<gamfit: wrong value for ALPHA.> gamfit (x, [0.01 0.02])
***** error<gamfit: X and FREQ vectors mismatch.>
 gamfit ([1 2 3], 0.05, [], [1 5])
***** error<gamfit: FREQ must not contain negative values.>
 gamfit ([1 2 3], 0.05, [], [1 5 -1])
***** error<gamfit: 'options' 5th argument must be a structure> ...
 gamfit ([1:10], 0.05, [], [], 5)
***** error<gamfit: X cannot contain negative values.> gamfit ([1 2 3 -4])
***** error<gamfit: X must contain positive values when censored.> ...
 gamfit ([1 2 0], [], [1 0 0])
23 tests, 23 passed, 0 known failure, 0 skipped
[inst/dist_fit/lognfit.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fit/lognfit.m
***** demo
 ## Sample 3 populations from 3 different log-normal distibutions
 randn ("seed", 1);    # for reproducibility
 r1 = lognrnd (0, 0.25, 1000, 1);
 randn ("seed", 2);    # for reproducibility
 r2 = lognrnd (0, 0.5, 1000, 1);
 randn ("seed", 3);    # for reproducibility
 r3 = lognrnd (0, 1, 1000, 1);
 r = [r1, r2, r3];

 ## Plot them normalized and fix their colors
 hist (r, 30, 2);
 h = findobj (gca, "Type", "patch");
 set (h(1), "facecolor", "c");
 set (h(2), "facecolor", "g");
 set (h(3), "facecolor", "r");
 hold on

 ## Estimate their mu and sigma parameters
 mu_sigmaA = lognfit (r(:,1));
 mu_sigmaB = lognfit (r(:,2));
 mu_sigmaC = lognfit (r(:,3));

 ## Plot their estimated PDFs
 x = [0:0.1:6];
 y = lognpdf (x, mu_sigmaA(1), mu_sigmaA(2));
 plot (x, y, "-pr");
 y = lognpdf (x, mu_sigmaB(1), mu_sigmaB(2));
 plot (x, y, "-sg");
 y = lognpdf (x, mu_sigmaC(1), mu_sigmaC(2));
 plot (x, y, "-^c");
 ylim ([0, 2])
 xlim ([0, 6])
 hold off
 legend ({"Normalized HIST of sample 1 with mu=0, σ=0.25", ...
          "Normalized HIST of sample 2 with mu=0, σ=0.5", ...
          "Normalized HIST of sample 3 with mu=0, σ=1", ...
          sprintf("PDF for sample 1 with estimated mu=%0.2f and σ=%0.2f", ...
                  mu_sigmaA(1), mu_sigmaA(2)), ...
          sprintf("PDF for sample 2 with estimated mu=%0.2f and σ=%0.2f", ...
                  mu_sigmaB(1), mu_sigmaB(2)), ...
          sprintf("PDF for sample 3 with estimated mu=%0.2f and σ=%0.2f", ...
                  mu_sigmaC(1), mu_sigmaC(2))}, "location", "northeast")
 title ("Three population samples from different log-normal distibutions")
 hold off
***** test
 randn ("seed", 1);
 x = lognrnd (3, 5, [1000, 1]);
 [paramhat, paramci] = lognfit (x, 0.01);
 assert (paramci(1,1) < 3);
 assert (paramci(1,2) > 3);
 assert (paramci(2,1) < 5);
 assert (paramci(2,2) > 5);
***** error<lognfit: X must be a numeric vector of positive values.> ...
 lognfit (ones (20,3))
***** error<lognfit: X must be a numeric vector of positive values.> ...
 lognfit ({1, 2, 3, 4, 5})
***** error<lognfit: X must be a numeric vector of positive values.> ...
 lognfit ([-1, 2, 3, 4, 5])
***** error<lognfit: wrong value for ALPHA.> lognfit (ones (20,1), 0)
***** error<lognfit: wrong value for ALPHA.> lognfit (ones (20,1), -0.3)
***** error<lognfit: wrong value for ALPHA.> lognfit (ones (20,1), 1.2)
***** error<lognfit: wrong value for ALPHA.> lognfit (ones (20,1), [0.05,  0.1])
***** error<lognfit: wrong value for ALPHA.> lognfit (ones (20,1), 0.02+i)
***** error<lognfit: X and CENSOR vectors mismatch.> ...
 lognfit (ones (20,1), [], zeros(15,1))
***** error<lognfit: X and FREQ vectors mismatch.> ...
 lognfit (ones (20,1), [], zeros(20,1), ones(25,1))
***** error<lognfit: > lognfit (ones (20,1), [], zeros(20,1), ones(20,1), "options")
12 tests, 12 passed, 0 known failure, 0 skipped
[inst/dist_fit/gumbellike.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fit/gumbellike.m
***** test
 x = 1:50;
 [nlogL, avar] = gumbellike ([2.3, 1.2], x);
 avar_out = [-1.2778e-13, 3.1859e-15; 3.1859e-15, -7.9430e-17];
 assert (nlogL, 3.242264755689906e+17, 1e-14);
 assert (avar, avar_out, 1e-3);
***** test
 x = 1:50;
 [nlogL, avar] = gumbellike ([2.3, 1.2], x * 0.5);
 avar_out = [-7.6094e-05, 3.9819e-06; 3.9819e-06, -2.0836e-07];
 assert (nlogL, 481898704.0472211, 1e-6);
 assert (avar, avar_out, 1e-3);
***** test
 x = 1:50;
 [nlogL, avar] = gumbellike ([21, 15], x);
 avar_out = [11.73913876598908, -5.9546128523121216; ...
             -5.954612852312121, 3.708060045170236];
 assert (nlogL, 223.7612479380652, 1e-13);
 assert (avar, avar_out, 1e-14);
***** error<gumbellike: too few input arguments.> gumbellike ([12, 15]);
***** error<gumbellike: wrong parameters length.> gumbellike ([12, 15, 3], [1:50]);
***** error<gumbellike: X must be a vector.> gumbellike ([12, 3], ones (10, 2));
***** error<gumbellike: X and CENSOR> gumbellike ([12, 15], [1:50], [1, 2, 3]);
***** error<gumbellike: X and FREQ> gumbellike ([12, 15], [1:50], [], [1, 2, 3]);
8 tests, 8 passed, 0 known failure, 0 skipped
[inst/dist_fit/gpfit.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fit/gpfit.m
***** demo
 ## Sample 2 populations from different generalized Pareto distibutions
 ## Assume location parameter θ is known
 theta = 0;
 rand ("seed", 5);    # for reproducibility
 r1 = gprnd (1, 2, theta, 20000, 1);
 rand ("seed", 2);    # for reproducibility
 r2 = gprnd (3, 1, theta, 20000, 1);
 r = [r1, r2];

 ## Plot them normalized and fix their colors
 hist (r, [0.1:0.2:100], 5);
 h = findobj (gca, "Type", "patch");
 set (h(1), "facecolor", "r");
 set (h(2), "facecolor", "c");
 ylim ([0, 1]);
 xlim ([0, 5]);
 hold on

 ## Estimate their α and β parameters
 k_sigmaA = gpfit (r(:,1), theta);
 k_sigmaB = gpfit (r(:,2), theta);

 ## Plot their estimated PDFs
 x = [0.01, 0.1:0.2:18];
 y = gppdf (x, k_sigmaA(1), k_sigmaA(2), theta);
 plot (x, y, "-pc");
 y = gppdf (x, k_sigmaB(1), k_sigmaB(2), theta);
 plot (x, y, "-sr");
 hold off
 legend ({"Normalized HIST of sample 1 with k=1 and σ=2", ...
          "Normalized HIST of sample 2 with k=2 and σ=2", ...
          sprintf("PDF for sample 1 with estimated k=%0.2f and σ=%0.2f", ...
                  k_sigmaA(1), k_sigmaA(2)), ...
          sprintf("PDF for sample 3 with estimated k=%0.2f and σ=%0.2f", ...
                  k_sigmaB(1), k_sigmaB(2))})
 title ("Three population samples from different generalized Pareto distibutions")
 text (2, 0.7, "Known location parameter θ = 0")
 hold off
***** test
 k = 0.8937; sigma = 1.3230; theta = 1;
 x = [2.2196, 11.9301, 4.3673, 1.0949, 6.5626, ...
      1.2109, 1.8576, 1.0039, 12.7917, 2.2590];
 [hat, ci] = gpfit (x, theta);
 assert (hat, [k, sigma, theta], 1e-4);
 assert (ci, [-0.7750, 0.2437, 1; 2.5624, 7.1820, 1], 1e-4);
***** error<gpfit: function called with too few input arguments.> gpfit ()
***** error<gpfit: function called with too few input arguments.> gpfit (1)
***** error<gpfit: X must be a vector of real values.> gpfit ([0.2, 0.5+i], 0);
***** error<gpfit: X must be a vector of real values.> gpfit (ones (2,2) * 0.5, 0);
***** error<gpfit: THETA must be a real scalar value.> ...
 gpfit ([0.5, 1.2], [0, 1]);
***** error<gpfit: THETA must be a real scalar value.> ...
 gpfit ([0.5, 1.2], 5+i);
***** error<gpfit: X cannot contain values less than THETA.> ...
 gpfit ([1:5], 2);
***** error<gpfit: wrong value for ALPHA.> gpfit ([0.01:0.1:0.99], 0, 1.2);
***** error<gpfit: wrong value for ALPHA.> gpfit ([0.01:0.1:0.99], 0, i);
***** error<gpfit: wrong value for ALPHA.> gpfit ([0.01:0.1:0.99], 0, -1);
***** error<gpfit: wrong value for ALPHA.> gpfit ([0.01:0.1:0.99], 0, [0.05, 0.01]);
***** error<gpfit: X and FREQ vectors mismatch.>
 gpfit ([1 2 3], 0, [], [1 5])
***** error<gpfit: FREQ must not contain negative values.>
 gpfit ([1 2 3], 0, [], [1 5 -1])
***** error<gpfit: 'options' 5th argument must be a structure> ...
 gpfit ([1:10], 1, 0.05, [], 5)
15 tests, 15 passed, 0 known failure, 0 skipped
[inst/dist_fit/gplike.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fit/gplike.m
***** test
 k = 0.8937; sigma = 1.3230; theta = 1;
 x = [2.2196, 11.9301, 4.3673, 1.0949, 6.5626, ...
      1.2109, 1.8576, 1.0039, 12.7917, 2.2590];
 [nlogL, acov] = gplike ([k, sigma, theta], x);
 assert (nlogL, 21.736, 1e-3);
 assert (acov, [0.7249, -0.7351, 0; -0.7351, 1.3040, 0; 0, 0, 0], 1e-4);
***** assert (gplike ([2, 3, 0], 4), 3.047536764863501, 1e-14)
***** assert (gplike ([2, 3, 4], 8), 3.047536764863501, 1e-14)
***** assert (gplike ([1, 2, 0], 4), 2.890371757896165, 1e-14)
***** assert (gplike ([1, 2, 4], 8), 2.890371757896165, 1e-14)
***** assert (gplike ([2, 3, 0], [1:10]), 32.57864322725392, 1e-14)
***** assert (gplike ([2, 3, 2], [1:10] + 2), 32.57864322725392, 1e-14)
***** assert (gplike ([2, 3, 0], [1:10], ones (1,10)), 32.57864322725392, 1e-14)
***** assert (gplike ([1, 2, 0], [1:10]), 31.65666282460443, 1e-14)
***** assert (gplike ([1, 2, 3], [1:10] + 3), 31.65666282460443, 1e-14)
***** assert (gplike ([1, 2, 0], [1:10], ones (1,10)), 31.65666282460443, 1e-14)
***** assert (gplike ([1, NaN, 0], [1:10]), NaN)
***** error<gplike: function called with too few input arguments.> gplike ()
***** error<gplike: function called with too few input arguments.> gplike (1)
***** error<gplike: X must be a vector.> gplike ([1, 2, 0], [])
***** error<gplike: X must be a vector.> gplike ([1, 2, 0], ones (2))
***** error<gplike: PARAMS must be a three-element vector.> gplike (2, [1:10])
***** error<gplike: PARAMS must be a three-element vector.> gplike ([2, 3], [1:10])
***** error<gplike: X and FREQ vectors mismatch.> ...
 gplike ([1, 2, 0], ones (10, 1), ones (8,1))
***** error<gplike: FREQ must not contain negative values.> ...
 gplike ([1, 2, 0], ones (1, 8), [1 1 1 1 1 1 1 -1])
20 tests, 20 passed, 0 known failure, 0 skipped
[inst/dist_fit/logifit.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fit/logifit.m
***** demo
 ## Sample 3 populations from different logistic distibutions
 rand ("seed", 5)  # for reproducibility
 r1 = logirnd (2, 1, 2000, 1);
 rand ("seed", 2)   # for reproducibility
 r2 = logirnd (5, 2, 2000, 1);
 rand ("seed", 7)   # for reproducibility
 r3 = logirnd (9, 4, 2000, 1);
 r = [r1, r2, r3];

 ## Plot them normalized and fix their colors
 hist (r, [-6:20], 1);
 h = findobj (gca, "Type", "patch");
 set (h(1), "facecolor", "c");
 set (h(2), "facecolor", "g");
 set (h(3), "facecolor", "r");
 ylim ([0, 0.3]);
 xlim ([-5, 20]);
 hold on

 ## Estimate their MU and LAMBDA parameters
 mu_sA = logifit (r(:,1));
 mu_sB = logifit (r(:,2));
 mu_sC = logifit (r(:,3));

 ## Plot their estimated PDFs
 x = [-5:0.5:20];
 y = logipdf (x, mu_sA(1), mu_sA(2));
 plot (x, y, "-pr");
 y = logipdf (x, mu_sB(1), mu_sB(2));
 plot (x, y, "-sg");
 y = logipdf (x, mu_sC(1), mu_sC(2));
 plot (x, y, "-^c");
 hold off
 legend ({"Normalized HIST of sample 1 with μ=1 and s=0.5", ...
          "Normalized HIST of sample 2 with μ=2 and s=0.3", ...
          "Normalized HIST of sample 3 with μ=4 and s=0.5", ...
          sprintf("PDF for sample 1 with estimated μ=%0.2f and s=%0.2f", ...
                  mu_sA(1), mu_sA(2)), ...
          sprintf("PDF for sample 2 with estimated μ=%0.2f and s=%0.2f", ...
                  mu_sB(1), mu_sB(2)), ...
          sprintf("PDF for sample 3 with estimated μ=%0.2f and s=%0.2f", ...
                  mu_sC(1), mu_sC(2))})
 title ("Three population samples from different logistic distibutions")
 hold off
***** test
 paramhat = logifit ([1:50]);
 paramhat_out = [25.5, 8.7724];
 assert (paramhat, paramhat_out, 1e-4);
***** test
 paramhat = logifit ([1:5]);
 paramhat_out = [3, 0.8645];
 assert (paramhat, paramhat_out, 1e-4);
***** test
 paramhat = logifit ([1:6], [], [], [1 1 1 1 1 0]);
 paramhat_out = [3, 0.8645];
 assert (paramhat, paramhat_out, 1e-4);
***** test
 paramhat = logifit ([1:5], [], [], [1 1 1 1 2]);
 paramhat_out = logifit ([1:5, 5]);
 assert (paramhat, paramhat_out, 1e-4);
***** error<logifit: X must be a vector.> logifit (ones (2,5));
***** error<logifit: wrong value for ALPHA.> logifit ([1, 2, 3, 4, 5], 1.2);
***** error<logifit: wrong value for ALPHA.> logifit ([1, 2, 3, 4, 5], 0);
***** error<logifit: wrong value for ALPHA.> logifit ([1, 2, 3, 4, 5], "alpha");
***** error<logifit: X and CENSOR vectors mismatch.> ...
 logifit ([1, 2, 3, 4, 5], 0.05, [1 1 0]);
***** error<logifit: X and CENSOR vectors mismatch.> ...
 logifit ([1, 2, 3, 4, 5], [], [1 1 0 1 1]');
***** error<logifit: X and FREQ vectors mismatch.> ...
 logifit ([1, 2, 3, 4, 5], 0.05, zeros (1,5), [1 1 0]);
***** error<logifit: X and FREQ vectors mismatch.> ...
 logifit ([1, 2, 3, 4, 5], [], [], [1 1 0 1 1]');
***** error<logifit: 'options' 5th argument must be a structure> ...
 logifit ([1, 2, 3, 4, 5], 0.05, [], [], 2);
13 tests, 13 passed, 0 known failure, 0 skipped
[inst/dist_fit/nbinlike.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fit/nbinlike.m
***** assert (nbinlike ([2.42086, 0.0867043], [1:50]), 205.5942, 1e-4)
***** assert (nbinlike ([3.58823, 0.254697], [1:20]), 63.6435, 1e-4)
***** assert (nbinlike ([8.80671, 0.615565], [1:10]), 24.7410, 1e-4)
***** assert (nbinlike ([22.1756, 0.831306], [1:8]), 17.9528, 1e-4)
***** assert (nbinlike ([22.1756, 0.831306], [1:9], [ones(1,8), 0]), 17.9528, 1e-4)
***** error<nbinlike: function called with too few input arguments.> nbinlike (3.25)
***** error<nbinlike: X must be a vector.> nbinlike ([5, 0.2], ones (2))
***** error<nbinlike: X cannot have negative values.> nbinlike ([5, 0.2], [-1, 3])
***** error<nbinlike: PARAMS must be a two-element vector.> ...
 nbinlike ([1, 0.2, 3], [1, 3, 5, 7])
***** error<nbinlike: number of successes,> nbinlike ([-5, 0.2], [1:15])
***** error<nbinlike: number of successes,> nbinlike ([0, 0.2], [1:15])
***** error<nbinlike: probability of success,> nbinlike ([5, 1.2], [3, 5])
***** error<nbinlike: probability of success,> nbinlike ([5, -0.2], [3, 5])
***** error<nbinlike: X and FREQ vectors mismatch.> ...
 nbinlike ([5, 0.2], ones (10, 1), ones (8,1))
***** error<nbinlike: FREQ must not contain negative values.> ...
 nbinlike ([5, 0.2], ones (1, 8), [1 1 1 1 1 1 1 -1])
***** error<nbinlike: FREQ must contain integer values.> ...
 nbinlike ([5, 0.2], ones (1, 8), [1 1 1 1 1 1 1 1.5])
16 tests, 16 passed, 0 known failure, 0 skipped
[inst/dist_fit/evfit.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fit/evfit.m
***** demo
 ## Sample 3 populations from different extreme value distibutions
 rand ("seed", 1);    # for reproducibility
 r1 = evrnd (2, 5, 400, 1);
 rand ("seed", 12);    # for reproducibility
 r2 = evrnd (-5, 3, 400, 1);
 rand ("seed", 13);    # for reproducibility
 r3 = evrnd (14, 8, 400, 1);
 r = [r1, r2, r3];

 ## Plot them normalized and fix their colors
 hist (r, 25, 0.4);
 h = findobj (gca, "Type", "patch");
 set (h(1), "facecolor", "c");
 set (h(2), "facecolor", "g");
 set (h(3), "facecolor", "r");
 ylim ([0, 0.28])
 xlim ([-30, 30]);
 hold on

 ## Estimate their MU and SIGMA parameters
 mu_sigmaA = evfit (r(:,1));
 mu_sigmaB = evfit (r(:,2));
 mu_sigmaC = evfit (r(:,3));

 ## Plot their estimated PDFs
 x = [min(r(:)):max(r(:))];
 y = evpdf (x, mu_sigmaA(1), mu_sigmaA(2));
 plot (x, y, "-pr");
 y = evpdf (x, mu_sigmaB(1), mu_sigmaB(2));
 plot (x, y, "-sg");
 y = evpdf (x, mu_sigmaC(1), mu_sigmaC(2));
 plot (x, y, "-^c");
 legend ({"Normalized HIST of sample 1 with μ=2 and σ=5", ...
          "Normalized HIST of sample 2 with μ=-5 and σ=3", ...
          "Normalized HIST of sample 3 with μ=14 and σ=8", ...
          sprintf("PDF for sample 1 with estimated μ=%0.2f and σ=%0.2f", ...
                  mu_sigmaA(1), mu_sigmaA(2)), ...
          sprintf("PDF for sample 2 with estimated μ=%0.2f and σ=%0.2f", ...
                  mu_sigmaB(1), mu_sigmaB(2)), ...
          sprintf("PDF for sample 3 with estimated μ=%0.2f and σ=%0.2f", ...
                  mu_sigmaC(1), mu_sigmaC(2))})
 title ("Three population samples from different extreme value distibutions")
 hold off
***** test
 x = 1:50;
 [paramhat, paramci] = evfit (x);
 paramhat_out = [32.6811, 13.0509];
 paramci_out = [28.8504, 10.5294; 36.5118, 16.1763];
 assert (paramhat, paramhat_out, 1e-4);
 assert (paramci, paramci_out, 1e-4);
***** test
 x = 1:50;
 [paramhat, paramci] = evfit (x, 0.01);
 paramci_out = [27.6468, 9.8426; 37.7155, 17.3051];
 assert (paramci, paramci_out, 1e-4);
***** error<evfit: X must be a double-precision vector.> evfit (ones (2,5));
***** error<evfit: X must be a double-precision vector.> evfit (single (ones (1,5)));
***** error<evfit: X must NOT contain missing values> evfit ([1, 2, 3, 4, NaN]);
***** error<evfit: wrong value for ALPHA.> evfit ([1, 2, 3, 4, 5], 1.2);
***** error<evfit: X and FREQ vectors mismatch.>
 evfit ([1 2 3], 0.05, [], [1 5])
***** error<evfit: FREQ must not contain negative values.>
 evfit ([1 2 3], 0.05, [], [1 5 -1])
***** error<evfit: 'options' 5th argument must be a structure> ...
 evfit ([1:10], 0.05, [], [], 5)
9 tests, 9 passed, 0 known failure, 0 skipped
[inst/dist_fit/betafit.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fit/betafit.m
***** demo
 ## Sample 2 populations from different Beta distibutions
 randg ("seed", 1);   # for reproducibility
 r1 = betarnd (2, 5, 500, 1);
 randg ("seed", 2);   # for reproducibility
 r2 = betarnd (2, 2, 500, 1);
 r = [r1, r2];

 ## Plot them normalized and fix their colors
 hist (r, 12, 15);
 h = findobj (gca, "Type", "patch");
 set (h(1), "facecolor", "c");
 set (h(2), "facecolor", "g");
 hold on

 ## Estimate their shape parameters
 a_b_A = betafit (r(:,1));
 a_b_B = betafit (r(:,2));

 ## Plot their estimated PDFs
 x = [min(r(:)):0.01:max(r(:))];
 y = betapdf (x, a_b_A(1), a_b_A(2));
 plot (x, y, "-pr");
 y = betapdf (x, a_b_B(1), a_b_B(2));
 plot (x, y, "-sg");
 ylim ([0, 4])
 legend ({"Normalized HIST of sample 1 with α=2 and β=5", ...
          "Normalized HIST of sample 2 with α=2 and β=2", ...
          sprintf("PDF for sample 1 with estimated α=%0.2f and β=%0.2f", ...
                  a_b_A(1), a_b_A(2)), ...
          sprintf("PDF for sample 2 with estimated α=%0.2f and β=%0.2f", ...
                  a_b_B(1), a_b_B(2))})
 title ("Two population samples from different Beta distibutions")
 hold off
***** test
 x = 0.01:0.02:0.99;
 [paramhat, paramci] = betafit (x);
 paramhat_out = [1.0199, 1.0199];
 paramci_out = [0.6947, 0.6947; 1.4974, 1.4974];
 assert (paramhat, paramhat_out, 1e-4);
 assert (paramci, paramci_out, 1e-4);
***** test
 x = 0.01:0.02:0.99;
 [paramhat, paramci] = betafit (x, 0.01);
 paramci_out = [0.6157, 0.6157; 1.6895, 1.6895];
 assert (paramci, paramci_out, 1e-4);
***** test
 x = 0.00:0.02:1;
 [paramhat, paramci] = betafit (x);
 paramhat_out = [0.0875, 0.1913];
 paramci_out = [0.0822, 0.1490; 0.0931, 0.2455];
 assert (paramhat, paramhat_out, 1e-4);
 assert (paramci, paramci_out, 1e-4);
***** error<betafit: X must be a vector of real values.> betafit ([0.2, 0.5+i]);
***** error<betafit: X must be a vector of real values.> betafit (ones (2,2) * 0.5);
***** error<betafit: X must be in the range> betafit ([0.5, 1.2]);
***** error<betafit: X must contain distinct values.> betafit ([0.1, 0.1]);
***** error<betafit: wrong value for ALPHA.> betafit ([0.01:0.1:0.99], 1.2);
***** error<betafit: X and FREQ vectors mismatch.> ...
 betafit ([0.01:0.01:0.05], 0.05, [1, 2, 3, 2]);
***** error<betafit: FREQ must not contain negative values.> ...
 betafit ([0.01:0.01:0.05], 0.05, [1, 2, 3, 2, -1]);
***** error<betafit: FREQ must contain integer values.> ...
 betafit ([0.01:0.01:0.05], 0.05, [1, 2, 3, 2, 1.5]);
***** error<betafit: 'options' argument must be a structure> ...
 betafit ([0.01:0.01:0.05], 0.05, struct ("option", 234));
***** error<betafit: 'options' argument must be a structure> ...
 betafit ([0.01:0.01:0.05], 0.05, ones (1,5), struct ("option", 234));
13 tests, 13 passed, 0 known failure, 0 skipped
[inst/dist_fit/gevlike.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fit/gevlike.m
***** test
 x = 1;
 k = 0.2;
 sigma = 0.3;
 mu = 0.5;
 [L, C] = gevlike ([k sigma mu], x);
 expected_L = 0.75942;
 expected_C = [-0.12547 1.77884 1.06731; 1.77884 16.40761 8.48877; 1.06731 8.48877 0.27979];
 assert (L, expected_L, 0.001);
 assert (C, inv (expected_C), 0.001);
***** test
 x = 1;
 k = 0;
 sigma = 0.3;
 mu = 0.5;
 [L, C] = gevlike ([k sigma mu], x);
 expected_L = 0.65157;
 expected_C = [0.090036 3.41229 2.047337; 3.412229 24.760027 12.510190; 2.047337 12.510190 2.098618];
 assert (L, expected_L, 0.001);
 assert (C, inv (expected_C), 0.001);
***** test
 x = -5:-1;
 k = -0.2;
 sigma = 0.3;
 mu = 0.5;
 [L, C] = gevlike ([k sigma mu], x);
 expected_L = 3786.4;
 expected_C = [1.6802e-07, 4.6110e-06, 8.7297e-05; ...
               4.6110e-06, 7.5693e-06, 1.2034e-05; ...
               8.7297e-05, 1.2034e-05, -0.0019125];
 assert (L, expected_L, -0.001);
 assert (C, expected_C, -0.001);
***** test
 x = -5:0;
 k = -0.2;
 sigma = 0.3;
 mu = 0.5;
 [L, C] = gevlike ([k sigma mu], x, [1, 1, 1, 1, 1, 0]);
 expected_L = 3786.4;
 expected_C = [1.6802e-07, 4.6110e-06, 8.7297e-05; ...
               4.6110e-06, 7.5693e-06, 1.2034e-05; ...
               8.7297e-05, 1.2034e-05, -0.0019125];
 assert (L, expected_L, -0.001);
 assert (C, expected_C, -0.001);
***** error<gevlike: function called with too few input arguments.> gevlike (3.25)
***** error<gevlike: X must be a vector.> gevlike ([1, 2, 3], ones (2))
***** error<gevlike: PARAMS must be a three-element vector.> ...
 gevlike ([1, 2], [1, 3, 5, 7])
***** error<gevlike: PARAMS must be a three-element vector.> ...
 gevlike ([1, 2, 3, 4], [1, 3, 5, 7])
***** error<gevlike: X and FREQ vectors mismatch.> ...
 gevlike ([5, 0.2, 1], ones (10, 1), ones (8,1))
***** error<gevlike: FREQ must not contain negative values.> ...
 gevlike ([5, 0.2, 1], ones (1, 8), [1 1 1 1 1 1 1 -1])
***** error<gevlike: FREQ must contain integer values.> ...
 gevlike ([5, 0.2, 1], ones (1, 8), [1 1 1 1 1 1 1 1.5])
11 tests, 11 passed, 0 known failure, 0 skipped
[inst/dist_fit/normfit.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fit/normfit.m
***** demo
 ## Sample 3 populations from 3 different normal distibutions
 randn ("seed", 1);    # for reproducibility
 r1 = normrnd (2, 5, 5000, 1);
 randn ("seed", 2);    # for reproducibility
 r2 = normrnd (5, 2, 5000, 1);
 randn ("seed", 3);    # for reproducibility
 r3 = normrnd (9, 4, 5000, 1);
 r = [r1, r2, r3];

 ## Plot them normalized and fix their colors
 hist (r, 15, 0.4);
 h = findobj (gca, "Type", "patch");
 set (h(1), "facecolor", "c");
 set (h(2), "facecolor", "g");
 set (h(3), "facecolor", "r");
 hold on

 ## Estimate their mu and sigma parameters
 [muhat, sigmahat] = normfit (r);

 ## Plot their estimated PDFs
 x = [min(r(:)):max(r(:))];
 y = normpdf (x, muhat(1), sigmahat(1));
 plot (x, y, "-pr");
 y = normpdf (x, muhat(2), sigmahat(2));
 plot (x, y, "-sg");
 y = normpdf (x, muhat(3), sigmahat(3));
 plot (x, y, "-^c");
 ylim ([0, 0.5])
 xlim ([-20, 20])
 hold off
 legend ({"Normalized HIST of sample 1 with mu=2, σ=5", ...
          "Normalized HIST of sample 2 with mu=5, σ=2", ...
          "Normalized HIST of sample 3 with mu=9, σ=4", ...
          sprintf("PDF for sample 1 with estimated mu=%0.2f and σ=%0.2f", ...
                  muhat(1), sigmahat(1)), ...
          sprintf("PDF for sample 2 with estimated mu=%0.2f and σ=%0.2f", ...
                  muhat(2), sigmahat(2)), ...
          sprintf("PDF for sample 3 with estimated mu=%0.2f and σ=%0.2f", ...
                  muhat(3), sigmahat(3))}, "location", "northwest")
 title ("Three population samples from different normal distibutions")
 hold off
***** test
 load lightbulb
 idx = find (lightbulb(:,2) == 0);
 censoring = lightbulb(idx,3) == 1;
 [muHat, sigmaHat] = normfit (lightbulb(idx,1), [], censoring);
 assert (muHat, 9496.59586737857, 1e-11);
 assert (sigmaHat, 3064.021012796456, 2e-12);
***** test
 randn ("seed", 234);
 x = normrnd (3, 5, [1000, 1]);
 [muHat, sigmaHat, muCI, sigmaCI] = normfit (x, 0.01);
 assert (muCI(1) < 3);
 assert (muCI(2) > 3);
 assert (sigmaCI(1) < 5);
 assert (sigmaCI(2) > 5);
***** error<normfit: X must not be a multi-dimensional array.> ...
 normfit (ones (3,3,3))
***** error<normfit: matrix data acceptable only under 2-arg syntax.> ...
 normfit (ones (20,3), [], zeros (20,1))
***** error<normfit: wrong value for ALPHA.> normfit (ones (20,1), 0)
***** error<normfit: wrong value for ALPHA.> normfit (ones (20,1), -0.3)
***** error<normfit: wrong value for ALPHA.> normfit (ones (20,1), 1.2)
***** error<normfit: wrong value for ALPHA.> normfit (ones (20,1), [0.05 0.1])
***** error<normfit: wrong value for ALPHA.> normfit (ones (20,1), 0.02+i)
***** error<normfit: X and CENSOR vectors mismatch.> ...
 normfit (ones (20,1), [], zeros(15,1))
***** error<normfit: X and FREQ vectors mismatch.> ...
 normfit (ones (20,1), [], zeros(20,1), ones(25,1))
***** error<normfit: FREQ must not contain negative values.> ...
 normfit (ones (5,1), [], zeros(5,1), [1, 2, 1, 2, -1]')
***** error<normfit: > normfit (ones (20,1), [], zeros(20,1), ones(20,1), "options")
13 tests, 13 passed, 0 known failure, 0 skipped
[inst/dist_fit/bisafit.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fit/bisafit.m
***** demo
 ## Sample 3 populations from different Birnbaum-Saunders distibutions
 rand ("seed", 5);    # for reproducibility
 r1 = bisarnd (1, 0.5, 2000, 1);
 rand ("seed", 2);    # for reproducibility
 r2 = bisarnd (2, 0.3, 2000, 1);
 rand ("seed", 7);    # for reproducibility
 r3 = bisarnd (4, 0.5, 2000, 1);
 r = [r1, r2, r3];

 ## Plot them normalized and fix their colors
 hist (r, 80, 4.2);
 h = findobj (gca, "Type", "patch");
 set (h(1), "facecolor", "c");
 set (h(2), "facecolor", "g");
 set (h(3), "facecolor", "r");
 ylim ([0, 1.1]);
 xlim ([0, 8]);
 hold on

 ## Estimate their α and β parameters
 beta_gammaA = bisafit (r(:,1));
 beta_gammaB = bisafit (r(:,2));
 beta_gammaC = bisafit (r(:,3));

 ## Plot their estimated PDFs
 x = [0:0.1:8];
 y = bisapdf (x, beta_gammaA(1), beta_gammaA(2));
 plot (x, y, "-pr");
 y = bisapdf (x, beta_gammaB(1), beta_gammaB(2));
 plot (x, y, "-sg");
 y = bisapdf (x, beta_gammaC(1), beta_gammaC(2));
 plot (x, y, "-^c");
 hold off
 legend ({"Normalized HIST of sample 1 with β=1 and γ=0.5", ...
          "Normalized HIST of sample 2 with β=2 and γ=0.3", ...
          "Normalized HIST of sample 3 with β=4 and γ=0.5", ...
          sprintf("PDF for sample 1 with estimated β=%0.2f and γ=%0.2f", ...
                  beta_gammaA(1), beta_gammaA(2)), ...
          sprintf("PDF for sample 2 with estimated β=%0.2f and γ=%0.2f", ...
                  beta_gammaB(1), beta_gammaB(2)), ...
          sprintf("PDF for sample 3 with estimated β=%0.2f and γ=%0.2f", ...
                  beta_gammaC(1), beta_gammaC(2))})
 title ("Three population samples from different Birnbaum-Saunders distibutions")
 hold off
***** test
 paramhat = bisafit ([1:50]);
 paramhat_out = [16.2649, 1.0156];
 assert (paramhat, paramhat_out, 1e-4);
***** test
 paramhat = bisafit ([1:5]);
 paramhat_out = [2.5585, 0.5839];
 assert (paramhat, paramhat_out, 1e-4);
***** error<bisafit: X must be a vector.> bisafit (ones (2,5));
***** error<bisafit: X must contain only positive values.> bisafit ([-1 2 3 4]);
***** error<bisafit: wrong value for ALPHA.> bisafit ([1, 2, 3, 4, 5], 1.2);
***** error<bisafit: wrong value for ALPHA.> bisafit ([1, 2, 3, 4, 5], 0);
***** error<bisafit: wrong value for ALPHA.> bisafit ([1, 2, 3, 4, 5], "alpha");
***** error<bisafit: X and CENSOR vectors mismatch.> ...
 bisafit ([1, 2, 3, 4, 5], 0.05, [1 1 0]);
***** error<bisafit: X and CENSOR vectors mismatch.> ...
 bisafit ([1, 2, 3, 4, 5], [], [1 1 0 1 1]');
***** error<bisafit: X and FREQ vectors mismatch.> ...
 bisafit ([1, 2, 3, 4, 5], 0.05, zeros (1,5), [1 1 0]);
***** error<bisafit: X and FREQ vectors mismatch.> ...
 bisafit ([1, 2, 3, 4, 5], [], [], [1 1 0 1 1]');
***** error<bisafit: 'options' 5th argument must be a structure> ...
 bisafit ([1, 2, 3, 4, 5], 0.05, [], [], 2);
12 tests, 12 passed, 0 known failure, 0 skipped
[inst/ecdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/ecdf.m
***** demo
 y = exprnd (10, 50, 1);    ## random failure times are exponential(10)
 d = exprnd (20, 50, 1);    ## drop-out times are exponential(20)
 t = min (y, d);            ## we observe the minimum of these times
 censored = (y > d);        ## we also observe whether the subject failed

 ## Calculate and plot the empirical cdf and confidence bounds
 [f, x, flo, fup] = ecdf (t, "censoring", censored);
 stairs (x, f);
 hold on;
 stairs (x, flo, "r:"); stairs (x, fup, "r:");

 ## Superimpose a plot of the known true cdf
 xx = 0:.1:max (t); yy = 1 - exp (-xx / 10); plot (xx, yy, "g-");
 hold off;
***** demo
 R = wblrnd (100, 2, 100, 1);
 ecdf (R, "Function", "survivor", "Alpha", 0.01, "Bounds", "on");
 hold on
 x = 1:1:250;
 wblsurv = 1 - cdf ("weibull", x, 100, 2);
 plot (x, wblsurv, "g-", "LineWidth", 2)
 legend ("Empirical survivor function", "Lower confidence bound", ...
         "Upper confidence bound", "Weibull survivor function", ...
         "Location", "northeast");
 hold off
***** error ecdf ();
***** error ecdf (randi (15,2));
***** error ecdf ([3,2,4,3+2i,5]);
***** error kstest ([2,3,4,5,6],"tail");
***** error kstest ([2,3,4,5,6],"tail", "whatever");
***** error kstest ([2,3,4,5,6],"function", "");
***** error kstest ([2,3,4,5,6],"badoption", 0.51);
***** error kstest ([2,3,4,5,6],"tail", 0);
***** error kstest ([2,3,4,5,6],"alpha", 0);
***** error kstest ([2,3,4,5,6],"alpha", NaN);
***** error kstest ([NaN,NaN,NaN,NaN,NaN],"tail", "unequal");
***** error kstest ([2,3,4,5,6],"alpha", 0.05, "CDF", [2,3,4;1,3,4;1,2,1]);
***** test
 hf = figure ("visible", "off");
 unwind_protect
   x = [2, 3, 4, 3, 5, 4, 6, 5, 8, 3, 7, 8, 9, 0];
   [F, x, Flo, Fup] = ecdf (x);
   F_out = [0; 0.0714; 0.1429; 0.3571; 0.5; 0.6429; 0.7143; 0.7857; 0.9286; 1];
   assert (F, F_out, ones (10,1) * 1e-4);
   x_out = [0 0 2 3 4 5 6 7 8 9]';
   assert (x, x_out);
   Flo_out = [NaN, 0, 0, 0.1061, 0.2381, 0.3919, 0.4776, 0.5708, 0.7937, NaN]';
   assert (Flo, Flo_out, ones (10,1) * 1e-4);
   Fup_out = [NaN, 0.2063, 0.3262, 0.6081, 0.7619, 0.8939, 0.9509, 1, 1, NaN]';
   assert (Fup, Fup_out, ones (10,1) * 1e-4);
 unwind_protect_cleanup
   close (hf);
 end_unwind_protect
***** test
 hf = figure ("visible", "off");
 unwind_protect
   x = [2, 3, 4, 3, 5, 4, 6, 5, 8, 3, 7, 8, 9, 0];
   ecdf (x);
 unwind_protect_cleanup
   close (hf);
 end_unwind_protect
14 tests, 14 passed, 0 known failure, 0 skipped
[inst/shadow9/median.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/shadow9/median.m
***** assert (median (1), 1)
***** assert (median ([1,2,3]), 2)
***** assert (median ([1,2,3]'), 2)
***** assert (median (cat(3,3,1,2)), 2)
***** assert (median ([3,1,2]), 2)
***** assert (median ([2,4,6,8]), 5)
***** assert (median ([8,2,6,4]), 5)
***** assert (median (single ([1,2,3])), single (2))
***** assert (median ([1,2], 3), [1,2])
***** test
 x = [1, 2, 3, 4, 5, 6];
 x2 = x';
 y = [1, 2, 3, 4, 5, 6, 7];
 y2 = y';

 assert (median (x) == median (x2) && median (x) == 3.5);
 assert (median (y) == median (y2) && median (y) == 4);
 assert (median ([x2, 2 * x2]), [3.5, 7]);
 assert (median ([y2, 3 * y2]), [4, 12]);
***** test
 in = [1 2 3];
 out = 2;
 assert (median (in, "default"), median (in));
 assert (median (in, "default"), out);
***** test
 in = single ([1 2 3]);
 out = 2;
 assert (median (in, "default"), single (median (in)));
 assert (median (in, "default"), single (out));
 assert (median (in, "double"), double (out));
 assert (median (in, "native"), single (out));
***** test
 in = uint8 ([1 2 3]);
 out = 2;
 assert (median (in, "default"), double (median (in)));
 assert (median (in, "default"), double (out));
 assert (median (in, "double"), out);
 assert (median (in, "native"), uint8 (out));
***** test
 in = logical ([1 0 1]);
 out = 1;
 assert (median (in, "default"), double (median (in)));
 assert (median (in, "default"), double (out));
 assert (median (in, "double"), double (out));
 assert (median (in, "native"), double (out));
***** test
 x = repmat ([2 2.1 2.2 2 NaN; 3 1 2 NaN 5; 1 1.1 1.4 5 3], [1, 1, 4]);
 y = repmat ([2 1.1 2 NaN NaN], [1, 1, 4]);
 assert (median (x), y);
 assert (median (x, 1), y);
 y = repmat ([2 1.1 2 3.5 4], [1, 1, 4]);
 assert (median (x, "omitnan"), y);
 assert (median (x, 1, "omitnan"), y);
 y = repmat ([2.05; 2.5; 1.4], [1, 1, 4]);
 assert (median (x, 2, "omitnan"), y);
 y = repmat ([NaN; NaN; 1.4], [1, 1, 4]);
 assert (median (x, 2), y);
 assert (median (x, "all"), NaN);
 assert (median (x, "all", "omitnan"), 2);
***** assert (median (cat (3, 3, 1, NaN, 2), "omitnan"), 2)
***** assert (median (cat (3, 3, 1, NaN, 2), 3, "omitnan"), 2)
***** test
 assert (median (true, "all"), logical (1));
 assert (median (false), logical (0));
 assert (median ([true false true]), true);
 assert (median ([true false true], 2), true);
 assert (median ([true false true], 1), logical ([1 0 1]));
 assert (median ([true false NaN], 1), [1 0 NaN]);
 assert (median ([true false NaN], 2), NaN);
 assert (median ([true false NaN], 2, "omitnan"), 0.5);
 assert (median ([true false NaN], 2, "omitnan", "native"), double(0.5));
***** test
 x = repmat ([1:20;6:25], [5 2 6 3]);
 assert (size (median (x, [3 2])), [10 1 1 3]);
 assert (size (median (x, [1 2])), [1 1 6 3]);
 assert (size (median (x, [1 2 4])), [1 1 6]);
 assert (size (median (x, [1 4 3])), [1 40]);
 assert (size (median (x, [1 2 3 4])), [1 1]);
***** assert (median (ones (2,2), 3), ones (2,2))
***** assert (median (ones (2,2,2), 99), ones (2,2,2))
***** assert (median (magic (3), 3), magic (3))
***** assert (median (magic (3), [1 3]), [4, 5, 6])
***** assert (median (magic (3), [1 99]), [4, 5, 6])
***** test
 x = repmat ([2 2.1 2.2 2 NaN; 3 1 2 NaN 5; 1 1.1 1.4 5 3], [1, 1, 4]);
 assert (median (x, [3 2]), [NaN NaN 1.4]');
 assert (median (x, [3 2], "omitnan"), [2.05 2.5 1.4]');
 assert (median (x, [1 3]), [2 1.1 2 NaN NaN]);
 assert (median (x, [1 3], "omitnan"), [2 1.1 2 3.5 4]);
***** assert (median (NaN), NaN)
***** assert (median (NaN, "omitnan"), NaN)
***** assert (median (NaN (2)), [NaN NaN])
***** assert (median (NaN (2), "omitnan"), [NaN NaN])
***** assert (median ([1 NaN 3]), NaN)
***** assert (median ([1 NaN 3], 1), [1 NaN 3])
***** assert (median ([1 NaN 3], 2), NaN)
***** assert (median ([1 NaN 3]'), NaN)
***** assert (median ([1 NaN 3]', 1), NaN)
***** assert (median ([1 NaN 3]', 2), [1; NaN; 3])
***** assert (median ([1 NaN 3], "omitnan"), 2)
***** assert (median ([1 NaN 3]', "omitnan"), 2)
***** assert (median ([1 NaN 3], 1, "omitnan"), [1 NaN 3])
***** assert (median ([1 NaN 3], 2, "omitnan"), 2)
***** assert (median ([1 NaN 3]', 1, "omitnan"), 2)
***** assert (median ([1 NaN 3]', 2, "omitnan"), [1; NaN; 3])
***** assert (median ([1 2 NaN 3]), NaN)
***** assert (median ([1 2 NaN 3], "omitnan"), 2)
***** assert (median ([1,2,NaN;4,5,6;NaN,8,9]), [NaN, 5, NaN])
***** assert <*64011> (median ([1,2,NaN;4,5,6;NaN,8,9], "omitnan"), [2.5, 5, 7.5], eps)
***** assert (median ([1 2 ; NaN 4]), [NaN 3])
***** assert (median ([1 2 ; NaN 4], "omitnan"), [1 3])
***** assert (median ([1 2 ; NaN 4], 1, "omitnan"), [1 3])
***** assert (median ([1 2 ; NaN 4], 2, "omitnan"), [1.5; 4], eps)
***** assert (median ([1 2 ; NaN 4], 3, "omitnan"), [1 2 ; NaN 4])
***** assert (median ([NaN 2 ; NaN 4]), [NaN 3])
***** assert (median ([NaN 2 ; NaN 4], "omitnan"), [NaN 3])
***** assert (median (ones (1, 0, 3)), NaN (1, 1, 3))
***** assert <*65405> (median ([NaN NaN], 1, "omitnan"), [NaN NaN])
***** assert <*65405> (median ([NaN NaN], 2, "omitnan"), NaN)
***** assert <*65405> (median ([NaN NaN]', 1, "omitnan"), NaN)
***** assert <*65405> (median ([NaN NaN]', 2, "omitnan"), [NaN; NaN])
***** assert <*65405> (median ([NaN NaN], "omitnan"), NaN)
***** assert <*65405> (median ([NaN NaN]', "omitnan"), NaN)
***** assert <*65405> (median (NaN(1,9), 1, "omitnan"), NaN(1,9))
***** assert <*65405> (median (NaN(1,9), 2, "omitnan"), NaN)
***** assert <*65405> (median (NaN(1,9), 3, "omitnan"), NaN(1,9))
***** assert <*65405> (median (NaN(9,1), 1, "omitnan"), NaN)
***** assert <*65405> (median (NaN(9,1), 2, "omitnan"), NaN(9,1))
***** assert <*65405> (median (NaN(9,1), 3, "omitnan"), NaN(9,1))
***** assert <*65405> (median (NaN(9,2), 1, "omitnan"), NaN(1,2))
***** assert <*65405> (median (NaN(9,2), 2, "omitnan"), NaN(9,1))
***** assert <*65405> (median (NaN(9,2), "omitnan"), NaN(1,2))
***** assert (median (NaN("single")), NaN("single"))
***** assert (median (NaN("single"), "omitnan"), NaN("single"))
***** assert (median (NaN("single"), "double"), NaN("double"))
***** assert (median (single([1 2 ; NaN 4])), single([NaN 3]))
***** assert (median (single([1 2 ; NaN 4]), "double"), double([NaN 3]))
***** assert (median (single([1 2 ; NaN 4]), "omitnan"), single([1 3]))
***** assert (median (single([1 2 ; NaN 4]), "omitnan", "double"), double([1 3]))
***** assert (median (single([NaN 2 ; NaN 4]), "double"), double([NaN 3]))
***** assert (median (single([NaN 2 ; NaN 4]), "omitnan"), single([NaN 3]))
***** assert (median (single([NaN 2 ; NaN 4]), "omitnan", "double"), double([NaN 3]))
***** test <*64011>
 x = [magic(3), magic(3)];
 x([3, 7, 11, 12, 16, 17]) = NaN;
 ynan = [NaN, 5, NaN, NaN, 5, NaN];
 yomitnan = [5.5, 5, 4.5, 8, 5, 2];
 assert (median (x), ynan);
 assert (median (x, "omitnan"), yomitnan, eps);
 assert (median (cat (3, x, x)), cat (3, ynan, ynan));
 assert (median (cat (3, x, x), "omitnan"), cat (3, yomitnan, yomitnan), eps);
***** assert (median (Inf), Inf)
***** assert (median (-Inf), -Inf)
***** assert (median ([-Inf Inf]), NaN)
***** assert (median ([3 Inf]), Inf)
***** assert (median ([3 4 Inf]), 4)
***** assert (median ([Inf 3 4]), 4)
***** assert (median ([Inf 3 Inf]), Inf)
***** assert (median ([1, 2, Inf]), 2)
***** assert (median ([1, 2, Inf, Inf]), Inf)
***** assert (median ([1, -Inf, Inf, Inf]), Inf)
***** assert (median ([-Inf, -Inf, Inf, Inf]), NaN)
***** assert (median([-Inf, Inf, Inf, Inf]), Inf)
***** assert (median([-Inf, -Inf, -Inf, Inf]), -Inf)
***** assert (median([-Inf, -Inf, -Inf, 2]), -Inf)
***** assert (median([-Inf, -Inf, 1, 2]), -Inf)
***** assert (median ([]), NaN)
***** assert (median (ones(1,0)), NaN)
***** assert (median (ones(0,1)), NaN)
***** assert (median ([], 1), NaN(1,0))
***** assert (median ([], 2), NaN(0,1))
***** assert (median ([], 3), NaN(0,0))
***** assert (median (ones(1,0), 1), NaN(1,0))
***** assert (median (ones(1,0), 2), NaN(1,1))
***** assert (median (ones(1,0), 3), NaN(1,0))
***** assert (median (ones(0,1), 1), NaN(1,1))
***** assert (median (ones(0,1), 2), NaN(0,1))
***** assert (median (ones(0,1), 3), NaN(0,1))
***** assert (median (ones(0,1,0,1), 1), NaN(1,1,0))
***** assert (median (ones(0,1,0,1), 2), NaN(0,1,0))
***** assert (median (ones(0,1,0,1), 3), NaN(0,1,1))
***** assert (median (ones(0,1,0,1), 4), NaN(0,1,0))
***** assert (median([1 3 3i 2 1i]), 2)
***** assert (median([1 2 4i; 3 2i 4]), [2, 1+1i, 2+2i])
***** shared a, b, x, y
 old_state = rand ("state");
 restore_state = onCleanup (@() rand ("state", old_state));
 rand ("state", 2);
 a = rand (2,3,4,5);
 b = rand (3,4,6,5);
 x = sort (a, 4);
 y = sort (b, 3);
***** assert <*35679> (median (a, 4), x(:, :, :, 3))
***** assert <*35679> (median (b, 3), (y(:, :, 3, :) + y(:, :, 4, :))/2)
***** shared   ## Clear shared to prevent variable echo for any later test failures
***** test
 x = ones(15,1,4);
 x([13,15],1,:) = NaN;
 assert (median (x, 1, "omitnan"), ones (1,1,4))
***** assert (median ([true, false]), true)
***** assert (median (logical ([])), false)
***** assert (median (uint8 ([1, 3])), uint8 (2))
***** assert (median (uint8 ([])), uint8 (NaN))
***** assert (median (uint8 ([NaN 10])), uint8 (5))
***** assert (median (int8 ([1, 3, 4])), int8 (3))
***** assert (median (int8 ([])), int8 (NaN))
***** assert (median (single ([1, 3, 4])), single (3))
***** assert (median (single ([1, 3, NaN])), single (NaN))
***** assert <54567> (median (uint8 ([253, 255])), uint8 (254))
***** assert <54567> (median (uint8 ([253, 254])), uint8 (254))
***** assert <54567> (median (int8 ([127, 126, 125, 124; 1 3 5 9])), ...
                  int8 ([64 65 65 67]))
***** assert <54567> (median (int8 ([127, 126, 125, 124; 1 3 5 9]), 2), ...
                  int8 ([126; 4]))
***** assert <54567> (median (int64 ([intmax("int64"), intmax("int64")-2])), ...
                  intmax ("int64") - 1)
***** assert <54567> (median ( ...
                 int64 ([intmax("int64"), intmax("int64")-2; 1 2]), 2), ...
                 int64([intmax("int64") - 1; 2]))
***** assert <54567> (median (uint64 ([intmax("uint64"), intmax("uint64")-2])), ...
                  intmax ("uint64") - 1)
***** assert <54567> (median ( ...
                 uint64 ([intmax("uint64"), intmax("uint64")-2; 1 2]), 2), ...
                 uint64([intmax("uint64") - 1; 2]))
***** assert <54567> (median (...
 [intmin('int8') intmin('int8')+5 intmax('int8')-5 intmax('int8')]), ...
 int8(-1))
***** assert <54567> (median ([int8([1 2 3 4]); ...
 intmin('int8') intmin('int8')+5 intmax('int8')-5 intmax('int8')], 2), ...
 int8([3;-1]))
***** assert <54567> (median (...
 [intmin('int64') intmin('int64')+5 intmax('int64')-5 intmax('int64')]), ...
 int64(-1))
***** assert <54567> (median ([int64([1 2 3 4]); ...
 intmin('int64') intmin('int64')+5 intmax('int64')-5 intmax('int64')], 2), ...
 int64([3;-1]))
***** assert <54567> (median ([intmax("uint64"), intmax("uint64")-2]), ...
  intmax("uint64")-1)
***** assert <54567> (median ([intmax("uint64"), intmax("uint64")-2], "default"), ...
  double(intmax("uint64")-1))
***** assert <54567> (median ([intmax("uint64"), intmax("uint64")-2], "double"), ...
  double(intmax("uint64")-1))
***** assert <54567> (median ([intmax("uint64"), intmax("uint64")-2], "native"), ...
  intmax("uint64")-1)
***** assert (median ([1 2 3], "aLL"), 2)
***** assert (median ([1 2 3], "OmitNan"), 2)
***** assert (median ([1 2 3], "DOUBle"), 2)
***** error <Invalid call> median ()
***** error <Invalid call> median (1, 2, 3)
***** error <Invalid call> median (1, 2, 3, 4)
***** error <Invalid call> median (1, "all", 3)
***** error <Invalid call> median (1, "b")
***** error <Invalid call> median (1, 1, "foo")
***** error <'all' cannot be used with> median (1, 3, "all")
***** error <'all' cannot be used with> median (1, [2 3], "all")
***** error <X must be either numeric or logical> median ({1:5})
***** error <X must be either numeric or logical> median ("char")
***** error <only one OUTTYPE can be specified> median(1, "double", "native")
***** error <DIM must be a positive integer> median (1, ones (2,2))
***** error <DIM must be a positive integer> median (1, 1.5)
***** error <DIM must be a positive integer> median (1, 0)
***** error <DIM must be a positive integer> median ([1 2 3], [-1 1])
***** error <VECDIM must contain non-repeating> median(1, [1 2 2])
159 tests, 159 passed, 0 known failure, 0 skipped
[inst/shadow9/std.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/shadow9/std.m
***** assert (std (13), 0)
***** assert (std (single (13)), single (0))
***** assert (std ([1,2,3]), 1)
***** assert (std ([1,2,3], 1), sqrt (2/3), eps)
***** assert (std ([1,2,3], [], 1), [0,0,0])
***** assert (std ([1,2,3], [], 3), [0,0,0])
***** assert (std (5, 99), 0)
***** assert (std (5, 99, 1), 0)
***** assert (std (5, 99, 2), 0)
***** assert (std ([5 3], [99 99], 2), 1)
***** assert (std ([1:7], [1:7]), sqrt (3))
***** assert (std ([eye(3)], [1:3]), sqrt ([5/36, 2/9, 1/4]), eps)
***** assert (std (ones (2,2,2), [1:2], 3), [(zeros (2,2))])
***** assert (std ([1 2; 3 4], 0, 'all'), std ([1:4]))
***** assert (std (reshape ([1:8], 2, 2, 2), 0, [1 3]), sqrt ([17/3 17/3]), eps)
***** assert (std ([1 2 3;1 2 3], [], [1 2]), sqrt (0.8), eps)
***** test
 x = [-10:10];
 y = [x;x+5;x-5];
 assert (std (x), sqrt (38.5), 1e-14);
 assert (std (y, [], 2), sqrt ([38.5; 38.5; 38.5]), 1e-14);
 assert (std (y, 0, 2), sqrt ([38.5; 38.5; 38.5]), 1e-14);
 assert (std (y, 1, 2), ones (3,1) * sqrt (36.66666666666666), 1e-14);
 assert (std (y, "all"), sqrt (54.19354838709678), 1e-14);
 y(2,4) = NaN;
 assert (std (y, "all"), NaN);
 assert (std (y, "all", "includenan"), NaN);
 assert (std (y, "all", "omitnan"), sqrt (55.01533580116342), 1e-14);
 assert (std (y, 0, 2, "includenan"), sqrt ([38.5; NaN; 38.5]), 1e-14);
 assert (std (y, [], 2), sqrt ([38.5; NaN; 38.5]), 1e-14);
 assert (std (y, [], 2, "omitnan"), ...
         sqrt ([38.5; 37.81842105263158; 38.5]), 1e-14);
***** assert (std ([4 NaN 6], [1 2 1], "omitnan"), 1, eps)
***** assert (std ([4 5 6], [1 NaN 1], "omitnan"), 1, eps)
***** assert (std (magic(3), [1 NaN 3], "omitnan"), sqrt(3)*[1 2 1], eps)
***** assert (std ([4 NaN 6], [1 2 1], "omitnan", "all"), 1, eps)
***** assert (std ([4 NaN 6], [1 2 1], "all", "omitnan"), 1, eps)
***** assert (std ([4 5 6], [1 NaN 1], "omitnan", "all"), 1, eps)
***** assert (std ([4 NaN 6], [1 2 1], 2, "omitnan"), 1, eps)
***** assert (std ([4 5 6], [1 NaN 1], 2, "omitnan"), 1, eps)
***** assert (std (magic(3), [1 NaN 3], 1, "omitnan"), sqrt(3)*[1 2 1], eps)
***** assert (std (magic(3), [1 NaN 3], 2, "omitnan"), sqrt(3)*[0.5;1;0.5], eps)
***** assert (std (4*[4 5; 6 7; 8 9], [1 3], 2, 'omitnan'), sqrt(3)*[1;1;1], eps)
***** assert (std ([4 NaN; 6 7; 8 9], [1 1 3], 1, 'omitnan'), [1.6 sqrt(3)/2], eps)
***** assert (std (4*[4 NaN; 6 7; 8 9], [1 3], 2, 'omitnan'), sqrt(3)*[0;1;1], eps)
***** assert (std (3*reshape(1:18, [3 3 2]), [1 2 3], 1, 'omitnan'), ...
          sqrt(5)*ones(1,3,2), eps)
***** assert (std (reshape(1:18, [3 3 2]), [1 2 3], 2, 'omitnan'), ...
          sqrt(5)*ones(3,1,2), eps)
***** assert (std (3*reshape(1:18, [3 3 2]), ones (3,3,2), [1 2], 'omitnan'), ...
          sqrt(60)*ones(1,1,2),eps)
***** assert (std (3*reshape(1:18, [3 3 2]), ones (3,3,2), [1 4], 'omitnan'), ...
          sqrt(6)*ones(1,3,2),eps)
***** assert (std (6*reshape(1:18, [3 3 2]), ones (3,3,2), [1:3], 'omitnan'), ...
          sqrt(969),eps)
***** test
 x = reshape(1:18, [3 3 2]);
 x([2, 14]) = NaN;
 w = ones (3,3,2);
 assert (std (16*x, w, [1:3], 'omitnan'), sqrt(6519), eps);
***** test
 x = reshape(1:18, [3 3 2]);
 w = ones (3,3,2);
 w([2, 14]) = NaN;
 assert (std (16*x, w, [1:3], 'omitnan'), sqrt(6519), eps);
***** assert (std ([1 2 3], "aLl"), 1);
***** assert (std ([1 2 3], "OmitNan"), 1);
***** assert (std ([1 2 3], "IncludeNan"), 1);
***** test
 x = repmat ([1:20;6:25], [5, 2, 6, 3]);
 assert (size (std (x, 0, [3 2])), [10, 1, 1, 3]);
 assert (size (std (x, 1, [1 2])), [1, 1, 6, 3]);
 assert (size (std (x, [], [1 2 4])), [1, 1, 6]);
 assert (size (std (x, 0, [1 4 3])), [1, 40]);
 assert (size (std (x, [], [1 2 3 4])), [1, 1]);
***** assert (std (3*magic(3)), sqrt([63 144 63]), eps)
***** assert (std (3*magic(3), 'omitnan'), sqrt([63 144 63]), eps)
***** assert (std (3*magic(3), 1), sqrt([42 96 42]), eps)
***** assert (std (3*magic(3), 1, 'omitnan'), sqrt([42 96 42]), eps)
***** assert (std (3*magic(3), ones(1,3), 1), sqrt([42 96 42]), eps)
***** assert (std (3*magic(3), ones(1,3), 1, 'omitnan'), sqrt([42 96 42]), eps)
***** assert (std (2*magic(3), [1 1 NaN], 1, 'omitnan'), [5 4 1], eps)
***** assert (std (3*magic(3), ones(3,3)), sqrt([42 96 42]), eps)
***** assert (std (3*magic(3), ones(3,3), 'omitnan'), sqrt([42 96 42]), eps)
***** assert (std (3*magic(3), [1 1 1; 1 1 1; 1 NaN 1], 'omitnan'), ...
         sqrt([42 36 42]), eps)
***** assert (std (3*magic(3), ones(3,3), 1), sqrt([42 96 42]), eps)
***** assert (std (3*magic(3), ones(3,3), 1, 'omitnan'), sqrt([42 96 42]), eps)
***** assert (std (3*magic(3), [1 1 1; 1 1 1; 1 NaN 1], 1, 'omitnan'), ...
         sqrt([42 36 42]), eps)
***** assert (std (3*magic(3), ones(3,3), [1 4]), sqrt([42 96 42]), eps)
***** assert (std (3*magic(3), ones(3,3), [1 4], 'omitnan'), sqrt([42 96 42]), eps)
***** assert (std (3*magic(3), [1 1 1; 1 1 1; 1 NaN 1],[1 4],'omitnan'), ...
         sqrt([42 36 42]), eps)
***** test
 x = repmat ([1:20;6:25], [5, 2, 6, 3]);
 v = repmat (sqrt (33.38912133891213), [10, 1, 1, 3]);
 assert (std (x, 0, [3, 2]), v, 1e-14);
 v = repmat (sqrt (33.250), [10, 1, 1, 3]);
 assert (std (x, 1, [3, 2]), v, 1e-14);
 x(2,5,6,3) = NaN;
 v(2,1,1,3) = NaN;
 assert (std (x, 1, [3, 2]), v, 1e-14);
 v = repmat (sqrt (33.38912133891213), [10 1 1 3]);
 v(2,1,1,3) = NaN;
 assert (std (x, [], [3, 2]), v, 1e-14);
 v(2,1,1,3) = sqrt (33.40177912169048);
 assert (std (x, [], [3, 2], "omitnan"), v, 1e-14);
***** assert (std (ones (2,2,2), [1:2], 3), [(zeros (2, 2))])
***** assert (std (magic (3), [1:9], "all"), 2.581988897471611, 1e-14)
***** assert (std (ones (2,2), [], 3), zeros (2,2))
***** assert (std (ones (2,2,2), [], 99), zeros (2,2,2))
***** assert (std (magic (3), [], 3), zeros (3,3))
***** assert (std (magic (3), [], 1), sqrt ([7, 16, 7]))
***** assert (std (magic (3), [], [1 3]), sqrt ([7, 16, 7]))
***** assert (std (magic (3), [], [1 99]), sqrt ([7, 16, 7]))
***** assert (std ([]), NaN)
***** assert (class (var (single ([]))), "single")
***** assert (std ([],[],1), NaN(1,0))
***** assert (std ([],[],2), NaN(0,1))
***** assert (std ([],[],3), [])
***** assert (class (var (single ([]), [], 1)), "single")
***** assert (std (ones (1,0)), NaN)
***** assert (std (ones (1,0), [], 1), NaN(1,0))
***** assert (std (ones (1,0), [], 2), NaN)
***** assert (std (ones (1,0), [], 3), NaN(1,0))
***** assert (class (var (ones (1, 0, "single"), [], 1)), "single")
***** assert (std (ones (0,1)), NaN)
***** assert (std (ones (0,1), [], 1), NaN)
***** assert (std (ones (0,1), [], 2), NaN(0,1))
***** assert (std (ones (0,1), [], 3), NaN(0,1))
***** assert (std (ones (1,3,0,2)), NaN(1,1,0,2))
***** assert (std (ones (1,3,0,2), [], 1), NaN(1,3,0,2))
***** assert (std (ones (1,3,0,2), [], 2), NaN(1,1,0,2))
***** assert (std (ones (1,3,0,2), [], 3), NaN(1,3,1,2))
***** assert (std (ones (1,3,0,2), [], 4), NaN(1,3,0))
***** test
 [~, m] = std ([]);
 assert (m, NaN);
***** test <*62395>
 [~, m] = std (13);
 assert (m, 13);
 [~, m] = std (single(13));
 assert (m, single(13));
 [~, m] = std ([1, 2, 3; 3 2 1], []);
 assert (m, [2 2 2]);
 [~, m] = std ([1, 2, 3; 3 2 1], [], 1);
 assert (m, [2 2 2]);
 [~, m] = std ([1, 2, 3; 3 2 1], [], 2);
 assert (m, [2 2]');
 [~, m] = std ([1, 2, 3; 3 2 1], [], 3);
 assert (m, [1 2 3; 3 2 1]);
***** test <*62395>
 [~, m] = std (5,99);
 assert (m, 5);
 [~, m] = std ([1:7], [1:7]);
 assert (m, 5);
 [~, m] = std ([eye(3)], [1:3]);
 assert (m, [1/6, 1/3, 0.5], eps);
 [~, m] = std (ones (2,2,2), [1:2], 3);
 assert (m, ones (2,2));
 [~, m] = std ([1 2; 3 4], 0, 'all');
 assert (m, 2.5, eps);
 [~, m] = std (reshape ([1:8], 2, 2, 2), 0, [1 3]);
 assert (m, [3.5, 5.5], eps);
***** test
 [v, m] = std (4 * eye (2), [1, 3]);
 assert (v, sqrt ([3, 3]), 1e-14);
 assert (m, [1, 3]);
***** test <*62395>
 [~, m] = std ([]);
 assert (m, NaN);
***** test
 x = repmat ([1:20;6:25], [5, 2, 6, 3]);
 [~, m] = std (x, 0, [3 2]);
 assert (m, mean (x, [3 2]));
 [~, m] = std (x, 0, [1 2]);
 assert (m, mean (x, [1 2]));
 [~, m] = std (x, 0, [1 3 4]);
 assert (m, mean (x, [1 3 4]));
***** test
 x = repmat ([1:20;6:25], [5, 2, 6, 3]);
 x(2,5,6,3) = NaN;
 [~, m] = std (x, 0, [3 2], "omitnan");
 assert (m, mean (x, [3 2], "omitnan"));
***** test <*63203>
 [v, m] = std (Inf);
 assert (v, NaN);
 assert (m, Inf);
***** test <*63203>
 [v, m] = std (NaN);
 assert (v, NaN);
 assert (m, NaN);
***** test <*63203>
 [v, m] = std ([1, Inf, 3]);
 assert (v, NaN);
 assert (m, Inf);
***** test <*63203>
 [v, m] = std ([1, Inf, 3]');
 assert (v, NaN);
 assert (m, Inf);
***** test <*63203>
 [v, m] = std ([1, NaN, 3]);
 assert (v, NaN);
 assert (m, NaN);
***** test <*63203>
 [v, m] = std ([1, NaN, 3]');
 assert (v, NaN);
 assert (m, NaN);
***** test <*63203>
 [v, m] = std ([1, Inf, 3], [], 1);
 assert (v, [0, NaN, 0]);
 assert (m, [1, Inf, 3]);
***** test <*63203>
 [v, m] = std ([1, Inf, 3], [], 2);
 assert (v, NaN);
 assert (m, Inf);
***** test <*63203>
 [v, m] = std ([1, Inf, 3], [], 3);
 assert (v, [0, NaN, 0]);
 assert (m, [1, Inf, 3]);
***** test <*63203>
 [v, m] = std ([1, NaN, 3], [], 1);
 assert (v, [0, NaN, 0]);
 assert (m, [1, NaN, 3]);
***** test <*63203>
 [v, m] = std ([1, NaN, 3], [], 2);
 assert (v, NaN);
 assert (m, NaN);
***** test <*63203>
 [v, m] = std ([1, NaN, 3], [], 3);
 assert (v, [0, NaN, 0]);
 assert (m, [1, NaN, 3]);
***** test <*63203>
 [v, m] = std ([1, 2, 3; 3, Inf, 5]);
 assert (v, sqrt ([2, NaN, 2]));
 assert (m, [2, Inf, 4]);
***** test <*63203>
 [v, m] = std ([1, Inf, 3; 3, Inf, 5]);
 assert (v, sqrt ([2, NaN, 2]));
 assert (m, [2, Inf, 4]);
***** test <*63203>
 [v, m] = std ([1, 2, 3; 3, NaN, 5]);
 assert (v, sqrt ([2, NaN, 2]));
 assert (m, [2, NaN, 4]);
***** test <*63203>
 [v, m] = std ([1, NaN, 3; 3, NaN, 5]);
 assert (v, sqrt ([2, NaN, 2]));
 assert (m, [2, NaN, 4]);
***** test <*63203>
 [v, m] = std ([Inf, 2, NaN]);
 assert (v, NaN);
 assert (m, NaN);
***** test <*63203>
 [v, m] = std ([Inf, 2, NaN]');
 assert (v, NaN);
 assert (m, NaN);
***** test <*63203>
 [v, m] = std ([NaN, 2, Inf]);
 assert (v, NaN);
 assert (m, NaN);
***** test <*63203>
 [v, m] = std ([NaN, 2, Inf]');
 assert (v, NaN);
 assert (m, NaN);
***** test <*63203>
 [v, m] = std ([Inf, 2, NaN], [], 1);
 assert (v, [NaN, 0, NaN]);
 assert (m, [Inf, 2, NaN]);
***** test <*63203>
 [v, m] = std ([Inf, 2, NaN], [], 2);
 assert (v, NaN);
 assert (m, NaN);
***** test <*63203>
 [v, m] = std ([NaN, 2, Inf], [], 1);
 assert (v, [NaN, 0, NaN]);
 assert (m, [NaN, 2, Inf]);
***** test <*63203>
 [v, m] = std ([NaN, 2, Inf], [], 2);
 assert (v, NaN);
 assert (m, NaN);
***** test <*63203>
 [v, m] = std ([1, 3, NaN; 3, 5, Inf]);
 assert (v, sqrt ([2, 2, NaN]));
 assert (m, [2, 4, NaN]);
***** test <*63203>
 [v, m] = std ([1, 3, Inf; 3, 5, NaN]);
 assert (v, sqrt ([2, 2, NaN]));
 assert (m, [2, 4, NaN]);
***** test <*63291>
 [v, m] = std (2 * eye (2));
 assert (v, sqrt ([2, 2]));
 assert (m, [1, 1]);
***** test <*63291>
 [v, m] = std (4 * eye (2), [1, 3]);
 assert (v, sqrt ([3, 3]));
 assert (m, [1, 3]);
***** test <*63291>
 [v, m] = std (sparse (2 * eye (2)));
 assert (full (v), sqrt ([2, 2]));
 assert (full (m), [1, 1]);
***** test <*63291>
 [v, m] = std (sparse (4 * eye (2)), [1, 3]);
 assert (full (v), sqrt ([3, 3]));
 assert (full (m), [1, 3]);
***** test <*63291>
 [v, m] = std (sparse (eye (2)));
 assert (issparse (v));
 assert (issparse (m));
***** test <*63291>
 [v, m] = std (sparse (eye (2)), [1, 3]);
 assert (issparse (v));
 assert (issparse (m));
***** error <Invalid call> std ()
***** error <Invalid call> std (1, 2, "omitnan", 3)
***** error <Invalid call> std (1, 2, 3, 4)
***** error <Invalid call> std (1, 2, 3, 4, 5)
***** error <Invalid call> std (1, "foo")
***** error <Invalid call> std (1, [], "foo")
***** error <normalization scalar must be either 0 or 1> std ([1 2 3], 2)
***** error <normalization scalar must be either 0 or 1> std ([1 2], 2, "all")
***** error <normalization scalar must be either 0 or 1> std ([1 2],0.5, "all")
***** error <weights must not contain any negative values> std (1, -1)
***** error <weights must not contain any negative values> std (1, [1 -1])
***** error <weights must not contain any negative values> ...
 std ([1 2 3], [1 -1 0])
***** error <X must be a numeric vector or matrix> std ({1:5})
***** error <X must be a numeric vector or matrix> std ("char")
***** error <X must be a numeric vector or matrix> std (['A'; 'B'])
***** error <DIM must be a positive integer> std (1, [], ones (2,2))
***** error <DIM must be a positive integer> std (1, 0, 1.5)
***** error <DIM must be a positive integer> std (1, [], 0)
***** error <DIM must be a positive integer> std (1, [], 1.5)
***** error <DIM must be a positive integer> std ([1 2 3], [], [-1 1])
***** error <VECDIM must contain non-repeating positive integers> ...
 std (repmat ([1:20;6:25], [5 2 6 3]), 0, [1 2 2 2])
***** error <weight matrix or array does not match X in size> ...
 std ([1 2], eye (2))
***** error <weight matrix or array does not match X in size> ...
 std ([1 2 3 4], [1 2; 3 4])
***** error <weight matrix or array does not match X in size> ...
 std ([1 2 3 4], [1 2; 3 4], 1)
***** error <weight matrix or array does not match X in size> ...
 std ([1 2 3 4], [1 2; 3 4], [2 3])
***** error <weight matrix or array does not match X in size> ...
 std (ones (2, 2), [1 2], [1 2])
***** error <weight matrix or array does not match X in size> ...
 std ([1 2 3 4; 5 6 7 8], [1 2 1 2 1; 1 2 1 2 1], 1)
***** error <weight matrix or array does not match X in size> ...
 std (repmat ([1:20;6:25], [5 2 6 3]), repmat ([1:20;6:25], [5 2 3]), [2 3])
***** error <weight vector length does not match> std ([1 2 3; 2 3 4], [1 3 4])
***** error <weight vector length does not match> std ([1 2], [1 2 3])
***** error <weight vector length does not match> std (1, [1 2])
***** error <weight vector length does not match> std ([1 2 3; 2 3 4], [1 3 4], 1)
***** error <weight vector length does not match> std ([1 2 3; 2 3 4], [1 3], 2)
***** error <weight vector length does not match> std ([1 2], [1 2], 1)
***** error <'all' flag cannot be used with DIM or VECDIM options> ...
 std (1, [], 1, "all")
***** error <weight vector element count does not match X> ...
 std ([1 2 3; 2 3 4], [1 3], "all")
***** error <weight matrix or array does not match X in size> ...
 std (repmat ([1:20;6:25], [5 2 6 3]), repmat ([1:20;6:25], [5 2 3]), "all")
162 tests, 162 passed, 0 known failure, 0 skipped
[inst/shadow9/var.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/shadow9/var.m
***** assert (var (13), 0)
***** assert (var (single (13)), single (0))
***** assert (var ([1,2,3]), 1)
***** assert (var ([1,2,3], 1), 2/3, eps)
***** assert (var ([1,2,3], [], 1), [0,0,0])
***** assert (var ([1,2,3], [], 3), [0,0,0])
***** assert (var (5, 99), 0)
***** assert (var (5, 99, 1), 0)
***** assert (var (5, 99, 2), 0)
***** assert (var ([5 3], [99 99], 2), 1)
***** assert (var ([1:7], [1:7]), 3)
***** assert (var ([eye(3)], [1:3]), [5/36, 2/9, 1/4], eps)
***** assert (var (ones (2,2,2), [1:2], 3), [(zeros (2,2))])
***** assert (var ([1 2; 3 4], 0, 'all'), var ([1:4]))
***** assert (var (reshape ([1:8], 2, 2, 2), 0, [1 3]), [17/3 17/3], eps)
***** assert (var ([1 2 3;1 2 3], [], [1 2]), 0.8, eps)
***** test
 x = [-10:10];
 y = [x;x+5;x-5];
 assert (var (x), 38.5);
 assert (var (y, [], 2), [38.5; 38.5; 38.5]);
 assert (var (y, 0, 2), [38.5; 38.5; 38.5]);
 assert (var (y, 1, 2), ones (3,1) * 36.66666666666666, 1e-14);
 assert (var (y, "all"), 54.19354838709678, 1e-14);
 y(2,4) = NaN;
 assert (var (y, "all"), NaN);
 assert (var (y, "all", "includenan"), NaN);
 assert (var (y, "all", "omitnan"), 55.01533580116342, 1e-14);
 assert (var (y, 0, 2, "includenan"), [38.5; NaN; 38.5]);
 assert (var (y, [], 2), [38.5; NaN; 38.5]);
 assert (var (y, [], 2, "omitnan"), [38.5; 37.81842105263158; 38.5], 1e-14);
***** assert (var ([1 NaN 3], [1 2 3], "omitnan"), 0.75, eps)
***** assert (var ([1 2 3], [1 NaN 3], "omitnan"), 0.75, eps)
***** assert (var (magic(3), [1 NaN 3], "omitnan"), [3 12 3], eps)
***** assert (var ([1 NaN 3], [1 2 3], "omitnan", "all"), 0.75, eps)
***** assert (var ([1 NaN 3], [1 2 3], "all", "omitnan"), 0.75, eps)
***** assert (var ([1 2 3], [1 NaN 3], "omitnan", "all"), 0.75, eps)
***** assert (var ([1 NaN 3], [1 2 3], 2, "omitnan"), 0.75, eps)
***** assert (var ([1 2 3], [1 NaN 3], 2, "omitnan"), 0.75, eps)
***** assert (var (magic(3), [1 NaN 3], 1, "omitnan"), [3 12 3], eps)
***** assert (var (magic(3), [1 NaN 3], 2, "omitnan"), [0.75;3;0.75], eps)
***** assert (var ([4 4; 4 6; 6 6], [1 3], 2, 'omitnan'), [0;0.75;0], eps)
***** assert (var ([4 NaN; 4 6; 6 6], [1 2 3], 1, 'omitnan'), [1 0])
***** assert (var ([4 NaN; 4 6; 6 6], [1 3], 2, 'omitnan'), [0;0.75;0], eps)
***** assert (var (3*reshape(1:18, [3 3 2]), [1 2 3], 1, 'omitnan'), ones(1,3,2)*5)
***** assert (var (reshape(1:18, [3 3 2]), [1 2 3], 2, 'omitnan'), 5*ones(3,1,2))
***** assert (var (3*reshape(1:18, [3 3 2]), ones (3,3,2), [1 2], 'omitnan'), ...
         60 * ones(1,1,2))
***** assert (var (3*reshape(1:18, [3 3 2]), ones (3,3,2), [1 4], 'omitnan'), ...
         6 * ones(1,3,2))
***** assert (var (6*reshape(1:18, [3 3 2]), ones (3,3,2), [1:3], 'omitnan'), 969)
***** test
 x = reshape(1:18, [3 3 2]);
 x([2, 14]) = NaN;
 w = ones (3,3,2);
 assert (var (16*x, w, [1:3], 'omitnan'), 6519);
***** test
 x = reshape(1:18, [3 3 2]);
 w = ones (3,3,2);
 w([2, 14]) = NaN;
 assert (var (16*x, w, [1:3], 'omitnan'), 6519);
***** assert (var ([1 2 3], "aLl"), 1);
***** assert (var ([1 2 3], "OmitNan"), 1);
***** assert (var ([1 2 3], "IncludeNan"), 1);
***** test
 x = repmat ([1:20;6:25], [5, 2, 6, 3]);
 assert (size (var (x, 0, [3 2])), [10, 1, 1, 3]);
 assert (size (var (x, 1, [1 2])), [1, 1, 6, 3]);
 assert (size (var (x, [], [1 2 4])), [1, 1, 6]);
 assert (size (var (x, 0, [1 4 3])), [1, 40]);
 assert (size (var (x, [], [1 2 3 4])), [1, 1]);
***** assert (var (3*magic(3)), [63 144 63])
***** assert (var (3*magic(3), 'omitnan'), [63 144 63])
***** assert (var (3*magic(3), 1), [42 96 42])
***** assert (var (3*magic(3), 1, 'omitnan'), [42 96 42])
***** assert (var (3*magic(3), ones(1,3), 1), [42 96 42])
***** assert (var (3*magic(3), ones(1,3), 1, 'omitnan'), [42 96 42])
***** assert (var (2*magic(3), [1 1 NaN], 1, 'omitnan'), [25 16 1])
***** assert (var (3*magic(3), ones(3,3)), [42 96 42])
***** assert (var (3*magic(3), ones(3,3), 'omitnan'), [42 96 42])
***** assert (var (3*magic(3), [1 1 1; 1 1 1; 1 NaN 1], 'omitnan'), [42 36 42])
***** assert (var (3*magic(3), ones(3,3), 1), [42 96 42])
***** assert (var (3*magic(3), ones(3,3), 1, 'omitnan'), [42 96 42])
***** assert (var (3*magic(3), [1 1 1; 1 1 1; 1 NaN 1], 1, 'omitnan'), [42 36 42])
***** assert (var (3*magic(3), ones(3,3), [1 4]), [42 96 42])
***** assert (var (3*magic(3), ones(3,3), [1 4], 'omitnan'), [42 96 42])
***** assert (var (3*magic(3), [1 1 1; 1 1 1; 1 NaN 1],[1 4],'omitnan'), [42 36 42])
***** test
 x = repmat ([1:20;6:25], [5, 2, 6, 3]);
 v = repmat (33.38912133891213, [10, 1, 1, 3]);
 assert (var (x, 0, [3, 2]), v, 1e-14);
 v = repmat (33.250, [10, 1, 1, 3]);
 assert (var (x, 1, [3, 2]), v, 1e-14);
 x(2,5,6,3) = NaN;
 v(2,1,1,3) = NaN;
 assert (var (x, 1, [3, 2]), v, 4e-14);
 v = repmat (33.38912133891213, [10 1 1 3]);
 v(2,1,1,3) = NaN;
 assert (var (x, [], [3, 2]), v, 4e-14);
 v(2,1,1,3) = 33.40177912169048;
 assert (var (x, [], [3, 2], "omitnan"), v, 4e-14);
***** assert (var (ones (2,2,2), [1:2], 3), [(zeros (2, 2))])
***** assert (var (magic (3), [1:9], "all"), 6.666666666666667, 1e-14)
***** assert (var (ones (2,2), [], 3), zeros (2,2))
***** assert (var (ones (2,2,2), [], 99), zeros (2,2,2))
***** assert (var (magic (3), [], 3), zeros (3,3))
***** assert (var (magic (3), [], 1), [7, 16, 7])
***** assert (var (magic (3), [], [1 3]), [7, 16, 7])
***** assert (var (magic (3), [], [1 99]), [7, 16, 7])
***** assert (var ([]), NaN)
***** assert (class (var (single ([]))), "single")
***** assert (var ([],[],1), NaN(1,0))
***** assert (var ([],[],2), NaN(0,1))
***** assert (var ([],[],3), [])
***** assert (class (var (single ([]), [], 1)), "single")
***** assert (var (ones (1,0)), NaN)
***** assert (var (ones (1,0), [], 1), NaN(1,0))
***** assert (var (ones (1,0), [], 2), NaN)
***** assert (var (ones (1,0), [], 3), NaN(1,0))
***** assert (class (var (ones (1, 0, "single"), [], 1)), "single")
***** assert (var (ones (0,1)), NaN)
***** assert (var (ones (0,1), [], 1), NaN)
***** assert (var (ones (0,1), [], 2), NaN(0,1))
***** assert (var (ones (0,1), [], 3), NaN(0,1))
***** assert (var (ones (1,3,0,2)), NaN(1,1,0,2))
***** assert (var (ones (1,3,0,2), [], 1), NaN(1,3,0,2))
***** assert (var (ones (1,3,0,2), [], 2), NaN(1,1,0,2))
***** assert (var (ones (1,3,0,2), [], 3), NaN(1,3,1,2))
***** assert (var (ones (1,3,0,2), [], 4), NaN(1,3,0))
***** test
 [~, m] = var ([]);
 assert (m, NaN);
***** test <*62395>
 [~, m] = var (13);
 assert (m, 13);
 [~, m] = var (single(13));
 assert (m, single(13));
 [~, m] = var ([1, 2, 3; 3 2 1], []);
 assert (m, [2 2 2]);
 [~, m] = var ([1, 2, 3; 3 2 1], [], 1);
 assert (m, [2 2 2]);
 [~, m] = var ([1, 2, 3; 3 2 1], [], 2);
 assert (m, [2 2]');
 [~, m] = var ([1, 2, 3; 3 2 1], [], 3);
 assert (m, [1 2 3; 3 2 1]);
***** test <*62395>
 [~, m] = var (5,99);
 assert (m, 5);
 [~, m] = var ([1:7], [1:7]);
 assert (m, 5);
 [~, m] = var ([eye(3)], [1:3]);
 assert (m, [1/6, 1/3, 0.5], eps);
 [~, m] = var (ones (2,2,2), [1:2], 3);
 assert (m, ones (2,2));
 [~, m] = var ([1 2; 3 4], 0, 'all');
 assert (m, 2.5, eps);
 [~, m] = var (reshape ([1:8], 2, 2, 2), 0, [1 3]);
 assert (m, [3.5, 5.5], eps);
***** test
 [v, m] = var (4 * eye (2), [1, 3]);
 assert (v, [3, 3]);
 assert (m, [1, 3]);
***** test <*62395>
 [~, m] = var ([]);
 assert (m, NaN);
***** test <*62395>
 x = repmat ([1:20;6:25], [5, 2, 6, 3]);
 [~, m] = var (x, 0, [3 2]);
 assert (m, mean (x, [3 2]));
 [~, m] = var (x, 0, [1 2]);
 assert (m, mean (x, [1 2]));
 [~, m] = var (x, 0, [1 3 4]);
 assert (m, mean (x, [1 3 4]));
***** test
 x = repmat ([1:20;6:25], [5, 2, 6, 3]);
 x(2,5,6,3) = NaN;
 [~, m] = var (x, 0, [3 2], "omitnan");
 assert (m, mean (x, [3 2], "omitnan"));
***** test <*63203>
 [v, m] = var (Inf);
 assert (v, NaN);
 assert (m, Inf);
***** test <*63203>
 [v, m] = var (NaN);
 assert (v, NaN);
 assert (m, NaN);
***** test <*63203>
 [v, m] = var ([1, Inf, 3]);
 assert (v, NaN);
 assert (m, Inf);
***** test <*63203>
 [v, m] = var ([1, Inf, 3]');
 assert (v, NaN);
 assert (m, Inf);
***** test <*63203>
 [v, m] = var ([1, NaN, 3]);
 assert (v, NaN);
 assert (m, NaN);
***** test <*63203>
 [v, m] = var ([1, NaN, 3]');
 assert (v, NaN);
 assert (m, NaN);
***** test <*63203>
 [v, m] = var ([1, Inf, 3], [], 1);
 assert (v, [0, NaN, 0]);
 assert (m, [1, Inf, 3]);
***** test <*63203>
 [v, m] = var ([1, Inf, 3], [], 2);
 assert (v, NaN);
 assert (m, Inf);
***** test <*63203>
 [v, m] = var ([1, Inf, 3], [], 3);
 assert (v, [0, NaN, 0]);
 assert (m, [1, Inf, 3]);
***** test <*63203>
 [v, m] = var ([1, NaN, 3], [], 1);
 assert (v, [0, NaN, 0]);
 assert (m, [1, NaN, 3]);
***** test <*63203>
 [v, m] = var ([1, NaN, 3], [], 2);
 assert (v, NaN);
 assert (m, NaN);
***** test <*63203>
 [v, m] = var ([1, NaN, 3], [], 3);
 assert (v, [0, NaN, 0]);
 assert (m, [1, NaN, 3]);
***** test <*63203>
 [v, m] = var ([1, 2, 3; 3, Inf, 5]);
 assert (v, [2, NaN, 2]);
 assert (m, [2, Inf, 4]);
***** test <*63203>
 [v, m] = var ([1, Inf, 3; 3, Inf, 5]);
 assert (v, [2, NaN, 2]);
 assert (m, [2, Inf, 4]);
***** test <*63203>
 [v, m] = var ([1, 2, 3; 3, NaN, 5]);
 assert (v, [2, NaN, 2]);
 assert (m, [2, NaN, 4]);
***** test <*63203>
 [v, m] = var ([1, NaN, 3; 3, NaN, 5]);
 assert (v, [2, NaN, 2]);
 assert (m, [2, NaN, 4]);
***** test <*63203>
 [v, m] = var ([Inf, 2, NaN]);
 assert (v, NaN);
 assert (m, NaN);
***** test <*63203>
 [v, m] = var ([Inf, 2, NaN]');
 assert (v, NaN);
 assert (m, NaN);
***** test <*63203>
 [v, m] = var ([NaN, 2, Inf]);
 assert (v, NaN);
 assert (m, NaN);
***** test <*63203>
 [v, m] = var ([NaN, 2, Inf]');
 assert (v, NaN);
 assert (m, NaN);
***** test <*63203>
 [v, m] = var ([Inf, 2, NaN], [], 1);
 assert (v, [NaN, 0, NaN]);
 assert (m, [Inf, 2, NaN]);
***** test <*63203>
 [v, m] = var ([Inf, 2, NaN], [], 2);
 assert (v, NaN);
 assert (m, NaN);
***** test <*63203>
 [v, m] = var ([NaN, 2, Inf], [], 1);
 assert (v, [NaN, 0, NaN]);
 assert (m, [NaN, 2, Inf]);
***** test <*63203>
 [v, m] = var ([NaN, 2, Inf], [], 2);
 assert (v, NaN);
 assert (m, NaN);
***** test <*63203>
 [v, m] = var ([1, 3, NaN; 3, 5, Inf]);
 assert (v, [2, 2, NaN]);
 assert (m, [2, 4, NaN]);
***** test <*63203>
 [v, m] = var ([1, 3, Inf; 3, 5, NaN]);
 assert (v, [2, 2, NaN]);
 assert (m, [2, 4, NaN]);
***** test <*63291>
 [v, m] = var (2 * eye (2));
 assert (v, [2, 2]);
 assert (m, [1, 1]);
***** test <*63291>
 [v, m] = var (4 * eye (2), [1, 3]);
 assert (v, [3, 3]);
 assert (m, [1, 3]);
***** test <*63291>
 [v, m] = var (sparse (2 * eye (2)));
 assert (full (v), [2, 2]);
 assert (full (m), [1, 1]);
***** test <*63291>
 [v, m] = var (sparse (4 * eye (2)), [1, 3]);
 assert (full (v), [3, 3]);
 assert (full (m), [1, 3]);
***** test<*63291>
 [v, m] = var (sparse (eye (2)));
 assert (issparse (v));
 assert (issparse (m));
***** test<*63291>
 [v, m] = var (sparse (eye (2)), [1, 3]);
 assert (issparse (v));
 assert (issparse (m));
***** error <Invalid call> var ()
***** error <Invalid call> var (1, 2, "omitnan", 3)
***** error <Invalid call> var (1, 2, 3, 4)
***** error <Invalid call> var (1, 2, 3, 4, 5)
***** error <Invalid call> var (1, "foo")
***** error <Invalid call> var (1, [], "foo")
***** error <normalization scalar must be either 0 or 1> var ([1 2 3], 2)
***** error <normalization scalar must be either 0 or 1> var ([1 2], 2, "all")
***** error <normalization scalar must be either 0 or 1> var ([1 2],0.5, "all")
***** error <weights must not contain any negative values> var (1, -1)
***** error <weights must not contain any negative values> var (1, [1 -1])
***** error <weights must not contain any negative values> ...
 var ([1 2 3], [1 -1 0])
***** error <X must be a numeric vector or matrix> var ({1:5})
***** error <X must be a numeric vector or matrix> var ("char")
***** error <X must be a numeric vector or matrix> var (['A'; 'B'])
***** error <DIM must be a positive integer> var (1, [], ones (2,2))
***** error <DIM must be a positive integer> var (1, 0, 1.5)
***** error <DIM must be a positive integer> var (1, [], 0)
***** error <DIM must be a positive integer> var (1, [], 1.5)
***** error <DIM must be a positive integer> var ([1 2 3], [], [-1 1])
***** error <VECDIM must contain non-repeating positive integers> ...
 var (repmat ([1:20;6:25], [5 2 6 3]), 0, [1 2 2 2])
***** error <weight matrix or array does not match X in size> ...
 var ([1 2], eye (2))
***** error <weight matrix or array does not match X in size> ...
 var ([1 2 3 4], [1 2; 3 4])
***** error <weight matrix or array does not match X in size> ...
 var ([1 2 3 4], [1 2; 3 4], 1)
***** error <weight matrix or array does not match X in size> ...
 var ([1 2 3 4], [1 2; 3 4], [2 3])
***** error <weight matrix or array does not match X in size> ...
 var (ones (2, 2), [1 2], [1 2])
***** error <weight matrix or array does not match X in size> ...
 var ([1 2 3 4; 5 6 7 8], [1 2 1 2 1; 1 2 1 2 1], 1)
***** error <weight matrix or array does not match X in size> ...
 var (repmat ([1:20;6:25], [5 2 6 3]), repmat ([1:20;6:25], [5 2 3]), [2 3])
***** error <weight vector length does not match> var ([1 2 3; 2 3 4], [1 3 4])
***** error <weight vector length does not match> var ([1 2], [1 2 3])
***** error <weight vector length does not match> var (1, [1 2])
***** error <weight vector length does not match> var ([1 2 3; 2 3 4], [1 3 4], 1)
***** error <weight vector length does not match> var ([1 2 3; 2 3 4], [1 3], 2)
***** error <weight vector length does not match> var ([1 2], [1 2], 1)
***** error <'all' flag cannot be used with DIM or VECDIM options> ...
 var (1, [], 1, "all")
***** error <weight vector element count does not match X> ...
 var ([1 2 3; 2 3 4], [1 3], "all")
***** error <weight matrix or array does not match X in size> ...
 var (repmat ([1:20;6:25], [5 2 6 3]), repmat ([1:20;6:25], [5 2 3]), "all")
162 tests, 162 passed, 0 known failure, 0 skipped
[inst/shadow9/mean.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/shadow9/mean.m
***** test
 x = -10:10;
 y = x';
 z = [y, y+10];
 assert (mean (x), 0);
 assert (mean (y), 0);
 assert (mean (z), [0, 10]);
***** assert (mean (magic (3), 1), [5, 5, 5])
***** assert (mean (magic (3), 2), [5; 5; 5])
***** assert (mean (logical ([1 0 1 1])), 0.75)
***** assert (mean (single ([1 0 1 1])), single (0.75))
***** assert (mean ([1 2], 3), [1 2])
***** test
 in = [1 2 3];
 out = 2;
 assert (mean (in, "default"), mean (in));
 assert (mean (in, "default"), out);
 assert (mean (in, "double"), out);
 assert (mean (in, "native"), out);
***** test
 in = single ([1 2 3]);
 out = 2;
 assert (mean (in, "default"), mean (in));
 assert (mean (in, "default"), single (out));
 assert (mean (in, "double"), out);
 assert (mean (in, "native"), single (out));
***** test
 in = logical ([1 0 1]);
 out = 2/3;
 assert (mean (in, "default"), mean (in), eps);
 assert (mean (in, "default"), out, eps);
 assert (mean (in, "double"), out, eps);
 assert (mean (in, "native"), out, eps);
***** test
 in = char ("ab");
 out = 97.5;
 assert (mean (in, "default"), mean (in), eps);
 assert (mean (in, "default"), out, eps);
 assert (mean (in, "double"), out, eps);
***** test
 in = uint8 ([1 2 3]);
 out = 2;
 assert (mean (in, "default"), mean (in));
 assert (mean (in, "default"), out);
 assert (mean (in, "double"), out);
 assert (mean (in, "native"), uint8 (out));
***** test
 in = uint8 ([0 1 2 3]);
 out = 1.5;
 out_u8 = 2;
 assert (mean (in, "default"), mean (in), eps);
 assert (mean (in, "default"), out, eps);
 assert (mean (in, "double"), out, eps);
 assert (mean (in, "native"), uint8 (out_u8));
 assert (class (mean (in, "native")), "uint8");
***** test # internal sum exceeding intmax
 in = uint8 ([3 141 141 255]);
 out = 135;
 assert (mean (in, "default"), mean (in));
 assert (mean (in, "default"), out);
 assert (mean (in, "double"), out);
 assert (mean (in, "native"), uint8 (out));
 assert (class (mean (in, "native")), "uint8");
***** test # fractional answer with internal sum exceeding intmax
 in = uint8 ([1 141 141 255]);
 out = 134.5;
 out_u8 = 135;
 assert (mean (in, "default"), mean (in));
 assert (mean (in, "default"), out);
 assert (mean (in, "double"), out);
 assert (mean (in, "native"), uint8 (out_u8));
 assert (class (mean (in, "native")), "uint8");
***** test <54567> # large int64 sum exceeding intmax and double precision limit
 in_same = uint64 ([intmax("uint64") intmax("uint64")-2]);
 out_same = intmax ("uint64")-1;
 in_opp = int64 ([intmin("int64"), intmax("int64")-1]);
 out_opp = -1;
 in_neg = int64 ([intmin("int64") intmin("int64")+2]);
 out_neg = intmin ("int64")+1;

 ## both positive
 assert (mean (in_same, "default"), mean (in_same));
 assert (mean (in_same, "default"), double (out_same));
 assert (mean (in_same, "double"), double (out_same));
 assert (mean (in_same, "native"), uint64 (out_same));
 assert (class (mean (in_same, "native")), "uint64");

 ## opposite signs
 assert (mean (in_opp, "default"), mean (in_opp));
 assert (mean (in_opp, "default"), double (out_opp));
 assert (mean (in_opp, "double"), double (out_opp));
 assert (mean (in_opp, "native"), int64 (out_opp));
 assert (class (mean (in_opp, "native")), "int64");

 ## both negative
 assert (mean (in_neg, "default"), mean (in_neg));
 assert (mean (in_neg, "default"), double(out_neg));
 assert (mean (in_neg, "double"), double(out_neg));
 assert (mean (in_neg, "native"), int64(out_neg));
 assert (class (mean (in_neg, "native")), "int64");
***** test <54567>
 in = [(intmin('int64')+5), (intmax('int64'))-5];
 assert (mean (in, "native"), int64(-1));
 assert (class (mean (in, "native")), "int64");
 assert (mean (double(in)), double(0) );
 assert (mean (in), double(-0.5) );
 assert (mean (in, "default"), double(-0.5) );
 assert (mean (in, "double"), double(-0.5) );
 assert (mean (in, "all", "native"), int64(-1));
 assert (mean (in, 2, "native"), int64(-1));
 assert (mean (in, [1 2], "native"), int64(-1));
 assert (mean (in, [2 3], "native"), int64(-1));
 assert (mean ([intmin("int64"), in, intmax("int64")]), double(-0.5))
 assert (mean ([in; int64([1 3])], 2, "native"), int64([-1; 2]));
***** test
 x = [-10:10];
 y = [x;x+5;x-5];
 assert (mean (x), 0);
 assert (mean (y, 2), [0, 5, -5]');
 assert (mean (y, "all"), 0);
 y(2,4) = NaN;
 assert (mean (y', "omitnan"), [0 5.35 -5]);
 z = y + 20;
 assert (mean (z, "all"), NaN);
 assert (mean (z, "all", "includenan"), NaN);
 assert (mean (z, "all", "omitnan"), 20.03225806451613, 4e-14);
 m = [20 NaN 15];
 assert (mean (z'), m);
 assert (mean (z', "includenan"), m);
 m = [20 25.35 15];
 assert (mean (z', "omitnan"), m);
 assert (mean (z, 2, "omitnan"), m');
 assert (mean (z, 2, "native", "omitnan"), m');
 assert (mean (z, 2, "omitnan", "native"), m');
***** test
 assert (mean (true, "all"), 1);
 assert (mean (false), 0);
 assert (mean ([true false true]), 2/3, 4e-14);
 assert (mean ([true false true], 1), [1 0 1]);
 assert (mean ([true false NaN], 1), [1 0 NaN]);
 assert (mean ([true false NaN], 2), NaN);
 assert (mean ([true false NaN], 2, "omitnan"), 0.5);
 assert (mean ([true false NaN], 2, "omitnan", "native"), 0.5);
***** assert (mean ("abc"), double (98))
***** assert (mean ("ab"), double (97.5), eps)
***** assert (mean ("abc", "double"), double (98))
***** assert (mean ("abc", "default"), double (98))
***** test
 x = magic (4);
 x([2, 9:12]) = NaN;
 assert (mean (x), [NaN 8.5, NaN, 8.5], eps);
 assert (mean (x,1), [NaN 8.5, NaN, 8.5], eps);
 assert (mean (x,2), NaN(4,1), eps);
 assert (mean (x,3), x, eps);
 assert (mean (x, 'omitnan'), [29/3, 8.5, NaN, 8.5], eps);
 assert (mean (x, 1, 'omitnan'), [29/3, 8.5, NaN, 8.5], eps);
 assert (mean (x, 2, 'omitnan'), [31/3; 9.5; 28/3; 19/3], eps);
 assert (mean (x, 3, 'omitnan'), x, eps);
***** assert (mean ([]), NaN(1,1))
***** assert (mean (single([])), NaN(1,1,"single"))
***** assert (mean ([], 1), NaN(1,0))
***** assert (mean ([], 2), NaN(0,1))
***** assert (mean ([], 3), NaN(0,0))
***** assert (mean (ones(1,0)), NaN(1,1))
***** assert (mean (ones(1,0), 1), NaN(1,0))
***** assert (mean (ones(1,0), 2), NaN(1,1))
***** assert (mean (ones(1,0), 3), NaN(1,0))
***** assert (mean (ones(0,1)), NaN(1,1))
***** assert (mean (ones(0,1), 1), NaN(1,1))
***** assert (mean (ones(0,1), 2), NaN(0,1))
***** assert (mean (ones(0,1), 3), NaN(0,1))
***** assert (mean (ones(0,1,0)), NaN(1,1,0))
***** assert (mean (ones(0,1,0), 1), NaN(1,1,0))
***** assert (mean (ones(0,1,0), 2), NaN(0,1,0))
***** assert (mean (ones(0,1,0), 3), NaN(0,1,1))
***** assert (mean (ones(0,0,1,0)), NaN(1,0,1,0))
***** assert (mean (ones(0,0,1,0), 1), NaN(1,0,1,0))
***** assert (mean (ones(0,0,1,0), 2), NaN(0,1,1,0))
***** assert (mean (ones(0,0,1,0), 3), NaN(0,0,1,0))
***** test
 x = repmat ([1:20;6:25], [5 2 6 3]);
 assert (size (mean (x, [3 2])), [10 1 1 3]);
 assert (size (mean (x, [1 2])), [1 1 6 3]);
 assert (size (mean (x, [1 2 4])), [1 1 6]);
 assert (size (mean (x, [1 4 3])), [1 40]);
 assert (size (mean (x, [1 2 3 4])), [1 1]);
***** assert (mean (ones (2,2), 3), ones (2,2))
***** assert (mean (ones (2,2,2), 99), ones (2,2,2))
***** assert (mean (magic (3), 3), magic (3))
***** assert (mean (magic (3), [1 3]), [5, 5, 5])
***** assert (mean (magic (3), [1 99]), [5, 5, 5])
***** test
 x = repmat ([1:20;6:25], [5 2 6 3]);
 m = repmat ([10.5;15.5], [5 1 1 3]);
 assert (mean (x, [3 2]), m, 4e-14);
 x(2,5,6,3) = NaN;
 m(2,1,1,3) = NaN;
 assert (mean (x, [3 2]), m, 4e-14);
 m(2,1,1,3) = 15.52301255230125;
 assert (mean (x, [3 2], "omitnan"), m, 4e-14);
***** assert (mean ([1 2 3], "aLL"), 2)
***** assert (mean ([1 2 3], "OmitNan"), 2)
***** assert (mean ([1 2 3], "DOUBle"), 2)
***** assert <*63848> (mean (ones (80e6, 1, "single")), 1, eps)
***** assert <*63848> (mean (ones (80e6, 1, "single"), "all"), 1, eps)
***** assert <*63848> (mean (ones (80e6, 1, "single"), 1), 1, eps)
***** assert <*63848> (mean (ones (80e6, 1, "single"), [1 2]), 1, eps)
***** assert <*63848> (mean (ones (80e6, 1, "single"), [1 3]), 1, eps)
***** assert <63848> (mean ([flintmax("double"), ones(1, 2^8-1, "double")]), ...
                               35184372088833-1/(2^8), eps(35184372088833))
***** error <Invalid call to mean.  Correct usage is> mean ()
***** error <Invalid call to mean.  Correct usage is> mean (1, 2, 3)
***** error <Invalid call to mean.  Correct usage is> mean (1, 2, 3, 4)
***** error <Invalid call to mean.  Correct usage is> mean (1, "all", 3)
***** error <Invalid call to mean.  Correct usage is> mean (1, "b")
***** error <Invalid call to mean.  Correct usage is> mean (1, 1, "foo")
***** error <OUTTYPE 'native' cannot be used with char> mean ("abc", "native")
***** error <X must be either a numeric, boolean, or character> mean ({1:5})
***** error <DIM must be a positive integer> mean (1, ones (2,2))
***** error <DIM must be a positive integer> mean (1, 1.5)
***** error <DIM must be a positive integer> mean (1, 0)
***** error <DIM must be a positive integer> mean (1, [])
***** error <DIM must be a positive integer> mean (1, -1)
***** error <DIM must be a positive integer> mean (1, -1.5)
***** error <DIM must be a positive integer> mean (1, NaN)
***** error <DIM must be a positive integer> mean (1, Inf)
***** error <DIM must be a positive integer> mean (repmat ([1:20;6:25], [5 2]), -1)
***** error <DIM must be a positive integer> mean (repmat ([1:5;5:9], [5 2]), [1 -1])
***** error <DIM must be a positive integer> mean (1, ones(1,0))
***** error <VECDIM must contain non-repeating> mean (1, [2 2])
80 tests, 80 passed, 0 known failure, 0 skipped
[inst/ridge.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/ridge.m
***** demo
 ## Perform ridge regression for a range of ridge parameters and observe
 ## how the coefficient estimates change based on the acetylene dataset.

 load acetylene

 X = [x1, x2, x3];

 x1x2 = x1 .* x2;
 x1x3 = x1 .* x3;
 x2x3 = x2 .* x3;

 D = [x1, x2, x3, x1x2, x1x3, x2x3];

 k = 0:1e-5:5e-3;

 b = ridge (y, D, k);

 figure
 plot (k, b, "LineWidth", 2)
 ylim ([-100, 100])
 grid on
 xlabel ("Ridge Parameter")
 ylabel ("Standardized Coefficient")
 title ("Ridge Trace")
 legend ("x1", "x2", "x3", "x1x2", "x1x3", "x2x3")

***** demo

 load carbig
 X = [Acceleration Weight Displacement Horsepower];
 y = MPG;

 n = length(y);

 rand("seed",1); % For reproducibility

 c = cvpartition(n,'HoldOut',0.3);
 idxTrain = training(c,1);
 idxTest = ~idxTrain;

 idxTrain = training(c,1);
 idxTest = ~idxTrain;

 k = 5;
 b = ridge(y(idxTrain),X(idxTrain,:),k,0);

 % Predict MPG values for the test data using the model.
 yhat = b(1) + X(idxTest,:)*b(2:end);
 scatter(y(idxTest),yhat)

 hold on
 plot(y(idxTest),y(idxTest),"r")
 xlabel('Actual MPG')
 ylabel('Predicted MPG')
 hold off

***** test
 b = ridge ([1 2 3 4]', [1 2 3 4; 2 3 4 5]', 1);
 assert (b, [0.5533; 0.5533], 1e-4);
***** test
 b = ridge ([1 2 3 4]', [1 2 3 4; 2 3 4 5]', 2);
 assert (b, [0.4841; 0.4841], 1e-4);
***** test
 load acetylene
 x = [x1, x2, x3];
 b = ridge (y, x, 0);
 assert (b,[10.2273;1.97128;-0.601818],1e-4);
***** test
 load acetylene
 x = [x1, x2, x3];
 b = ridge (y, x, 0.0005);
 assert (b,[10.2233;1.9712;-0.6056],1e-4);
***** test
 load acetylene
 x = [x1, x2, x3];
 b = ridge (y, x, 0.001);
 assert (b,[10.2194;1.9711;-0.6094],1e-4);
***** test
 load acetylene
 x = [x1, x2, x3];
 b = ridge (y, x, 0.002);
 assert (b,[10.2116;1.9709;-0.6169],1e-4);
***** test
 load acetylene
 x = [x1, x2, x3];
 b = ridge (y, x, 0.005);
 assert (b,[10.1882;1.9704;-0.6393],1e-4);
***** test
 load acetylene
 x = [x1, x2, x3];
 b = ridge (y, x, 0.01);
 assert (b,[10.1497;1.9695;-0.6761],1e-4);
***** error<ridge: function called with too few input arguments.> ridge (1)
***** error<ridge: function called with too few input arguments.> ridge (1, 2)
***** error<ridge: Y must be a numeric column vector.> ridge (ones (3), ones (3), 2)
***** error<ridge: Y must be a numeric column vector.> ridge ([1, 2], ones (2), 2)
***** error<ridge: Y must be a numeric column vector.> ridge ([], ones (3), 2)
***** error<ridge: X must be a numeric matrix.> ridge (ones (5,1), [], 2)
***** error<ridge: Y and X must contain the same number of rows.> ...
 ridge ([1; 2; 3; 4; 5], ones (3), 3)
***** error<ridge: wrong value for SCALED argument.> ...
 ridge ([1; 2; 3], ones (3), 3, 2)
***** error<ridge: wrong value for SCALED argument.> ...
 ridge ([1; 2; 3], ones (3), 3, "some")
17 tests, 17 passed, 0 known failure, 0 skipped
[inst/dist_wrap/mle.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_wrap/mle.m
***** error <mle: X must be a numeric vector of real values.> mle (ones (2))
***** error <mle: X must be a numeric vector of real values.> mle ("text")
***** error <mle: X must be a numeric vector of real values.> mle ([1, 2, 3, i, 5])
***** error <mle: optional arguments must be in NAME-VALUE pairs.> ...
 mle ([1:50], "distribution")
***** error <mle: 'censoring' argument must have the same size as the input data in X.> ...
 mle ([1:50], "censoring", logical ([1,0,1,0]))
***** error <mle: 'frequency' argument must have the same size as the input data in X.> ...
 mle ([1:50], "frequency", [1,0,1,0])
***** error <mle: 'frequency' argument must contain non-negative integer values.> ...
 mle ([1 0 1 0], "frequency", [-1 1 0 0])
***** error <mle: 'frequency' argument must contain non-negative integer values.> ...
 mle ([1 0 1 0], "distribution", "nbin", "frequency", [-1 1 0 0])
***** error <mle: invalid value for 'alpha' argument.> mle ([1:50], "alpha", [0.05, 0.01])
***** error <mle: invalid value for 'alpha' argument.> mle ([1:50], "alpha", 1)
***** error <mle: invalid value for 'alpha' argument.> mle ([1:50], "alpha", -1)
***** error <mle: invalid value for 'alpha' argument.> mle ([1:50], "alpha", i)
***** error <mle: 'ntrials' argument must be a positive integer scalar value.> ...
 mle ([1:50], "ntrials", -1)
***** error <mle: 'ntrials' argument must be a positive integer scalar value.> ...
 mle ([1:50], "ntrials", [20, 50])
***** error <mle: 'ntrials' argument must be a positive integer scalar value.> ...
 mle ([1:50], "ntrials", [20.3])
***** error <mle: 'ntrials' argument must be a positive integer scalar value.> ...
 mle ([1:50], "ntrials", 3i)
***** error <mle: 'options' argument must be a structure compatible for 'fminsearch'.> ...
 mle ([1:50], "options", 4)
***** error <mle: 'options' argument must be a structure compatible for 'fminsearch'.> ...
 mle ([1:50], "options", struct ("x", 3))
***** error <mle: unknown parameter name.> mle ([1:50], "NAME", "value")
***** error <mle: censoring is not supported for the Bernoulli distribution.> ...
 mle ([1 0 1 0], "distribution", "bernoulli", "censoring", [1 1 0 0])
***** error <mle: invalid data for the Bernoulli distribution.> ...
 mle ([1 2 1 0], "distribution", "bernoulli")
***** error <mle: censoring is not supported for the Beta distribution.> ...
 mle ([1 0 1 0], "distribution", "beta", "censoring", [1 1 0 0])
***** error <mle: censoring is not supported for the Binomial distribution.> ...
 mle ([1 0 1 0], "distribution", "bino", "censoring", [1 1 0 0])
***** error <mle: 'Ntrials' parameter is required for the Binomial distribution.> ...
 mle ([1 0 1 0], "distribution", "bino")
***** error <mle: censoring is not supported for the Geometric distribution.> ...
 mle ([1 0 1 0], "distribution", "geo", "censoring", [1 1 0 0])
***** error <mle: censoring is not supported for the Generalized Extreme Value distribution.> ...
 mle ([1 0 1 0], "distribution", "gev", "censoring", [1 1 0 0])
***** error <mle: censoring is not supported for the Generalized Pareto distribution.> ...
 mle ([1 0 1 0], "distribution", "gp", "censoring", [1 1 0 0])
***** error <mle: invalid 'theta' location parameter for the Generalized Pareto distribution.> ...
 mle ([1 0 -1 0], "distribution", "gp")
***** error <mle: censoring is not supported for the Half Normal distribution.> ...
 mle ([1 0 1 0], "distribution", "hn", "censoring", [1 1 0 0])
***** error <mle: invalid 'mu' location parameter for the Half Normal distribution.> ...
 mle ([1 0 -1 0], "distribution", "hn")
***** error <mle: censoring is not supported for the Negative Binomial distribution.> ...
 mle ([1 0 1 0], "distribution", "nbin", "censoring", [1 1 0 0])
***** error <mle: censoring is not supported for the Poisson distribution.> ...
 mle ([1 0 1 0], "distribution", "poisson", "censoring", [1 1 0 0])
***** error <mle: censoring is not supported for the Discrete Uniform distribution.> ...
 mle ([1 0 1 0], "distribution", "unid", "censoring", [1 1 0 0])
***** error <mle: censoring is not supported for the Continuous Uniform distribution.> ...
 mle ([1 0 1 0], "distribution", "unif", "censoring", [1 1 0 0])
***** error <mle: unrecognized distribution name.> mle ([1:50], "distribution", "value")
***** error <mle: censoring is not supported for the Continuous Uniform distribution.> ...
 mle ([1 0 1 0], "distribution", "unif", "censoring", [1 1 0 0])
36 tests, 36 passed, 0 known failure, 0 skipped
[inst/dist_wrap/makedist.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_wrap/makedist.m
***** test
 pd = makedist ("beta");
 assert (class (pd), "BetaDistribution");
 assert (pd.a, 1);
 assert (pd.b, 1);
***** test
 pd = makedist ("beta", "a", 5);
 assert (pd.a, 5);
 assert (pd.b, 1);
***** test
 pd = makedist ("beta", "b", 5);
 assert (pd.a, 1);
 assert (pd.b, 5);
***** test
 pd = makedist ("beta", "a", 3, "b", 5);
 assert (pd.a, 3);
 assert (pd.b, 5);
***** test
 pd = makedist ("binomial");
 assert (class (pd), "BinomialDistribution");
 assert (pd.N, 1);
 assert (pd.p, 0.5);
***** test
 pd = makedist ("binomial", "N", 5);
 assert (pd.N, 5);
 assert (pd.p, 0.5);
***** test
 pd = makedist ("binomial", "p", 0.2);
 assert (pd.N, 1);
 assert (pd.p, 0.2);
***** test
 pd = makedist ("binomial", "N", 3, "p", 0.3);
 assert (pd.N, 3);
 assert (pd.p, 0.3);
***** test
 pd = makedist ("birnbaumsaunders");
 assert (class (pd), "BirnbaumSaundersDistribution");
 assert (pd.beta, 1);
 assert (pd.gamma, 1);
***** test
 pd = makedist ("birnbaumsaunders", "beta", 5);
 assert (pd.beta, 5);
 assert (pd.gamma, 1);
***** test
 pd = makedist ("birnbaumsaunders", "gamma", 5);
 assert (pd.beta, 1);
 assert (pd.gamma, 5);
***** test
 pd = makedist ("birnbaumsaunders", "beta", 3, "gamma", 5);
 assert (pd.beta, 3);
 assert (pd.gamma, 5);
***** test
 pd = makedist ("burr");
 assert (class (pd), "BurrDistribution");
 assert (pd.alpha, 1);
 assert (pd.c, 1);
 assert (pd.k, 1);
***** test
 pd = makedist ("burr", "k", 5);
 assert (pd.alpha, 1);
 assert (pd.c, 1);
 assert (pd.k, 5);
***** test
 pd = makedist ("burr", "c", 5);
 assert (pd.alpha, 1);
 assert (pd.c, 5);
 assert (pd.k, 1);
***** test
 pd = makedist ("burr", "alpha", 3, "c", 5);
 assert (pd.alpha, 3);
 assert (pd.c, 5);
 assert (pd.k, 1);
***** test
 pd = makedist ("burr", "k", 3, "c", 5);
 assert (pd.alpha, 1);
 assert (pd.c, 5);
 assert (pd.k, 3);
***** test
 pd = makedist ("exponential");
 assert (class (pd), "ExponentialDistribution");
 assert (pd.mu, 1);
***** test
 pd = makedist ("exponential", "mu", 5);
 assert (pd.mu, 5);
***** test
 pd = makedist ("extremevalue");
 assert (class (pd), "ExtremeValueDistribution");
 assert (pd.mu, 0);
 assert (pd.sigma, 1);
***** test
 pd = makedist ("extremevalue", "mu", 5);
 assert (class (pd), "ExtremeValueDistribution");
 assert (pd.mu, 5);
 assert (pd.sigma, 1);
***** test
 pd = makedist ("ev", "sigma", 5);
 assert (class (pd), "ExtremeValueDistribution");
 assert (pd.mu, 0);
 assert (pd.sigma, 5);
***** test
 pd = makedist ("ev", "mu", -3, "sigma", 5);
 assert (class (pd), "ExtremeValueDistribution");
 assert (pd.mu, -3);
 assert (pd.sigma, 5);
***** test
 pd = makedist ("gamma");
 assert (class (pd), "GammaDistribution");
 assert (pd.a, 1);
 assert (pd.b, 1);
***** test
 pd = makedist ("gamma", "a", 5);
 assert (pd.a, 5);
 assert (pd.b, 1);
***** test
 pd = makedist ("gamma", "b", 5);
 assert (pd.a, 1);
 assert (pd.b, 5);
***** test
 pd = makedist ("gamma", "a", 3, "b", 5);
 assert (pd.a, 3);
 assert (pd.b, 5);
***** test
 pd = makedist ("GeneralizedExtremeValue");
 assert (class (pd), "GeneralizedExtremeValueDistribution");
 assert (pd.k, 0);
 assert (pd.sigma, 1);
 assert (pd.mu, 0);
***** test
 pd = makedist ("GeneralizedExtremeValue", "k", 5);
 assert (pd.k, 5);
 assert (pd.sigma, 1);
 assert (pd.mu, 0);
***** test
 pd = makedist ("GeneralizedExtremeValue", "sigma", 5);
 assert (pd.k, 0);
 assert (pd.sigma, 5);
 assert (pd.mu, 0);
***** test
 pd = makedist ("GeneralizedExtremeValue", "k", 3, "sigma", 5);
 assert (pd.k, 3);
 assert (pd.sigma, 5);
 assert (pd.mu, 0);
***** test
 pd = makedist ("GeneralizedExtremeValue", "mu", 3, "sigma", 5);
 assert (pd.k, 0);
 assert (pd.sigma, 5);
 assert (pd.mu, 3);
***** test
 pd = makedist ("GeneralizedPareto");
 assert (class (pd), "GeneralizedParetoDistribution");
 assert (pd.k, 1);
 assert (pd.sigma, 1);
 assert (pd.theta, 1);
***** test
 pd = makedist ("GeneralizedPareto", "k", 5);
 assert (pd.k, 5);
 assert (pd.sigma, 1);
 assert (pd.theta, 1);
***** test
 pd = makedist ("GeneralizedPareto", "sigma", 5);
 assert (pd.k, 1);
 assert (pd.sigma, 5);
 assert (pd.theta, 1);
***** test
 pd = makedist ("GeneralizedPareto", "k", 3, "sigma", 5);
 assert (pd.k, 3);
 assert (pd.sigma, 5);
 assert (pd.theta, 1);
***** test
 pd = makedist ("GeneralizedPareto", "theta", 3, "sigma", 5);
 assert (pd.k, 1);
 assert (pd.sigma, 5);
 assert (pd.theta, 3);
***** test
 pd = makedist ("HalfNormal");
 assert (class (pd), "HalfNormalDistribution");
 assert (pd.mu, 0);
 assert (pd.sigma, 1);
***** test
 pd = makedist ("HalfNormal", "mu", 5);
 assert (pd.mu, 5);
 assert (pd.sigma, 1);
***** test
 pd = makedist ("HalfNormal", "sigma", 5);
 assert (pd.mu, 0);
 assert (pd.sigma, 5);
***** test
 pd = makedist ("HalfNormal", "mu", 3, "sigma", 5);
 assert (pd.mu, 3);
 assert (pd.sigma, 5);
***** test
 pd = makedist ("InverseGaussian");
 assert (class (pd), "InverseGaussianDistribution");
 assert (pd.mu, 1);
 assert (pd.lambda, 1);
***** test
 pd = makedist ("InverseGaussian", "mu", 5);
 assert (pd.mu, 5);
 assert (pd.lambda, 1);
***** test
 pd = makedist ("InverseGaussian", "lambda", 5);
 assert (pd.mu, 1);
 assert (pd.lambda, 5);
***** test
 pd = makedist ("InverseGaussian", "mu", 3, "lambda", 5);
 assert (pd.mu, 3);
 assert (pd.lambda, 5);
***** test
 pd = makedist ("logistic");
 assert (class (pd), "LogisticDistribution");
 assert (pd.mu, 0);
 assert (pd.sigma, 1);
***** test
 pd = makedist ("logistic", "mu", 5);
 assert (pd.mu, 5);
 assert (pd.sigma, 1);
***** test
 pd = makedist ("logistic", "sigma", 5);
 assert (pd.mu, 0);
 assert (pd.sigma, 5);
***** test
 pd = makedist ("logistic", "mu", 3, "sigma", 5);
 assert (pd.mu, 3);
 assert (pd.sigma, 5);
***** test
 pd = makedist ("loglogistic");
 assert (class (pd), "LoglogisticDistribution");
 assert (pd.mu, 0);
 assert (pd.sigma, 1);
***** test
 pd = makedist ("loglogistic", "mu", 5);
 assert (pd.mu, 5);
 assert (pd.sigma, 1);
***** test
 pd = makedist ("loglogistic", "sigma", 5);
 assert (pd.mu, 0);
 assert (pd.sigma, 5);
***** test
 pd = makedist ("loglogistic", "mu", 3, "sigma", 5);
 assert (pd.mu, 3);
 assert (pd.sigma, 5);
***** test
 pd = makedist ("Lognormal");
 assert (class (pd), "LognormalDistribution");
 assert (pd.mu, 0);
 assert (pd.sigma, 1);
***** test
 pd = makedist ("Lognormal", "mu", 5);
 assert (pd.mu, 5);
 assert (pd.sigma, 1);
***** test
 pd = makedist ("Lognormal", "sigma", 5);
 assert (pd.mu, 0);
 assert (pd.sigma, 5);
***** test
 pd = makedist ("Lognormal", "mu", -3, "sigma", 5);
 assert (pd.mu, -3);
 assert (pd.sigma, 5);
***** test
 pd = makedist ("Loguniform");
 assert (class (pd), "LoguniformDistribution");
 assert (pd.Lower, 1);
 assert (pd.Upper, 4);
***** test
 pd = makedist ("Loguniform", "Lower", 2);
 assert (pd.Lower, 2);
 assert (pd.Upper, 4);
***** test
 pd = makedist ("Loguniform", "Lower", 1, "Upper", 3);
 assert (pd.Lower, 1);
 assert (pd.Upper, 3);
***** test
 pd = makedist ("Multinomial");
 assert (class (pd), "MultinomialDistribution");
 assert (pd.Probabilities, [0.5, 0.5]);
***** test
 pd = makedist ("Multinomial", "Probabilities", [0.2, 0.3, 0.1, 0.4]);
 assert (class (pd), "MultinomialDistribution");
 assert (pd.Probabilities, [0.2, 0.3, 0.1, 0.4]);
***** test
 pd = makedist ("Nakagami");
 assert (class (pd), "NakagamiDistribution");
 assert (pd.mu, 1);
 assert (pd.omega, 1);
***** test
 pd = makedist ("Nakagami", "mu", 5);
 assert (class (pd), "NakagamiDistribution");
 assert (pd.mu, 5);
 assert (pd.omega, 1);
***** test
 pd = makedist ("Nakagami", "omega", 0.3);
 assert (class (pd), "NakagamiDistribution");
 assert (pd.mu, 1);
 assert (pd.omega, 0.3);
***** test
 pd = makedist ("NegativeBinomial");
 assert (class (pd), "NegativeBinomialDistribution");
 assert (pd.R, 1);
 assert (pd.P, 0.5);
***** test
 pd = makedist ("NegativeBinomial", "R", 5);
 assert (class (pd), "NegativeBinomialDistribution");
 assert (pd.R, 5);
 assert (pd.P, 0.5);
***** test
 pd = makedist ("NegativeBinomial", "p", 0.3);
 assert (class (pd), "NegativeBinomialDistribution");
 assert (pd.R, 1);
 assert (pd.P, 0.3);
***** test
 pd = makedist ("Normal");
 assert (class (pd), "NormalDistribution");
 assert (pd.mu, 0);
 assert (pd.sigma, 1);
***** test
 pd = makedist ("Normal", "mu", 5);
 assert (class (pd), "NormalDistribution");
 assert (pd.mu, 5);
 assert (pd.sigma, 1);
***** test
 pd = makedist ("Normal", "sigma", 5);
 assert (class (pd), "NormalDistribution");
 assert (pd.mu, 0);
 assert (pd.sigma, 5);
***** test
 pd = makedist ("Normal", "mu", -3, "sigma", 5);
 assert (class (pd), "NormalDistribution");
 assert (pd.mu, -3);
 assert (pd.sigma, 5);
***** test
 pd = makedist ("PiecewiseLinear");
 assert (class (pd), "PiecewiseLinearDistribution");
 assert (pd.x, [0; 1]);
 assert (pd.Fx, [0; 1]);
***** test
 pd = makedist ("PiecewiseLinear", "x", [0, 1, 2], "Fx", [0, 0.5, 1]);
 assert (pd.x, [0; 1; 2]);
 assert (pd.Fx, [0; 0.5; 1]);
***** test
 pd = makedist ("Poisson");
 assert (class (pd), "PoissonDistribution");
 assert (pd.lambda, 1);
***** test
 pd = makedist ("Poisson", "lambda", 5);
 assert (pd.lambda, 5);
***** test
 pd = makedist ("Rayleigh");
 assert (class (pd), "RayleighDistribution");
 assert (pd.sigma, 1);
***** test
 pd = makedist ("Rayleigh", "sigma", 5);
 assert (pd.sigma, 5);
***** test
 pd = makedist ("Rician");
 assert (class (pd), "RicianDistribution");
 assert (pd.s, 1);
 assert (pd.sigma, 1);
***** test
 pd = makedist ("Rician", "s", 3);
 assert (pd.s, 3);
 assert (pd.sigma, 1);
***** test
 pd = makedist ("Rician", "sigma", 3);
 assert (pd.s, 1);
 assert (pd.sigma, 3);
***** test
 pd = makedist ("Rician", "s", 2, "sigma", 3);
 assert (pd.s, 2);
 assert (pd.sigma, 3);
***** warning
 pd = makedist ("stable");
 assert (class (pd), "double");
 assert (isempty (pd), true);
***** test
 pd = makedist ("tlocationscale");
 assert (class (pd), "tLocationScaleDistribution");
 assert (pd.mu, 0);
 assert (pd.sigma, 1);
 assert (pd.nu, 5);
***** test
 pd = makedist ("tlocationscale", "mu", 5);
 assert (pd.mu, 5);
 assert (pd.sigma, 1);
 assert (pd.nu, 5);
***** test
 pd = makedist ("tlocationscale", "sigma", 2);
 assert (pd.mu, 0);
 assert (pd.sigma, 2);
 assert (pd.nu, 5);
***** test
 pd = makedist ("tlocationscale", "mu", 5, "sigma", 2);
 assert (pd.mu, 5);
 assert (pd.sigma, 2);
 assert (pd.nu, 5);
***** test
 pd = makedist ("tlocationscale", "nu", 1, "sigma", 2);
 assert (pd.mu, 0);
 assert (pd.sigma, 2);
 assert (pd.nu, 1);
***** test
 pd = makedist ("tlocationscale", "mu", -2, "sigma", 3, "nu", 1);
 assert (pd.mu, -2);
 assert (pd.sigma, 3);
 assert (pd.nu, 1);
***** test
 pd = makedist ("Triangular");
 assert (class (pd), "TriangularDistribution");
 assert (pd.A, 0);
 assert (pd.B, 0.5);
 assert (pd.C, 1);
***** test
 pd = makedist ("Triangular", "A", -2);
 assert (pd.A, -2);
 assert (pd.B, 0.5);
 assert (pd.C, 1);
***** test
 pd = makedist ("Triangular", "A", 0.5, "B", 0.9);
 assert (pd.A, 0.5);
 assert (pd.B, 0.9);
 assert (pd.C, 1);
***** test
 pd = makedist ("Triangular", "A", 1, "B", 2, "C", 5);
 assert (pd.A, 1);
 assert (pd.B, 2);
 assert (pd.C, 5);
***** test
 pd = makedist ("Uniform");
 assert (class (pd), "UniformDistribution");
 assert (pd.Lower, 0);
 assert (pd.Upper, 1);
***** test
 pd = makedist ("Uniform", "Lower", -2);
 assert (pd.Lower, -2);
 assert (pd.Upper, 1);
***** test
 pd = makedist ("Uniform", "Lower", 1, "Upper", 3);
 assert (pd.Lower, 1);
 assert (pd.Upper, 3);
***** test
 pd = makedist ("Weibull");
 assert (class (pd), "WeibullDistribution");
 assert (pd.lambda, 1);
 assert (pd.k, 1);
***** test
 pd = makedist ("Weibull", "lambda", 3);
 assert (pd.lambda, 3);
 assert (pd.k, 1);
***** test
 pd = makedist ("Weibull", "lambda", 3, "k", 2);
 assert (pd.lambda, 3);
 assert (pd.k, 2);
***** error <makedist: DISTNAME must be a character vector.> makedist (1)
***** error <makedist: DISTNAME must be a character vector.> makedist (["as";"sd"])
***** error <makedist: unrecognized distribution name.> makedist ("some")
***** error <makedist: optional arguments must be in NAME-VALUE pairs.> ...
 makedist ("Beta", "a")
***** error <makedist: unknown parameter for 'Beta' distribution.> ...
 makedist ("Beta", "a", 1, "Q", 23)
***** error <makedist: unknown parameter for 'Binomial' distribution.> ...
 makedist ("Binomial", "N", 1, "Q", 23)
***** error <makedist: unknown parameter for 'BirnbaumSaunders' distribution.> ...
 makedist ("BirnbaumSaunders", "N", 1)
***** error <makedist: unknown parameter for 'Burr' distribution.> ...
 makedist ("Burr", "lambda", 1, "sdfs", 34)
***** error <makedist: unknown parameter for 'ExtremeValue' distribution.> ...
 makedist ("extremevalue", "mu", 1, "sdfs", 34)
***** error <makedist: unknown parameter for 'Exponential' distribution.> ...
 makedist ("exponential", "mu", 1, "sdfs", 34)
***** error <makedist: unknown parameter for 'Gamma' distribution.> ...
 makedist ("Gamma", "k", 1, "sdfs", 34)
***** error <makedist: unknown parameter for 'GeneralizedExtremeValue' distribution.> ...
 makedist ("GeneralizedExtremeValue", "k", 1, "sdfs", 34)
***** error <makedist: unknown parameter for 'GeneralizedPareto' distribution.> ...
 makedist ("GeneralizedPareto", "k", 1, "sdfs", 34)
***** error <makedist: unknown parameter for 'HalfNormal' distribution.> ...
 makedist ("HalfNormal", "k", 1, "sdfs", 34)
***** error <makedist: unknown parameter for 'InverseGaussian' distribution.> ...
 makedist ("InverseGaussian", "k", 1, "sdfs", 34)
***** error <makedist: unknown parameter for 'Logistic' distribution.> ...
 makedist ("Logistic", "k", 1, "sdfs", 34)
***** error <makedist: unknown parameter for 'Loglogistic' distribution.> ...
 makedist ("Loglogistic", "k", 1, "sdfs", 34)
***** error <makedist: unknown parameter for 'Lognormal' distribution.> ...
 makedist ("Lognormal", "k", 1, "sdfs", 34)
***** error <makedist: unknown parameter for 'Loguniform' distribution.> ...
 makedist ("Loguniform", "k", 1, "sdfs", 34)
***** error <makedist: unknown parameter for 'Multinomial' distribution.> ...
 makedist ("Multinomial", "k", 1, "sdfs", 34)
***** error <makedist: unknown parameter for 'Nakagami' distribution.> ...
 makedist ("Nakagami", "mu", 1, "sdfs", 34)
***** error <makedist: unknown parameter for 'NegativeBinomial' distribution.> ...
 makedist ("NegativeBinomial", "mu", 1, "sdfs", 34)
***** error <makedist: unknown parameter for 'Normal' distribution.> ...
 makedist ("Normal", "mu", 1, "sdfs", 34)
***** error <makedist: unknown parameter for 'PiecewiseLinear' distribution.> ...
 makedist ("PiecewiseLinear", "mu", 1, "sdfs", 34)
***** error <makedist: unknown parameter for 'Poisson' distribution.> ...
 makedist ("Poisson", "mu", 1, "sdfs", 34)
***** error <makedist: unknown parameter for 'Rayleigh' distribution.> ...
 makedist ("Rayleigh", "mu", 1, "sdfs", 34)
***** error <makedist: unknown parameter for 'Rician' distribution.> ...
 makedist ("Rician", "mu", 1, "sdfs", 34)
***** error <makedist: unknown parameter for 'Stable' distribution.> ...
 makedist ("Stable", "mu", 1, "sdfs", 34)
***** error <makedist: unknown parameter for 'tLocationScale' distribution.> ...
 makedist ("tLocationScale", "mu", 1, "sdfs", 34)
***** error <makedist: unknown parameter for 'Triangular' distribution.> ...
 makedist ("Triangular", "mu", 1, "sdfs", 34)
***** error <makedist: unknown parameter for 'Uniform' distribution.> ...
 makedist ("Uniform", "mu", 1, "sdfs", 34)
***** error <makedist: unknown parameter for 'Weibull' distribution.> ...
 makedist ("Weibull", "mu", 1, "sdfs", 34)
131 tests, 131 passed, 0 known failure, 0 skipped
[inst/dist_wrap/icdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_wrap/icdf.m
***** shared p
 p = [0.05:0.05:0.5];
***** assert (icdf ("Beta", p, 5, 2), betainv (p, 5, 2))
***** assert (icdf ("beta", p, 5, 2), betainv (p, 5, 2))
***** assert (icdf ("Binomial", p, 5, 2), binoinv (p, 5, 2))
***** assert (icdf ("bino", p, 5, 2), binoinv (p, 5, 2))
***** assert (icdf ("Birnbaum-Saunders", p, 5, 2), bisainv (p, 5, 2))
***** assert (icdf ("bisa", p, 5, 2), bisainv (p, 5, 2))
***** assert (icdf ("Burr", p, 5, 2, 2), burrinv (p, 5, 2, 2))
***** assert (icdf ("burr", p, 5, 2, 2), burrinv (p, 5, 2, 2))
***** assert (icdf ("Cauchy", p, 5, 2), cauchyinv (p, 5, 2))
***** assert (icdf ("cauchy", p, 5, 2), cauchyinv (p, 5, 2))
***** assert (icdf ("Chi-squared", p, 5), chi2inv (p, 5))
***** assert (icdf ("chi2", p, 5), chi2inv (p, 5))
***** assert (icdf ("Extreme Value", p, 5, 2), evinv (p, 5, 2))
***** assert (icdf ("ev", p, 5, 2), evinv (p, 5, 2))
***** assert (icdf ("Exponential", p, 5), expinv (p, 5))
***** assert (icdf ("exp", p, 5), expinv (p, 5))
***** assert (icdf ("F-Distribution", p, 5, 2), finv (p, 5, 2))
***** assert (icdf ("f", p, 5, 2), finv (p, 5, 2))
***** assert (icdf ("Gamma", p, 5, 2), gaminv (p, 5, 2))
***** assert (icdf ("gam", p, 5, 2), gaminv (p, 5, 2))
***** assert (icdf ("Geometric", p, 5), geoinv (p, 5))
***** assert (icdf ("geo", p, 5), geoinv (p, 5))
***** assert (icdf ("Generalized Extreme Value", p, 5, 2, 2), gevinv (p, 5, 2, 2))
***** assert (icdf ("gev", p, 5, 2, 2), gevinv (p, 5, 2, 2))
***** assert (icdf ("Generalized Pareto", p, 5, 2, 2), gpinv (p, 5, 2, 2))
***** assert (icdf ("gp", p, 5, 2, 2), gpinv (p, 5, 2, 2))
***** assert (icdf ("Gumbel", p, 5, 2), gumbelinv (p, 5, 2))
***** assert (icdf ("gumbel", p, 5, 2), gumbelinv (p, 5, 2))
***** assert (icdf ("Half-normal", p, 5, 2), hninv (p, 5, 2))
***** assert (icdf ("hn", p, 5, 2), hninv (p, 5, 2))
***** assert (icdf ("Hypergeometric", p, 5, 2, 2), hygeinv (p, 5, 2, 2))
***** assert (icdf ("hyge", p, 5, 2, 2), hygeinv (p, 5, 2, 2))
***** assert (icdf ("Inverse Gaussian", p, 5, 2), invginv (p, 5, 2))
***** assert (icdf ("invg", p, 5, 2), invginv (p, 5, 2))
***** assert (icdf ("Laplace", p, 5, 2), laplaceinv (p, 5, 2))
***** assert (icdf ("laplace", p, 5, 2), laplaceinv (p, 5, 2))
***** assert (icdf ("Logistic", p, 5, 2), logiinv (p, 5, 2))
***** assert (icdf ("logi", p, 5, 2), logiinv (p, 5, 2))
***** assert (icdf ("Log-Logistic", p, 5, 2), loglinv (p, 5, 2))
***** assert (icdf ("logl", p, 5, 2), loglinv (p, 5, 2))
***** assert (icdf ("Lognormal", p, 5, 2), logninv (p, 5, 2))
***** assert (icdf ("logn", p, 5, 2), logninv (p, 5, 2))
***** assert (icdf ("Nakagami", p, 5, 2), nakainv (p, 5, 2))
***** assert (icdf ("naka", p, 5, 2), nakainv (p, 5, 2))
***** assert (icdf ("Negative Binomial", p, 5, 2), nbininv (p, 5, 2))
***** assert (icdf ("nbin", p, 5, 2), nbininv (p, 5, 2))
***** assert (icdf ("Noncentral F-Distribution", p, 5, 2, 2), ncfinv (p, 5, 2, 2))
***** assert (icdf ("ncf", p, 5, 2, 2), ncfinv (p, 5, 2, 2))
***** assert (icdf ("Noncentral Student T", p, 5, 2), nctinv (p, 5, 2))
***** assert (icdf ("nct", p, 5, 2), nctinv (p, 5, 2))
***** assert (icdf ("Noncentral Chi-Squared", p, 5, 2), ncx2inv (p, 5, 2))
***** assert (icdf ("ncx2", p, 5, 2), ncx2inv (p, 5, 2))
***** assert (icdf ("Normal", p, 5, 2), norminv (p, 5, 2))
***** assert (icdf ("norm", p, 5, 2), norminv (p, 5, 2))
***** assert (icdf ("Poisson", p, 5), poissinv (p, 5))
***** assert (icdf ("poiss", p, 5), poissinv (p, 5))
***** assert (icdf ("Rayleigh", p, 5), raylinv (p, 5))
***** assert (icdf ("rayl", p, 5), raylinv (p, 5))
***** assert (icdf ("Rician", p, 5, 1), riceinv (p, 5, 1))
***** assert (icdf ("rice", p, 5, 1), riceinv (p, 5, 1))
***** assert (icdf ("Student T", p, 5), tinv (p, 5))
***** assert (icdf ("t", p, 5), tinv (p, 5))
***** assert (icdf ("location-scale T", p, 5, 1, 2), tlsinv (p, 5, 1, 2))
***** assert (icdf ("tls", p, 5, 1, 2), tlsinv (p, 5, 1, 2))
***** assert (icdf ("Triangular", p, 5, 2, 2), triinv (p, 5, 2, 2))
***** assert (icdf ("tri", p, 5, 2, 2), triinv (p, 5, 2, 2))
***** assert (icdf ("Discrete Uniform", p, 5), unidinv (p, 5))
***** assert (icdf ("unid", p, 5), unidinv (p, 5))
***** assert (icdf ("Uniform", p, 5, 2), unifinv (p, 5, 2))
***** assert (icdf ("unif", p, 5, 2), unifinv (p, 5, 2))
***** assert (icdf ("Von Mises", p, 5, 2), vminv (p, 5, 2))
***** assert (icdf ("vm", p, 5, 2), vminv (p, 5, 2))
***** assert (icdf ("Weibull", p, 5, 2), wblinv (p, 5, 2))
***** assert (icdf ("wbl", p, 5, 2), wblinv (p, 5, 2))
***** error<icdf: distribution NAME must a char string.> icdf (1)
***** error<icdf: distribution NAME must a char string.> icdf ({"beta"})
***** error<icdf: P must be numeric.> icdf ("beta", {[1 2 3 4 5]})
***** error<icdf: P must be numeric.> icdf ("beta", "text")
***** error<icdf: values in P must be real.> icdf ("beta", 1+i)
***** error<icdf: distribution parameters must be numeric.> ...
 icdf ("Beta", p, "a", 2)
***** error<icdf: distribution parameters must be numeric.> ...
 icdf ("Beta", p, 5, "")
***** error<icdf: distribution parameters must be numeric.> ...
 icdf ("Beta", p, 5, {2})
***** error<icdf: chi2 distribution requires 1 parameter.> icdf ("chi2", p)
***** error<icdf: Beta distribution requires 2 parameters.> icdf ("Beta", p, 5)
***** error<icdf: Burr distribution requires 3 parameters.> icdf ("Burr", p, 5)
***** error<icdf: Burr distribution requires 3 parameters.> icdf ("Burr", p, 5, 2)
86 tests, 86 passed, 0 known failure, 0 skipped
[inst/dist_wrap/fitdist.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_wrap/fitdist.m
***** test
 x = betarnd (1, 1, 100, 1);
 pd = fitdist (x, "Beta");
 [phat, pci] = betafit (x);
 assert ([pd.a, pd.b], phat);
 assert (paramci (pd), pci);
***** test
 x1 = betarnd (1, 1, 100, 1);
 x2 = betarnd (5, 2, 100, 1);
 pd = fitdist ([x1; x2], "Beta", "By", [ones(100,1); 2*ones(100,1)]);
 [phat, pci] = betafit (x1);
 assert ([pd(1).a, pd(1).b], phat);
 assert (paramci (pd(1)), pci);
 [phat, pci] = betafit (x2);
 assert ([pd(2).a, pd(2).b], phat);
 assert (paramci (pd(2)), pci);
***** test
 N = 1;
 x = binornd (N, 0.5, 100, 1);
 pd = fitdist (x, "binomial");
 [phat, pci] = binofit (sum (x), numel (x));
 assert ([pd.N, pd.p], [N, phat]);
 assert (paramci (pd), pci);
***** test
 N = 3;
 x = binornd (N, 0.4, 100, 1);
 pd = fitdist (x, "binomial", "ntrials", N);
 [phat, pci] = binofit (sum (x), numel (x) * N);
 assert ([pd.N, pd.p], [N, phat]);
 assert (paramci (pd), pci);
***** test
 N = 1;
 x1 = binornd (N, 0.5, 100, 1);
 x2 = binornd (N, 0.7, 100, 1);
 pd = fitdist ([x1; x2], "binomial", "By", [ones(100,1); 2*ones(100,1)]);
 [phat, pci] = binofit (sum (x1), numel (x1));
 assert ([pd(1).N, pd(1).p], [N, phat]);
 assert (paramci (pd(1)), pci);
 [phat, pci] = binofit (sum (x2), numel (x2));
 assert ([pd(2).N, pd(2).p], [N, phat]);
 assert (paramci (pd(2)), pci);
***** test
 N = 5;
 x1 = binornd (N, 0.5, 100, 1);
 x2 = binornd (N, 0.8, 100, 1);
 pd = fitdist ([x1; x2], "binomial", "ntrials", N, ...
               "By", [ones(100,1); 2*ones(100,1)]);
 [phat, pci] = binofit (sum (x1), numel (x1) * N);
 assert ([pd(1).N, pd(1).p], [N, phat]);
 assert (paramci (pd(1)), pci);
 [phat, pci] = binofit (sum (x2), numel (x2) * N);
 assert ([pd(2).N, pd(2).p], [N, phat]);
 assert (paramci (pd(2)), pci);
***** test
 x = bisarnd (1, 1, 100, 1);
 pd = fitdist (x, "BirnbaumSaunders");
 [phat, pci] = bisafit (x);
 assert ([pd.beta, pd.gamma], phat);
 assert (paramci (pd), pci);
***** test
 x1 = bisarnd (1, 1, 100, 1);
 x2 = bisarnd (5, 2, 100, 1);
 pd = fitdist ([x1; x2], "bisa", "By", [ones(100,1); 2*ones(100,1)]);
 [phat, pci] = bisafit (x1);
 assert ([pd(1).beta, pd(1).gamma], phat);
 assert (paramci (pd(1)), pci);
 [phat, pci] = bisafit (x2);
 assert ([pd(2).beta, pd(2).gamma], phat);
 assert (paramci (pd(2)), pci);
***** test
 x = burrrnd (1, 2, 1, 100, 1);
 pd = fitdist (x, "Burr");
 [phat, pci] = burrfit (x);
 assert ([pd.alpha, pd.c, pd.k], phat);
 assert (paramci (pd), pci);
***** test
 x1 = burrrnd (1, 2, 1, 100, 1);
 x2 = burrrnd (1, 0.5, 2, 100, 1);
 pd = fitdist ([x1; x2], "burr", "By", [ones(100,1); 2*ones(100,1)]);
 [phat, pci] = burrfit (x1);
 assert ([pd(1).alpha, pd(1).c, pd(1).k], phat);
 assert (paramci (pd(1)), pci);
 [phat, pci] = burrfit (x2);
 assert ([pd(2).alpha, pd(2).c, pd(2).k], phat);
 assert (paramci (pd(2)), pci);
***** test
 x = exprnd (1, 100, 1);
 pd = fitdist (x, "exponential");
 [muhat, muci] = expfit (x);
 assert ([pd.mu], muhat);
 assert (paramci (pd), muci);
***** test
 x1 = exprnd (1, 100, 1);
 x2 = exprnd (5, 100, 1);
 pd = fitdist ([x1; x2], "exponential", "By", [ones(100,1); 2*ones(100,1)]);
 [muhat, muci] = expfit (x1);
 assert ([pd(1).mu], muhat);
 assert (paramci (pd(1)), muci);
 [muhat, muci] = expfit (x2);
 assert ([pd(2).mu], muhat);
 assert (paramci (pd(2)), muci);
***** test
 x = evrnd (1, 1, 100, 1);
 pd = fitdist (x, "ev");
 [phat, pci] = evfit (x);
 assert ([pd.mu, pd.sigma], phat);
 assert (paramci (pd), pci);
***** test
 x1 = evrnd (1, 1, 100, 1);
 x2 = evrnd (5, 2, 100, 1);
 pd = fitdist ([x1; x2], "extremevalue", "By", [ones(100,1); 2*ones(100,1)]);
 [phat, pci] = evfit (x1);
 assert ([pd(1).mu, pd(1).sigma], phat);
 assert (paramci (pd(1)), pci);
 [phat, pci] = evfit (x2);
 assert ([pd(2).mu, pd(2).sigma], phat);
 assert (paramci (pd(2)), pci);
***** test
 x = gamrnd (1, 1, 100, 1);
 pd = fitdist (x, "Gamma");
 [phat, pci] = gamfit (x);
 assert ([pd.a, pd.b], phat);
 assert (paramci (pd), pci);
***** test
 x1 = gamrnd (1, 1, 100, 1);
 x2 = gamrnd (5, 2, 100, 1);
 pd = fitdist ([x1; x2], "Gamma", "By", [ones(100,1); 2*ones(100,1)]);
 [phat, pci] = gamfit (x1);
 assert ([pd(1).a, pd(1).b], phat);
 assert (paramci (pd(1)), pci);
 [phat, pci] = gamfit (x2);
 assert ([pd(2).a, pd(2).b], phat);
 assert (paramci (pd(2)), pci);
***** test
 rand ("seed", 4);   # for reproducibility
 x = gevrnd (-0.5, 1, 2, 1000, 1);
 pd = fitdist (x, "generalizedextremevalue");
 [phat, pci] = gevfit (x);
 assert ([pd.k, pd.sigma, pd.mu], phat);
 assert (paramci (pd), pci);
***** test
 rand ("seed", 5);   # for reproducibility
 x1 = gevrnd (-0.5, 1, 2, 1000, 1);
 rand ("seed", 9);   # for reproducibility
 x2 = gevrnd (0, 1, -4, 1000, 1);
 pd = fitdist ([x1; x2], "gev", "By", [ones(1000,1); 2*ones(1000,1)]);
 [phat, pci] = gevfit (x1);
 assert ([pd(1).k, pd(1).sigma, pd(1).mu], phat);
 assert (paramci (pd(1)), pci);
 [phat, pci] = gevfit (x2);
 assert ([pd(2).k, pd(2).sigma, pd(2).mu], phat);
 assert (paramci (pd(2)), pci);
***** test
 x = gprnd (1, 1, 1, 100, 1);
 pd = fitdist (x, "GeneralizedPareto");
 [phat, pci] = gpfit (x, 1);
 assert ([pd.k, pd.sigma, pd.theta], phat);
 assert (paramci (pd), pci);
***** test
 x = gprnd (1, 1, 2, 100, 1);
 pd = fitdist (x, "GeneralizedPareto", "theta", 2);
 [phat, pci] = gpfit (x, 2);
 assert ([pd.k, pd.sigma, pd.theta], phat);
 assert (paramci (pd), pci);
***** test
 x1 = gprnd (1, 1, 1, 100, 1);
 x2 = gprnd (0, 2, 1, 100, 1);
 pd = fitdist ([x1; x2], "gp", "By", [ones(100,1); 2*ones(100,1)]);
 [phat, pci] = gpfit (x1, 1);
 assert ([pd(1).k, pd(1).sigma, pd(1).theta], phat);
 assert (paramci (pd(1)), pci);
 [phat, pci] = gpfit (x2, 1);
 assert ([pd(2).k, pd(2).sigma, pd(2).theta], phat);
 assert (paramci (pd(2)), pci);
***** test
 x1 = gprnd (3, 2, 2, 100, 1);
 x2 = gprnd (2, 3, 2, 100, 1);
 pd = fitdist ([x1; x2], "GeneralizedPareto", "theta", 2, ...
               "By", [ones(100,1); 2*ones(100,1)]);
 [phat, pci] = gpfit (x1, 2);
 assert ([pd(1).k, pd(1).sigma, pd(1).theta], phat);
 assert (paramci (pd(1)), pci);
 [phat, pci] = gpfit (x2, 2);
 assert ([pd(2).k, pd(2).sigma, pd(2).theta], phat);
 assert (paramci (pd(2)), pci);
***** test
 x = hnrnd (0, 1, 100, 1);
 pd = fitdist (x, "HalfNormal");
 [phat, pci] = hnfit (x, 0);
 assert ([pd.mu, pd.sigma], phat);
 assert (paramci (pd), pci);
***** test
 x = hnrnd (1, 1, 100, 1);
 pd = fitdist (x, "HalfNormal", "mu", 1);
 [phat, pci] = hnfit (x, 1);
 assert ([pd.mu, pd.sigma], phat);
 assert (paramci (pd), pci);
***** test
 x1 = hnrnd (0, 1, 100, 1);
 x2 = hnrnd (0, 2, 100, 1);
 pd = fitdist ([x1; x2], "HalfNormal", "By", [ones(100,1); 2*ones(100,1)]);
 [phat, pci] = hnfit (x1, 0);
 assert ([pd(1).mu, pd(1).sigma], phat);
 assert (paramci (pd(1)), pci);
 [phat, pci] = hnfit (x2, 0);
 assert ([pd(2).mu, pd(2).sigma], phat);
 assert (paramci (pd(2)), pci);
***** test
 x1 = hnrnd (2, 1, 100, 1);
 x2 = hnrnd (2, 2, 100, 1);
 pd = fitdist ([x1; x2], "HalfNormal", "mu", 2, ...
               "By", [ones(100,1); 2*ones(100,1)]);
 [phat, pci] = hnfit (x1, 2);
 assert ([pd(1).mu, pd(1).sigma], phat);
 assert (paramci (pd(1)), pci);
 [phat, pci] = hnfit (x2, 2);
 assert ([pd(2).mu, pd(2).sigma], phat);
 assert (paramci (pd(2)), pci);
***** test
 x = invgrnd (1, 1, 100, 1);
 pd = fitdist (x, "InverseGaussian");
 [phat, pci] = invgfit (x);
 assert ([pd.mu, pd.lambda], phat);
 assert (paramci (pd), pci);
***** test
 x1 = invgrnd (1, 1, 100, 1);
 x2 = invgrnd (5, 2, 100, 1);
 pd = fitdist ([x1; x2], "InverseGaussian", "By", [ones(100,1); 2*ones(100,1)]);
 [phat, pci] = invgfit (x1);
 assert ([pd(1).mu, pd(1).lambda], phat);
 assert (paramci (pd(1)), pci);
 [phat, pci] = invgfit (x2);
 assert ([pd(2).mu, pd(2).lambda], phat);
 assert (paramci (pd(2)), pci);
***** test
 x = logirnd (1, 1, 100, 1);
 pd = fitdist (x, "logistic");
 [phat, pci] = logifit (x);
 assert ([pd.mu, pd.sigma], phat);
 assert (paramci (pd), pci);
***** test
 x1 = logirnd (1, 1, 100, 1);
 x2 = logirnd (5, 2, 100, 1);
 pd = fitdist ([x1; x2], "logistic", "By", [ones(100,1); 2*ones(100,1)]);
 [phat, pci] = logifit (x1);
 assert ([pd(1).mu, pd(1).sigma], phat);
 assert (paramci (pd(1)), pci);
 [phat, pci] = logifit (x2);
 assert ([pd(2).mu, pd(2).sigma], phat);
 assert (paramci (pd(2)), pci);
***** test
 x = loglrnd (1, 1, 100, 1);
 pd = fitdist (x, "loglogistic");
 [phat, pci] = loglfit (x);
 assert ([pd.mu, pd.sigma], phat);
 assert (paramci (pd), pci);
***** test
 x1 = loglrnd (1, 1, 100, 1);
 x2 = loglrnd (5, 2, 100, 1);
 pd = fitdist ([x1; x2], "loglogistic", "By", [ones(100,1); 2*ones(100,1)]);
 [phat, pci] = loglfit (x1);
 assert ([pd(1).mu, pd(1).sigma], phat);
 assert (paramci (pd(1)), pci);
 [phat, pci] = loglfit (x2);
 assert ([pd(2).mu, pd(2).sigma], phat);
 assert (paramci (pd(2)), pci);
***** test
 x = lognrnd (1, 1, 100, 1);
 pd = fitdist (x, "lognormal");
 [phat, pci] = lognfit (x);
 assert ([pd.mu, pd.sigma], phat);
 assert (paramci (pd), pci);
***** test
 x1 = lognrnd (1, 1, 100, 1);
 x2 = lognrnd (5, 2, 100, 1);
 pd = fitdist ([x1; x2], "lognormal", "By", [ones(100,1); 2*ones(100,1)]);
 [phat, pci] = lognfit (x1);
 assert ([pd(1).mu, pd(1).sigma], phat);
 assert (paramci (pd(1)), pci);
 [phat, pci] = lognfit (x2);
 assert ([pd(2).mu, pd(2).sigma], phat);
 assert (paramci (pd(2)), pci);
***** test
 x = nakarnd (2, 0.5, 100, 1);
 pd = fitdist (x, "Nakagami");
 [phat, pci] = nakafit (x);
 assert ([pd.mu, pd.omega], phat);
 assert (paramci (pd), pci);
***** test
 x1 = nakarnd (2, 0.5, 100, 1);
 x2 = nakarnd (5, 0.8, 100, 1);
 pd = fitdist ([x1; x2], "Nakagami", "By", [ones(100,1); 2*ones(100,1)]);
 [phat, pci] = nakafit (x1);
 assert ([pd(1).mu, pd(1).omega], phat);
 assert (paramci (pd(1)), pci);
 [phat, pci] = nakafit (x2);
 assert ([pd(2).mu, pd(2).omega], phat);
 assert (paramci (pd(2)), pci);
***** test
 randp ("seed", 123);
 randg ("seed", 321);
 x = nbinrnd (2, 0.5, 100, 1);
 pd = fitdist (x, "negativebinomial");
 [phat, pci] = nbinfit (x);
 assert ([pd.R, pd.P], phat);
 assert (paramci (pd), pci);
***** test
 randp ("seed", 345);
 randg ("seed", 543);
 x1 = nbinrnd (2, 0.5, 100, 1);
 randp ("seed", 432);
 randg ("seed", 234);
 x2 = nbinrnd (5, 0.8, 100, 1);
 pd = fitdist ([x1; x2], "nbin", "By", [ones(100,1); 2*ones(100,1)]);
 [phat, pci] = nbinfit (x1);
 assert ([pd(1).R, pd(1).P], phat);
 assert (paramci (pd(1)), pci);
 [phat, pci] = nbinfit (x2);
 assert ([pd(2).R, pd(2).P], phat);
 assert (paramci (pd(2)), pci);
***** test
 x = normrnd (1, 1, 100, 1);
 pd = fitdist (x, "normal");
 [muhat, sigmahat, muci, sigmaci] = normfit (x);
 assert ([pd.mu, pd.sigma], [muhat, sigmahat]);
 assert (paramci (pd), [muci, sigmaci]);
***** test
 x1 = normrnd (1, 1, 100, 1);
 x2 = normrnd (5, 2, 100, 1);
 pd = fitdist ([x1; x2], "normal", "By", [ones(100,1); 2*ones(100,1)]);
 [muhat, sigmahat, muci, sigmaci] = normfit (x1);
 assert ([pd(1).mu, pd(1).sigma], [muhat, sigmahat]);
 assert (paramci (pd(1)), [muci, sigmaci]);
 [muhat, sigmahat, muci, sigmaci] = normfit (x2);
 assert ([pd(2).mu, pd(2).sigma], [muhat, sigmahat]);
 assert (paramci (pd(2)), [muci, sigmaci]);
***** test
 x = poissrnd (1, 100, 1);
 pd = fitdist (x, "poisson");
 [phat, pci] = poissfit (x);
 assert (pd.lambda, phat);
 assert (paramci (pd), pci);
***** test
 x1 = poissrnd (1, 100, 1);
 x2 = poissrnd (5, 100, 1);
 pd = fitdist ([x1; x2], "poisson", "By", [ones(100,1); 2*ones(100,1)]);
 [phat, pci] = poissfit (x1);
 assert (pd(1).lambda, phat);
 assert (paramci (pd(1)), pci);
 [phat, pci] = poissfit (x2);
 assert (pd(2).lambda, phat);
 assert (paramci (pd(2)), pci);
***** test
 x = raylrnd (1, 100, 1);
 pd = fitdist (x, "rayleigh");
 [phat, pci] = raylfit (x);
 assert (pd.sigma, phat);
 assert (paramci (pd), pci);
***** test
 x1 = raylrnd (1, 100, 1);
 x2 = raylrnd (5, 100, 1);
 pd = fitdist ([x1; x2], "rayleigh", "By", [ones(100,1); 2*ones(100,1)]);
 [phat, pci] = raylfit (x1);
 assert ( pd(1).sigma, phat);
 assert (paramci (pd(1)), pci);
 [phat, pci] = raylfit (x2);
 assert (pd(2).sigma, phat);
 assert (paramci (pd(2)), pci);
***** test
 x = ricernd (1, 1, 100, 1);
 pd = fitdist (x, "rician");
 [phat, pci] = ricefit (x);
 assert ([pd.s, pd.sigma], phat);
 assert (paramci (pd), pci);
***** test
 x1 = ricernd (1, 1, 100, 1);
 x2 = ricernd (5, 2, 100, 1);
 pd = fitdist ([x1; x2], "rician", "By", [ones(100,1); 2*ones(100,1)]);
 [phat, pci] = ricefit (x1);
 assert ([pd(1).s, pd(1).sigma], phat);
 assert (paramci (pd(1)), pci);
 [phat, pci] = ricefit (x2);
 assert ([pd(2).s, pd(2).sigma], phat);
 assert (paramci (pd(2)), pci);
***** warning <fitdist: 'Stable' distribution not supported yet.> ...
 fitdist ([1 2 3 4 5], "Stable");
***** test
 x = tlsrnd (0, 1, 1, 100, 1);
 pd = fitdist (x, "tlocationscale");
 [phat, pci] = tlsfit (x);
 assert ([pd.mu, pd.sigma, pd.nu], phat);
 assert (paramci (pd), pci);
***** test
 x1 = tlsrnd (0, 1, 1, 100, 1);
 x2 = tlsrnd (5, 2, 1, 100, 1);
 pd = fitdist ([x1; x2], "tlocationscale", "By", [ones(100,1); 2*ones(100,1)]);
 [phat, pci] = tlsfit (x1);
 assert ([pd(1).mu, pd(1).sigma, pd(1).nu], phat);
 assert (paramci (pd(1)), pci);
 [phat, pci] = tlsfit (x2);
 assert ([pd(2).mu, pd(2).sigma, pd(2).nu], phat);
 assert (paramci (pd(2)), pci);
***** test
 x = [1 2 3 4 5];
 pd = fitdist (x, "weibull");
 [phat, pci] = wblfit (x);
 assert ([pd.lambda, pd.k], phat);
 assert (paramci (pd), pci);
***** test
 x = [1 2 3 4 5 6 7 8 9 10];
 pd = fitdist (x, "weibull", "By", [1 1 1 1 1 2 2 2 2 2]);
 [phat, pci] = wblfit (x(1:5));
 assert ([pd(1).lambda, pd(1).k], phat);
 assert (paramci (pd(1)), pci);
 [phat, pci] = wblfit (x(6:10));
 assert ([pd(2).lambda, pd(2).k], phat);
 assert (paramci (pd(2)), pci);
***** error <fitdist: DISTNAME is required.> fitdist (1)
***** error <fitdist: DISTNAME must be a character vector.> fitdist (1, ["as";"sd"])
***** error <fitdist: unrecognized distribution name.> fitdist (1, "some")
***** error <fitdist: X must be a numeric vector of real values.> ...
 fitdist (ones (2), "normal")
***** error <fitdist: X must be a numeric vector of real values.> ...
 fitdist ([i, 2, 3], "normal")
***** error <fitdist: X must be a numeric vector of real values.> ...
 fitdist (["a", "s", "d"], "normal")
***** error <fitdist: optional arguments must be in NAME-VALUE pairs.> ...
 fitdist ([1, 2, 3], "normal", "By")
***** error <fitdist: GROUPVAR argument must have the same size as the input data in X.> ...
 fitdist ([1, 2, 3], "normal", "By", [1, 2])
***** error <fitdist: 'censoring' argument must have the same size as the input data in X.> ...
 fitdist ([1, 2, 3], "normal", "Censoring", [1, 2])
***** error <fitdist: 'frequency' argument must have the same size as the input data in X.> ...
 fitdist ([1, 2, 3], "normal", "frequency", [1, 2])
***** error <fitdist: 'frequency' argument must contain non-negative integer values.> ...
 fitdist ([1, 2, 3], "negativebinomial", "frequency", [1, -2, 3])
***** error <fitdist: invalid value for 'alpha' argument.> ...
 fitdist ([1, 2, 3], "normal", "alpha", [1, 2])
***** error <fitdist: invalid value for 'alpha' argument.> ...
 fitdist ([1, 2, 3], "normal", "alpha", i)
***** error <fitdist: invalid value for 'alpha' argument.> ...
 fitdist ([1, 2, 3], "normal", "alpha", -0.5)
***** error <fitdist: invalid value for 'alpha' argument.> ...
 fitdist ([1, 2, 3], "normal", "alpha", 1.5)
***** error <fitdist: 'ntrials' argument must be a positive integer scalar value.> ...
 fitdist ([1, 2, 3], "normal", "ntrials", [1, 2])
***** error <fitdist: 'ntrials' argument must be a positive integer scalar value.> ...
 fitdist ([1, 2, 3], "normal", "ntrials", 0)
***** error <fitdist: 'options' argument must be a structure compatible for 'fminsearch'.> ...
 fitdist ([1, 2, 3], "normal", "options", 0)
***** error <fitdist: 'options' argument must be a structure compatible for 'fminsearch'.> ...
 fitdist ([1, 2, 3], "normal", "options", struct ("options", 1))
***** warning fitdist ([1, 2, 3], "kernel", "kernel", "normal");
***** warning fitdist ([1, 2, 3], "kernel", "support", "positive");
***** warning fitdist ([1, 2, 3], "kernel", "width", 1);
***** error <fitdist: unknown parameter name.> ...
 fitdist ([1, 2, 3], "normal", "param", struct ("options", 1))
***** error <fitdist: must define GROUPVAR for more than one output arguments.> ...
 [pdca, gn, gl] = fitdist ([1, 2, 3], "normal");
***** error <fitdist: invalid THETA value for generalized Pareto distribution.> ...
 fitdist ([1, 2, 3], "generalizedpareto", "theta", 2);
***** error <fitdist: invalid MU value for half-normal distribution.> ...
 fitdist ([1, 2, 3], "halfnormal", "mu", 2);
77 tests, 77 passed, 0 known failure, 0 skipped
[inst/dist_wrap/random.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_wrap/random.m
***** assert (size (random ("Beta", 5, 2, 2, 10)), size (betarnd (5, 2, 2, 10)))
***** assert (size (random ("beta", 5, 2, 2, 10)), size (betarnd (5, 2, 2, 10)))
***** assert (size (random ("Binomial", 5, 2, [10, 20])), size (binornd (5, 2, 10, 20)))
***** assert (size (random ("bino", 5, 2, [10, 20])), size (binornd (5, 2, 10, 20)))
***** assert (size (random ("Birnbaum-Saunders", 5, 2, [10, 20])), size (bisarnd (5, 2, 10, 20)))
***** assert (size (random ("bisa", 5, 2, [10, 20])), size (bisarnd (5, 2, 10, 20)))
***** assert (size (random ("Burr", 5, 2, 2, [10, 20])), size (burrrnd (5, 2, 2, 10, 20)))
***** assert (size (random ("burr", 5, 2, 2, [10, 20])), size (burrrnd (5, 2, 2, 10, 20)))
***** assert (size (random ("Cauchy", 5, 2, [10, 20])), size (cauchyrnd (5, 2, 10, 20)))
***** assert (size (random ("cauchy", 5, 2, [10, 20])), size (cauchyrnd (5, 2, 10, 20)))
***** assert (size (random ("Chi-squared", 5, [10, 20])), size (chi2rnd (5, 10, 20)))
***** assert (size (random ("chi2", 5, [10, 20])), size (chi2rnd (5, 10, 20)))
***** assert (size (random ("Extreme Value", 5, 2, [10, 20])), size (evrnd (5, 2, 10, 20)))
***** assert (size (random ("ev", 5, 2, [10, 20])), size (evrnd (5, 2, 10, 20)))
***** assert (size (random ("Exponential", 5, [10, 20])), size (exprnd (5, 10, 20)))
***** assert (size (random ("exp", 5, [10, 20])), size (exprnd (5, 10, 20)))
***** assert (size (random ("F-Distribution", 5, 2, [10, 20])), size (frnd (5, 2, 10, 20)))
***** assert (size (random ("f", 5, 2, [10, 20])), size (frnd (5, 2, 10, 20)))
***** assert (size (random ("Gamma", 5, 2, [10, 20])), size (gamrnd (5, 2, 10, 20)))
***** assert (size (random ("gam", 5, 2, [10, 20])), size (gamrnd (5, 2, 10, 20)))
***** assert (size (random ("Geometric", 5, [10, 20])), size (geornd (5, 10, 20)))
***** assert (size (random ("geo", 5, [10, 20])), size (geornd (5, 10, 20)))
***** assert (size (random ("Generalized Extreme Value", 5, 2, 2, [10, 20])), size (gevrnd (5, 2, 2, 10, 20)))
***** assert (size (random ("gev", 5, 2, 2, [10, 20])), size (gevrnd (5, 2, 2, 10, 20)))
***** assert (size (random ("Generalized Pareto", 5, 2, 2, [10, 20])), size (gprnd (5, 2, 2, 10, 20)))
***** assert (size (random ("gp", 5, 2, 2, [10, 20])), size (gprnd (5, 2, 2, 10, 20)))
***** assert (size (random ("Gumbel", 5, 2, [10, 20])), size (gumbelrnd (5, 2, 10, 20)))
***** assert (size (random ("gumbel", 5, 2, [10, 20])), size (gumbelrnd (5, 2, 10, 20)))
***** assert (size (random ("Half-normal", 5, 2, [10, 20])), size (hnrnd (5, 2, 10, 20)))
***** assert (size (random ("hn", 5, 2, [10, 20])), size (hnrnd (5, 2, 10, 20)))
***** assert (size (random ("Hypergeometric", 5, 2, 2, [10, 20])), size (hygernd (5, 2, 2, 10, 20)))
***** assert (size (random ("hyge", 5, 2, 2, [10, 20])), size (hygernd (5, 2, 2, 10, 20)))
***** assert (size (random ("Inverse Gaussian", 5, 2, [10, 20])), size (invgrnd (5, 2, 10, 20)))
***** assert (size (random ("invg", 5, 2, [10, 20])), size (invgrnd (5, 2, 10, 20)))
***** assert (size (random ("Laplace", 5, 2, [10, 20])), size (laplacernd (5, 2, 10, 20)))
***** assert (size (random ("laplace", 5, 2, [10, 20])), size (laplacernd (5, 2, 10, 20)))
***** assert (size (random ("Logistic", 5, 2, [10, 20])), size (logirnd (5, 2, 10, 20)))
***** assert (size (random ("logi", 5, 2, [10, 20])), size (logirnd (5, 2, 10, 20)))
***** assert (size (random ("Log-Logistic", 5, 2, [10, 20])), size (loglrnd (5, 2, 10, 20)))
***** assert (size (random ("logl", 5, 2, [10, 20])), size (loglrnd (5, 2, 10, 20)))
***** assert (size (random ("Lognormal", 5, 2, [10, 20])), size (lognrnd (5, 2, 10, 20)))
***** assert (size (random ("logn", 5, 2, [10, 20])), size (lognrnd (5, 2, 10, 20)))
***** assert (size (random ("Nakagami", 5, 2, [10, 20])), size (nakarnd (5, 2, 10, 20)))
***** assert (size (random ("naka", 5, 2, [10, 20])), size (nakarnd (5, 2, 10, 20)))
***** assert (size (random ("Negative Binomial", 5, 2, [10, 20])), size (nbinrnd (5, 2, 10, 20)))
***** assert (size (random ("nbin", 5, 2, [10, 20])), size (nbinrnd (5, 2, 10, 20)))
***** assert (size (random ("Noncentral F-Distribution", 5, 2, 2, [10, 20])), size (ncfrnd (5, 2, 2, 10, 20)))
***** assert (size (random ("ncf", 5, 2, 2, [10, 20])), size (ncfrnd (5, 2, 2, 10, 20)))
***** assert (size (random ("Noncentral Student T", 5, 2, [10, 20])), size (nctrnd (5, 2, 10, 20)))
***** assert (size (random ("nct", 5, 2, [10, 20])), size (nctrnd (5, 2, 10, 20)))
***** assert (size (random ("Noncentral Chi-Squared", 5, 2, [10, 20])), size (ncx2rnd (5, 2, 10, 20)))
***** assert (size (random ("ncx2", 5, 2, [10, 20])), size (ncx2rnd (5, 2, 10, 20)))
***** assert (size (random ("Normal", 5, 2, [10, 20])), size (normrnd (5, 2, 10, 20)))
***** assert (size (random ("norm", 5, 2, [10, 20])), size (normrnd (5, 2, 10, 20)))
***** assert (size (random ("Poisson", 5, [10, 20])), size (poissrnd (5, 10, 20)))
***** assert (size (random ("poiss", 5, [10, 20])), size (poissrnd (5, 10, 20)))
***** assert (size (random ("Rayleigh", 5, [10, 20])), size (raylrnd (5, 10, 20)))
***** assert (size (random ("rayl", 5, [10, 20])), size (raylrnd (5, 10, 20)))
***** assert (size (random ("Rician", 5, 1, [10, 20])), size (ricernd (5, 1, 10, 20)))
***** assert (size (random ("rice", 5, 1, [10, 20])), size (ricernd (5, 1, 10, 20)))
***** assert (size (random ("Student T", 5, [10, 20])), size (trnd (5, 10, 20)))
***** assert (size (random ("t", 5, [10, 20])), size (trnd (5, 10, 20)))
***** assert (size (random ("location-scale T", 5, 1, 2, [10, 20])), size (tlsrnd (5, 1, 2, 10, 20)))
***** assert (size (random ("tls", 5, 1, 2, [10, 20])), size (tlsrnd (5, 1, 2, 10, 20)))
***** assert (size (random ("Triangular", 5, 2, 2, [10, 20])), size (trirnd (5, 2, 2, 10, 20)))
***** assert (size (random ("tri", 5, 2, 2, [10, 20])), size (trirnd (5, 2, 2, 10, 20)))
***** assert (size (random ("Discrete Uniform", 5, [10, 20])), size (unidrnd (5, 10, 20)))
***** assert (size (random ("unid", 5, [10, 20])), size (unidrnd (5, 10, 20)))
***** assert (size (random ("Uniform", 5, 2, [10, 20])), size (unifrnd (5, 2, 10, 20)))
***** assert (size (random ("unif", 5, 2, [10, 20])), size (unifrnd (5, 2, 10, 20)))
***** assert (size (random ("Von Mises", 5, 2, [10, 20])), size (vmrnd (5, 2, 10, 20)))
***** assert (size (random ("vm", 5, 2, [10, 20])), size (vmrnd (5, 2, 10, 20)))
***** assert (size (random ("Weibull", 5, 2, [10, 20])), size (wblrnd (5, 2, 10, 20)))
***** assert (size (random ("wbl", 5, 2, [10, 20])), size (wblrnd (5, 2, 10, 20)))
***** error<random: distribution NAME must a char string.> random (1)
***** error<random: distribution NAME must a char string.> random ({"beta"})
***** error<random: distribution parameters must be numeric.> ...
 random ("Beta", "a", 2)
***** error<random: distribution parameters must be numeric.> ...
 random ("Beta", 5, "")
***** error<random: distribution parameters must be numeric.> ...
 random ("Beta", 5, {2})
***** error<random: distribution parameters must be numeric.> ...
 random ("Beta", "a", 2, 2, 10)
***** error<random: distribution parameters must be numeric.> ...
 random ("Beta", 5, "", 2, 10)
***** error<random: distribution parameters must be numeric.> ...
 random ("Beta", 5, {2}, 2, 10)
***** error<random: distribution parameters must be numeric.> ...
 random ("Beta", 5, "", 2, 10)
***** error<random: chi2 distribution requires 1 parameter.> random ("chi2")
***** error<random: Beta distribution requires 2 parameters.> random ("Beta", 5)
***** error<random: Burr distribution requires 3 parameters.> random ("Burr", 5)
***** error<random: Burr distribution requires 3 parameters.> random ("Burr", 5, 2)
87 tests, 87 passed, 0 known failure, 0 skipped
[inst/dist_wrap/pdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_wrap/pdf.m
***** shared x
 x = [1:5];
***** assert (pdf ("Beta", x, 5, 2), betapdf (x, 5, 2))
***** assert (pdf ("beta", x, 5, 2), betapdf (x, 5, 2))
***** assert (pdf ("Binomial", x, 5, 2), binopdf (x, 5, 2))
***** assert (pdf ("bino", x, 5, 2), binopdf (x, 5, 2))
***** assert (pdf ("Birnbaum-Saunders", x, 5, 2), bisapdf (x, 5, 2))
***** assert (pdf ("bisa", x, 5, 2), bisapdf (x, 5, 2))
***** assert (pdf ("Burr", x, 5, 2, 2), burrpdf (x, 5, 2, 2))
***** assert (pdf ("burr", x, 5, 2, 2), burrpdf (x, 5, 2, 2))
***** assert (pdf ("Cauchy", x, 5, 2), cauchypdf (x, 5, 2))
***** assert (pdf ("cauchy", x, 5, 2), cauchypdf (x, 5, 2))
***** assert (pdf ("Chi-squared", x, 5), chi2pdf (x, 5))
***** assert (pdf ("chi2", x, 5), chi2pdf (x, 5))
***** assert (pdf ("Extreme Value", x, 5, 2), evpdf (x, 5, 2))
***** assert (pdf ("ev", x, 5, 2), evpdf (x, 5, 2))
***** assert (pdf ("Exponential", x, 5), exppdf (x, 5))
***** assert (pdf ("exp", x, 5), exppdf (x, 5))
***** assert (pdf ("F-Distribution", x, 5, 2), fpdf (x, 5, 2))
***** assert (pdf ("f", x, 5, 2), fpdf (x, 5, 2))
***** assert (pdf ("Gamma", x, 5, 2), gampdf (x, 5, 2))
***** assert (pdf ("gam", x, 5, 2), gampdf (x, 5, 2))
***** assert (pdf ("Geometric", x, 5), geopdf (x, 5))
***** assert (pdf ("geo", x, 5), geopdf (x, 5))
***** assert (pdf ("Generalized Extreme Value", x, 5, 2, 2), gevpdf (x, 5, 2, 2))
***** assert (pdf ("gev", x, 5, 2, 2), gevpdf (x, 5, 2, 2))
***** assert (pdf ("Generalized Pareto", x, 5, 2, 2), gppdf (x, 5, 2, 2))
***** assert (pdf ("gp", x, 5, 2, 2), gppdf (x, 5, 2, 2))
***** assert (pdf ("Gumbel", x, 5, 2), gumbelpdf (x, 5, 2))
***** assert (pdf ("gumbel", x, 5, 2), gumbelpdf (x, 5, 2))
***** assert (pdf ("Half-normal", x, 5, 2), hnpdf (x, 5, 2))
***** assert (pdf ("hn", x, 5, 2), hnpdf (x, 5, 2))
***** assert (pdf ("Hypergeometric", x, 5, 2, 2), hygepdf (x, 5, 2, 2))
***** assert (pdf ("hyge", x, 5, 2, 2), hygepdf (x, 5, 2, 2))
***** assert (pdf ("Inverse Gaussian", x, 5, 2), invgpdf (x, 5, 2))
***** assert (pdf ("invg", x, 5, 2), invgpdf (x, 5, 2))
***** assert (pdf ("Laplace", x, 5, 2), laplacepdf (x, 5, 2))
***** assert (pdf ("laplace", x, 5, 2), laplacepdf (x, 5, 2))
***** assert (pdf ("Logistic", x, 5, 2), logipdf (x, 5, 2))
***** assert (pdf ("logi", x, 5, 2), logipdf (x, 5, 2))
***** assert (pdf ("Log-Logistic", x, 5, 2), loglpdf (x, 5, 2))
***** assert (pdf ("logl", x, 5, 2), loglpdf (x, 5, 2))
***** assert (pdf ("Lognormal", x, 5, 2), lognpdf (x, 5, 2))
***** assert (pdf ("logn", x, 5, 2), lognpdf (x, 5, 2))
***** assert (pdf ("Nakagami", x, 5, 2), nakapdf (x, 5, 2))
***** assert (pdf ("naka", x, 5, 2), nakapdf (x, 5, 2))
***** assert (pdf ("Negative Binomial", x, 5, 2), nbinpdf (x, 5, 2))
***** assert (pdf ("nbin", x, 5, 2), nbinpdf (x, 5, 2))
***** assert (pdf ("Noncentral F-Distribution", x, 5, 2, 2), ncfpdf (x, 5, 2, 2))
***** assert (pdf ("ncf", x, 5, 2, 2), ncfpdf (x, 5, 2, 2))
***** assert (pdf ("Noncentral Student T", x, 5, 2), nctpdf (x, 5, 2))
***** assert (pdf ("nct", x, 5, 2), nctpdf (x, 5, 2))
***** assert (pdf ("Noncentral Chi-Squared", x, 5, 2), ncx2pdf (x, 5, 2))
***** assert (pdf ("ncx2", x, 5, 2), ncx2pdf (x, 5, 2))
***** assert (pdf ("Normal", x, 5, 2), normpdf (x, 5, 2))
***** assert (pdf ("norm", x, 5, 2), normpdf (x, 5, 2))
***** assert (pdf ("Poisson", x, 5), poisspdf (x, 5))
***** assert (pdf ("poiss", x, 5), poisspdf (x, 5))
***** assert (pdf ("Rayleigh", x, 5), raylpdf (x, 5))
***** assert (pdf ("rayl", x, 5), raylpdf (x, 5))
***** assert (pdf ("Rician", x, 5, 1), ricepdf (x, 5, 1))
***** assert (pdf ("rice", x, 5, 1), ricepdf (x, 5, 1))
***** assert (pdf ("Student T", x, 5), tpdf (x, 5))
***** assert (pdf ("t", x, 5), tpdf (x, 5))
***** assert (pdf ("location-scale T", x, 5, 1, 2), tlspdf (x, 5, 1, 2))
***** assert (pdf ("tls", x, 5, 1, 2), tlspdf (x, 5, 1, 2))
***** assert (pdf ("Triangular", x, 5, 2, 2), tripdf (x, 5, 2, 2))
***** assert (pdf ("tri", x, 5, 2, 2), tripdf (x, 5, 2, 2))
***** assert (pdf ("Discrete Uniform", x, 5), unidpdf (x, 5))
***** assert (pdf ("unid", x, 5), unidpdf (x, 5))
***** assert (pdf ("Uniform", x, 5, 2), unifpdf (x, 5, 2))
***** assert (pdf ("unif", x, 5, 2), unifpdf (x, 5, 2))
***** assert (pdf ("Von Mises", x, 5, 2), vmpdf (x, 5, 2))
***** assert (pdf ("vm", x, 5, 2), vmpdf (x, 5, 2))
***** assert (pdf ("Weibull", x, 5, 2), wblpdf (x, 5, 2))
***** assert (pdf ("wbl", x, 5, 2), wblpdf (x, 5, 2))
***** error<pdf: distribution NAME must a char string.> pdf (1)
***** error<pdf: distribution NAME must a char string.> pdf ({"beta"})
***** error<pdf: X must be numeric.> pdf ("beta", {[1 2 3 4 5]})
***** error<pdf: X must be numeric.> pdf ("beta", "text")
***** error<pdf: values in X must be real.> pdf ("beta", 1+i)
***** error<pdf: distribution parameters must be numeric.> ...
 pdf ("Beta", x, "a", 2)
***** error<pdf: distribution parameters must be numeric.> ...
 pdf ("Beta", x, 5, "")
***** error<pdf: distribution parameters must be numeric.> ...
 pdf ("Beta", x, 5, {2})
***** error<pdf: chi2 distribution requires 1 parameter.> pdf ("chi2", x)
***** error<pdf: Beta distribution requires 2 parameters.> pdf ("Beta", x, 5)
***** error<pdf: Burr distribution requires 3 parameters.> pdf ("Burr", x, 5)
***** error<pdf: Burr distribution requires 3 parameters.> pdf ("Burr", x, 5, 2)
86 tests, 86 passed, 0 known failure, 0 skipped
[inst/dist_wrap/cdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_wrap/cdf.m
***** shared x
 x = [1:5];
***** assert (cdf ("Beta", x, 5, 2), betacdf (x, 5, 2))
***** assert (cdf ("beta", x, 5, 2, "upper"), betacdf (x, 5, 2, "upper"))
***** assert (cdf ("Binomial", x, 5, 2), binocdf (x, 5, 2))
***** assert (cdf ("bino", x, 5, 2, "upper"), binocdf (x, 5, 2, "upper"))
***** assert (cdf ("Birnbaum-Saunders", x, 5, 2), bisacdf (x, 5, 2))
***** assert (cdf ("bisa", x, 5, 2, "upper"), bisacdf (x, 5, 2, "upper"))
***** assert (cdf ("Burr", x, 5, 2, 2), burrcdf (x, 5, 2, 2))
***** assert (cdf ("burr", x, 5, 2, 2, "upper"), burrcdf (x, 5, 2, 2, "upper"))
***** assert (cdf ("Cauchy", x, 5, 2), cauchycdf (x, 5, 2))
***** assert (cdf ("cauchy", x, 5, 2, "upper"), cauchycdf (x, 5, 2, "upper"))
***** assert (cdf ("Chi-squared", x, 5), chi2cdf (x, 5))
***** assert (cdf ("chi2", x, 5, "upper"), chi2cdf (x, 5, "upper"))
***** assert (cdf ("Extreme Value", x, 5, 2), evcdf (x, 5, 2))
***** assert (cdf ("ev", x, 5, 2, "upper"), evcdf (x, 5, 2, "upper"))
***** assert (cdf ("Exponential", x, 5), expcdf (x, 5))
***** assert (cdf ("exp", x, 5, "upper"), expcdf (x, 5, "upper"))
***** assert (cdf ("F-Distribution", x, 5, 2), fcdf (x, 5, 2))
***** assert (cdf ("f", x, 5, 2, "upper"), fcdf (x, 5, 2, "upper"))
***** assert (cdf ("Gamma", x, 5, 2), gamcdf (x, 5, 2))
***** assert (cdf ("gam", x, 5, 2, "upper"), gamcdf (x, 5, 2, "upper"))
***** assert (cdf ("Geometric", x, 5), geocdf (x, 5))
***** assert (cdf ("geo", x, 5, "upper"), geocdf (x, 5, "upper"))
***** assert (cdf ("Generalized Extreme Value", x, 5, 2, 2), gevcdf (x, 5, 2, 2))
***** assert (cdf ("gev", x, 5, 2, 2, "upper"), gevcdf (x, 5, 2, 2, "upper"))
***** assert (cdf ("Generalized Pareto", x, 5, 2, 2), gpcdf (x, 5, 2, 2))
***** assert (cdf ("gp", x, 5, 2, 2, "upper"), gpcdf (x, 5, 2, 2, "upper"))
***** assert (cdf ("Gumbel", x, 5, 2), gumbelcdf (x, 5, 2))
***** assert (cdf ("gumbel", x, 5, 2, "upper"), gumbelcdf (x, 5, 2, "upper"))
***** assert (cdf ("Half-normal", x, 5, 2), hncdf (x, 5, 2))
***** assert (cdf ("hn", x, 5, 2, "upper"), hncdf (x, 5, 2, "upper"))
***** assert (cdf ("Hypergeometric", x, 5, 2, 2), hygecdf (x, 5, 2, 2))
***** assert (cdf ("hyge", x, 5, 2, 2, "upper"), hygecdf (x, 5, 2, 2, "upper"))
***** assert (cdf ("Inverse Gaussian", x, 5, 2), invgcdf (x, 5, 2))
***** assert (cdf ("invg", x, 5, 2, "upper"), invgcdf (x, 5, 2, "upper"))
***** assert (cdf ("Laplace", x, 5, 2), laplacecdf (x, 5, 2))
***** assert (cdf ("laplace", x, 5, 2, "upper"), laplacecdf (x, 5, 2, "upper"))
***** assert (cdf ("Logistic", x, 5, 2), logicdf (x, 5, 2))
***** assert (cdf ("logi", x, 5, 2, "upper"), logicdf (x, 5, 2, "upper"))
***** assert (cdf ("Log-Logistic", x, 5, 2), loglcdf (x, 5, 2))
***** assert (cdf ("logl", x, 5, 2, "upper"), loglcdf (x, 5, 2, "upper"))
***** assert (cdf ("Lognormal", x, 5, 2), logncdf (x, 5, 2))
***** assert (cdf ("logn", x, 5, 2, "upper"), logncdf (x, 5, 2, "upper"))
***** assert (cdf ("Nakagami", x, 5, 2), nakacdf (x, 5, 2))
***** assert (cdf ("naka", x, 5, 2, "upper"), nakacdf (x, 5, 2, "upper"))
***** assert (cdf ("Negative Binomial", x, 5, 2), nbincdf (x, 5, 2))
***** assert (cdf ("nbin", x, 5, 2, "upper"), nbincdf (x, 5, 2, "upper"))
***** assert (cdf ("Noncentral F-Distribution", x, 5, 2, 2), ncfcdf (x, 5, 2, 2))
***** assert (cdf ("ncf", x, 5, 2, 2, "upper"), ncfcdf (x, 5, 2, 2, "upper"))
***** assert (cdf ("Noncentral Student T", x, 5, 2), nctcdf (x, 5, 2))
***** assert (cdf ("nct", x, 5, 2, "upper"), nctcdf (x, 5, 2, "upper"))
***** assert (cdf ("Noncentral Chi-Squared", x, 5, 2), ncx2cdf (x, 5, 2))
***** assert (cdf ("ncx2", x, 5, 2, "upper"), ncx2cdf (x, 5, 2, "upper"))
***** assert (cdf ("Normal", x, 5, 2), normcdf (x, 5, 2))
***** assert (cdf ("norm", x, 5, 2, "upper"), normcdf (x, 5, 2, "upper"))
***** assert (cdf ("Poisson", x, 5), poisscdf (x, 5))
***** assert (cdf ("poiss", x, 5, "upper"), poisscdf (x, 5, "upper"))
***** assert (cdf ("Rayleigh", x, 5), raylcdf (x, 5))
***** assert (cdf ("rayl", x, 5, "upper"), raylcdf (x, 5, "upper"))
***** assert (cdf ("Rician", x, 5, 1), ricecdf (x, 5, 1))
***** assert (cdf ("rice", x, 5, 1, "upper"), ricecdf (x, 5, 1, "upper"))
***** assert (cdf ("Student T", x, 5), tcdf (x, 5))
***** assert (cdf ("t", x, 5, "upper"), tcdf (x, 5, "upper"))
***** assert (cdf ("location-scale T", x, 5, 1, 2), tlscdf (x, 5, 1, 2))
***** assert (cdf ("tls", x, 5, 1, 2, "upper"), tlscdf (x, 5, 1, 2, "upper"))
***** assert (cdf ("Triangular", x, 5, 2, 2), tricdf (x, 5, 2, 2))
***** assert (cdf ("tri", x, 5, 2, 2, "upper"), tricdf (x, 5, 2, 2, "upper"))
***** assert (cdf ("Discrete Uniform", x, 5), unidcdf (x, 5))
***** assert (cdf ("unid", x, 5, "upper"), unidcdf (x, 5, "upper"))
***** assert (cdf ("Uniform", x, 5, 2), unifcdf (x, 5, 2))
***** assert (cdf ("unif", x, 5, 2, "upper"), unifcdf (x, 5, 2, "upper"))
***** assert (cdf ("Von Mises", x, 5, 2), vmcdf (x, 5, 2))
***** assert (cdf ("vm", x, 5, 2, "upper"), vmcdf (x, 5, 2, "upper"))
***** assert (cdf ("Weibull", x, 5, 2), wblcdf (x, 5, 2))
***** assert (cdf ("wbl", x, 5, 2, "upper"), wblcdf (x, 5, 2, "upper"))
***** error<cdf: distribution NAME must a char string.> cdf (1)
***** error<cdf: distribution NAME must a char string.> cdf ({"beta"})
***** error<cdf: X must be numeric.> cdf ("beta", {[1 2 3 4 5]})
***** error<cdf: X must be numeric.> cdf ("beta", "text")
***** error<cdf: values in X must be real.> cdf ("beta", 1+i)
***** error<cdf: distribution parameters must be numeric.> ...
 cdf ("Beta", x, "a", 2)
***** error<cdf: distribution parameters must be numeric.> ...
 cdf ("Beta", x, 5, "")
***** error<cdf: distribution parameters must be numeric.> ...
 cdf ("Beta", x, 5, {2})
***** error<cdf: chi2 distribution requires 1 parameter.> cdf ("chi2", x)
***** error<cdf: Beta distribution requires 2 parameters.> cdf ("Beta", x, 5)
***** error<cdf: Burr distribution requires 3 parameters.> cdf ("Burr", x, 5)
***** error<cdf: Burr distribution requires 3 parameters.> cdf ("Burr", x, 5, 2)
86 tests, 86 passed, 0 known failure, 0 skipped
[inst/linkage.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/linkage.m
***** shared x, t
 x = reshape (mod (magic (6),5), [], 3);
 t = 1e-6;
***** assert (cond (linkage (pdist (x))),                   34.119045, t);
***** assert (cond (linkage (pdist (x), "complete")),       21.793345, t);
***** assert (cond (linkage (pdist (x), "average")),        27.045012, t);
***** assert (cond (linkage (pdist (x), "weighted")),       27.412889, t);
 lastwarn(); # Clear last warning before the test
***** warning <cluster distances> linkage (pdist (x), "centroid");
***** test
 warning off Octave:clustering
 assert (cond (linkage (pdist (x), "centroid")),      27.457477, t);
 warning on Octave:clustering
***** warning <cluster distances> linkage (pdist (x), "median");
***** test
 warning off Octave:clustering
 assert (cond (linkage (pdist (x), "median")),        27.683325, t);
 warning on Octave:clustering
***** assert (cond (linkage (pdist (x), "ward")),           17.195198, t);
***** assert (cond (linkage (x, "ward", "euclidean")),      17.195198, t);
***** assert (cond (linkage (x, "ward", {"euclidean"})),    17.195198, t);
***** assert (cond (linkage (x, "ward", {"minkowski", 2})), 17.195198, t);
12 tests, 12 passed, 0 known failure, 0 skipped
[inst/ttest2.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/ttest2.m
***** test
 a = 1:5;
 b = 6:10;
 b(5) = NaN;
 [h,p,ci,stats] = ttest2 (a,b);
 assert (h, 1);
 assert (p, 0.002535996080258229, 1e-14);
 assert (ci, [-6.822014919225481, -2.17798508077452], 1e-14);
 assert (stats.tstat, -4.582575694955839, 1e-14);
 assert (stats.df, 7);
 assert (stats.sd, 1.4638501094228, 1e-13);
***** error ttest2 ([8:0.1:12], [8:0.1:12], "tail", "invalid");
***** error ttest2 ([8:0.1:12], [8:0.1:12], "tail", 25);
3 tests, 3 passed, 0 known failure, 0 skipped
[inst/normplot.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/normplot.m
***** demo
 h = normplot([1:20]);
***** demo
 h = normplot([1:20;5:2:44]');
***** demo
 ax = newplot();
 h = normplot(ax, [1:20]);
 ax = gca;
 h = normplot(ax, [-10:10]);
 set (ax, "xlim", [-11, 21]);
***** error normplot ();
***** error normplot (23);
***** error normplot (23, [1:20]);
***** error normplot (ones(3,4,5));
***** test
 hf = figure ("visible", "off");
 unwind_protect
   ax = newplot (hf);
   h = normplot (ax, [1:20]);
   ax = gca;
   h = normplot(ax, [-10:10]);
   set (ax, "xlim", [-11, 21]);
 unwind_protect_cleanup
   close (hf);
 end_unwind_protect
***** test
 hf = figure ("visible", "off");
 unwind_protect
   h = normplot([1:20;5:2:44]');
 unwind_protect_cleanup
   close (hf);
 end_unwind_protect
6 tests, 6 passed, 0 known failure, 0 skipped
[inst/ztest2.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/ztest2.m
***** error ztest2 ();
***** error ztest2 (1);
***** error ztest2 (1, 2);
***** error ztest2 (1, 2, 3);
***** error<ztest2: optional arguments must be in NAME-VALUE pairs.> ...
 ztest2 (1, 2, 3, 4, "alpha")
***** error<ztest2: invalid VALUE for alpha.> ...
 ztest2 (1, 2, 3, 4, "alpha", 0);
***** error<ztest2: invalid VALUE for alpha.> ...
 ztest2 (1, 2, 3, 4, "alpha", 1.2);
***** error<ztest2: invalid VALUE for alpha.> ...
 ztest2 (1, 2, 3, 4, "alpha", "val");
***** error<ztest2: invalid VALUE for tail.>  ...
 ztest2 (1, 2, 3, 4, "tail", "val");
***** error<ztest2: invalid VALUE for tail.>  ...
 ztest2 (1, 2, 3, 4, "alpha", 0.01, "tail", "val");
***** error<ztest2: invalid NAME for optional arguments.> ...
 ztest2 (1, 2, 3, 4, "alpha", 0.01, "tail", "both", "badoption", 3);
11 tests, 11 passed, 0 known failure, 0 skipped
[inst/qqplot.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/qqplot.m
***** test
 hf = figure ("visible", "off");
 unwind_protect
   qqplot ([2 3 3 4 4 5 6 5 6 7 8 9 8 7 8 9 0 8 7 6 5 4 6 13 8 15 9 9]);
 unwind_protect_cleanup
   close (hf);
 end_unwind_protect
***** error qqplot ()
***** error <qqplot: X must be a numeric vector> qqplot ({1})
***** error <qqplot: X must be a numeric vector> qqplot (ones (2,2))
***** error <qqplot: X must be a numeric vecto> qqplot (1, "foobar")
***** error <qqplot: no inverse CDF found> qqplot ([1 2 3], "foobar")
6 tests, 6 passed, 0 known failure, 0 skipped
[inst/procrustes.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/procrustes.m
***** demo
 ## Create some random points in two dimensions
 n = 10;
 randn ("seed", 1);
 X = normrnd (0, 1, [n, 2]);

 ## Those same points, rotated, scaled, translated, plus some noise
 S = [0.5, -sqrt(3)/2; sqrt(3)/2, 0.5]; # rotate 60 degrees
 Y = normrnd (0.5*X*S + 2, 0.05, n, 2);

 ## Conform Y to X, plot original X and Y, and transformed Y
 [d, Z] = procrustes (X, Y);
 plot (X(:,1), X(:,2), "rx", Y(:,1), Y(:,2), "b.", Z(:,1), Z(:,2), "bx");
***** demo
 ## Find Procrustes distance and plot superimposed shape

 X = [40 88; 51 88; 35 78; 36 75; 39 72; 44 71; 48 71; 52 74; 55 77];
 Y = [36 43; 48 42; 31 26; 33 28; 37 30; 40 31; 45 30; 48 28; 51 24];
 plot (X(:,1),X(:,2),"x");
 hold on
 plot (Y(:,1),Y(:,2),"o");
 xlim ([0 100]);
 ylim ([0 100]);
 legend ("Target shape (X)", "Source shape (Y)");
 [d, Z] = procrustes (X, Y)
 plot (Z(:,1), Z(:,2), "s");
 legend ("Target shape (X)", "Source shape (Y)", "Transformed shape (Z)");
 hold off
***** demo
 ## Apply Procrustes transformation to larger set of points

 ## Create matrices with landmark points for two triangles
 X = [5, 0; 5, 5; 8, 5];   # target
 Y = [0, 0; 1, 0; 1, 1];   # source

 ## Create a matrix with more points on the source triangle
 Y_mp = [linspace(Y(1,1),Y(2,1),10)', linspace(Y(1,2),Y(2,2),10)'; ...
         linspace(Y(2,1),Y(3,1),10)', linspace(Y(2,2),Y(3,2),10)'; ...
         linspace(Y(3,1),Y(1,1),10)', linspace(Y(3,2),Y(1,2),10)'];

 ## Plot both shapes, including the larger set of points for the source shape
 plot ([X(:,1); X(1,1)], [X(:,2); X(1,2)], "bx-");
 hold on
 plot ([Y(:,1); Y(1,1)], [Y(:,2); Y(1,2)], "ro-", "MarkerFaceColor", "r");
 plot (Y_mp(:,1), Y_mp(:,2), "ro");
 xlim ([-1 10]);
 ylim ([-1 6]);
 legend ("Target shape (X)", "Source shape (Y)", ...
         "More points on Y", "Location", "northwest");
 hold off

 ## Obtain the Procrustes transformation
 [d, Z, transform] = procrustes (X, Y)

 ## Use the Procrustes transformation to superimpose the more points (Y_mp)
 ## on the source shape onto the target shape, and then visualize the results.
 Z_mp = transform.b * Y_mp * transform.T + transform.c(1,:);
 figure
 plot ([X(:,1); X(1,1)], [X(:,2); X(1,2)], "bx-");
 hold on
 plot ([Y(:,1); Y(1,1)], [Y(:,2); Y(1,2)], "ro-", "MarkerFaceColor", "r");
 plot (Y_mp(:,1), Y_mp(:,2), "ro");
 xlim ([-1 10]);
 ylim ([-1 6]);
 plot ([Z(:,1); Z(1,1)],[Z(:,2); Z(1,2)],"ks-","MarkerFaceColor","k");
 plot (Z_mp(:,1),Z_mp(:,2),"ks");
 legend ("Target shape (X)", "Source shape (Y)", ...
         "More points on Y", "Transformed source shape (Z)", ...
         "Transformed additional points", "Location", "northwest");
 hold off
***** demo
 ## Compare shapes without reflection

 T = [33, 93; 33, 87; 33, 80; 31, 72; 32, 65; 32, 58; 30, 72; ...
      28, 72; 25, 69; 22, 64; 23, 59; 26, 57; 30, 57];
 S = [48, 83; 48, 77; 48, 70; 48, 65; 49, 59; 49, 56; 50, 66; ...
      52, 66; 56, 65; 58, 61; 57, 57; 54, 56; 51, 55];
 plot (T(:,1), T(:,2), "x-");
 hold on
 plot (S(:,1), S(:,2), "o-");
 legend ("Target shape (d)", "Source shape (b)");
 hold off
 d_false = procrustes (T, S, "reflection", false);
 printf ("Procrustes distance without reflection: %f\n", d_false);
 d_true = procrustes (T, S, "reflection", true);
 printf ("Procrustes distance with reflection: %f\n", d_true);
 d_best = procrustes (T, S, "reflection", "best");
 printf ("Procrustes distance with best fit: %f\n", d_true);
***** error procrustes ();
***** error procrustes (1, 2, 3, 4, 5, 6);
***** error<procrustes: X and Y must be 2-dimensional matrices.> ...
 procrustes (ones (2, 2, 2), ones (2, 2, 2));
***** error<procrustes: values in X and Y must be real.> ...
 procrustes ([1, 2; -3, 4; 2, 3], [1, 2; -3, 4; 2, 3+i]);
***** error<procrustes: values in X and Y must be real.> ...
 procrustes ([1, 2; -3, 4; 2, 3], [1, 2; -3, 4; 2, NaN]);
***** error<procrustes: values in X and Y must be real.> ...
 procrustes ([1, 2; -3, 4; 2, 3], [1, 2; -3, 4; 2, Inf]);
***** error<procrustes: X and Y must have equal number of rows.> ...
 procrustes (ones (10 ,3), ones (11, 3));
***** error<procrustes: X must have at least as many columns as Y.> ...
 procrustes (ones (10 ,3), ones (10, 4));
***** error<procrustes: optional arguments must be in Name-Value pairs.> ...
 procrustes (ones (10 ,3), ones (10, 3), "reflection");
***** error<procrustes: optional arguments must be in Name-Value pairs.> ...
 procrustes (ones (10 ,3), ones (10, 3), true);
***** error<procrustes: invalid value for scaling.> ...
 procrustes (ones (10 ,3), ones (10, 3), "scaling", 0);
***** error<procrustes: invalid value for scaling.> ...
 procrustes (ones (10 ,3), ones (10, 3), "scaling", [true true]);
***** error<procrustes: invalid value for reflection.> ...
 procrustes (ones (10 ,3), ones (10, 3), "reflection", 1);
***** error<procrustes: invalid value for reflection.> ...
 procrustes (ones (10 ,3), ones (10, 3), "reflection", "some");
***** error<procrustes: invalid name for optional arguments.> ...
 procrustes (ones (10 ,3), ones (10, 3), "param1", "some");
15 tests, 15 passed, 0 known failure, 0 skipped
[inst/logistic_regression.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/logistic_regression.m
***** test
 # Output compared to following MATLAB commands
 # [B, DEV, STATS] = mnrfit(X,Y+1,'model','ordinal');
 # P = mnrval(B,X)
 X = [1.489381332449196, 1.1534152241851305; ...
      1.8110085304863965, 0.9449666896938425; ...
      -0.04453299665130296, 0.34278203449678646; ...
      -0.36616019468850347, 1.130254275908322; ...
       0.15339143291005095, -0.7921044310668951; ...
      -1.6031878794469698, -1.8343471035233376; ...
      -0.14349521143198166, -0.6762996896828459; ...
      -0.4403818557740143, -0.7921044310668951; ...
      -0.7372685001160434, -0.027793137932169563; ...
      -0.11875465773681024, 0.5512305689880763];
 Y = [1,1,1,1,1,0,0,0,0,0]';
 [INTERCEPT, SLOPE, DEV, DL, D2L, P] = logistic_regression (Y, X, false);
***** test
 # Output compared to following MATLAB commands
 # [B, DEV, STATS] = mnrfit(X,Y+1,'model','ordinal');
 load carbig
 X = [Acceleration Displacement Horsepower Weight];
 miles = [1,1,1,1,1,1,1,1,1,1,NaN,NaN,NaN,NaN,NaN,1,1,NaN,1,1,2,2,1,2,2,2, ...
          2,2,2,2,2,1,1,1,1,2,2,2,2,NaN,2,1,1,2,1,1,1,1,1,1,1,1,1,2,2,1,2, ...
          2,3,3,3,3,2,2,2,2,2,2,2,1,1,1,1,1,1,1,1,1,2,1,1,1,1,1,2,2,2,2,2, ...
          2,2,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,2,1,1,1,1,1,2,2,2,1,2,2, ...
          2,1,1,3,2,2,2,1,2,2,1,2,2,2,1,3,2,3,2,1,1,1,1,1,1,1,1,3,2,2,3,3, ...
          2,2,2,2,2,3,2,1,1,1,1,1,1,1,1,1,1,1,2,2,1,3,2,2,2,2,2,2,1,3,2,2, ...
          2,2,2,3,2,2,2,2,2,1,1,1,1,2,2,2,2,3,2,3,3,2,1,1,1,3,3,2,2,2,1,2, ...
          2,1,1,1,1,1,3,3,3,2,3,1,1,1,1,1,2,2,1,1,1,1,1,3,2,2,2,3,3,3,3,2, ...
          2,2,4,3,3,4,3,2,2,2,2,2,2,2,2,2,2,2,1,1,2,1,1,1,3,2,2,3,2,2,2,2, ...
          2,1,2,1,3,3,2,2,2,2,2,1,1,1,1,1,1,2,1,3,3,3,2,2,2,2,2,3,3,3,3,2, ...
          2,2,3,4,3,3,3,2,2,2,2,3,3,3,3,3,4,2,4,4,4,3,3,4,4,3,3,3,2,3,2,3, ...
          2,2,2,2,3,4,4,3,3,3,3,3,3,3,3,3,3,3,3,3,3,2,NaN,3,2,2,2,2,2,1,2, ...
          2,3,3,3,2,2,2,3,3,3,3,3,3,3,3,3,3,3,2,3,2,2,3,3,2,2,4,3,2,3]';
 [INTERCEPT, SLOPE, DEV, DL, D2L, P] = logistic_regression (miles, X, false);
 assert (DEV, 433.197174495549, 1e-05);
 assert (INTERCEPT(1), -16.6895155618903, 1e-05);
 assert (INTERCEPT(2), -11.7207818178493, 1e-05);
 assert (INTERCEPT(3), -8.0605768506075, 1e-05);
 assert (SLOPE(1), 0.104762463756714, 1e-05);
 assert (SLOPE(2), 0.0103357623191891, 1e-05);
 assert (SLOPE(3), 0.0645199313242276, 1e-05);
 assert (SLOPE(4), 0.00166377028388103, 1e-05);
2 tests, 2 passed, 0 known failure, 0 skipped
[inst/isoutlier.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/isoutlier.m
***** demo
 A = [57 59 60 100 59 58 57 58 300 61 62 60 62 58 57];
 TF = isoutlier (A, "mean")
***** demo
 ## Use a moving detection method to detect local outliers in a sine wave

 x = -2*pi:0.1:2*pi;
 A = sin(x);
 A(47) = 0;
 time = datenum (2023,1,1,0,0,0) + (1/24)*[0:length(x)-1] - 730485;
 TF = isoutlier (A, "movmedian", 5*(1/24), "SamplePoints", time);
 plot (time, A)
 hold on
 plot (time(TF), A(TF), "x")
 datetick ('x', 20, 'keepticks')
 legend ("Original Data", "Outlier Data")
***** demo
 ## Locate an outlier in a vector of data and visualize the outlier

 x = 1:10;
 A = [60 59 49 49 58 100 61 57 48 58];
 [TF, L, U, C] = isoutlier (A);
 plot (x, A);
 hold on
 plot (x(TF), A(TF), "x");
 xlim ([1,10]);
 line ([1,10], [L, L], "Linestyle", ":");
 text (1.1, L-2, "Lower Threshold");
 line ([1,10], [U, U], "Linestyle", ":");
 text (1.1, U-2, "Upper Threshold");
 line ([1,10], [C, C], "Linestyle", ":");
 text (1.1, C-3, "Center Value");
 legend ("Original Data", "Outlier Data");
***** test
 A = [57 59 60 100 59 58 57 58 300 61 62 60 62 58 57];
 assert (isoutlier (A, "mean"), logical([zeros(1,8) 1 zeros(1,6)]))
 assert (isoutlier (A, "median"), ...
 logical([zeros(1,3) 1 zeros(1,4) 1 zeros(1,6)]))
***** test
 A = [57 59 60 100 59 58 57 58 300 61 62 60 62 58 57];
 [TF, L, U, C] = isoutlier (A, "mean");
 assert (L, -109.2459044922864, 1e-12)
 assert (U, 264.9792378256198, 1e-12)
 assert (C, 77.8666666666666, 1e-12)
***** test
 A = [57 59 60 100 59 58 57 58 300 61 62 60 62 58 57];
 [TF, L, U, C] = isoutlier (A, "median");
 assert (L, 50.104386688966386, 1e-12)
 assert (U, 67.895613311033610, 1e-12)
 assert (C, 59)
***** test
 A = magic(5) + diag(200*ones(1,5));
 T = logical (eye (5));
 assert (isoutlier (A, 2), T)
***** test
 A = [57 59 60 100 59 58 57 58 300 61 62 60 62 58 57];
 [TF, L, U, C] = isoutlier (A, "movmedian", 5);
 l = [54.5522, 52.8283, 54.5522, 54.5522, 54.5522, 53.5522, 53.5522, ...
      53.5522, 47.6566, 56.5522, 57.5522, 56.5522, 51.1044, 52.3283, 53.5522];
 u = [63.4478, 66.1717, 63.4478, 63.4478, 63.4478, 62.4478, 62.4478, ...
      62.4478, 74.3434, 65.4478, 66.4478, 65.4478, 68.8956, 65.6717, 62.4478];
 c = [59, 59.5, 59, 59, 59, 58, 58, 58, 61, 61, 62, 61, 60, 59, 58];
 assert (L, l, 1e-4)
 assert (U, u, 1e-4)
 assert (C, c)
***** test
 A = [57 59 60 100 59 58 57 58 300 61 62 60 62 58 57];
 [TF, L, U, C] = isoutlier (A, "movmedian", 5, "SamplePoints", [1:15]);
 l = [54.5522, 52.8283, 54.5522, 54.5522, 54.5522, 53.5522, 53.5522, ...
      53.5522, 47.6566, 56.5522, 57.5522, 56.5522, 51.1044, 52.3283, 53.5522];
 u = [63.4478, 66.1717, 63.4478, 63.4478, 63.4478, 62.4478, 62.4478, ...
      62.4478, 74.3434, 65.4478, 66.4478, 65.4478, 68.8956, 65.6717, 62.4478];
 c = [59, 59.5, 59, 59, 59, 58, 58, 58, 61, 61, 62, 61, 60, 59, 58];
 assert (L, l, 1e-4)
 assert (U, u, 1e-4)
 assert (C, c)
***** test
 A = [57 59 60 100 59 58 57 58 300 61 62 60 62 58 57];
 [TF, L, U, C] = isoutlier (A, "movmean", 5);
 l = [54.0841,  6.8872, 11.5608, 12.1518, 11.0210, 10.0112, -218.2840, ...
      -217.2375, -215.1239, -213.4890, -211.3264, 55.5800, 52.9589, ...
      52.5979, 51.0627];
 u = [63.2492, 131.1128, 122.4392, 122.2482, 122.5790, 122.7888, 431.0840, ...
      430.8375, 430.3239, 429.8890, 429.3264, 65.6200, 66.6411, 65.9021, ...
      66.9373];
 c = [58.6667, 69, 67, 67.2, 66.8, 66.4, 106.4, 106.8, 107.6, 108.2, 109, ...
      60.6, 59.8, 59.25, 59];
 assert (L, l, 1e-4)
 assert (U, u, 1e-4)
 assert (C, c, 1e-4)
***** test
 A = [57 59 60 100 59 58 57 58 300 61 62 60 62 58 57];
 [TF, L, U, C] = isoutlier (A, "movmean", 5, "SamplePoints", [1:15]);
 l = [54.0841,  6.8872, 11.5608, 12.1518, 11.0210, 10.0112, -218.2840, ...
      -217.2375, -215.1239, -213.4890, -211.3264, 55.5800, 52.9589, ...
      52.5979, 51.0627];
 u = [63.2492, 131.1128, 122.4392, 122.2482, 122.5790, 122.7888, 431.0840, ...
      430.8375, 430.3239, 429.8890, 429.3264, 65.6200, 66.6411, 65.9021, ...
      66.9373];
 c = [58.6667, 69, 67, 67.2, 66.8, 66.4, 106.4, 106.8, 107.6, 108.2, 109, ...
      60.6, 59.8, 59.25, 59];
 assert (L, l, 1e-4)
 assert (U, u, 1e-4)
 assert (C, c, 1e-4)
***** test
 A = [57 59 60 100 59 58 57 58 300 61 62 60 62 58 57];
 [TF, L, U, C] = isoutlier (A, "gesd");
 assert (TF, logical ([0 0 0 1 0 0 0 0 1 0 0 0 0 0 0]))
 assert (L, 34.235977035439944, 1e-12)
 assert (U, 89.764022964560060, 1e-12)
 assert (C, 62)
***** test
 A = [57 59 60 100 59 58 57 58 300 61 62 60 62 58 57];
 [TF, L, U, C] = isoutlier (A, "gesd", "ThresholdFactor", 0.01);
 assert (TF, logical ([0 0 0 1 0 0 0 0 1 0 0 0 0 0 0]))
 assert (L, 31.489256770616173, 1e-12)
 assert (U, 92.510743229383820, 1e-12)
 assert (C, 62)
***** test
 A = [57 59 60 100 59 58 57 58 300 61 62 60 62 58 57];
 [TF, L, U, C] = isoutlier (A, "gesd", "ThresholdFactor", 5e-10);
 assert (TF, logical ([0 0 0 0 0 0 0 0 1 0 0 0 0 0 0]))
 assert (L, 23.976664158788935, 1e-12)
 assert (U, 100.02333584121110, 1e-12)
 assert (C, 62)
***** test
 A = [57 59 60 100 59 58 57 58 300 61 62 60 62 58 57];
 [TF, L, U, C] = isoutlier (A, "grubbs");
 assert (TF, logical ([0 0 0 1 0 0 0 0 1 0 0 0 0 0 0]))
 assert (L, 54.642809574646606, 1e-12)
 assert (U, 63.511036579199555, 1e-12)
 assert (C, 59.076923076923080, 1e-12)
***** test
 A = [57 59 60 100 59 58 57 58 300 61 62 60 62 58 57];
 [TF, L, U, C] = isoutlier (A, "grubbs", "ThresholdFactor", 0.01);
 assert (TF, logical ([0 0 0 1 0 0 0 0 1 0 0 0 0 0 0]))
 assert (L, 54.216083184201850, 1e-12)
 assert (U, 63.937762969644310, 1e-12)
 assert (C, 59.076923076923080, 1e-12)
***** test
 A = [57 59 60 100 59 58 57 58 300 61 62 60 62 58 57];
 [TF, L, U, C] = isoutlier (A,  "percentiles", [10 90]);
 assert (TF, logical ([0 0 0 0 0 0 0 0 1 0 0 0 0 0 0]))
 assert (L, 57)
 assert (U, 100)
 assert (C, 78.5)
***** test
 A = [57 59 60 100 59 58 57 58 300 61 62 60 62 58 57];
 [TF, L, U, C] = isoutlier (A,  "percentiles", [20 80]);
 assert (TF, logical ([1 0 0 1 0 0 1 0 1 0 0 0 0 0 1]))
 assert (L, 57.5)
 assert (U, 62)
 assert (C, 59.75)
***** shared A
 A = [57 59 60 100 59 58 57 58 300 61 62 60 62 58 57];
***** error<isoutlier: WINDOW must be a positive scalar> ...
 isoutlier (A, "movmedian", 0);
***** error<isoutlier: WINDOW must be a positive scalar> ...
 isoutlier (A, "movmedian", []);
***** error<isoutlier: WINDOW must be a positive scalar> ...
 isoutlier (A, "movmedian", [2 3 4]);
***** error<isoutlier: WINDOW must be a positive integer> ...
 isoutlier (A, "movmedian", 1.4);
***** error<isoutlier: WINDOW must be a positive scalar> ...
 isoutlier (A, "movmedian", [0 1]);
***** error<isoutlier: WINDOW must be a positive scalar> ...
 isoutlier (A, "movmedian", [2 -1]);
***** error<isoutlier: WINDOW must be a positive scalar> ...
 isoutlier (A, "movmedian", {2 3});
***** error<isoutlier: WINDOW must be a positive scalar> ...
 isoutlier (A, "movmedian", "char");

***** error<isoutlier: WINDOW must be a positive scalar> ...
 isoutlier (A, "movmean", 0);
***** error<isoutlier: WINDOW must be a positive scalar> ...
 isoutlier (A, "movmean", []);
***** error<isoutlier: WINDOW must be a positive scalar> ...
 isoutlier (A, "movmean", [2 3 4]);
***** error<isoutlier: WINDOW must be a positive integer> ...
 isoutlier (A, "movmean", 1.4);
***** error<isoutlier: WINDOW must be a positive scalar> ...
 isoutlier (A, "movmean", [0 1]);
***** error<isoutlier: WINDOW must be a positive scalar> ...
 isoutlier (A, "movmean", [2 -1]);
***** error<isoutlier: WINDOW must be a positive scalar> ...
 isoutlier (A, "movmean", {2 3});
***** error<isoutlier: WINDOW must be a positive scalar> ...
 isoutlier (A, "movmean", "char");

***** error<isoutlier: THRESHOLD must be a two-element> ...
 isoutlier (A, "percentiles", [-1 90]);
***** error<isoutlier: THRESHOLD must be a two-element> ...
 isoutlier (A, "percentiles", [10 -90]);
***** error<isoutlier: THRESHOLD must be a two-element> ...
 isoutlier (A, "percentiles", [90]);
***** error<isoutlier: THRESHOLD must be a two-element> ...
 isoutlier (A, "percentiles", [90 20]);
***** error<isoutlier: THRESHOLD must be a two-element> ...
 isoutlier (A, "percentiles", [90 20]);
***** error<isoutlier: THRESHOLD must be a two-element> ...
 isoutlier (A, "percentiles", [10 20 90]);
***** error<isoutlier: THRESHOLD must be a two-element> ...
 isoutlier (A, "percentiles", {10 90});
***** error<isoutlier: THRESHOLD must be a two-element> ...
 isoutlier (A, "percentiles", "char");

***** error<isoutlier: sample points must be a vector.> ...
 isoutlier (A, "movmean", 5, "SamplePoints", ones(3,15));
***** error<isoutlier: sample points must be a vector.> ...
 isoutlier (A, "movmean", 5, "SamplePoints", 15);
***** error<isoutlier: sample points must be unique.> ...
 isoutlier (A, "movmean", 5, "SamplePoints", [1,1:14]);
***** error<isoutlier: sample points must be sorted.> ...
 isoutlier (A, "movmean", 5, "SamplePoints", [2,1,3:15]);
***** error<isoutlier: sample points must have the same size> ...
 isoutlier (A, "movmean", 5, "SamplePoints", [1:14]);

***** error<isoutlier: threshold factor must be a nonnegative scalar.> ...
 isoutlier (A, "movmean", 5, "ThresholdFactor", [1:14]);
***** error<isoutlier: threshold factor must be a nonnegative scalar.> ...
 isoutlier (A, "movmean", 5, "ThresholdFactor", -1);
***** error<isoutlier: threshold factor must must be in> ...
 isoutlier (A, "gesd", "ThresholdFactor", 3);
***** error<isoutlier: threshold factor must must be in> ...
 isoutlier (A, "grubbs", "ThresholdFactor", 3);

***** error<isoutlier: maximum outlier count must be a positive integer scalar.> ...
 isoutlier (A, "movmean", 5, "MaxNumOutliers", [1:14]);
***** error<isoutlier: maximum outlier count must be a positive integer scalar.> ...
 isoutlier (A, "movmean", 5, "MaxNumOutliers", -1);
***** error<isoutlier: maximum outlier count must be a positive integer scalar.> ...
 isoutlier (A, "movmean", 5, "MaxNumOutliers", 0);
***** error<isoutlier: maximum outlier count must be a positive integer scalar.> ...
 isoutlier (A, "movmean", 5, "MaxNumOutliers", 1.5);

***** error<isoutlier: invalid input argument.> ...
 isoutlier (A, {"movmean"}, 5, "SamplePoints", [1:15]);
***** error<isoutlier: invalid input argument.> isoutlier (A, {1});
***** error<isoutlier: invalid input argument.> isoutlier (A, true);
***** error<isoutlier: invalid input argument.> isoutlier (A, false);
***** error<isoutlier: DIM must be a positive integer scalar.> isoutlier (A, 0);
***** error<isoutlier: DIM must be a positive integer scalar.> isoutlier (A, [1 2]);
***** error<isoutlier: DIM must be a positive integer scalar.> isoutlier (A, -2);
59 tests, 59 passed, 0 known failure, 0 skipped
[inst/bar3.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/bar3.m
***** demo
 ## Ploting 5 bars in the same series.

 z = [50; 40; 30; 20; 10];
 bar3 (z);
***** demo
 ## Ploting 5 bars in different groups.

 z = [50, 40, 30, 20, 10];
 bar3 (z);
***** demo
 ## A 3D bar graph with each series corresponding to a column in z.

 z = [1, 4, 7; 2, 5, 8; 3, 6, 9; 4, 7, 10];
 bar3 (z);
***** demo
 ## Specify y-axis locations as tick names. y must be a column vector!

 y = [1950, 1960, 1970, 1980, 1990]';
 z = [16, 8, 4, 2, 1]';
 bar3 (y, z);
***** demo
 ## Plot 3 series as a grouped plot without any space between the grouped bars

 z = [70 50 33 10; 75 55 35 15; 80 60 40 20];
 bar3 (z, 1, 'grouped');
***** demo
 ## Plot a stacked style 3D bar graph

 z = [19, 30, 21, 30; 40, 16, 32, 12];
 b = bar3 (z, 0.5, 'stacked');
***** error <bar3: Z must be numeric.> bar3 ("A")
***** error <bar3: Z must be numeric.> bar3 ({2,3,4,5})
***** error <bar3: inconsistent size in Y and Z input arguments.> ...
 bar3 ([1,2,3]', ones (2))
***** error <bar3: WIDTH must be a scalar in the range> ...
 bar3 ([1:5], 1.2)
***** error <bar3: WIDTH must be a scalar in the range> ...
 bar3 ([1:5]', ones (5), 1.2)
***** error <bar3: numeric COLOR must be a 1x3 vector of an Nx3 matrix> ...
 bar3 ([1:5]', ones (5), [0.8, 0.7])
***** error <bar3: missing value for optional argument 'width'.> ...
 bar3 (ones (5), 'width')
***** error <bar3: invalid value for optional argument 'width'.> ...
 bar3 (ones (5), 'width', 1.2)
***** error <bar3: invalid value for optional argument 'width'.> ...
 bar3 (ones (5), 'width', [0.8, 0.8, 0.8])
***** error <bar3: missing value for optional argument 'color'.> ...
 bar3 (ones (5), 'color')
***** error <bar3: numeric COLOR must be a 1x3 vector of an Nx3 matrix> ...
 bar3 (ones (5), 'color', [0.8, 0.8])
***** error <bar3: invalid value for optional argument 'color'.> ...
 bar3 (ones (5), 'color', "brown")
***** error <bar3: invalid value for optional argument 'color'.> ...
 bar3 (ones (5), 'color', {"r", "k", "c", "m", "brown"})
***** error <bar3: missing value for optional argument 'xlabel'.> ...
 bar3 (ones (5), 'xlabel')
***** error <bar3: invalid value for optional argument 'xlabel'.> ...
 bar3 (ones (5), 'xlabel', 4)
***** error <bar3: missing value for optional argument 'ylabel'.> ...
 bar3 (ones (5), 'ylabel')
***** error <bar3: invalid value for optional argument 'ylabel'.> ...
 bar3 (ones (5), 'ylabel', 4)
***** error <bar3: invalid optional argument.> bar3 (ones (5), 'this', 4)
***** error <bar3: the elements in 'xlabel' must equal the columns in Z.> ...
 bar3 (ones (5), 'xlabel', {"A", "B", "C"})
***** error <bar3: the elements in 'ylabel' must equal the rows in Z.> ...
 bar3 (ones (5), 'ylabel', {"A", "B", "C"})
20 tests, 20 passed, 0 known failure, 0 skipped
[inst/hmmestimate.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/hmmestimate.m
***** test
 sequence = [1, 2, 1, 1, 1, 2, 2, 1, 2, 3, 3, ...
             3, 3, 2, 3, 1, 1, 1, 1, 3, 3, 2, 3, 1, 3];
 states =   [1, 1, 2, 2, 2, 1, 1, 1, 1, 1, 1, ...
             1, 1, 1, 1, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1];
 [transprobest, outprobest] = hmmestimate (sequence, states);
 expectedtransprob = [0.88889, 0.11111; 0.28571, 0.71429];
 expectedoutprob = [0.16667, 0.33333, 0.50000; 1.00000, 0.00000, 0.00000];
 assert (transprobest, expectedtransprob, 0.001);
 assert (outprobest, expectedoutprob, 0.001);
***** test
 sequence = {"A", "B", "A", "A", "A", "B", "B", "A", "B", "C", "C", "C", ...
             "C", "B", "C", "A", "A", "A", "A", "C", "C", "B", "C", "A", "C"};
 states = {"One", "One", "Two", "Two", "Two", "One", "One", "One", "One", ...
           "One", "One", "One", "One", "One", "One", "Two", "Two", "Two", ...
           "Two", "One", "One", "One", "One", "One", "One"};
 symbols = {"A", "B", "C"};
 statenames = {"One", "Two"};
 [transprobest, outprobest] = hmmestimate (sequence, states, "symbols", ...
                                           symbols, "statenames", statenames);
 expectedtransprob = [0.88889, 0.11111; 0.28571, 0.71429];
 expectedoutprob = [0.16667, 0.33333, 0.50000; 1.00000, 0.00000, 0.00000];
 assert (transprobest, expectedtransprob, 0.001);
 assert (outprobest, expectedoutprob, 0.001);
***** test
 sequence = [1, 2, 1, 1, 1, 2, 2, 1, 2, 3, 3, 3, ...
             3, 2, 3, 1, 1, 1, 1, 3, 3, 2, 3, 1, 3];
 states =   [1, 1, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, ...
             1, 1, 1, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1];
 pseudotransitions = [8, 2; 4, 6];
 pseudoemissions = [2, 4, 4; 7, 2, 1];
 [transprobest, outprobest] = hmmestimate (sequence, states, ...
  "pseudotransitions", pseudotransitions, "pseudoemissions", pseudoemissions);
 expectedtransprob = [0.85714, 0.14286; 0.35294, 0.64706];
 expectedoutprob = [0.178571, 0.357143, 0.464286; ...
                    0.823529, 0.117647, 0.058824];
 assert (transprobest, expectedtransprob, 0.001);
 assert (outprobest, expectedoutprob, 0.001);
3 tests, 3 passed, 0 known failure, 0 skipped
[inst/ppplot.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/ppplot.m
***** test
 hf = figure ("visible", "off");
 unwind_protect
   ppplot ([2 3 3 4 4 5 6 5 6 7 8 9 8 7 8 9 0 8 7 6 5 4 6 13 8 15 9 9]);
 unwind_protect_cleanup
   close (hf);
 end_unwind_protect
***** error ppplot ()
***** error <ppplot: X must be a numeric vector> ppplot (ones (2,2))
***** error <ppplot: X must be a numeric vector> ppplot (1, 2)
***** error <ppplot: DIST must be a strin> ppplot ([1 2 3 4], 2)
5 tests, 5 passed, 0 known failure, 0 skipped
[inst/fitgmdist.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/fitgmdist.m
***** demo
 ## Generate a two-cluster problem
 C1 = randn (100, 2) + 2;
 C2 = randn (100, 2) - 2;
 data = [C1; C2];

 ## Perform clustering
 GMModel = fitgmdist (data, 2);

 ## Plot the result
 figure
 [heights, bins] = hist3([C1; C2]);
 [xx, yy] = meshgrid(bins{1}, bins{2});
 bbins = [xx(:), yy(:)];
 contour (reshape (GMModel.pdf (bbins), size (heights)));
***** demo
 Angle_Theta = [ 30 + 10 * randn(1, 10),  60 + 10 * randn(1, 10) ]';
 nbOrientations = 2;
 initial_orientations = [38.0; 18.0];
 initial_weights = ones (1, nbOrientations) / nbOrientations;
 initial_Sigma = 10 * ones (1, 1, nbOrientations);
 start = struct ("mu", initial_orientations, "Sigma", initial_Sigma, ...
                 "ComponentProportion", initial_weights);
 GMModel_Theta = fitgmdist (Angle_Theta, nbOrientations, "Start", start , ...
                            "RegularizationValue", 0.0001)
***** test
 load fisheriris
 classes = unique (species);
 [~, score] = pca (meas, "NumComponents", 2);
 options.MaxIter = 1000;
 options.TolFun = 1e-6;
 options.Display = "off";
 GMModel = fitgmdist (score, 2, "Options", options);
 assert (isa (GMModel, "gmdistribution"), true);
 assert (GMModel.mu, [1.3212, -0.0954; -2.6424, 0.1909], 1e-4);
1 test, 1 passed, 0 known failure, 0 skipped
[inst/fitcdiscr.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/fitcdiscr.m
***** demo
 ## Train a linear discriminant classifier for Gamma = 0.5
 ## and plot the decision boundaries.

 load fisheriris
 idx = ! strcmp (species, "setosa");
 X = meas(idx,3:4);
 Y = cast (strcmpi (species(idx), "virginica"), "double");
 obj = fitcdiscr (X, Y, "Gamma", 0.5)
 x1 = [min(X(:,1)):0.03:max(X(:,1))];
 x2 = [min(X(:,2)):0.02:max(X(:,2))];
 [x1G, x2G] = meshgrid (x1, x2);
 XGrid = [x1G(:), x2G(:)];
 pred = predict (obj, XGrid);
 gidx = logical (str2num (cell2mat (pred)));

 figure
 scatter (XGrid(gidx,1), XGrid(gidx,2), "markerfacecolor", "magenta");
 hold on
 scatter (XGrid(!gidx,1), XGrid(!gidx,2), "markerfacecolor", "red");
 plot (X(Y == 0, 1), X(Y == 0, 2), "ko", X(Y == 1, 1), X(Y == 1, 2), "kx");
 xlabel ("Petal length (cm)");
 ylabel ("Petal width (cm)");
 title ("Linear Discriminant Analysis Decision Boundary");
 legend ({"Versicolor Region", "Virginica Region", ...
         "Sampled Versicolor", "Sampled Virginica"}, ...
         "location", "northwest")
 axis tight
 hold off
***** test
 load fisheriris
 Mdl = fitcdiscr (meas, species, "Gamma", 0.5);
 [label, score, cost] = predict (Mdl, [2, 2, 2, 2]);
 assert (label, {'versicolor'})
 assert (score, [0, 0.9999, 0.0001], 1e-4)
 assert (cost, [1, 0.0001, 0.9999], 1e-4)
 [label, score, cost] = predict (Mdl, [2.5, 2.5, 2.5, 2.5]);
 assert (label, {'versicolor'})
 assert (score, [0, 0.6368, 0.3632], 1e-4)
 assert (cost, [1, 0.3632, 0.6368], 1e-4)
 assert (class (Mdl), "ClassificationDiscriminant");
 assert ({Mdl.X, Mdl.Y, Mdl.NumObservations}, {meas, species, 150})
 assert ({Mdl.DiscrimType, Mdl.ResponseName}, {"linear", "Y"})
 assert ({Mdl.Gamma, Mdl.MinGamma}, {0.5, 0})
 assert (Mdl.ClassNames, unique (species))
 sigma = [0.265008, 0.046361, 0.083757, 0.019201; ...
          0.046361, 0.115388, 0.027622, 0.016355; ...
          0.083757, 0.027622, 0.185188, 0.021333; ...
          0.019201, 0.016355, 0.021333, 0.041882];
 assert (Mdl.Sigma, sigma, 1e-6)
 mu = [5.0060, 3.4280, 1.4620, 0.2460; ...
       5.9360, 2.7700, 4.2600, 1.3260; ...
       6.5880, 2.9740, 5.5520, 2.0260];
 assert (Mdl.Mu, mu, 1e-14)
 assert (Mdl.LogDetSigma, -8.6884, 1e-4)
***** error<fitcdiscr: too few arguments.> fitcdiscr ()
***** error<fitcdiscr: too few arguments.> fitcdiscr (ones (4,1))
***** error<fitcdiscr: name-value arguments must be in pairs.>
 fitcdiscr (ones (4,2), ones (4, 1), "K")
***** error<fitcdiscr: number of rows in X and Y must be equal.>
 fitcdiscr (ones (4,2), ones (3, 1))
***** error<fitcdiscr: number of rows in X and Y must be equal.>
 fitcdiscr (ones (4,2), ones (3, 1), "K", 2)
6 tests, 6 passed, 0 known failure, 0 skipped
[inst/dcov.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dcov.m
***** demo
 base=@(x) (x- min(x))./(max(x)-min(x));
 N = 5e2;
 x = randn (N,1); x = base (x);
 z = randn (N,1); z = base (z);
 # Linear relations
 cy = [1 0.55 0.3 0 -0.3 -0.55 -1];
 ly = x .* cy;
 ly(:,[1:3 5:end]) = base (ly(:,[1:3 5:end]));
 # Correlated Gaussian
 cz = 1 - abs (cy);
 gy = base ( ly + cz.*z);
 # Shapes
 sx      = repmat (x,1,7);
 sy      = zeros (size (ly));
 v       = 2 * rand (size(x,1),2) - 1;
 sx(:,1) = v(:,1); sy(:,1) = cos(2*pi*sx(:,1)) + 0.5*v(:,2).*exp(-sx(:,1).^2/0.5);
 R       =@(d) [cosd(d) sind(d); -sind(d) cosd(d)];
 tmp     = R(35) * v.';
 sx(:,2) = tmp(1,:); sy(:,2) = tmp(2,:);
 tmp     = R(45) * v.';
 sx(:,3) = tmp(1,:); sy(:,3) = tmp(2,:);
 sx(:,4) = v(:,1); sy(:,4) = sx(:,4).^2 + 0.5*v(:,2);
 sx(:,5) = v(:,1); sy(:,5) = 3*sign(v(:,2)).*(sx(:,5)).^2  + v(:,2);
 sx(:,6) = cos (2*pi*v(:,1)) + 0.5*(x-0.5);
 sy(:,6) = sin (2*pi*v(:,1)) + 0.5*(z-0.5);
 sx(:,7) = x + sign(v(:,1)); sy(:,7) = z + sign(v(:,2));
 sy      = base (sy);
 sx      = base (sx);
 # scaled shape
 sc  = 1/3;
 ssy = (sy-0.5) * sc + 0.5;
 n = size (ly,2);
 ym = 1.2;
 xm = 0.5;
 fmt={'horizontalalignment','center'};
 ff = "% .2f";
 figure (1)
 for i=1:n
   subplot(4,n,i);
   plot (x, gy(:,i), '.b');
   axis tight
   axis off
   text (xm,ym,sprintf (ff, dcov (x,gy(:,i))),fmt{:})

   subplot(4,n,i+n);
   plot (x, ly(:,i), '.b');
   axis tight
   axis off
   text (xm,ym,sprintf (ff, dcov (x,ly(:,i))),fmt{:})

   subplot(4,n,i+2*n);
   plot (sx(:,i), sy(:,i), '.b');
   axis tight
   axis off
   text (xm,ym,sprintf (ff, dcov (sx(:,i),sy(:,i))),fmt{:})
   v = axis ();

   subplot(4,n,i+3*n);
   plot (sx(:,i), ssy(:,i), '.b');
   axis (v)
   axis off
   text (xm,ym,sprintf (ff, dcov (sx(:,i),ssy(:,i))),fmt{:})
 endfor
***** error dcov (randn (30, 5), randn (25,5))
1 test, 1 passed, 0 known failure, 0 skipped
[inst/vartestn.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/vartestn.m
***** demo
 ## Test the null hypothesis that the variances are equal across the five
 ## columns of data in the students’ exam grades matrix, grades.

 load examgrades
 vartestn (grades)
***** demo
 ## Test the null hypothesis that the variances in miles per gallon (MPG) are
 ## equal across different model years.

 load carsmall
 vartestn (MPG, Model_Year)
***** demo
 ## Use Levene’s test to test the null hypothesis that the variances in miles
 ## per gallon (MPG) are equal across different model years.

 load carsmall
 p = vartestn (MPG, Model_Year, "TestType", "LeveneAbsolute")
***** demo
 ## Test the null hypothesis that the variances are equal across the five
 ## columns of data in the students’ exam grades matrix, grades, using the
 ## Brown-Forsythe test.  Suppress the display of the summary table of
 ## statistics and the box plot.

 load examgrades
 [p, stats] = vartestn (grades, "TestType", "BrownForsythe", "Display", "off")
***** error<vartestn: too few input arguments.> vartestn ();
***** error<vartestn: X must be a vector or a matrix.> vartestn (1);
***** error<vartestn: if X is a vector then a group vector is required.> ...
 vartestn ([1, 2, 3, 4, 5, 6, 7]);
***** error<vartestn: if X is a vector then a group vector is required.> ...
 vartestn ([1, 2, 3, 4, 5, 6, 7], []);
***** error<vartestn: if X is a vector then a group vector is required.> ...
 vartestn ([1, 2, 3, 4, 5, 6, 7], "TestType", "LeveneAbsolute");
***** error<vartestn: if X is a vector then a group vector is required.> ...
 vartestn ([1, 2, 3, 4, 5, 6, 7], [], "TestType", "LeveneAbsolute");
***** error<vartestn: invalid value for display.> ...
 vartestn ([1, 2, 3, 4, 5, 6, 7], [1, 1, 1, 2, 2, 2, 2], "Display", "some");
***** error<vartestn: invalid value for display.> ...
 vartestn (ones (50,3), "Display", "some");
***** error<vartestn: invalid value for testtype.> ...
 vartestn (ones (50,3), "Display", "off", "testtype", "some");
***** error<vartestn: optional arguments must be in name/value pairs.> ...
 vartestn (ones (50,3), [], "som");
***** error<vartestn: invalid name for optional arguments.> ...
 vartestn (ones (50,3), [], "some", "some");
***** error<vartestn: columns in X and GROUP length do not match.> ...
 vartestn (ones (50,3), [1, 2], "Display", "off");
***** test
 load examgrades
 [p, stat] = vartestn (grades, "Display", "off");
 assert (p, 7.908647337018238e-08, 1e-14);
 assert (stat.chisqstat, 38.7332, 1e-4);
 assert (stat.df, 4);
***** test
 load examgrades
 [p, stat] = vartestn (grades, "Display", "off", "TestType", "LeveneAbsolute");
 assert (p, 9.523239714592791e-07, 1e-14);
 assert (stat.fstat, 8.5953, 1e-4);
 assert (stat.df, [4, 595]);
***** test
 load examgrades
 [p, stat] = vartestn (grades, "Display", "off", "TestType", "LeveneQuadratic");
 assert (p, 7.219514351897161e-07, 1e-14);
 assert (stat.fstat, 8.7503, 1e-4);
 assert (stat.df, [4, 595]);
***** test
 load examgrades
 [p, stat] = vartestn (grades, "Display", "off", "TestType", "BrownForsythe");
 assert (p, 1.312093241723211e-06, 1e-14);
 assert (stat.fstat, 8.4160, 1e-4);
 assert (stat.df, [4, 595]);
***** test
 load examgrades
 [p, stat] = vartestn (grades, "Display", "off", "TestType", "OBrien");
 assert (p, 8.235660885480556e-07, 1e-14);
 assert (stat.fstat, 8.6766, 1e-4);
 assert (stat.df, [4, 595]);
17 tests, 17 passed, 0 known failure, 0 skipped
[inst/runstest.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/runstest.m
***** test
 ## NIST beam deflection data
 ## http://www.itl.nist.gov/div898/handbook/eda/section4/eda425.htm
 data = [-213, -564, -35, -15, 141, 115, -420, -360, 203, -338, -431, ...
          194, -220, -513, 154, -125, -559, 92, -21, -579, -52, 99, -543, ...
         -175, 162, -457, -346, 204, -300, -474, 164, -107, -572, -8, 83, ...
         -541, -224, 180, -420, -374, 201, -236, -531, 83, 27, -564, -112, ...
          131, -507, -254, 199, -311, -495, 143, -46, -579, -90, 136, ...
         -472, -338, 202, -287, -477, 169, -124, -568, 17, 48, -568, -135, ...
          162, -430, -422, 172, -74, -577, -13, 92, -534, -243, 194, -355, ...
         -465, 156, -81, -578, -64, 139, -449, -384, 193, -198, -538, 110, ...
          -44, -577, -6, 66, -552, -164, 161, -460, -344, 205, -281, -504, ...
          134, -28, -576, -118, 156, -437, -381, 200, -220, -540, 83, 11, ...
         -568, -160, 172, -414, -408, 188, -125, -572, -32, 139, -492, ...
         -321, 205, -262, -504, 142, -83, -574, 0, 48, -571, -106, 137, ...
         -501, -266, 190, -391, -406, 194, -186, -553, 83, -13, -577, -49, ...
          103, -515, -280, 201, 300, -506, 131, -45, -578, -80, 138, -462, ...
         -361, 201, -211, -554, 32, 74, -533, -235, 187, -372, -442, 182, ...
         -147, -566, 25, 68, -535, -244, 194, -351, -463, 174, -125, -570, ...
           15, 72, -550, -190, 172, -424, -385, 198, -218, -536, 96];
 [h, p, stats] = runstest (data, median (data));
 expected_h = 1;
 expected_p = 0.008562;
 expected_z = 2.6229;
 assert (h, expected_h);
 assert (p, expected_p, 1E-6);
 assert (stats.z, expected_z, 1E-4);
***** shared x
 x = [45, -60, 1.225, 55.4, -9 27];
***** test
 [h, p, stats] = runstest (x);
 assert (h, 0);
 assert (p, 0.6, 1e-14);
 assert (stats.nruns, 5);
 assert (stats.n1, 3);
 assert (stats.n0, 3);
 assert (stats.z, 0.456435464587638, 1e-14);
***** test
 [h, p, stats] = runstest (x, [], "method", "approximate");
 assert (h, 0);
 assert (p, 0.6481, 1e-4);
 assert (stats.z, 0.456435464587638, 1e-14);
***** test
 [h, p, stats] = runstest (x, [], "tail", "left");
 assert (h, 0);
 assert (p, 0.9, 1e-14);
 assert (stats.z, 1.369306393762915, 1e-14);
***** error<runstest: X must be a vector a scalar values.> runstest (ones (2,20))
***** error<runstest: X must be a vector a scalar values.> runstest (["asdasda"])
***** error<runstest: V must be either a scalar number or> ...
 runstest ([2 3 4 3 2 3 4], "updown")
***** error<runstest: invalid value for alpha.> ...
 runstest ([2 3 4 3 2 3 4], [], "alpha", 0)
***** error<runstest: invalid value for alpha.> ...
 runstest ([2 3 4 3 2 3 4], [], "alpha", [0.02 0.2])
***** error<runstest: invalid value for alpha.> ...
 runstest ([2 3 4 3 2 3 4], [], "alpha", 1.2)
***** error<runstest: invalid value for alpha.> ...
 runstest ([2 3 4 3 2 3 4], [], "alpha", -0.05)
***** error<runstest: invalid value for method.> ...
 runstest ([2 3 4 3 2 3 4], [], "method", "some")
***** error<runstest: invalid value for tail.> ...
 runstest ([2 3 4 3 2 3 4], [], "tail", "some")
***** error<runstest: invalid optional argument.> ...
 runstest ([2 3 4 3 2 3 4], [], "option", "some")
14 tests, 14 passed, 0 known failure, 0 skipped
[inst/einstein.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/einstein.m
***** demo
 einstein (0.4, 0.6)
***** demo
 einstein (0.2, 0.5)
***** demo
 einstein (0.6, 0.1)
***** test
 hf = figure ("visible", "off");
 unwind_protect
   tiles = einstein (0.4, 0.6);
   assert (isstruct (tiles), true);
 unwind_protect_cleanup
   close (hf);
 end_unwind_protect
***** error<Invalid call to einstein.  Correct usage is> einstein
***** error<Invalid call to einstein.  Correct usage is> einstein (0.5)
***** error<einstein: A and B must be within the open interval> einstein (0, 0.9)
***** error<einstein: A and B must be within the open interval> einstein (0.4, 1)
***** error<einstein: A and B must be within the open interval> einstein (-0.4, 1)
6 tests, 6 passed, 0 known failure, 0 skipped
[inst/tiedrank.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/tiedrank.m
***** test
 [r,tieadj] = tiedrank ([10, 20, 30, 40, 20]);
 assert (r, [1, 2.5, 4, 5, 2.5]);
 assert (tieadj, 3);
***** test
 [r,tieadj] = tiedrank ([10; 20; 30; 40; 20]);
 assert (r, [1; 2.5; 4; 5; 2.5]);
 assert (tieadj, 3);
***** test
 [r,tieadj] = tiedrank ([10, 20, 30, 40, 20], 1);
 assert (r, [1, 2.5, 4, 5, 2.5]);
 assert (tieadj, [1; 0; 18]);
***** test
 [r,tieadj] = tiedrank ([10, 20, 30, 40, 20], 0, 1);
 assert (r, [1, 2.5, 2, 1, 2.5]);
 assert (tieadj, 3);
***** test
 [r,tieadj] = tiedrank ([10, 20, 30, 40, 20], 1, 1);
 assert (r, [1, 2.5, 2, 1, 2.5]);
 assert (tieadj, [1; 0; 18]);
***** error <tiedrank: X must be a vector.> tiedrank (ones (2))
***** error <tiedrank: TIEFLAG must be a numeric or boolean scalar.> ...
 tiedrank ([1, 2, 3, 4, 5], [1, 1])
***** error <tiedrank: TIEFLAG must be a numeric or boolean scalar.> ...
 tiedrank ([1, 2, 3, 4, 5], "A")
***** error <tiedrank: TIEFLAG must be a numeric or boolean scalar.> ...
 tiedrank ([1, 2, 3, 4, 5], [true, true])
***** error <tiedrank: BIDIR must be a numeric or boolean scalar.> ...
 tiedrank ([1, 2, 3, 4, 5], 0, [1, 1])
***** error <tiedrank: BIDIR must be a numeric or boolean scalar.> ...
 tiedrank ([1, 2, 3, 4, 5], 0, "A")
***** error <tiedrank: BIDIR must be a numeric or boolean scalar.> ...
 tiedrank ([1, 2, 3, 4, 5], 0, [true, true])
12 tests, 12 passed, 0 known failure, 0 skipped
[inst/standardizeMissing.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/standardizeMissing.m
***** assert (standardizeMissing (1, 1), NaN)
***** assert (standardizeMissing (1, 0), 1)
***** assert (standardizeMissing (eye(2), 1), [NaN 0;0 NaN])
***** assert (standardizeMissing ([1:3;4:6], [2 3; 4 5]), [1, NaN, NaN; NaN, NaN, 6])
***** assert (standardizeMissing (cat (3,1,2,3,4), 3), cat (3,1,2,NaN,4))
***** assert (standardizeMissing ('foo', 'a'), 'foo')
***** assert (standardizeMissing ('foo', 'f'), ' oo')
***** assert (standardizeMissing ('foo', 'o'), 'f  ')
***** assert (standardizeMissing ('foo', 'oo'), 'f  ')
***** assert (standardizeMissing ({'foo'}, 'f'), {'foo'})
***** assert (standardizeMissing ({'foo'}, {'f'}), {'foo'})
***** assert (standardizeMissing ({'foo'}, 'test'), {'foo'})
***** assert (standardizeMissing ({'foo'}, {'test'}), {'foo'})
***** assert (standardizeMissing ({'foo'}, 'foo'), {''})
***** assert (standardizeMissing ({'foo'}, {'foo'}), {''})
***** assert (standardizeMissing (['foo';'bar'], 'oar'), ['f  ';'b  '])
***** assert (standardizeMissing (['foo';'bar'], ['o';'a';'r']), ['f  ';'b  '])
***** assert (standardizeMissing (['foo';'bar'], ['o ';'ar']), ['f  ';'b  '])
***** assert (standardizeMissing ({'foo','bar'}, 'foo'), {'','bar'})
***** assert (standardizeMissing ({'foo','bar'}, 'f'), {'foo','bar'})
***** assert (standardizeMissing ({'foo','bar'}, {'foo', 'a'}), {'','bar'})
***** assert (standardizeMissing ({'foo'}, {'f', 'oo'}), {'foo'})
***** assert (standardizeMissing ({'foo','bar'}, {'foo'}), {'','bar'})
***** assert (standardizeMissing ({'foo','bar'}, {'foo', 'a'}), {'','bar'})
***** assert (standardizeMissing (double (1), single (1)), double (NaN))
***** assert (standardizeMissing (single (1), single (1)), single (NaN))
***** assert (standardizeMissing (single (1), double (1)), single (NaN))
***** assert (standardizeMissing (single (1), true), single (NaN))
***** assert (standardizeMissing (double (1), int32(1)), double (NaN))
***** assert (standardizeMissing (true, true), true)
***** assert (standardizeMissing (true, 1), true)
***** assert (standardizeMissing (int32 (1), int32 (1)), int32 (1))
***** assert (standardizeMissing (int32 (1), 1), int32 (1))
***** assert (standardizeMissing (uint32 (1), uint32 (1)), uint32 (1))
***** assert (standardizeMissing (uint32 (1), 1), uint32 (1))
***** error standardizeMissing ();
***** error standardizeMissing (1);
***** error standardizeMissing (1,2,3);
***** error <only cells of strings> standardizeMissing ({'abc', 1}, 1);
***** error <unsupported data type> standardizeMissing (struct ('a','b'), 1);
***** error <'indicator' and 'A' must have > standardizeMissing ([1 2 3], {1});
***** error <'indicator' and 'A' must have > standardizeMissing ([1 2 3], 'a');
***** error <'indicator' and 'A' must have > standardizeMissing ([1 2 3], struct ('a', 1));
***** error <'indicator' and 'A' must have > standardizeMissing ('foo', 1);
***** error <'indicator' and 'A' must have > standardizeMissing ('foo', {1});
***** error <'indicator' and 'A' must have > standardizeMissing ('foo', {'f'});
***** error <'indicator' and 'A' must have > standardizeMissing ('foo', struct ('a', 1));
***** error <'indicator' and 'A' must have > standardizeMissing ({'foo'}, 1);
***** error <'indicator' and 'A' must have > standardizeMissing ({'foo'}, 1);
49 tests, 49 passed, 0 known failure, 0 skipped
[inst/cholcov.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/cholcov.m
***** demo
 C1 = [2, 1, 1, 2; 1, 2, 1, 2; 1, 1, 2, 2; 2, 2, 2, 3]
 T = cholcov (C1)
 C2 = T'*T
***** test
 C1 = [2, 1, 1, 2; 1, 2, 1, 2; 1, 1, 2, 2; 2, 2, 2, 3];
 T = cholcov (C1);
 assert (C1, T'*T, 1e-15 * ones (size (C1)));
1 test, 1 passed, 0 known failure, 0 skipped
[inst/pdist.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/pdist.m
***** shared xy, t, eucl, x
 xy = [0 1; 0 2; 7 6; 5 6];
 t = 1e-3;
 eucl = @(v,m) sqrt(sumsq(repmat(v,rows(m),1)-m,2));
 x = [1 2 3; 4 5 6; 7 8 9; 3 2 1];
***** assert (pdist (xy),                 [1.000 8.602 7.071 8.062 6.403 2.000], t);
***** assert (pdist (xy, eucl),           [1.000 8.602 7.071 8.062 6.403 2.000], t);
***** assert (pdist (xy, "euclidean"),    [1.000 8.602 7.071 8.062 6.403 2.000], t);
***** assert (pdist (xy, "seuclidean"),   [0.380 2.735 2.363 2.486 2.070 0.561], t);
***** assert (pdist (xy, "mahalanobis"),  [1.384 1.967 2.446 2.384 1.535 2.045], t);
***** assert (pdist (xy, "cityblock"),    [1.000 12.00 10.00 11.00 9.000 2.000], t);
***** assert (pdist (xy, "minkowski"),    [1.000 8.602 7.071 8.062 6.403 2.000], t);
***** assert (pdist (xy, "minkowski", 3), [1.000 7.763 6.299 7.410 5.738 2.000], t);
***** assert (pdist (xy, "cosine"),       [0.000 0.349 0.231 0.349 0.231 0.013], t);
***** assert (pdist (xy, "correlation"),  [0.000 2.000 0.000 2.000 0.000 2.000], t);
***** assert (pdist (xy, "spearman"),     [0.000 2.000 0.000 2.000 0.000 2.000], t);
***** assert (pdist (xy, "hamming"),      [0.500 1.000 1.000 1.000 1.000 0.500], t);
***** assert (pdist (xy, "jaccard"),      [1.000 1.000 1.000 1.000 1.000 0.500], t);
***** assert (pdist (xy, "chebychev"),    [1.000 7.000 5.000 7.000 5.000 2.000], t);
***** assert (pdist (x), [5.1962, 10.3923, 2.8284, 5.1962, 5.9161, 10.7703], 1e-4);
***** assert (pdist (x, "euclidean"), ...
        [5.1962, 10.3923, 2.8284, 5.1962, 5.9161, 10.7703], 1e-4);
***** assert (pdist (x, eucl), ...
        [5.1962, 10.3923, 2.8284, 5.1962, 5.9161, 10.7703], 1e-4);
***** assert (pdist (x, "squaredeuclidean"), [27, 108, 8, 27, 35, 116]);
***** assert (pdist (x, "seuclidean"), ...
        [1.8071, 3.6142, 0.9831, 1.8071, 1.8143, 3.4854], 1e-4);
***** warning<pdist: matrix is close to singular> ...
 pdist (x, "mahalanobis");
***** assert (pdist (x, "cityblock"), [9, 18, 4, 9, 9, 18]);
***** assert (pdist (x, "minkowski"), ...
        [5.1962, 10.3923, 2.8284, 5.1962, 5.9161, 10.7703], 1e-4);
***** assert (pdist (x, "minkowski", 3), ...
        [4.3267, 8.6535, 2.5198, 4.3267, 5.3485, 9.2521], 1e-4);
***** assert (pdist (x, "cosine"), ...
        [0.0254, 0.0406, 0.2857, 0.0018, 0.1472, 0.1173], 1e-4);
***** assert (pdist (x, "correlation"), [0, 0, 2, 0, 2, 2], 1e-14);
***** assert (pdist (x, "spearman"), [0, 0, 2, 0, 2, 2], 1e-14);
***** assert (pdist (x, "hamming"), [1, 1, 2/3, 1, 1, 1]);
***** assert (pdist (x, "jaccard"), [1, 1, 2/3, 1, 1, 1]);
***** assert (pdist (x, "chebychev"), [3, 6, 2, 3, 5, 8]);
29 tests, 29 passed, 0 known failure, 0 skipped
[inst/ismissing.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/ismissing.m
***** assert (ismissing ([1,NaN,3]), [false,true,false])
***** assert (ismissing ('abcd f'), [false,false,false,false,true,false])
***** assert (ismissing ({'xxx','','xyz'}), [false,true,false])
***** assert (ismissing ({'x','','y'}), [false,true,false])
***** assert (ismissing ({'x','','y';'z','a',''}), logical([0,1,0;0,0,1]))
***** assert (ismissing ([1,2;NaN,2]), [false,false;true,false])
***** assert (ismissing ([1,2;NaN,2], 2), [false,true;false,true])
***** assert (ismissing ([1,2;NaN,2], [1 2]), [true,true;false,true])
***** assert (ismissing ([1,2;NaN,2], NaN), [false,false;true,false])
***** assert (ismissing (cat(3,magic(2),magic(2))), logical (zeros (2,2,2)))
***** assert (ismissing (cat(3,magic(2),[1 2;3 NaN])), logical (cat(3,[0,0;0,0],[0,0;0,1])))
***** assert (ismissing ([1 2; 3 4], [5 1; 2 0]), logical([1 1; 0 0]))
***** assert (ismissing (cat(3,'f oo','ba r')), logical(cat(3,[0 1 0 0],[0 0 1 0])))
***** assert (ismissing (cat(3,{'foo'},{''},{'bar'})), logical(cat(3,0,1,0)))
***** assert (ismissing (double (NaN)), true)
***** assert (ismissing (single (NaN)), true)
***** assert (ismissing (' '), true)
***** assert (ismissing ({''}), true)
***** assert (ismissing ({' '}), false)
***** assert (ismissing (double (eye(3)), single (1)), logical(eye(3)))
***** assert (ismissing (double (eye(3)), true), logical(eye(3)))
***** assert (ismissing (double (eye(3)), int32 (1)), logical(eye(3)))
***** assert (ismissing (single (eye(3)), true), logical(eye(3)))
***** assert (ismissing (single (eye(3)), double (1)), logical(eye(3)))
***** assert (ismissing (single(eye(3)), int32 (1)), logical(eye(3)))
***** assert (ismissing ({'123', '', 123}), [false false false])
***** assert (ismissing (logical ([1 0 1])), [false false false])
***** assert (ismissing (int32 ([1 2 3])), [false false false])
***** assert (ismissing (uint32 ([1 2 3])), [false false false])
***** assert (ismissing ({1, 2, 3}), [false false false])
***** assert (ismissing ([struct struct struct]), [false false false])
***** assert (ismissing (logical (eye(3)), true), logical(eye(3)))
***** assert (ismissing (logical (eye(3)), double (1)), logical(eye(3)))
***** assert (ismissing (logical (eye(3)), single (1)), logical(eye(3)))
***** assert (ismissing (logical (eye(3)), int32 (1)), logical(eye(3)))
***** assert (ismissing (int32 (eye(3)), int32 (1)), logical(eye(3)))
***** assert (ismissing (int32 (eye(3)), true), logical(eye(3)))
***** assert (ismissing (int32 (eye(3)), double (1)), logical(eye(3)))
***** assert (ismissing (int32 (eye(3)), single (1)), logical(eye(3)))
***** assert (ismissing ([]), logical([]))
***** assert (ismissing (''), logical([]))
***** assert (ismissing (ones (0,1)), logical(ones(0,1)))
***** assert (ismissing (ones (1,0)), logical(ones(1,0)))
***** assert (ismissing (ones (1,2,0)), logical(ones(1,2,0)))
***** error ismissing ()
***** error <'indicator' and 'A' must have the same> ismissing ([1 2; 3 4], "abc")
***** error <'indicator' and 'A' must have the same> ismissing ({"", "", ""}, 1)
***** error <'indicator' and 'A' must have the same> ismissing (1, struct)
***** error <indicators not supported for data type> ismissing (struct, 1)
49 tests, 49 passed, 0 known failure, 0 skipped
[inst/dist_fun/laplaceinv.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/laplaceinv.m
***** demo
 ## Plot various iCDFs from the Laplace distribution
 p = 0.001:0.001:0.999;
 x1 = cauchyinv (p, 0, 1);
 x2 = cauchyinv (p, 0, 2);
 x3 = cauchyinv (p, 0, 4);
 x4 = cauchyinv (p, -5, 4);
 plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-c")
 grid on
 ylim ([-10, 10])
 legend ({"μ = 0, β = 1", "μ = 0, β = 2", ...
          "μ = 0, β = 4", "μ = -5, β = 4"}, "location", "northwest")
 title ("Laplace iCDF")
 xlabel ("probability")
 ylabel ("values in x")
***** shared p, x
 p = [-1 0 0.5 1 2];
 x = [NaN, -Inf, 0, Inf, NaN];
***** assert (laplaceinv (p, 0, 1), x)
***** assert (laplaceinv (p, 0, [-2, -1, 0, 1, 2]), [nan(1, 3), Inf, NaN])
***** assert (laplaceinv ([p, NaN], 0, 1), [x, NaN])
***** assert (laplaceinv (single ([p, NaN]), 0, 1), single ([x, NaN]))
***** assert (laplaceinv ([p, NaN], single (0), 1), single ([x, NaN]))
***** assert (laplaceinv ([p, NaN], 0, single (1)), single ([x, NaN]))
***** error<laplaceinv: function called with too few input arguments.> laplaceinv ()
***** error<laplaceinv: function called with too few input arguments.> laplaceinv (1)
***** error<laplaceinv: function called with too few input arguments.> ...
 laplaceinv (1, 2)
***** error<laplaceinv: function called with too many inputs> laplaceinv (1, 2, 3, 4)
***** error<laplaceinv: P, MU, and BETA must be of common size or scalars.> ...
 laplaceinv (1, ones (2), ones (3))
***** error<laplaceinv: P, MU, and BETA must be of common size or scalars.> ...
 laplaceinv (ones (2), 1, ones (3))
***** error<laplaceinv: P, MU, and BETA must be of common size or scalars.> ...
 laplaceinv (ones (2), ones (3), 1)
***** error<laplaceinv: P, MU, and BETA must not be complex.> laplaceinv (i, 2, 3)
***** error<laplaceinv: P, MU, and BETA must not be complex.> laplaceinv (1, i, 3)
***** error<laplaceinv: P, MU, and BETA must not be complex.> laplaceinv (1, 2, i)
16 tests, 16 passed, 0 known failure, 0 skipped
[inst/dist_fun/mvtcdfqmc.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/mvtcdfqmc.m
***** error mvtcdfqmc (1, 2, 3);
***** error mvtcdfqmc (1, 2, 3, 4, 5, 6, 7, 8);
2 tests, 2 passed, 0 known failure, 0 skipped
[inst/dist_fun/copulacdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/copulacdf.m
***** test
 x = [0.2:0.2:0.6; 0.2:0.2:0.6];
 theta = [1; 2];
 p = copulacdf ("Clayton", x, theta);
 expected_p = [0.1395; 0.1767];
 assert (p, expected_p, 0.001);
***** test
 x = [0.2:0.2:0.6; 0.2:0.2:0.6];
 p = copulacdf ("Gumbel", x, 2);
 expected_p = [0.1464; 0.1464];
 assert (p, expected_p, 0.001);
***** test
 x = [0.2:0.2:0.6; 0.2:0.2:0.6];
 theta = [1; 2];
 p = copulacdf ("Frank", x, theta);
 expected_p = [0.0699; 0.0930];
 assert (p, expected_p, 0.001);
***** test
 x = [0.2:0.2:0.6; 0.2:0.2:0.6];
 theta = [0.3; 0.7];
 p = copulacdf ("AMH", x, theta);
 expected_p = [0.0629; 0.0959];
 assert (p, expected_p, 0.001);
***** test
 x = [0.2:0.2:0.6; 0.2:0.1:0.4];
 theta = [0.2, 0.1, 0.1, 0.05];
 p = copulacdf ("FGM", x, theta);
 expected_p = [0.0558; 0.0293];
 assert (p, expected_p, 0.001);
5 tests, 5 passed, 0 known failure, 0 skipped
[inst/dist_fun/laplacepdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/laplacepdf.m
***** demo
 ## Plot various PDFs from the Laplace distribution
 x = -10:0.01:10;
 y1 = laplacepdf (x, 0, 1);
 y2 = laplacepdf (x, 0, 2);
 y3 = laplacepdf (x, 0, 4);
 y4 = laplacepdf (x, -5, 4);
 plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-c")
 grid on
 xlim ([-10, 10])
 ylim ([0, 0.6])
 legend ({"μ = 0, β = 1", "μ = 0, β = 2", ...
          "μ = 0, β = 4", "μ = -5, β = 4"}, "location", "northeast")
 title ("Laplace PDF")
 xlabel ("values in x")
 ylabel ("density")
***** shared x, y
 x = [-Inf -log(2) 0 log(2) Inf];
 y = [0, 1/4, 1/2, 1/4, 0];
***** assert (laplacepdf ([x, NaN], 0, 1), [y, NaN])
***** assert (laplacepdf (x, 0, [-2, -1, 0, 1, 2]), [nan(1, 3), 0.25, 0])
***** assert (laplacepdf (single ([x, NaN]), 0, 1), single ([y, NaN]))
***** assert (laplacepdf ([x, NaN], single (0), 1), single ([y, NaN]))
***** assert (laplacepdf ([x, NaN], 0, single (1)), single ([y, NaN]))
***** error<laplacepdf: function called with too few input arguments.> laplacepdf ()
***** error<laplacepdf: function called with too few input arguments.> laplacepdf (1)
***** error<laplacepdf: function called with too few input arguments.> ...
 laplacepdf (1, 2)
***** error<laplacepdf: function called with too many inputs> laplacepdf (1, 2, 3, 4)
***** error<laplacepdf: X, MU, and BETA must be of common size or scalars.> ...
 laplacepdf (1, ones (2), ones (3))
***** error<laplacepdf: X, MU, and BETA must be of common size or scalars.> ...
 laplacepdf (ones (2), 1, ones (3))
***** error<laplacepdf: X, MU, and BETA must be of common size or scalars.> ...
 laplacepdf (ones (2), ones (3), 1)
***** error<laplacepdf: X, MU, and BETA must not be complex.> laplacepdf (i, 2, 3)
***** error<laplacepdf: X, MU, and BETA must not be complex.> laplacepdf (1, i, 3)
***** error<laplacepdf: X, MU, and BETA must not be complex.> laplacepdf (1, 2, i)
15 tests, 15 passed, 0 known failure, 0 skipped
[inst/dist_fun/riceinv.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/riceinv.m
***** demo
 ## Plot various iCDFs from the Rician distribution
 p = 0.001:0.001:0.999;
 x1 = riceinv (p, 0, 1);
 x2 = riceinv (p, 0.5, 1);
 x3 = riceinv (p, 1, 1);
 x4 = riceinv (p, 2, 1);
 x5 = riceinv (p, 4, 1);
 plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-m", p, x5, "-k")
 grid on
 legend ({"s = 0, σ = 1", "s = 0.5, σ = 1", "s = 1, σ = 1", ...
          "s = 2, σ = 1", "s = 4, σ = 1"}, "location", "northwest")
 title ("Rician iCDF")
 xlabel ("probability")
 ylabel ("values in x")
***** shared p
 p = [-1 0 0.75 1 2];
***** assert (riceinv (p, ones (1,5), 2*ones (1,5)), [NaN 0 3.5354 Inf NaN], 1e-4)
***** assert (riceinv (p, 1, 2*ones (1,5)), [NaN 0 3.5354 Inf NaN], 1e-4)
***** assert (riceinv (p, ones (1,5), 2), [NaN 0 3.5354 Inf NaN], 1e-4)
***** assert (riceinv (p, [1 0 NaN 1 1], 2), [NaN 0 NaN Inf NaN])
***** assert (riceinv (p, 1, 2*[1 0 NaN 1 1]), [NaN NaN NaN Inf NaN])
***** assert (riceinv ([p(1:2) NaN p(4:5)], 1, 2), [NaN 0 NaN Inf NaN])
***** assert (riceinv ([p, NaN], 1, 2), [NaN 0 3.5354 Inf NaN NaN], 1e-4)
***** assert (riceinv (single ([p, NaN]), 1, 2), ...
        single ([NaN 0 3.5354 Inf NaN NaN]), 1e-4)
***** assert (riceinv ([p, NaN], single (1), 2), ...
        single ([NaN 0 3.5354 Inf NaN NaN]), 1e-4)
***** assert (riceinv ([p, NaN], 1, single (2)), ...
        single ([NaN 0 3.5354 Inf NaN NaN]), 1e-4)
***** error<riceinv: function called with too few input arguments.> riceinv ()
***** error<riceinv: function called with too few input arguments.> riceinv (1)
***** error<riceinv: function called with too few input arguments.> riceinv (1,2)
***** error<riceinv: function called with too many inputs> riceinv (1,2,3,4)
***** error<riceinv: P, S, and B must be of common size or scalars.> ...
 riceinv (ones (3), ones (2), ones (2))
***** error<riceinv: P, S, and B must be of common size or scalars.> ...
 riceinv (ones (2), ones (3), ones (2))
***** error<riceinv: P, S, and B must be of common size or scalars.> ...
 riceinv (ones (2), ones (2), ones (3))
***** error<riceinv: P, S, and B must not be complex.> riceinv (i, 2, 2)
***** error<riceinv: P, S, and B must not be complex.> riceinv (2, i, 2)
***** error<riceinv: P, S, and B must not be complex.> riceinv (2, 2, i)
20 tests, 20 passed, 0 known failure, 0 skipped
[inst/dist_fun/hygeinv.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/hygeinv.m
***** demo
 ## Plot various iCDFs from the hypergeometric distribution
 p = 0.001:0.001:0.999;
 x1 = hygeinv (p, 500, 50, 100);
 x2 = hygeinv (p, 500, 60, 200);
 x3 = hygeinv (p, 500, 70, 300);
 plot (p, x1, "-b", p, x2, "-g", p, x3, "-r")
 grid on
 ylim ([0, 60])
 legend ({"m = 500, k = 50, n = 100", "m = 500, k = 60, n = 200", ...
          "m = 500, k = 70, n = 300"}, "location", "northwest")
 title ("Hypergeometric iCDF")
 xlabel ("probability")
 ylabel ("values in p (number of successes)")
***** shared p
 p = [-1 0 0.5 1 2];
***** assert (hygeinv (p, 4*ones (1,5), 2*ones (1,5), 2*ones (1,5)), [NaN 0 1 2 NaN])
***** assert (hygeinv (p, 4*ones (1,5), 2, 2), [NaN 0 1 2 NaN])
***** assert (hygeinv (p, 4, 2*ones (1,5), 2), [NaN 0 1 2 NaN])
***** assert (hygeinv (p, 4, 2, 2*ones (1,5)), [NaN 0 1 2 NaN])
***** assert (hygeinv (p, 4*[1 -1 NaN 1.1 1], 2, 2), [NaN NaN NaN NaN NaN])
***** assert (hygeinv (p, 4, 2*[1 -1 NaN 1.1 1], 2), [NaN NaN NaN NaN NaN])
***** assert (hygeinv (p, 4, 5, 2), [NaN NaN NaN NaN NaN])
***** assert (hygeinv (p, 4, 2, 2*[1 -1 NaN 1.1 1]), [NaN NaN NaN NaN NaN])
***** assert (hygeinv (p, 4, 2, 5), [NaN NaN NaN NaN NaN])
***** assert (hygeinv ([p(1:2) NaN p(4:5)], 4, 2, 2), [NaN 0 NaN 2 NaN])
***** assert (hygeinv ([p, NaN], 4, 2, 2), [NaN 0 1 2 NaN NaN])
***** assert (hygeinv (single ([p, NaN]), 4, 2, 2), single ([NaN 0 1 2 NaN NaN]))
***** assert (hygeinv ([p, NaN], single (4), 2, 2), single ([NaN 0 1 2 NaN NaN]))
***** assert (hygeinv ([p, NaN], 4, single (2), 2), single ([NaN 0 1 2 NaN NaN]))
***** assert (hygeinv ([p, NaN], 4, 2, single (2)), single ([NaN 0 1 2 NaN NaN]))
***** error<hygeinv: function called with too few input arguments.> hygeinv ()
***** error<hygeinv: function called with too few input arguments.> hygeinv (1)
***** error<hygeinv: function called with too few input arguments.> hygeinv (1,2)
***** error<hygeinv: function called with too few input arguments.> hygeinv (1,2,3)
***** error<hygeinv: P, T, M, and N must be of common size or scalars.> ...
 hygeinv (ones (2), ones (3), 1, 1)
***** error<hygeinv: P, T, M, and N must be of common size or scalars.> ...
 hygeinv (1, ones (2), ones (3), 1)
***** error<hygeinv: P, T, M, and N must be of common size or scalars.> ...
 hygeinv (1, 1, ones (2), ones (3))
***** error<hygeinv: P, T, M, and N must not be complex.> hygeinv (i, 2, 2, 2)
***** error<hygeinv: P, T, M, and N must not be complex.> hygeinv (2, i, 2, 2)
***** error<hygeinv: P, T, M, and N must not be complex.> hygeinv (2, 2, i, 2)
***** error<hygeinv: P, T, M, and N must not be complex.> hygeinv (2, 2, 2, i)
26 tests, 26 passed, 0 known failure, 0 skipped
[inst/dist_fun/bisainv.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/bisainv.m
***** demo
 ## Plot various iCDFs from the Birnbaum-Saunders distribution
 p = 0.001:0.001:0.999;
 x1 = bisainv (p, 1, 0.5);
 x2 = bisainv (p, 1, 1);
 x3 = bisainv (p, 1, 2);
 x4 = bisainv (p, 1, 5);
 x5 = bisainv (p, 1, 10);
 plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-c", p, x5, "-m")
 grid on
 ylim ([0, 10])
 legend ({"β = 1, γ = 0.5", "β = 1, γ = 1", "β = 1, γ = 2", ...
          "β = 1, γ = 5", "β = 1, γ = 10"}, "location", "northwest")
 title ("Birnbaum-Saunders iCDF")
 xlabel ("probability")
 ylabel ("values in x")
***** demo
 ## Plot various iCDFs from the Birnbaum-Saunders distribution
 p = 0.001:0.001:0.999;
 x1 = bisainv (p, 1, 0.3);
 x2 = bisainv (p, 2, 0.3);
 x3 = bisainv (p, 1, 0.5);
 x4 = bisainv (p, 3, 0.5);
 x5 = bisainv (p, 5, 0.5);
 plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-c", p, x5, "-m")
 grid on
 ylim ([0, 10])
 legend ({"β = 1, γ = 0.3", "β = 2, γ = 0.3", "β = 1, γ = 0.5", ...
          "β = 3, γ = 0.5", "β = 5, γ = 0.5"}, "location", "northwest")
 title ("Birnbaum-Saunders iCDF")
 xlabel ("probability")
 ylabel ("values in x")
***** shared p, y, f
 f = @(p,b,c) (b * (c * norminv (p) + sqrt (4 + (c * norminv(p))^2))^2) / 4;
 p = [-1, 0, 1/4, 1/2, 1, 2];
 y = [NaN, 0, f(1/4, 1, 1), 1, Inf, NaN];
***** assert (bisainv (p, ones (1,6), ones (1,6)), y)
***** assert (bisainv (p, 1, ones (1,6)), y)
***** assert (bisainv (p, ones (1,6), 1), y)
***** assert (bisainv (p, 1, 1), y)
***** assert (bisainv (p, 1, [1, 1, 1, NaN, 1, 1]), [y(1:3), NaN, y(5:6)])
***** assert (bisainv (p, [1, 1, 1, NaN, 1, 1], 1), [y(1:3), NaN, y(5:6)])
***** assert (bisainv ([p, NaN], 1, 1), [y, NaN])
***** assert (bisainv (single ([p, NaN]), 1, 1), single ([y, NaN]), eps ("single"))
***** assert (bisainv ([p, NaN], 1, single (1)), single ([y, NaN]), eps ("single"))
***** assert (bisainv ([p, NaN], single (1), 1), single ([y, NaN]), eps ("single"))
***** error<bisainv: function called with too few input arguments.> bisainv ()
***** error<bisainv: function called with too few input arguments.> bisainv (1)
***** error<bisainv: function called with too few input arguments.> bisainv (1, 2)
***** error<bisainv: function called with too many inputs> bisainv (1, 2, 3, 4)
***** error<bisainv: P, BETA, and GAMMA must be of common size or scalars.> ...
 bisainv (ones (3), ones (2), ones(2))
***** error<bisainv: P, BETA, and GAMMA must be of common size or scalars.> ...
 bisainv (ones (2), ones (3), ones(2))
***** error<bisainv: P, BETA, and GAMMA must be of common size or scalars.> ...
 bisainv (ones (2), ones (2), ones(3))
***** error<bisainv: P, BETA, and GAMMA must not be complex.> bisainv (i, 4, 3)
***** error<bisainv: P, BETA, and GAMMA must not be complex.> bisainv (1, i, 3)
***** error<bisainv: P, BETA, and GAMMA must not be complex.> bisainv (1, 4, i)
20 tests, 20 passed, 0 known failure, 0 skipped
[inst/dist_fun/bvtcdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/bvtcdf.m
***** test
 x = [1, 2];
 rho = [1, 0.5; 0.5, 1];
 df = 4;
 assert (bvtcdf(x, rho(2), df), mvtcdf(x, rho, df), 1e-14);
***** test
 x = [3, 2;2, 4;1, 5];
 rho = [1, 0.5; 0.5, 1];
 df = 4;
 assert (bvtcdf(x, rho(2), df), mvtcdf(x, rho, df), 1e-14);
2 tests, 2 passed, 0 known failure, 0 skipped
[inst/dist_fun/gamcdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/gamcdf.m
***** demo
 ## Plot various CDFs from the Gamma distribution
 x = 0:0.01:20;
 p1 = gamcdf (x, 1, 2);
 p2 = gamcdf (x, 2, 2);
 p3 = gamcdf (x, 3, 2);
 p4 = gamcdf (x, 5, 1);
 p5 = gamcdf (x, 9, 0.5);
 p6 = gamcdf (x, 7.5, 1);
 p7 = gamcdf (x, 0.5, 1);
 plot (x, p1, "-r", x, p2, "-g", x, p3, "-y", x, p4, "-m", ...
       x, p5, "-k", x, p6, "-b", x, p7, "-c")
 grid on
 legend ({"α = 1, β = 2", "α = 2, β = 2", "α = 3, β = 2", ...
          "α = 5, β = 1", "α = 9, β = 0.5", "α = 7.5, β = 1", ...
          "α = 0.5, β = 1"}, "location", "southeast")
 title ("Gamma CDF")
 xlabel ("values in x")
 ylabel ("probability")
***** shared x, y, u
 x = [-1, 0, 0.5, 1, 2, Inf];
 y = [0, gammainc(x(2:end), 1)];
 u = [0, NaN, NaN, 1, 0.1353352832366127, 0];
***** assert (gamcdf (x, ones (1,6), ones (1,6)), y, eps)
***** assert (gamcdf (x, ones (1,6), ones (1,6), []), y, eps)
***** assert (gamcdf (x, 1, ones (1,6)), y, eps)
***** assert (gamcdf (x, ones (1,6), 1), y, eps)
***** assert (gamcdf (x, [0, -Inf, NaN, Inf, 1, 1], 1), [1, NaN, NaN, 0, y(5:6)], eps)
***** assert (gamcdf (x, [0, -Inf, NaN, Inf, 1, 1], 1, "upper"), u, eps)
***** assert (gamcdf (x, 1, [0, -Inf, NaN, Inf, 1, 1]), [NaN, NaN, NaN, 0, y(5:6)], eps)
***** assert (gamcdf ([x(1:2), NaN, x(4:6)], 1, 1), [y(1:2), NaN, y(4:6)], eps)
***** assert (gamcdf ([x, NaN], 1, 1), [y, NaN])
***** assert (gamcdf (single ([x, NaN]), 1, 1), single ([y, NaN]), eps ("single"))
***** assert (gamcdf ([x, NaN], single (1), 1), single ([y, NaN]), eps ("single"))
***** assert (gamcdf ([x, NaN], 1, single (1)), single ([y, NaN]), eps ("single"))
***** error<gamcdf: invalid number of input arguments.> gamcdf ()
***** error<gamcdf: invalid number of input arguments.> gamcdf (1)
***** error<gamcdf: invalid number of input arguments.> gamcdf (1, 2, 3, 4, 5, 6, 7)
***** error<gamcdf: invalid argument for upper tail.> gamcdf (1, 2, 3, "uper")
***** error<gamcdf: invalid argument for upper tail.> gamcdf (1, 2, 3, 4, 5, "uper")
***** error<gamcdf: invalid size of covariance matrix.> gamcdf (2, 3, 4, [1, 2])
***** error<gamcdf: covariance matrix is required for confidence bounds.> ...
 [p, plo, pup] = gamcdf (1, 2, 3)
***** error<gamcdf: covariance matrix is required for confidence bounds.> ...
 [p, plo, pup] = gamcdf (1, 2, 3, "upper")
***** error<gamcdf: invalid value for alpha.> [p, plo, pup] = ...
 gamcdf (1, 2, 3, [1, 0; 0, 1], 0)
***** error<gamcdf: invalid value for alpha.> [p, plo, pup] = ...
 gamcdf (1, 2, 3, [1, 0; 0, 1], 1.22)
***** error<gamcdf: invalid value for alpha.> [p, plo, pup] = ...
 gamcdf (1, 2, 3, [1, 0; 0, 1], "alpha", "upper")
***** error<gamcdf: X, A, and B must be of common size or scalars.> ...
 gamcdf (ones (3), ones (2), ones (2))
***** error<gamcdf: X, A, and B must be of common size or scalars.> ...
 gamcdf (ones (2), ones (3), ones (2))
***** error<gamcdf: X, A, and B must be of common size or scalars.> ...
 gamcdf (ones (2), ones (2), ones (3))
***** error<gamcdf: X, A, and B must not be complex.> gamcdf (i, 2, 2)
***** error<gamcdf: X, A, and B must not be complex.> gamcdf (2, i, 2)
***** error<gamcdf: X, A, and B must not be complex.> gamcdf (2, 2, i)
***** error<gamcdf: bad covariance matrix.> ...
 [p, plo, pup] = gamcdf (1, 2, 3, [1, 0; 0, -inf], 0.04)
30 tests, 30 passed, 0 known failure, 0 skipped
[inst/dist_fun/tricdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/tricdf.m
***** demo
 ## Plot various CDFs from the triangular distribution
 x = 0.001:0.001:10;
 p1 = tricdf (x, 3, 4, 6);
 p2 = tricdf (x, 1, 2, 5);
 p3 = tricdf (x, 2, 3, 9);
 p4 = tricdf (x, 2, 5, 9);
 plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", x, p4, "-c")
 grid on
 xlim ([0, 10])
 legend ({"a = 3, b = 4, c = 6", "a = 1, b = 2, c = 5", ...
          "a = 2, b = 3, c = 9", "a = 2, b = 5, c = 9"}, ...
         "location", "southeast")
 title ("Triangular CDF")
 xlabel ("values in x")
 ylabel ("probability")
***** shared x, y
 x = [-1, 0, 0.1, 0.5, 0.9, 1, 2] + 1;
 y = [0, 0, 0.02, 0.5, 0.98, 1 1];
***** assert (tricdf (x, ones (1,7), 1.5 * ones (1, 7), 2 * ones (1, 7)), y, eps)
***** assert (tricdf (x, 1 * ones (1, 7), 1.5, 2), y, eps)
***** assert (tricdf (x, 1 * ones (1, 7), 1.5, 2, "upper"), 1 - y, eps)
***** assert (tricdf (x, 1, 1.5, 2 * ones (1, 7)), y, eps)
***** assert (tricdf (x, 1, 1.5 * ones (1, 7), 2), y, eps)
***** assert (tricdf (x, 1, 1.5, 2), y, eps)
***** assert (tricdf (x, [1, 1, NaN, 1, 1, 1, 1], 1.5, 2), ...
 [y(1:2), NaN, y(4:7)], eps)
***** assert (tricdf (x, 1, 1.5, 2*[1, 1, NaN, 1, 1, 1, 1]), ...
 [y(1:2), NaN, y(4:7)], eps)
***** assert (tricdf (x, 1, 1.5, 2*[1, 1, NaN, 1, 1, 1, 1]), ...
 [y(1:2), NaN, y(4:7)], eps)
***** assert (tricdf ([x, NaN], 1, 1.5, 2), [y, NaN], eps)
***** assert (tricdf (single ([x, NaN]), 1, 1.5, 2), ...
 single ([y, NaN]), eps("single"))
***** assert (tricdf ([x, NaN], single (1), 1.5, 2), ...
 single ([y, NaN]), eps("single"))
***** assert (tricdf ([x, NaN], 1, single (1.5), 2), ...
 single ([y, NaN]), eps("single"))
***** assert (tricdf ([x, NaN], 1, 1.5, single (2)), ...
 single ([y, NaN]), eps("single"))
***** error<tricdf: function called with too few input arguments.> tricdf ()
***** error<tricdf: function called with too few input arguments.> tricdf (1)
***** error<tricdf: function called with too few input arguments.> tricdf (1, 2)
***** error<tricdf: function called with too few input arguments.> tricdf (1, 2, 3)
***** error<tricdf: function called with too many inputs> ...
 tricdf (1, 2, 3, 4, 5, 6)
***** error<tricdf: invalid argument for upper tail.> tricdf (1, 2, 3, 4, "tail")
***** error<tricdf: invalid argument for upper tail.> tricdf (1, 2, 3, 4, 5)
***** error<tricdf: X, A, B, and C must be of common size or scalars.> ...
 tricdf (ones (3), ones (2), ones(2), ones(2))
***** error<tricdf: X, A, B, and C must be of common size or scalars.> ...
 tricdf (ones (2), ones (3), ones(2), ones(2))
***** error<tricdf: X, A, B, and C must be of common size or scalars.> ...
 tricdf (ones (2), ones (2), ones(3), ones(2))
***** error<tricdf: X, A, B, and C must be of common size or scalars.> ...
 tricdf (ones (2), ones (2), ones(2), ones(3))
***** error<tricdf: X, A, B, and C must not be complex.> tricdf (i, 2, 3, 4)
***** error<tricdf: X, A, B, and C must not be complex.> tricdf (1, i, 3, 4)
***** error<tricdf: X, A, B, and C must not be complex.> tricdf (1, 2, i, 4)
***** error<tricdf: X, A, B, and C must not be complex.> tricdf (1, 2, 3, i)
29 tests, 29 passed, 0 known failure, 0 skipped
[inst/dist_fun/loglinv.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/loglinv.m
***** demo
 ## Plot various iCDFs from the log-logistic distribution
 p = 0.001:0.001:0.999;
 x1 = loglinv (p, log (1), 1/0.5);
 x2 = loglinv (p, log (1), 1);
 x3 = loglinv (p, log (1), 1/2);
 x4 = loglinv (p, log (1), 1/4);
 x5 = loglinv (p, log (1), 1/8);
 plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-c", p, x5, "-m")
 ylim ([0, 20])
 grid on
 legend ({"σ = 2 (β = 0.5)", "σ = 1 (β = 1)", "σ = 0.5 (β = 2)", ...
          "σ = 0.25 (β = 4)", "σ = 0.125 (β = 8)"}, "location", "northwest")
 title ("Log-logistic iCDF")
 xlabel ("probability")
 ylabel ("x")
 text (0.03, 12.5, "μ = 0 (α = 1), values of σ (β) as shown in legend")
***** shared p, out1, out2
 p = [-1, 0, 0.2, 0.5, 0.8, 0.95, 1, 2];
 out1 = [NaN, 0, 0.25, 1, 4, 19, Inf, NaN];
 out2 = [NaN, 0, 0.0424732, 2.718282, 173.970037, 18644.695061, Inf, NaN];
***** assert (loglinv (p, 0, 1), out1, 1e-8)
***** assert (loglinv (p, 0, 1), out1, 1e-8)
***** assert (loglinv (p, 1, 3), out2, 1e-6)
***** assert (class (loglinv (single (1), 2, 3)), "single")
***** assert (class (loglinv (1, single (2), 3)), "single")
***** assert (class (loglinv (1, 2, single (3))), "single")
***** error<loglinv: function called with too few input arguments.> loglinv (1)
***** error<loglinv: function called with too few input arguments.> loglinv (1, 2)
***** error<loglinv: P, MU, and SIGMA must be of common size or scalars.> ...
 loglinv (1, ones (2), ones (3))
***** error<loglinv: P, MU, and SIGMA must be of common size or scalars.> ...
 loglinv (ones (2), 1, ones (3))
***** error<loglinv: P, MU, and SIGMA must be of common size or scalars.> ...
 loglinv (ones (2), ones (3), 1)
***** error<loglinv: P, MU, and SIGMA must not be complex.> loglinv (i, 2, 3)
***** error<loglinv: P, MU, and SIGMA must not be complex.> loglinv (1, i, 3)
***** error<loglinv: P, MU, and SIGMA must not be complex.> loglinv (1, 2, i)
14 tests, 14 passed, 0 known failure, 0 skipped
[inst/dist_fun/plcdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/plcdf.m
***** demo
 ## Plot various CDFs from the Piecewise linear distribution
 data = 0:0.01:10;
 x1 = [0, 1, 3, 4, 7, 10];
 Fx1 = [0, 0.2, 0.5, 0.6, 0.7, 1];
 x2 = [0, 2, 5, 6, 7, 8];
 Fx2 = [0, 0.1, 0.3, 0.6, 0.9, 1];
 p1 = plcdf (data, x1, Fx1);
 p2 = plcdf (data, x2, Fx2);
 plot (data, p1, "-b", data, p2, "g")
 grid on
 ylim ([0, 1])
 xlim ([0, 10])
 legend ({"x1, Fx1", "x2, Fx2"}, "location", "southeast")
 title ("Piecewise linear CDF")
 xlabel ("values in data")
 ylabel ("probability")
***** test
 data = 0:0.2:1;
 p = plcdf (data, [0, 1], [0, 1]);
 assert (p, data);
***** test
 data = 0:0.2:1;
 p = plcdf (data, [0, 2], [0, 1]);
 assert (p, 0.5 * data);
***** test
 data = 0:0.2:1;
 p = plcdf (data, [0, 1], [0, 0.5]);
 assert (p, 0.5 * data);
***** test
 data = 0:0.2:1;
 p = plcdf (data, [0, 0.5], [0, 1]);
 assert (p, [0, 0.4, 0.8, 1, 1, 1]);
***** test
 data = 0:0.2:1;
 p = plcdf (data, [0, 1], [0, 1], "upper");
 assert (p, 1 - data);
***** error<plcdf: function called with too few input arguments.> plcdf ()
***** error<plcdf: function called with too few input arguments.> plcdf (1)
***** error<plcdf: function called with too few input arguments.> plcdf (1, 2)
***** error<plcdf: invalid argument for upper tail.> plcdf (1, 2, 3, "uper")
***** error<plcdf: invalid argument for upper tail.> plcdf (1, 2, 3, 4)
***** error<plcdf: X and FX must be vectors of equal size.> ...
 plcdf (1, [0, 1, 2], [0, 1])
***** error<plcdf: X and FX must be at least two-elements long.> ...
 plcdf (1, [0], [1])
***** error<plcdf: FX must be bounded in the range> ...
 plcdf (1, [0, 1, 2], [0, 1, 1.5])
***** error<plcdf: FX must be bounded in the range> ...
 plcdf (1, [0, 1, 2], [0, i, 1])
***** error<plcdf: DATA, X, and FX must not be complex.> ...
 plcdf (i, [0, 1, 2], [0, 0.5, 1])
***** error<plcdf: DATA, X, and FX must not be complex.> ...
 plcdf (1, [0, i, 2], [0, 0.5, 1])
***** error<plcdf: DATA, X, and FX must not be complex.> ...
 plcdf (1, [0, 1, 2], [0, 0.5i, 1])
17 tests, 17 passed, 0 known failure, 0 skipped
[inst/dist_fun/ncfrnd.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/ncfrnd.m
***** assert (size (ncfrnd (1, 1, 1)), [1 1])
***** assert (size (ncfrnd (1, ones (2,1), 1)), [2, 1])
***** assert (size (ncfrnd (1, ones (2,2), 1)), [2, 2])
***** assert (size (ncfrnd (ones (2,1), 1, 1)), [2, 1])
***** assert (size (ncfrnd (ones (2,2), 1, 1)), [2, 2])
***** assert (size (ncfrnd (1, 1, 1, 3)), [3, 3])
***** assert (size (ncfrnd (1, 1, 1, [4, 1])), [4, 1])
***** assert (size (ncfrnd (1, 1, 1, 4, 1)), [4, 1])
***** assert (size (ncfrnd (1, 1, 1, 4, 1, 5)), [4, 1, 5])
***** assert (size (ncfrnd (1, 1, 1, 0, 1)), [0, 1])
***** assert (size (ncfrnd (1, 1, 1, 1, 0)), [1, 0])
***** assert (size (ncfrnd (1, 1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
***** assert (class (ncfrnd (1, 1, 1)), "double")
***** assert (class (ncfrnd (1, single (1), 1)), "single")
***** assert (class (ncfrnd (1, 1, single (1))), "single")
***** assert (class (ncfrnd (1, single ([1, 1]), 1)), "single")
***** assert (class (ncfrnd (1, 1, single ([1, 1]))), "single")
***** assert (class (ncfrnd (single (1), 1, 1)), "single")
***** assert (class (ncfrnd (single ([1, 1]), 1, 1)), "single")
***** error<ncfrnd: function called with too few input arguments.> ncfrnd ()
***** error<ncfrnd: function called with too few input arguments.> ncfrnd (1)
***** error<ncfrnd: function called with too few input arguments.> ncfrnd (1, 2)
***** error<ncfrnd: DF1, DF2, and LAMBDA must be of common size or scalars.> ...
 ncfrnd (ones (3), ones (2), ones (2))
***** error<ncfrnd: DF1, DF2, and LAMBDA must be of common size or scalars.> ...
 ncfrnd (ones (2), ones (3), ones (2))
***** error<ncfrnd: DF1, DF2, and LAMBDA must be of common size or scalars.> ...
 ncfrnd (ones (2), ones (2), ones (3))
***** error<ncfrnd: DF1, DF2, and LAMBDA must not be complex.> ncfrnd (i, 2, 3)
***** error<ncfrnd: DF1, DF2, and LAMBDA must not be complex.> ncfrnd (1, i, 3)
***** error<ncfrnd: DF1, DF2, and LAMBDA must not be complex.> ncfrnd (1, 2, i)
***** error<ncfrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 ncfrnd (1, 2, 3, -1)
***** error<ncfrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 ncfrnd (1, 2, 3, 1.2)
***** error<ncfrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 ncfrnd (1, 2, 3, ones (2))
***** error<ncfrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 ncfrnd (1, 2, 3, [2 -1 2])
***** error<ncfrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 ncfrnd (1, 2, 3, [2 0 2.5])
***** error<ncfrnd: dimensions must be non-negative integers.> ...
 ncfrnd (1, 2, 3, 2, -1, 5)
***** error<ncfrnd: dimensions must be non-negative integers.> ...
 ncfrnd (1, 2, 3, 2, 1.5, 5)
***** error<ncfrnd: DF1, DF2, and LAMBDA must be scalars or of size SZ.> ...
 ncfrnd (2, ones (2), 2, 3)
***** error<ncfrnd: DF1, DF2, and LAMBDA must be scalars or of size SZ.> ...
 ncfrnd (2, ones (2), 2, [3, 2])
***** error<ncfrnd: DF1, DF2, and LAMBDA must be scalars or of size SZ.> ...
 ncfrnd (2, ones (2), 2, 3, 2)
38 tests, 38 passed, 0 known failure, 0 skipped
[inst/dist_fun/betacdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/betacdf.m
***** demo
 ## Plot various CDFs from the Beta distribution
 x = 0:0.005:1;
 p1 = betacdf (x, 0.5, 0.5);
 p2 = betacdf (x, 5, 1);
 p3 = betacdf (x, 1, 3);
 p4 = betacdf (x, 2, 2);
 p5 = betacdf (x, 2, 5);
 plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", x, p4, "-c", x, p5, "-m")
 grid on
 legend ({"α = β = 0.5", "α = 5, β = 1", "α = 1, β = 3", ...
          "α = 2, β = 2", "α = 2, β = 5"}, "location", "northwest")
 title ("Beta CDF")
 xlabel ("values in x")
 ylabel ("probability")
***** shared x, y, x1, x2
 x = [-1 0 0.5 1 2];
 y = [0 0 0.75 1 1];
***** assert (betacdf (x, ones (1, 5), 2 * ones (1, 5)), y)
***** assert (betacdf (x, 1, 2 * ones (1, 5)), y)
***** assert (betacdf (x, ones (1, 5), 2), y)
***** assert (betacdf (x, [0 1 NaN 1 1], 2), [NaN 0 NaN 1 1])
***** assert (betacdf (x, 1, 2 * [0 1 NaN 1 1]), [NaN 0 NaN 1 1])
***** assert (betacdf ([x(1:2) NaN x(4:5)], 1, 2), [y(1:2) NaN y(4:5)])
 x1 = [0.1:0.2:0.9];
***** assert (betacdf (x1, 2, 2), [0.028, 0.216, 0.5, 0.784, 0.972], 1e-14);
***** assert (betacdf (x1, 2, 2, "upper"), 1 - [0.028, 0.216, 0.5, 0.784, 0.972],...
        1e-14);
 x2 = [1, 2, 3];
***** assert (betacdf (0.5, x2, x2), [0.5, 0.5, 0.5], 1e-14);
***** assert (betacdf ([x, NaN], 1, 2), [y, NaN])
***** assert (betacdf (single ([x, NaN]), 1, 2), single ([y, NaN]))
***** assert (betacdf ([x, NaN], single (1), 2), single ([y, NaN]))
***** assert (betacdf ([x, NaN], 1, single (2)), single ([y, NaN]))
***** error<betacdf: function called with too few input arguments.> betacdf ()
***** error<betacdf: function called with too few input arguments.> betacdf (1)
***** error<betacdf: function called with too few input arguments.> betacdf (1, 2)
***** error<betacdf: function called with too many inputs> betacdf (1, 2, 3, 4, 5)
***** error<betacdf: invalid argument for upper tail.> betacdf (1, 2, 3, "tail")
***** error<betacdf: invalid argument for upper tail.> betacdf (1, 2, 3, 4)
***** error<betacdf: X, A, and B must be of common size or scalars.> ...
 betacdf (ones (3), ones (2), ones (2))
***** error<betacdf: X, A, and B must be of common size or scalars.> ...
 betacdf (ones (2), ones (3), ones (2))
***** error<betacdf: X, A, and B must be of common size or scalars.> ...
 betacdf (ones (2), ones (2), ones (3))
***** error<betacdf: X, A, and B must not be complex.> betacdf (i, 2, 2)
***** error<betacdf: X, A, and B must not be complex.> betacdf (2, i, 2)
***** error<betacdf: X, A, and B must not be complex.> betacdf (2, 2, i)
25 tests, 25 passed, 0 known failure, 0 skipped
[inst/dist_fun/fpdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/fpdf.m
***** demo
 ## Plot various PDFs from the F distribution
 x = 0.01:0.01:4;
 y1 = fpdf (x, 1, 1);
 y2 = fpdf (x, 2, 1);
 y3 = fpdf (x, 5, 2);
 y4 = fpdf (x, 10, 1);
 y5 = fpdf (x, 100, 100);
 plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-c", x, y5, "-m")
 grid on
 ylim ([0, 2.5])
 legend ({"df1 = 1, df2 = 2", "df1 = 2, df2 = 1", ...
          "df1 = 5, df2 = 2", "df1 = 10, df2 = 1", ...
          "df1 = 100, df2 = 100"}, "location", "northeast")
 title ("F PDF")
 xlabel ("values in x")
 ylabel ("density")
***** shared x, y
 x = [-1 0 0.5 1 2];
 y = [0 0 4/9 1/4 1/9];
***** assert (fpdf (x, 2*ones (1,5), 2*ones (1,5)), y, eps)
***** assert (fpdf (x, 2, 2*ones (1,5)), y, eps)
***** assert (fpdf (x, 2*ones (1,5), 2), y, eps)
***** assert (fpdf (x, [0 NaN Inf 2 2], 2), [NaN NaN NaN y(4:5)], eps)
***** assert (fpdf (x, 2, [0 NaN Inf 2 2]), [NaN NaN NaN y(4:5)], eps)
***** assert (fpdf ([x, NaN], 2, 2), [y, NaN], eps)
***** test #F (x, 1, df1) == T distribution (sqrt (x), df1) / sqrt (x)
 rand ("seed", 1234);    # for reproducibility
 xr = rand (10,1);
 xr = xr(x > 0.1 & x < 0.9);
 yr = tpdf (sqrt (xr), 2) ./ sqrt (xr);
 assert (fpdf (xr, 1, 2), yr, 5*eps);
***** assert (fpdf (single ([x, NaN]), 2, 2), single ([y, NaN]), eps ("single"))
***** assert (fpdf ([x, NaN], single (2), 2), single ([y, NaN]), eps ("single"))
***** assert (fpdf ([x, NaN], 2, single (2)), single ([y, NaN]), eps ("single"))
***** error<fpdf: function called with too few input arguments.> fpdf ()
***** error<fpdf: function called with too few input arguments.> fpdf (1)
***** error<fpdf: function called with too few input arguments.> fpdf (1,2)
***** error<fpdf: X, DF1, and DF2 must be of common size or scalars.> ...
 fpdf (ones (3), ones (2), ones (2))
***** error<fpdf: X, DF1, and DF2 must be of common size or scalars.> ...
 fpdf (ones (2), ones (3), ones (2))
***** error<fpdf: X, DF1, and DF2 must be of common size or scalars.> ...
 fpdf (ones (2), ones (2), ones (3))
***** error<fpdf: X, DF1, and DF2 must not be complex.> fpdf (i, 2, 2)
***** error<fpdf: X, DF1, and DF2 must not be complex.> fpdf (2, i, 2)
***** error<fpdf: X, DF1, and DF2 must not be complex.> fpdf (2, 2, i)
19 tests, 19 passed, 0 known failure, 0 skipped
[inst/dist_fun/logncdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/logncdf.m
***** demo
 ## Plot various CDFs from the log-normal distribution
 x = 0:0.01:3;
 p1 = logncdf (x, 0, 1);
 p2 = logncdf (x, 0, 0.5);
 p3 = logncdf (x, 0, 0.25);
 plot (x, p1, "-b", x, p2, "-g", x, p3, "-r")
 grid on
 legend ({"μ = 0, σ = 1", "μ = 0, σ = 0.5", "μ = 0, σ = 0.25"}, ...
         "location", "southeast")
 title ("Log-normal CDF")
 xlabel ("values in x")
 ylabel ("probability")
***** shared x, y
 x = [-1, 0, 1, e, Inf];
 y = [0, 0, 0.5, 1/2+1/2*erf(1/2), 1];
***** assert (logncdf (x, zeros (1,5), sqrt(2)*ones (1,5)), y, eps)
***** assert (logncdf (x, zeros (1,5), sqrt(2)*ones (1,5), []), y, eps)
***** assert (logncdf (x, 0, sqrt(2)*ones (1,5)), y, eps)
***** assert (logncdf (x, zeros (1,5), sqrt(2)), y, eps)
***** assert (logncdf (x, [0 1 NaN 0 1], sqrt(2)), [0 0 NaN y(4:5)], eps)
***** assert (logncdf (x, 0, sqrt(2)*[0 NaN Inf 1 1]), [NaN NaN y(3:5)], eps)
***** assert (logncdf ([x(1:3) NaN x(5)], 0, sqrt(2)), [y(1:3) NaN y(5)], eps)
***** assert (logncdf ([x, NaN], 0, sqrt(2)), [y, NaN], eps)
***** assert (logncdf (single ([x, NaN]), 0, sqrt(2)), single ([y, NaN]), eps ("single"))
***** assert (logncdf ([x, NaN], single (0), sqrt(2)), single ([y, NaN]), eps ("single"))
***** assert (logncdf ([x, NaN], 0, single (sqrt(2))), single ([y, NaN]), eps ("single"))
***** error<logncdf: invalid number of input arguments.> logncdf ()
***** error<logncdf: invalid number of input arguments.> logncdf (1,2,3,4,5,6,7)
***** error<logncdf: invalid argument for upper tail.> logncdf (1, 2, 3, 4, "uper")
***** error<logncdf: X, MU, and SIGMA must be of common size or scalars.> ...
 logncdf (ones (3), ones (2), ones (2))
***** error<logncdf: invalid size of covariance matrix.> logncdf (2, 3, 4, [1, 2])
***** error<logncdf: covariance matrix is required for confidence bounds.> ...
 [p, plo, pup] = logncdf (1, 2, 3)
***** error<logncdf: invalid value for alpha.> [p, plo, pup] = ...
 logncdf (1, 2, 3, [1, 0; 0, 1], 0)
***** error<logncdf: invalid value for alpha.> [p, plo, pup] = ...
 logncdf (1, 2, 3, [1, 0; 0, 1], 1.22)
***** error<logncdf: invalid value for alpha.> [p, plo, pup] = ...
 logncdf (1, 2, 3, [1, 0; 0, 1], "alpha", "upper")
***** error<logncdf: X, MU, and SIGMA must not be complex.> logncdf (i, 2, 2)
***** error<logncdf: X, MU, and SIGMA must not be complex.> logncdf (2, i, 2)
***** error<logncdf: X, MU, and SIGMA must not be complex.> logncdf (2, 2, i)
***** error<logncdf: bad covariance matrix.> ...
 [p, plo, pup] =logncdf (1, 2, 3, [1, 0; 0, -inf], 0.04)
24 tests, 24 passed, 0 known failure, 0 skipped
[inst/dist_fun/ncx2rnd.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/ncx2rnd.m
***** assert (size (ncx2rnd (1, 1)), [1 1])
***** assert (size (ncx2rnd (1, ones (2,1))), [2, 1])
***** assert (size (ncx2rnd (1, ones (2,2))), [2, 2])
***** assert (size (ncx2rnd (ones (2,1), 1)), [2, 1])
***** assert (size (ncx2rnd (ones (2,2), 1)), [2, 2])
***** assert (size (ncx2rnd (1, 1, 3)), [3, 3])
***** assert (size (ncx2rnd (1, 1, [4, 1])), [4, 1])
***** assert (size (ncx2rnd (1, 1, 4, 1)), [4, 1])
***** assert (size (ncx2rnd (1, 1, 4, 1, 5)), [4, 1, 5])
***** assert (size (ncx2rnd (1, 1, 0, 1)), [0, 1])
***** assert (size (ncx2rnd (1, 1, 1, 0)), [1, 0])
***** assert (size (ncx2rnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
***** assert (class (ncx2rnd (1, 1)), "double")
***** assert (class (ncx2rnd (1, single (1))), "single")
***** assert (class (ncx2rnd (1, single ([1, 1]))), "single")
***** assert (class (ncx2rnd (single (1), 1)), "single")
***** assert (class (ncx2rnd (single ([1, 1]), 1)), "single")
***** error<ncx2rnd: function called with too few input arguments.> ncx2rnd ()
***** error<ncx2rnd: function called with too few input arguments.> ncx2rnd (1)
***** error<ncx2rnd: DF and LAMBDA must be of common size or scalars.> ...
 ncx2rnd (ones (3), ones (2))
***** error<ncx2rnd: DF and LAMBDA must be of common size or scalars.> ...
 ncx2rnd (ones (2), ones (3))
***** error<ncx2rnd: DF and LAMBDA must not be complex.> ncx2rnd (i, 2)
***** error<ncx2rnd: DF and LAMBDA must not be complex.> ncx2rnd (1, i)
***** error<ncx2rnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 ncx2rnd (1, 2, -1)
***** error<ncx2rnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 ncx2rnd (1, 2, 1.2)
***** error<ncx2rnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 ncx2rnd (1, 2, ones (2))
***** error<ncx2rnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 ncx2rnd (1, 2, [2 -1 2])
***** error<ncx2rnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 ncx2rnd (1, 2, [2 0 2.5])
***** error<ncx2rnd: dimensions must be non-negative integers.> ...
 ncx2rnd (1, 2, 2, -1, 5)
***** error<ncx2rnd: dimensions must be non-negative integers.> ...
 ncx2rnd (1, 2, 2, 1.5, 5)
***** error<ncx2rnd: DF and LAMBDA must be scalars or of size SZ.> ...
 ncx2rnd (2, ones (2), 3)
***** error<ncx2rnd: DF and LAMBDA must be scalars or of size SZ.> ...
 ncx2rnd (2, ones (2), [3, 2])
***** error<ncx2rnd: DF and LAMBDA must be scalars or of size SZ.> ...
 ncx2rnd (2, ones (2), 3, 2)
33 tests, 33 passed, 0 known failure, 0 skipped
[inst/dist_fun/raylpdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/raylpdf.m
***** demo
 ## Plot various PDFs from the Rayleigh distribution
 x = 0:0.01:10;
 y1 = raylpdf (x, 0.5);
 y2 = raylpdf (x, 1);
 y3 = raylpdf (x, 2);
 y4 = raylpdf (x, 3);
 y5 = raylpdf (x, 4);
 plot (x, y1, "-b", x, y2, "g", x, y3, "-r", x, y4, "-m", x, y5, "-k")
 grid on
 ylim ([0, 1.25])
 legend ({"σ = 0,5", "σ = 1", "σ = 2", ...
          "σ = 3", "σ = 4"}, "location", "northeast")
 title ("Rayleigh PDF")
 xlabel ("values in x")
 ylabel ("density")
***** test
 x = 0:0.5:2.5;
 sigma = 1:6;
 y = raylpdf (x, sigma);
 expected_y = [0.0000, 0.1212, 0.1051, 0.0874, 0.0738, 0.0637];
 assert (y, expected_y, 0.001);
***** test
 x = 0:0.5:2.5;
 y = raylpdf (x, 0.5);
 expected_y = [0.0000, 1.2131, 0.5413, 0.0667, 0.0027, 0.0000];
 assert (y, expected_y, 0.001);
***** error<raylpdf: function called with too few input arguments.> raylpdf ()
***** error<raylpdf: function called with too few input arguments.> raylpdf (1)
***** error<raylpdf: X and SIGMA must be of common size or scalars.> ...
 raylpdf (ones (3), ones (2))
***** error<raylpdf: X and SIGMA must be of common size or scalars.> ...
 raylpdf (ones (2), ones (3))
***** error<raylpdf: X and SIGMA must not be complex.> raylpdf (i, 2)
***** error<raylpdf: X and SIGMA must not be complex.> raylpdf (2, i)
8 tests, 8 passed, 0 known failure, 0 skipped
[inst/dist_fun/tinv.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/tinv.m
***** demo
 ## Plot various iCDFs from the Student's T distribution
 p = 0.001:0.001:0.999;
 x1 = tinv (p, 1);
 x2 = tinv (p, 2);
 x3 = tinv (p, 5);
 x4 = tinv (p, Inf);
 plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-m")
 grid on
 xlim ([0, 1])
 ylim ([-5, 5])
 legend ({"df = 1", "df = 2", ...
          "df = 5", 'df = \infty'}, "location", "northwest")
 title ("Student's T iCDF")
 xlabel ("probability")
 ylabel ("values in x")
***** shared p
 p = [-1 0 0.5 1 2];
***** assert (tinv (p, ones (1,5)), [NaN -Inf 0 Inf NaN])
***** assert (tinv (p, 1), [NaN -Inf 0 Inf NaN], eps)
***** assert (tinv (p, [1 0 NaN 1 1]), [NaN NaN NaN Inf NaN], eps)
***** assert (tinv ([p(1:2) NaN p(4:5)], 1), [NaN -Inf NaN Inf NaN])
***** assert (tinv ([p, NaN], 1), [NaN -Inf 0 Inf NaN NaN], eps)
***** assert (tinv (single ([p, NaN]), 1), single ([NaN -Inf 0 Inf NaN NaN]), eps ("single"))
***** assert (tinv ([p, NaN], single (1)), single ([NaN -Inf 0 Inf NaN NaN]), eps ("single"))
***** error<tinv: function called with too few input arguments.> tinv ()
***** error<tinv: function called with too few input arguments.> tinv (1)
***** error<tinv: P and DF must be of common size or scalars.> ...
 tinv (ones (3), ones (2))
***** error<tinv: P and DF must be of common size or scalars.> ...
 tinv (ones (2), ones (3))
***** error<tinv: P and DF must not be complex.> tinv (i, 2)
***** error<tinv: P and DF must not be complex.> tinv (2, i)
13 tests, 13 passed, 0 known failure, 0 skipped
[inst/dist_fun/nakacdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/nakacdf.m
***** demo
 ## Plot various CDFs from the Nakagami distribution
 x = 0:0.01:3;
 p1 = nakacdf (x, 0.5, 1);
 p2 = nakacdf (x, 1, 1);
 p3 = nakacdf (x, 1, 2);
 p4 = nakacdf (x, 1, 3);
 p5 = nakacdf (x, 2, 1);
 p6 = nakacdf (x, 2, 2);
 p7 = nakacdf (x, 5, 1);
 plot (x, p1, "-r", x, p2, "-g", x, p3, "-y", x, p4, "-m", ...
       x, p5, "-k", x, p6, "-b", x, p7, "-c")
 grid on
 xlim ([0, 3])
 legend ({"μ = 0.5, ω = 1", "μ = 1, ω = 1", "μ = 1, ω = 2", ...
          "μ = 1, ω = 3", "μ = 2, ω = 1", "μ = 2, ω = 2", ...
          "μ = 5, ω = 1"}, "location", "southeast")
 title ("Nakagami CDF")
 xlabel ("values in x")
 ylabel ("probability")
***** shared x, y
 x = [-1, 0, 1, 2, Inf];
 y = [0, 0, 0.63212055882855778, 0.98168436111126578, 1];
***** assert (nakacdf (x, ones (1,5), ones (1,5)), y, eps)
***** assert (nakacdf (x, 1, 1), y, eps)
***** assert (nakacdf (x, [1, 1, NaN, 1, 1], 1), [y(1:2), NaN, y(4:5)])
***** assert (nakacdf (x, 1, [1, 1, NaN, 1, 1]), [y(1:2), NaN, y(4:5)])
***** assert (nakacdf ([x, NaN], 1, 1), [y, NaN], eps)
***** assert (nakacdf (single ([x, NaN]), 1, 1), single ([y, NaN]), eps("single"))
***** assert (nakacdf ([x, NaN], single (1), 1), single ([y, NaN]), eps("single"))
***** assert (nakacdf ([x, NaN], 1, single (1)), single ([y, NaN]), eps("single"))
***** error<nakacdf: function called with too few input arguments.> nakacdf ()
***** error<nakacdf: function called with too few input arguments.> nakacdf (1)
***** error<nakacdf: function called with too few input arguments.> nakacdf (1, 2)
***** error<nakacdf: invalid argument for upper tail.> nakacdf (1, 2, 3, "tail")
***** error<nakacdf: invalid argument for upper tail.> nakacdf (1, 2, 3, 4)
***** error<nakacdf: X, MU, and OMEGA must be of common size or scalars.> ...
 nakacdf (ones (3), ones (2), ones (2))
***** error<nakacdf: X, MU, and OMEGA must be of common size or scalars.> ...
 nakacdf (ones (2), ones (3), ones (2))
***** error<nakacdf: X, MU, and OMEGA must be of common size or scalars.> ...
 nakacdf (ones (2), ones (2), ones (3))
***** error<nakacdf: X, MU, and OMEGA must not be complex.> nakacdf (i, 2, 2)
***** error<nakacdf: X, MU, and OMEGA must not be complex.> nakacdf (2, i, 2)
***** error<nakacdf: X, MU, and OMEGA must not be complex.> nakacdf (2, 2, i)
19 tests, 19 passed, 0 known failure, 0 skipped
[inst/dist_fun/triinv.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/triinv.m
***** demo
 ## Plot various iCDFs from the triangular distribution
 p = 0.001:0.001:0.999;
 x1 = triinv (p, 3, 6, 4);
 x2 = triinv (p, 1, 5, 2);
 x3 = triinv (p, 2, 9, 3);
 x4 = triinv (p, 2, 9, 5);
 plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-c")
 grid on
 ylim ([0, 10])
 legend ({"a = 3, b = 6, c = 4", "a = 1, b = 5, c = 2", ...
          "a = 2, b = 9, c = 3", "a = 2, b = 9, c = 5"}, ...
         "location", "northwest")
 title ("Triangular CDF")
 xlabel ("probability")
 ylabel ("values in x")
***** shared p, y
 p = [-1, 0, 0.02, 0.5, 0.98, 1, 2];
 y = [NaN, 0, 0.1, 0.5, 0.9, 1, NaN] + 1;
***** assert (triinv (p, ones (1, 7), 1.5 * ones (1, 7), 2 * ones (1, 7)), y, eps)
***** assert (triinv (p, 1 * ones (1, 7), 1.5, 2), y, eps)
***** assert (triinv (p, 1, 1.5, 2 * ones (1, 7)), y, eps)
***** assert (triinv (p, 1, 1.5*ones (1,7), 2), y, eps)
***** assert (triinv (p, 1, 1.5, 2), y, eps)
***** assert (triinv (p, [1, 1, NaN, 1, 1, 1, 1], 1.5, 2), [y(1:2), NaN, y(4:7)], eps)
***** assert (triinv (p, 1, 1.5 * [1, 1, NaN, 1, 1, 1, 1], 2), [y(1:2), NaN, y(4:7)], eps)
***** assert (triinv (p, 1, 1.5, 2 * [1, 1, NaN, 1, 1, 1, 1]), [y(1:2), NaN, y(4:7)], eps)
***** assert (triinv ([p, NaN], 1, 1.5, 2), [y, NaN], eps)
***** assert (triinv (single ([p, NaN]), 1, 1.5, 2), single ([y, NaN]), eps('single'))
***** assert (triinv ([p, NaN], single (1), 1.5, 2), single ([y, NaN]), eps('single'))
***** assert (triinv ([p, NaN], 1, single (1.5), 2), single ([y, NaN]), eps('single'))
***** assert (triinv ([p, NaN], 1, 1.5, single (2)), single ([y, NaN]), eps('single'))
***** error<triinv: function called with too few input arguments.> triinv ()
***** error<triinv: function called with too few input arguments.> triinv (1)
***** error<triinv: function called with too few input arguments.> triinv (1, 2)
***** error<triinv: function called with too few input arguments.> triinv (1, 2, 3)
***** error<triinv: function called with too many inputs> ...
 triinv (1, 2, 3, 4, 5)
***** error<triinv: P, A, B, and C must be of common size or scalars.> ...
 triinv (ones (3), ones (2), ones(2), ones(2))
***** error<triinv: P, A, B, and C must be of common size or scalars.> ...
 triinv (ones (2), ones (3), ones(2), ones(2))
***** error<triinv: P, A, B, and C must be of common size or scalars.> ...
 triinv (ones (2), ones (2), ones(3), ones(2))
***** error<triinv: P, A, B, and C must be of common size or scalars.> ...
 triinv (ones (2), ones (2), ones(2), ones(3))
***** error<triinv: P, A, B, and C must not be complex.> triinv (i, 2, 3, 4)
***** error<triinv: P, A, B, and C must not be complex.> triinv (1, i, 3, 4)
***** error<triinv: P, A, B, and C must not be complex.> triinv (1, 2, i, 4)
***** error<triinv: P, A, B, and C must not be complex.> triinv (1, 2, 3, i)
26 tests, 26 passed, 0 known failure, 0 skipped
[inst/dist_fun/unidinv.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/unidinv.m
***** demo
 ## Plot various iCDFs from the discrete uniform distribution
 p = 0.001:0.001:0.999;
 x1 = unidinv (p, 5);
 x2 = unidinv (p, 9);
 plot (p, x1, "-b", p, x2, "-g")
 grid on
 xlim ([0, 1])
 ylim ([0, 10])
 legend ({"N = 5", "N = 9"}, "location", "northwest")
 title ("Discrete uniform iCDF")
 xlabel ("probability")
 ylabel ("values in x")
***** shared p
 p = [-1 0 0.5 1 2];
***** assert (unidinv (p, 10*ones (1,5)), [NaN NaN 5 10 NaN], eps)
***** assert (unidinv (p, 10), [NaN NaN 5 10 NaN], eps)
***** assert (unidinv (p, 10*[0 1 NaN 1 1]), [NaN NaN NaN 10 NaN], eps)
***** assert (unidinv ([p(1:2) NaN p(4:5)], 10), [NaN NaN NaN 10 NaN], eps)
***** assert (unidinv ([p, NaN], 10), [NaN NaN 5 10 NaN NaN], eps)
***** assert (unidinv (single ([p, NaN]), 10), single ([NaN NaN 5 10 NaN NaN]), eps)
***** assert (unidinv ([p, NaN], single (10)), single ([NaN NaN 5 10 NaN NaN]), eps)
***** error<unidinv: function called with too few input arguments.> unidinv ()
***** error<unidinv: function called with too few input arguments.> unidinv (1)
***** error<unidinv: P and N must be of common size or scalars.> ...
 unidinv (ones (3), ones (2))
***** error<unidinv: P and N must be of common size or scalars.> ...
 unidinv (ones (2), ones (3))
***** error<unidinv: P and N must not be complex.> unidinv (i, 2)
***** error<unidinv: P and N must not be complex.> unidinv (2, i)
13 tests, 13 passed, 0 known failure, 0 skipped
[inst/dist_fun/poissrnd.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/poissrnd.m
***** assert (size (poissrnd (2)), [1, 1])
***** assert (size (poissrnd (ones (2,1))), [2, 1])
***** assert (size (poissrnd (ones (2,2))), [2, 2])
***** assert (size (poissrnd (1, 3)), [3, 3])
***** assert (size (poissrnd (1, [4 1])), [4, 1])
***** assert (size (poissrnd (1, 4, 1)), [4, 1])
***** assert (size (poissrnd (1, 4, 1)), [4, 1])
***** assert (size (poissrnd (1, 4, 1, 5)), [4, 1, 5])
***** assert (size (poissrnd (1, 0, 1)), [0, 1])
***** assert (size (poissrnd (1, 1, 0)), [1, 0])
***** assert (size (poissrnd (1, 1, 2, 0, 5)), [1, 2, 0, 5])
***** assert (poissrnd (0, 1, 1), 0)
***** assert (poissrnd ([0, 0, 0], [1, 3]), [0 0 0])
***** assert (class (poissrnd (2)), "double")
***** assert (class (poissrnd (single (2))), "single")
***** assert (class (poissrnd (single ([2 2]))), "single")
***** error<poissrnd: function called with too few input arguments.> poissrnd ()
***** error<poissrnd: LAMBDA must not be complex.> poissrnd (i)
***** error<poissrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 poissrnd (1, -1)
***** error<poissrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 poissrnd (1, 1.2)
***** error<poissrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 poissrnd (1, ones (2))
***** error<poissrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 poissrnd (1, [2 -1 2])
***** error<poissrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 poissrnd (1, [2 0 2.5])
***** error<poissrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 poissrnd (ones (2), ones (2))
***** error<poissrnd: dimensions must be non-negative integers.> ...
 poissrnd (1, 2, -1, 5)
***** error<poissrnd: dimensions must be non-negative integers.> ...
 poissrnd (1, 2, 1.5, 5)
***** error<poissrnd: LAMBDA must be scalar or of size SZ.> poissrnd (ones (2,2), 3)
***** error<poissrnd: LAMBDA must be scalar or of size SZ.> poissrnd (ones (2,2), [3, 2])
***** error<poissrnd: LAMBDA must be scalar or of size SZ.> poissrnd (ones (2,2), 2, 3)
29 tests, 29 passed, 0 known failure, 0 skipped
[inst/dist_fun/raylcdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/raylcdf.m
***** demo
 ## Plot various CDFs from the Rayleigh distribution
 x = 0:0.01:10;
 p1 = raylcdf (x, 0.5);
 p2 = raylcdf (x, 1);
 p3 = raylcdf (x, 2);
 p4 = raylcdf (x, 3);
 p5 = raylcdf (x, 4);
 plot (x, p1, "-b", x, p2, "g", x, p3, "-r", x, p4, "-m", x, p5, "-k")
 grid on
 ylim ([0, 1])
 legend ({"σ = 0.5", "σ = 1", "σ = 2", ...
          "σ = 3", "σ = 4"}, "location", "southeast")
 title ("Rayleigh CDF")
 xlabel ("values in x")
 ylabel ("probability")
***** test
 x = 0:0.5:2.5;
 sigma = 1:6;
 p = raylcdf (x, sigma);
 expected_p = [0.0000, 0.0308, 0.0540, 0.0679, 0.0769, 0.0831];
 assert (p, expected_p, 0.001);
***** test
 x = 0:0.5:2.5;
 p = raylcdf (x, 0.5);
 expected_p = [0.0000, 0.3935, 0.8647, 0.9889, 0.9997, 1.0000];
 assert (p, expected_p, 0.001);
***** shared x, p
 x = [-1, 0, 1, 2, Inf];
 p = [0, 0, 0.39346934028737, 0.86466471676338, 1];
***** assert (raylcdf (x, 1), p, 1e-14)
***** assert (raylcdf (x, 1, "upper"), 1 - p, 1e-14)
***** error<raylcdf: function called with too few input arguments.> raylcdf ()
***** error<raylcdf: function called with too few input arguments.> raylcdf (1)
***** error<raylcdf: invalid argument for upper tail.> raylcdf (1, 2, "uper")
***** error<raylcdf: invalid argument for upper tail.> raylcdf (1, 2, 3)
***** error<raylcdf: X and SIGMA must be of common size or scalars.> ...
 raylcdf (ones (3), ones (2))
***** error<raylcdf: X and SIGMA must be of common size or scalars.> ...
 raylcdf (ones (2), ones (3))
***** error<raylcdf: X and SIGMA must not be complex.> raylcdf (i, 2)
***** error<raylcdf: X and SIGMA must not be complex.> raylcdf (2, i)
12 tests, 12 passed, 0 known failure, 0 skipped
[inst/dist_fun/betainv.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/betainv.m
***** demo
 ## Plot various iCDFs from the Beta distribution
 p = 0.001:0.001:0.999;
 x1 = betainv (p, 0.5, 0.5);
 x2 = betainv (p, 5, 1);
 x3 = betainv (p, 1, 3);
 x4 = betainv (p, 2, 2);
 x5 = betainv (p, 2, 5);
 plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-c", p, x5, "-m")
 grid on
 legend ({"α = β = 0.5", "α = 5, β = 1", "α = 1, β = 3", ...
          "α = 2, β = 2", "α = 2, β = 5"}, "location", "southeast")
 title ("Beta iCDF")
 xlabel ("probability")
 ylabel ("values in x")
***** shared p
 p = [-1 0 0.75 1 2];
***** assert (betainv (p, ones (1,5), 2*ones (1,5)), [NaN 0 0.5 1 NaN], eps)
***** assert (betainv (p, 1, 2*ones (1,5)), [NaN 0 0.5 1 NaN], eps)
***** assert (betainv (p, ones (1,5), 2), [NaN 0 0.5 1 NaN], eps)
***** assert (betainv (p, [1 0 NaN 1 1], 2), [NaN NaN NaN 1 NaN])
***** assert (betainv (p, 1, 2*[1 0 NaN 1 1]), [NaN NaN NaN 1 NaN])
***** assert (betainv ([p(1:2) NaN p(4:5)], 1, 2), [NaN 0 NaN 1 NaN])
***** assert (betainv ([p, NaN], 1, 2), [NaN 0 0.5 1 NaN NaN], eps)
***** assert (betainv (single ([p, NaN]), 1, 2), single ([NaN 0 0.5 1 NaN NaN]))
***** assert (betainv ([p, NaN], single (1), 2), single ([NaN 0 0.5 1 NaN NaN]), eps("single"))
***** assert (betainv ([p, NaN], 1, single (2)), single ([NaN 0 0.5 1 NaN NaN]), eps("single"))
***** error<betainv: function called with too few input arguments.> betainv ()
***** error<betainv: function called with too few input arguments.> betainv (1)
***** error<betainv: function called with too few input arguments.> betainv (1,2)
***** error<betainv: function called with too many inputs> betainv (1,2,3,4)
***** error<betainv: P, A, and B must be of common size or scalars.> ...
 betainv (ones (3), ones (2), ones (2))
***** error<betainv: P, A, and B must be of common size or scalars.> ...
 betainv (ones (2), ones (3), ones (2))
***** error<betainv: P, A, and B must be of common size or scalars.> ...
 betainv (ones (2), ones (2), ones (3))
***** error<betainv: P, A, and B must not be complex.> betainv (i, 2, 2)
***** error<betainv: P, A, and B must not be complex.> betainv (2, i, 2)
***** error<betainv: P, A, and B must not be complex.> betainv (2, 2, i)
20 tests, 20 passed, 0 known failure, 0 skipped
[inst/dist_fun/exppdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/exppdf.m
***** demo
 ## Plot various PDFs from the exponential distribution
 x = 0:0.01:5;
 y1 = exppdf (x, 2/3);
 y2 = exppdf (x, 1.0);
 y3 = exppdf (x, 2.0);
 plot (x, y1, "-b", x, y2, "-g", x, y3, "-r")
 grid on
 ylim ([0, 1.5])
 legend ({"μ = 2/3", "μ = 1", "μ = 2"}, "location", "northeast")
 title ("Exponential PDF")
 xlabel ("values in x")
 ylabel ("density")
***** shared x,y
 x = [-1 0 0.5 1 Inf];
 y = gampdf (x, 1, 2);
***** assert (exppdf (x, 2*ones (1,5)), y)
***** assert (exppdf (x, 2*[1 0 NaN 1 1]), [y(1) NaN NaN y(4:5)])
***** assert (exppdf ([x, NaN], 2), [y, NaN])
***** assert (exppdf (single ([x, NaN]), 2), single ([y, NaN]))
***** assert (exppdf ([x, NaN], single (2)), single ([y, NaN]))
***** error<exppdf: function called with too few input arguments.> exppdf ()
***** error<exppdf: function called with too many inputs> exppdf (1,2,3)
***** error<exppdf: X and MU must be of common size or scalars.> ...
 exppdf (ones (3), ones (2))
***** error<exppdf: X and MU must be of common size or scalars.> ...
 exppdf (ones (2), ones (3))
***** error<exppdf: X and MU must not be complex.> exppdf (i, 2)
***** error<exppdf: X and MU must not be complex.> exppdf (2, i)
11 tests, 11 passed, 0 known failure, 0 skipped
[inst/dist_fun/mvtrnd.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/mvtrnd.m
***** test
 rho = [1, 0.5; 0.5, 1];
 df = 3;
 n = 10;
 r = mvtrnd (rho, df, n);
 assert (size (r), [10, 2]);
***** test
 rho = [1, 0.5; 0.5, 1];
 df = [2; 3];
 n = 2;
 r = mvtrnd (rho, df, 2);
 assert (size (r), [2, 2]);
2 tests, 2 passed, 0 known failure, 0 skipped
[inst/dist_fun/mvnrnd.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/mvnrnd.m
***** error<mvnrnd: too few input arguments.> mvnrnd ()
***** error<mvnrnd: too few input arguments.> mvnrnd ([2, 3, 4])
***** error<mvnrnd: wrong size of MU.> mvnrnd (ones (2, 2, 2), ones (1, 2, 3, 4))
***** error<mvnrnd: wrong size of SIGMA.> mvnrnd (ones (1, 3), ones (1, 2, 3, 4))
***** assert (size (mvnrnd ([2, 3, 4], [2, 2, 2])), [1, 3])
***** assert (size (mvnrnd ([2, 3, 4], [2, 2, 2], 10)), [10, 3])
6 tests, 6 passed, 0 known failure, 0 skipped
[inst/dist_fun/unidpdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/unidpdf.m
***** demo
 ## Plot various PDFs from the discrete uniform distribution
 x = 0:10;
 y1 = unidpdf (x, 5);
 y2 = unidpdf (x, 9);
 plot (x, y1, "*b", x, y2, "*g")
 grid on
 xlim ([0, 10])
 ylim ([0, 0.25])
 legend ({"N = 5", "N = 9"}, "location", "northeast")
 title ("Descrete uniform PDF")
 xlabel ("values in x")
 ylabel ("density")
***** shared x, y
 x = [-1 0 1 2 10 11];
 y = [0 0 0.1 0.1 0.1 0];
***** assert (unidpdf (x, 10*ones (1,6)), y)
***** assert (unidpdf (x, 10), y)
***** assert (unidpdf (x, 10*[0 NaN 1 1 1 1]), [NaN NaN y(3:6)])
***** assert (unidpdf ([x, NaN], 10), [y, NaN])
***** assert (unidpdf (single ([x, NaN]), 10), single ([y, NaN]))
***** assert (unidpdf ([x, NaN], single (10)), single ([y, NaN]))
***** error<unidpdf: function called with too few input arguments.> unidpdf ()
***** error<unidpdf: function called with too few input arguments.> unidpdf (1)
***** error<unidpdf: X and N must be of common size or scalars.> ...
 unidpdf (ones (3), ones (2))
***** error<unidpdf: X and N must be of common size or scalars.> ...
 unidpdf (ones (2), ones (3))
***** error<unidpdf: X and N must not be complex.> unidpdf (i, 2)
***** error<unidpdf: X and N must not be complex.> unidpdf (2, i)
12 tests, 12 passed, 0 known failure, 0 skipped
[inst/dist_fun/evcdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/evcdf.m
***** demo
 ## Plot various CDFs from the extreme value distribution
 x = -10:0.01:10;
 p1 = evcdf (x, 0.5, 2);
 p2 = evcdf (x, 1.0, 2);
 p3 = evcdf (x, 1.5, 3);
 p4 = evcdf (x, 3.0, 4);
 plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", x, p4, "-c")
 grid on
 legend ({"μ = 0.5, σ = 2", "μ = 1.0, σ = 2", ...
          "μ = 1.5, σ = 3", "μ = 3.0, σ = 4"}, "location", "southeast")
 title ("Extreme value CDF")
 xlabel ("values in x")
 ylabel ("probability")
***** shared x, y
 x = [-Inf, 1, 2, Inf];
 y = [0, 0.6321, 0.9340, 1];
***** assert (evcdf (x, ones (1,4), ones (1,4)), y, 1e-4)
***** assert (evcdf (x, 1, ones (1,4)), y, 1e-4)
***** assert (evcdf (x, ones (1,4), 1), y, 1e-4)
***** assert (evcdf (x, [0, -Inf, NaN, Inf], 1), [0, 1, NaN, NaN], 1e-4)
***** assert (evcdf (x, 1, [Inf, NaN, -1, 0]), [NaN, NaN, NaN, NaN], 1e-4)
***** assert (evcdf ([x(1:2), NaN, x(4)], 1, 1), [y(1:2), NaN, y(4)], 1e-4)
***** assert (evcdf (x, "upper"), [1, 0.0660, 0.0006, 0], 1e-4)
***** assert (evcdf ([x, NaN], 1, 1), [y, NaN], 1e-4)
***** assert (evcdf (single ([x, NaN]), 1, 1), single ([y, NaN]), 1e-4)
***** assert (evcdf ([x, NaN], single (1), 1), single ([y, NaN]), 1e-4)
***** assert (evcdf ([x, NaN], 1, single (1)), single ([y, NaN]), 1e-4)
***** error<evcdf: invalid number of input arguments.> evcdf ()
***** error<evcdf: invalid number of input arguments.> evcdf (1,2,3,4,5,6,7)
***** error<evcdf: invalid argument for upper tail.> evcdf (1, 2, 3, 4, "uper")
***** error<evcdf: X, MU, and SIGMA must be of common size or scalars.> ...
 evcdf (ones (3), ones (2), ones (2))
***** error<evcdf: invalid size of covariance matrix.> evcdf (2, 3, 4, [1, 2])
***** error<evcdf: covariance matrix is required for confidence bounds.> ...
 [p, plo, pup] = evcdf (1, 2, 3)
***** error<evcdf: invalid value for alpha.> [p, plo, pup] = ...
 evcdf (1, 2, 3, [1, 0; 0, 1], 0)
***** error<evcdf: invalid value for alpha.> [p, plo, pup] = ...
 evcdf (1, 2, 3, [1, 0; 0, 1], 1.22)
***** error<evcdf: invalid value for alpha.> [p, plo, pup] = ...
 evcdf (1, 2, 3, [1, 0; 0, 1], "alpha", "upper")
***** error<evcdf: X, MU, and SIGMA must not be complex.> evcdf (i, 2, 2)
***** error<evcdf: X, MU, and SIGMA must not be complex.> evcdf (2, i, 2)
***** error<evcdf: X, MU, and SIGMA must not be complex.> evcdf (2, 2, i)
***** error<evcdf: bad covariance matrix.> ...
 [p, plo, pup] = evcdf (1, 2, 3, [1, 0; 0, -inf], 0.04)
24 tests, 24 passed, 0 known failure, 0 skipped
[inst/dist_fun/mvtpdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/mvtpdf.m
***** demo
 ## Compute the pdf of a multivariate t distribution with correlation
 ## parameters rho = [1 .4; .4 1] and 2 degrees of freedom.

 rho = [1, 0.4; 0.4, 1];
 df = 2;
 [X1, X2] = meshgrid (linspace (-2, 2, 25)', linspace (-2, 2, 25)');
 X = [X1(:), X2(:)];
 y = mvtpdf (X, rho, df);
 surf (X1, X2, reshape (y, 25, 25));
 title ("Bivariate Student's t probability density function");
***** assert (mvtpdf ([0 0], eye(2), 1), 0.1591549, 1E-7)
***** assert (mvtpdf ([1 0], [1 0.5; 0.5 1], 2), 0.06615947, 1E-7)
***** assert (mvtpdf ([1 0.4 0; 1.2 0.5 0.5; 1.4 0.6 1], ...
 [1 0.5 0.3; 0.5 1 0.6; 0.3 0.6 1], [5 6 7]), ...
 [0.04713313 0.03722421 0.02069011]', 1E-7)
3 tests, 3 passed, 0 known failure, 0 skipped
[inst/dist_fun/geornd.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/geornd.m
***** assert (size (geornd (0.5)), [1, 1])
***** assert (size (geornd (0.5*ones (2,1))), [2, 1])
***** assert (size (geornd (0.5*ones (2,2))), [2, 2])
***** assert (size (geornd (0.5, 3)), [3, 3])
***** assert (size (geornd (0.5, [4 1])), [4, 1])
***** assert (size (geornd (0.5, 4, 1)), [4, 1])
***** assert (class (geornd (0.5)), "double")
***** assert (class (geornd (single (0.5))), "single")
***** assert (class (geornd (single ([0.5 0.5]))), "single")
***** assert (class (geornd (single (0))), "single")
***** assert (class (geornd (single (1))), "single")
***** error<geornd: function called with too few input arguments.> geornd ()
***** error<geornd: PS must not be complex.> geornd (i)
***** error<geornd: SZ must be a scalar or a row vector of non-negative integers.> ...
 geornd (1, -1)
***** error<geornd: SZ must be a scalar or a row vector of non-negative integers.> ...
 geornd (1, 1.2)
***** error<geornd: SZ must be a scalar or a row vector of non-negative integers.> ...
 geornd (1, ones (2))
***** error<geornd: SZ must be a scalar or a row vector of non-negative integers.> ...
 geornd (1, [2 -1 2])
***** error<geornd: SZ must be a scalar or a row vector of non-negative integers.> ...
 geornd (1, [2 0 2.5])
***** error<geornd: SZ must be a scalar or a row vector of non-negative integers.> ...
 geornd (ones (2), ones (2))
***** error<geornd: dimensions must be non-negative integers.> ...
 geornd (1, 2, -1, 5)
***** error<geornd: dimensions must be non-negative integers.> ...
 geornd (1, 2, 1.5, 5)
***** error<geornd: PS must be scalar or of size SZ.> geornd (ones (2,2), 3)
***** error<geornd: PS must be scalar or of size SZ.> geornd (ones (2,2), [3, 2])
***** error<geornd: PS must be scalar or of size SZ.> geornd (ones (2,2), 2, 3)
24 tests, 24 passed, 0 known failure, 0 skipped
[inst/dist_fun/trirnd.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/trirnd.m
***** assert (size (trirnd (1, 1.5, 2)), [1, 1])
***** assert (size (trirnd (1 * ones (2, 1), 1.5, 2)), [2, 1])
***** assert (size (trirnd (1 * ones (2, 2), 1.5, 2)), [2, 2])
***** assert (size (trirnd (1, 1.5 * ones (2, 1), 2)), [2, 1])
***** assert (size (trirnd (1, 1.5 * ones (2, 2), 2)), [2, 2])
***** assert (size (trirnd (1, 1.5, 2 * ones (2, 1))), [2, 1])
***** assert (size (trirnd (1, 1.5, 2 * ones (2, 2))), [2, 2])
***** assert (size (trirnd (1, 1.5, 2, 3)), [3, 3])
***** assert (size (trirnd (1, 1.5, 2, [4, 1])), [4, 1])
***** assert (size (trirnd (1, 1.5, 2, 4, 1)), [4, 1])
***** assert (class (trirnd (1, 1.5, 2)), "double")
***** assert (class (trirnd (single (1), 1.5, 2)), "single")
***** assert (class (trirnd (single ([1, 1]), 1.5, 2)), "single")
***** assert (class (trirnd (1, single (1.5), 2)), "single")
***** assert (class (trirnd (1, single ([1.5, 1.5]), 2)), "single")
***** assert (class (trirnd (1, 1.5, single (1.5))), "single")
***** assert (class (trirnd (1, 1.5, single ([2, 2]))), "single")
***** error<trirnd: function called with too few input arguments.> trirnd ()
***** error<trirnd: function called with too few input arguments.> trirnd (1)
***** error<trirnd: function called with too few input arguments.> trirnd (1, 2)
***** error<trirnd: A, B, and C must be of common size or scalars.> ...
 trirnd (ones (3), 5 * ones (2), ones (2))
***** error<trirnd: A, B, and C must be of common size or scalars.> ...
 trirnd (ones (2), 5 * ones (3), ones (2))
***** error<trirnd: A, B, and C must be of common size or scalars.> ...
 trirnd (ones (2), 5 * ones (2), ones (3))
***** error<trirnd: A, B, and C must not be complex.> trirnd (i, 5, 3)
***** error<trirnd: A, B, and C must not be complex.> trirnd (1, 5+i, 3)
***** error<trirnd: A, B, and C must not be complex.> trirnd (1, 5, i)
***** error<trirnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 trirnd (1, 5, 3, -1)
***** error<trirnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 trirnd (1, 5, 3, 1.2)
***** error<trirnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 trirnd (1, 5, 3, ones (2))
***** error<trirnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 trirnd (1, 5, 3, [2 -1 2])
***** error<trirnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 trirnd (1, 5, 3, [2 0 2.5])
***** error<trirnd: dimensions must be non-negative integers.> ...
 trirnd (1, 5, 3, 2, -1, 5)
***** error<trirnd: dimensions must be non-negative integers.> ...
 trirnd (1, 5, 3, 2, 1.5, 5)
***** error<trirnd: A, B, and C must be scalar or of size SZ.> ...
 trirnd (2, 5 * ones (2), 2, 3)
***** error<trirnd: A, B, and C must be scalar or of size SZ.> ...
 trirnd (2, 5 * ones (2), 2, [3, 2])
***** error<trirnd: A, B, and C must be scalar or of size SZ.> ...
 trirnd (2, 5 * ones (2), 2, 3, 2)
36 tests, 36 passed, 0 known failure, 0 skipped
[inst/dist_fun/logiinv.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/logiinv.m
***** demo
 ## Plot various iCDFs from the logistic distribution
 p = 0.001:0.001:0.999;
 x1 = logiinv (p, 5, 2);
 x2 = logiinv (p, 9, 3);
 x3 = logiinv (p, 9, 4);
 x4 = logiinv (p, 6, 2);
 x5 = logiinv (p, 2, 1);
 plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-c", p, x5, "-m")
 grid on
 legend ({"μ = 5, σ = 2", "μ = 9, σ = 3", "μ = 9, σ = 4", ...
          "μ = 6, σ = 2", "μ = 2, σ = 1"}, "location", "southeast")
 title ("Logistic iCDF")
 xlabel ("probability")
 ylabel ("x")
***** test
 p = [0.01:0.01:0.99];
 assert (logiinv (p, 0, 1), log (p ./ (1-p)), 25*eps);
***** shared p
 p = [-1 0 0.5 1 2];
***** assert (logiinv (p, 0, 1), [NaN -Inf 0 Inf NaN])
***** assert (logiinv (p, 0, [-1, 0, 1, 2, 3]), [NaN NaN 0 Inf NaN])
***** assert (logiinv ([p, NaN], 0, 1), [NaN -Inf 0 Inf NaN NaN])
***** assert (logiinv (single ([p, NaN]), 0, 1), single ([NaN -Inf 0 Inf NaN NaN]))
***** assert (logiinv ([p, NaN], single (0), 1), single ([NaN -Inf 0 Inf NaN NaN]))
***** assert (logiinv ([p, NaN], 0, single (1)), single ([NaN -Inf 0 Inf NaN NaN]))
***** error<logiinv: function called with too few input arguments.> logiinv ()
***** error<logiinv: function called with too few input arguments.> logiinv (1)
***** error<logiinv: function called with too few input arguments.> ...
 logiinv (1, 2)
***** error<logiinv: P, MU, and SIGMA must be of common size or scalars.> ...
 logiinv (1, ones (2), ones (3))
***** error<logiinv: P, MU, and SIGMA must be of common size or scalars.> ...
 logiinv (ones (2), 1, ones (3))
***** error<logiinv: P, MU, and SIGMA must be of common size or scalars.> ...
 logiinv (ones (2), ones (3), 1)
***** error<logiinv: P, MU, and SIGMA must not be complex.> logiinv (i, 2, 3)
***** error<logiinv: P, MU, and SIGMA must not be complex.> logiinv (1, i, 3)
***** error<logiinv: P, MU, and SIGMA must not be complex.> logiinv (1, 2, i)
16 tests, 16 passed, 0 known failure, 0 skipped
[inst/dist_fun/jsupdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/jsupdf.m
***** error jsupdf ()
***** error jsupdf (1, 2, 3, 4)
***** error<jsupdf: X, ALPHA1, and ALPHA2 must be of common size or scalars.> ...
 jsupdf (1, ones (2), ones (3))
3 tests, 3 passed, 0 known failure, 0 skipped
[inst/dist_fun/ricecdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/ricecdf.m
***** demo
 ## Plot various CDFs from the Rician distribution
 x = 0:0.01:10;
 p1 = ricecdf (x, 0, 1);
 p2 = ricecdf (x, 0.5, 1);
 p3 = ricecdf (x, 1, 1);
 p4 = ricecdf (x, 2, 1);
 p5 = ricecdf (x, 4, 1);
 plot (x, p1, "-b", x, p2, "g", x, p3, "-r", x, p4, "-m", x, p5, "-k")
 grid on
 ylim ([0, 1])
 xlim ([0, 8])
 legend ({"s = 0, σ = 1", "s = 0.5, σ = 1", "s = 1, σ = 1", ...
          "s = 2, σ = 1", "s = 4, σ = 1"}, "location", "southeast")
 title ("Rician CDF")
 xlabel ("values in x")
 ylabel ("probability")
***** demo
 ## Plot various CDFs from the Rician distribution
 x = 0:0.01:10;
 p1 = ricecdf (x, 0, 0.5);
 p2 = ricecdf (x, 0, 2);
 p3 = ricecdf (x, 0, 3);
 p4 = ricecdf (x, 2, 2);
 p5 = ricecdf (x, 4, 2);
 plot (x, p1, "-b", x, p2, "g", x, p3, "-r", x, p4, "-m", x, p5, "-k")
 grid on
 ylim ([0, 1])
 xlim ([0, 8])
 legend ({"ν = 0, σ = 0.5", "ν = 0, σ = 2", "ν = 0, σ = 3", ...
          "ν = 2, σ = 2", "ν = 4, σ = 2"}, "location", "southeast")
 title ("Rician CDF")
 xlabel ("values in x")
 ylabel ("probability")
***** test
 x = 0:0.5:2.5;
 s = 1:6;
 p = ricecdf (x, s, 1);
 expected_p = [0.0000, 0.0179, 0.0108, 0.0034, 0.0008, 0.0001];
 assert (p, expected_p, 0.001);
***** test
 x = 0:0.5:2.5;
 sigma = 1:6;
 p = ricecdf (x, 1, sigma);
 expected_p = [0.0000, 0.0272, 0.0512, 0.0659, 0.0754, 0.0820];
 assert (p, expected_p, 0.001);
***** test
 x = 0:0.5:2.5;
 p = ricecdf (x, 0, 1);
 expected_p = [0.0000, 0.1175, 0.3935, 0.6753, 0.8647, 0.9561];
 assert (p, expected_p, 0.001);
***** test
 x = 0:0.5:2.5;
 p = ricecdf (x, 1, 1);
 expected_p = [0.0000, 0.0735, 0.2671, 0.5120, 0.7310, 0.8791];
 assert (p, expected_p, 0.001);
***** shared x, p
 x = [-1, 0, 1, 2, Inf];
 p = [0, 0, 0.26712019620318, 0.73098793996409, 1];
***** assert (ricecdf (x, 1, 1), p, 1e-14)
***** assert (ricecdf (x, 1, 1, "upper"), 1 - p, 1e-14)
***** error<ricecdf: function called with too few input arguments.> ricecdf ()
***** error<ricecdf: function called with too few input arguments.> ricecdf (1)
***** error<ricecdf: function called with too few input arguments.> ricecdf (1, 2)
***** error<ricecdf: invalid argument for upper tail.> ricecdf (1, 2, 3, "uper")
***** error<ricecdf: invalid argument for upper tail.> ricecdf (1, 2, 3, 4)
***** error<ricecdf: X, S, and SIGMA must be of common size or scalars.> ...
 ricecdf (ones (3), ones (2), ones (2))
***** error<ricecdf: X, S, and SIGMA must be of common size or scalars.> ...
 ricecdf (ones (2), ones (3), ones (2))
***** error<ricecdf: X, S, and SIGMA must be of common size or scalars.> ...
 ricecdf (ones (2), ones (2), ones (3))
***** error<ricecdf: X, S, and SIGMA must not be complex.> ricecdf (i, 2, 3)
***** error<ricecdf: X, S, and SIGMA must not be complex.> ricecdf (2, i, 3)
***** error<ricecdf: X, S, and SIGMA must not be complex.> ricecdf (2, 2, i)
17 tests, 17 passed, 0 known failure, 0 skipped
[inst/dist_fun/binopdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/binopdf.m
***** demo
 ## Plot various PDFs from the binomial distribution
 x = 0:40;
 y1 = binopdf (x, 20, 0.5);
 y2 = binopdf (x, 20, 0.7);
 y3 = binopdf (x, 40, 0.5);
 plot (x, y1, "*b", x, y2, "*g", x, y3, "*r")
 grid on
 ylim ([0, 0.25])
 legend ({"n = 20, ps = 0.5", "n = 20, ps = 0.7", ...
          "n = 40, ps = 0.5"}, "location", "northeast")
 title ("Binomial PDF")
 xlabel ("values in x (number of successes)")
 ylabel ("density")
***** shared x, y
 x = [-1 0 1 2 3];
 y = [0 1/4 1/2 1/4 0];
***** assert (binopdf (x, 2 * ones (1, 5), 0.5 * ones (1, 5)), y, eps)
***** assert (binopdf (x, 2, 0.5 * ones (1, 5)), y, eps)
***** assert (binopdf (x, 2 * ones (1, 5), 0.5), y, eps)
***** assert (binopdf (x, 2 * [0 -1 NaN 1.1 1], 0.5), [0 NaN NaN NaN 0])
***** assert (binopdf (x, 2, 0.5 * [0 -1 NaN 3 1]), [0 NaN NaN NaN 0])
***** assert (binopdf ([x, NaN], 2, 0.5), [y, NaN], eps)
***** assert (binopdf (cat (3, x, x), 2, 0.5), cat (3, y, y), eps)
***** assert (binopdf (1, 1, 1), 1)
***** assert (binopdf (0, 3, 0), 1)
***** assert (binopdf (2, 2, 1), 1)
***** assert (binopdf (1, 2, 1), 0)
***** assert (binopdf (0, 1.1, 0), NaN)
***** assert (binopdf (1, 2, -1), NaN)
***** assert (binopdf (1, 2, 1.5), NaN)
***** assert (binopdf ([], 1, 1), [])
***** assert (binopdf (1, [], 1), [])
***** assert (binopdf (1, 1, []), [])
***** assert (binopdf (ones (1, 0), 2, .5), ones(1, 0))
***** assert (binopdf (ones (0, 1), 2, .5), ones(0, 1))
***** assert (binopdf (ones (0, 1, 2), 2, .5), ones(0, 1, 2))
***** assert (binopdf (1, ones (0, 1, 2), .5), ones(0, 1, 2))
***** assert (binopdf (1, 2, ones (0, 1, 2)), ones(0, 1, 2))
***** assert (binopdf (ones (1, 0, 2), 2, .5), ones(1, 0, 2))
***** assert (binopdf (ones (1, 2, 0), 2, .5), ones(1, 2, 0))
***** assert (binopdf (ones (0, 1, 2), NaN, .5), ones(0, 1, 2))
***** assert (binopdf (ones (0, 1, 2), 2, NaN), ones(0, 1, 2))
***** assert (binopdf (single ([x, NaN]), 2, 0.5), single ([y, NaN]))
***** assert (binopdf ([x, NaN], single (2), 0.5), single ([y, NaN]))
***** assert (binopdf ([x, NaN], 2, single (0.5)), single ([y, NaN]))
***** error<binopdf: function called with too few input arguments.> binopdf ()
***** error<binopdf: function called with too few input arguments.> binopdf (1)
***** error<binopdf: function called with too few input arguments.> binopdf (1, 2)
***** error<binopdf: function called with too many inputs> binopdf (1, 2, 3, 4)
***** error<binopdf: X, N, and PS must be of common size or scalars.> ...
 binopdf (ones (3), ones (2), ones (2))
***** error<binopdf: X, N, and PS must be of common size or scalars.> ...
 binopdf (ones (2), ones (3), ones (2))
***** error<binopdf: X, N, and PS must be of common size or scalars.> ...
 binopdf (ones (2), ones (2), ones (3))
***** error<binopdf: X, N, and PS must not be complex.> binopdf (i, 2, 2)
***** error<binopdf: X, N, and PS must not be complex.> binopdf (2, i, 2)
***** error<binopdf: X, N, and PS must not be complex.> binopdf (2, 2, i)
39 tests, 39 passed, 0 known failure, 0 skipped
[inst/dist_fun/mvncdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/mvncdf.m
***** demo
 mu = [1, -1];
 Sigma = [0.9, 0.4; 0.4, 0.3];
 [X1, X2] = meshgrid (linspace (-1, 3, 25)', linspace (-3, 1, 25)');
 X = [X1(:), X2(:)];
 p = mvncdf (X, mu, Sigma);
 Z = reshape (p, 25, 25);
 surf (X1, X2, Z);
 title ("Bivariate Normal Distribution");
 ylabel "X1"
 xlabel "X2"
***** demo
 mu = [0, 0];
 Sigma = [0.25, 0.3; 0.3, 1];
 p = mvncdf ([0 0], [1 1], mu, Sigma);
 x1 = -3:.2:3;
 x2 = -3:.2:3;
 [X1, X2] = meshgrid (x1, x2);
 X = [X1(:), X2(:)];
 p = mvnpdf (X, mu, Sigma);
 p = reshape (p, length (x2), length (x1));
 contour (x1, x2, p, [0.0001, 0.001, 0.01, 0.05, 0.15, 0.25, 0.35]);
 xlabel ("x");
 ylabel ("p");
 title ("Probability over Rectangular Region");
 line ([0, 0, 1, 1, 0], [1, 0, 0, 1, 1], "Linestyle", "--", "Color", "k");
***** test
 fD = (-2:2)';
 X = repmat (fD, 1, 4);
 p = mvncdf (X);
 assert (p, [0; 0.0006; 0.0625; 0.5011; 0.9121], ones (5, 1) * 1e-4);
***** test
 mu = [1, -1];
 Sigma = [0.9, 0.4; 0.4, 0.3];
 [X1,X2] = meshgrid (linspace (-1, 3, 25)', linspace (-3, 1, 25)');
 X = [X1(:), X2(:)];
 p = mvncdf (X, mu, Sigma);
 p_out = [0.00011878988774500, 0.00034404112322371, ...
          0.00087682502191813, 0.00195221905058185, ...
          0.00378235566873474, 0.00638175749734415, ...
          0.00943764224329656, 0.01239164888125426, ...
          0.01472750274376648, 0.01623228313374828]';
 assert (p([1:10]), p_out, 1e-16);
***** test
 mu = [1, -1];
 Sigma = [0.9, 0.4; 0.4, 0.3];
 [X1,X2] = meshgrid (linspace (-1, 3, 25)', linspace (-3, 1, 25)');
 X = [X1(:), X2(:)];
 p = mvncdf (X, mu, Sigma);
 p_out = [0.8180695783608276, 0.8854485749482751, ...
          0.9308108777385832, 0.9579855743025508, ...
          0.9722897881414742, 0.9788150170059926, ...
          0.9813597788804785, 0.9821977956568989, ...
          0.9824283794464095, 0.9824809345614861]';
 assert (p([616:625]), p_out, 3e-16);
***** test
 mu = [0, 0];
 Sigma = [0.25, 0.3; 0.3, 1];
 [p, err] = mvncdf ([0, 0], [1, 1], mu, Sigma);
 assert (p, 0.2097424404755626, 1e-16);
 assert (err, 1e-08);
***** test
 x = [1 2];
 mu = [0.5 1.5];
 sigma = [1.0, 0.5; 0.5, 1.0];
 p = mvncdf (x, mu, sigma);
 assert (p, 0.546244443857090, 1e-15);
***** test
 x = [1 2];
 mu = [0.5 1.5];
 sigma = [1.0, 0.5; 0.5, 1.0];
 a = [-inf 0];
 p = mvncdf (a, x, mu, sigma);
 assert (p, 0.482672935215631, 1e-15);
***** error p = mvncdf (randn (25,26), [], eye (26));
***** error p = mvncdf (randn (25,8), [], eye (9));
***** error p = mvncdf (randn (25,4), randn (25,5), [], eye (4));
***** error p = mvncdf (randn (25,4), randn (25,4), [2, 3; 2, 3], eye (4));
***** error p = mvncdf (randn (25,4), randn (25,4), ones (1, 5), eye (4));
***** error p = mvncdf ([-inf, 0], [1, 2], [0.5, 1.5], [1.0, 0.5; 0.5, 1.0], option)
12 tests, 12 passed, 0 known failure, 0 skipped
[inst/dist_fun/nctrnd.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/nctrnd.m
***** assert (size (nctrnd (1, 1)), [1 1])
***** assert (size (nctrnd (1, ones (2,1))), [2, 1])
***** assert (size (nctrnd (1, ones (2,2))), [2, 2])
***** assert (size (nctrnd (ones (2,1), 1)), [2, 1])
***** assert (size (nctrnd (ones (2,2), 1)), [2, 2])
***** assert (size (nctrnd (1, 1, 3)), [3, 3])
***** assert (size (nctrnd (1, 1, [4, 1])), [4, 1])
***** assert (size (nctrnd (1, 1, 4, 1)), [4, 1])
***** assert (size (nctrnd (1, 1, 4, 1, 5)), [4, 1, 5])
***** assert (size (nctrnd (1, 1, 0, 1)), [0, 1])
***** assert (size (nctrnd (1, 1, 1, 0)), [1, 0])
***** assert (size (nctrnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
***** assert (class (nctrnd (1, 1)), "double")
***** assert (class (nctrnd (1, single (1))), "single")
***** assert (class (nctrnd (1, single ([1, 1]))), "single")
***** assert (class (nctrnd (single (1), 1)), "single")
***** assert (class (nctrnd (single ([1, 1]), 1)), "single")
***** error<nctrnd: function called with too few input arguments.> nctrnd ()
***** error<nctrnd: function called with too few input arguments.> nctrnd (1)
***** error<nctrnd: DF and MU must be of common size or scalars.> ...
 nctrnd (ones (3), ones (2))
***** error<nctrnd: DF and MU must be of common size or scalars.> ...
 nctrnd (ones (2), ones (3))
***** error<nctrnd: DF and MU must not be complex.> nctrnd (i, 2)
***** error<nctrnd: DF and MU must not be complex.> nctrnd (1, i)
***** error<nctrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 nctrnd (1, 2, -1)
***** error<nctrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 nctrnd (1, 2, 1.2)
***** error<nctrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 nctrnd (1, 2, ones (2))
***** error<nctrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 nctrnd (1, 2, [2 -1 2])
***** error<nctrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 nctrnd (1, 2, [2 0 2.5])
***** error<nctrnd: dimensions must be non-negative integers.> ...
 nctrnd (1, 2, 2, -1, 5)
***** error<nctrnd: dimensions must be non-negative integers.> ...
 nctrnd (1, 2, 2, 1.5, 5)
***** error<nctrnd: DF and MU must be scalars or of size SZ.> ...
 nctrnd (2, ones (2), 3)
***** error<nctrnd: DF and MU must be scalars or of size SZ.> ...
 nctrnd (2, ones (2), [3, 2])
***** error<nctrnd: DF and MU must be scalars or of size SZ.> ...
 nctrnd (2, ones (2), 3, 2)
33 tests, 33 passed, 0 known failure, 0 skipped
[inst/dist_fun/plinv.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/plinv.m
***** demo
 ## Plot various iCDFs from the Piecewise linear distribution
 p = 0.001:0.001:0.999;
 x1 = [0, 1, 3, 4, 7, 10];
 Fx1 = [0, 0.2, 0.5, 0.6, 0.7, 1];
 x2 = [0, 2, 5, 6, 7, 8];
 Fx2 = [0, 0.1, 0.3, 0.6, 0.9, 1];
 data1 = plinv (p, x1, Fx1);
 data2 = plinv (p, x2, Fx2);
 plot (p, data1, "-b", p, data2, "-g")
 grid on
 legend ({"x1, Fx1", "x2, Fx2"}, "location", "northwest")
 title ("Piecewise linear iCDF")
 xlabel ("probability")
 ylabel ("values in data")
***** test
 p = 0:0.2:1;
 data = plinv (p, [0, 1], [0, 1]);
 assert (data, p);
***** test
 p = 0:0.2:1;
 data = plinv (p, [0, 2], [0, 1]);
 assert (data, 2 * p);
***** test
 p = 0:0.2:1;
 data_out = 1:6;
 data = plinv (p, [0, 1], [0, 0.5]);
 assert (data, [0, 0.4, 0.8, NA, NA, NA]);
***** test
 p = 0:0.2:1;
 data_out = 1:6;
 data = plinv (p, [0, 0.5], [0, 1]);
 assert (data, [0:0.1:0.5]);
***** error<plinv: function called with too few input arguments.> plinv ()
***** error<plinv: function called with too few input arguments.> plinv (1)
***** error<plinv: function called with too few input arguments.> plinv (1, 2)
***** error<plinv: X and FX must be vectors of equal size.> ...
 plinv (1, [0, 1, 2], [0, 1])
***** error<plinv: X and FX must be at least two-elements long.> ...
 plinv (1, [0], [1])
***** error<plinv: FX must be bounded in the range> ...
 plinv (1, [0, 1, 2], [0, 1, 1.5])
***** error<plinv: FX must be bounded in the range> ...
 plinv (1, [0, 1, 2], [0, i, 1])
***** error<plinv: P, X, and FX must not be complex.> ...
 plinv (i, [0, 1, 2], [0, 0.5, 1])
***** error<plinv: P, X, and FX must not be complex.> ...
 plinv (1, [0, i, 2], [0, 0.5, 1])
***** error<plinv: P, X, and FX must not be complex.> ...
 plinv (1, [0, 1, 2], [0, 0.5i, 1])
14 tests, 14 passed, 0 known failure, 0 skipped
[inst/dist_fun/betapdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/betapdf.m
***** demo
 ## Plot various PDFs from the Beta distribution
 x = 0.001:0.001:0.999;
 y1 = betapdf (x, 0.5, 0.5);
 y2 = betapdf (x, 5, 1);
 y3 = betapdf (x, 1, 3);
 y4 = betapdf (x, 2, 2);
 y5 = betapdf (x, 2, 5);
 plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-c", x, y5, "-m")
 grid on
 ylim ([0, 2.5])
 legend ({"α = β = 0.5", "α = 5, β = 1", "α = 1, β = 3", ...
          "α = 2, β = 2", "α = 2, β = 5"}, "location", "north")
 title ("Beta PDF")
 xlabel ("values in x")
 ylabel ("density")
***** shared x, y
 x = [-1 0 0.5 1 2];
 y = [0 2 1 0 0];
***** assert (betapdf (x, ones (1, 5), 2 * ones (1, 5)), y)
***** assert (betapdf (x, 1, 2 * ones (1, 5)), y)
***** assert (betapdf (x, ones (1, 5), 2), y)
***** assert (betapdf (x, [0 NaN 1 1 1], 2), [NaN NaN y(3:5)])
***** assert (betapdf (x, 1, 2 * [0 NaN 1 1 1]), [NaN NaN y(3:5)])
***** assert (betapdf ([x, NaN], 1, 2), [y, NaN])
***** assert (betapdf (single ([x, NaN]), 1, 2), single ([y, NaN]))
***** assert (betapdf ([x, NaN], single (1), 2), single ([y, NaN]))
***** assert (betapdf ([x, NaN], 1, single (2)), single ([y, NaN]))
***** test
 x = rand (10,1);
 y = 1 ./ (pi * sqrt (x .* (1 - x)));
 assert (betapdf (x, 1/2, 1/2), y, 1e-12);
***** assert (betapdf (0.5, 1000, 1000), 35.678, 1e-3)
***** error<betapdf: function called with too few input arguments.> betapdf ()
***** error<betapdf: function called with too few input arguments.> betapdf (1)
***** error<betapdf: function called with too few input arguments.> betapdf (1,2)
***** error<betapdf: function called with too many inputs> betapdf (1,2,3,4)
***** error<betapdf: X, A, and B must be of common size or scalars.> ...
 betapdf (ones (3), ones (2), ones (2))
***** error<betapdf: X, A, and B must be of common size or scalars.> ...
 betapdf (ones (2), ones (3), ones (2))
***** error<betapdf: X, A, and B must be of common size or scalars.> ...
 betapdf (ones (2), ones (2), ones (3))
***** error<betapdf: X, A, and B must not be complex.> betapdf (i, 2, 2)
***** error<betapdf: X, A, and B must not be complex.> betapdf (2, i, 2)
***** error<betapdf: X, A, and B must not be complex.> betapdf (2, 2, i)
21 tests, 21 passed, 0 known failure, 0 skipped
[inst/dist_fun/hnrnd.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/hnrnd.m
***** assert (size (hnrnd (1, 1, 1)), [1, 1])
***** assert (size (hnrnd (1, 1, 2)), [2, 2])
***** assert (size (hnrnd (1, 1, [2, 1])), [2, 1])
***** assert (size (hnrnd (1, zeros (2, 2))), [2, 2])
***** assert (size (hnrnd (1, ones (2, 1))), [2, 1])
***** assert (size (hnrnd (1, ones (2, 2))), [2, 2])
***** assert (size (hnrnd (ones (2, 1), 1)), [2, 1])
***** assert (size (hnrnd (ones (2, 2), 1)), [2, 2])
***** assert (size (hnrnd (1, 1, 3)), [3, 3])
***** assert (size (hnrnd (1, 1, [4 1])), [4, 1])
***** assert (size (hnrnd (1, 1, 4, 1)), [4, 1])
***** test
 r =  hnrnd (1, [1, 0, -1]);
 assert (r([2:3]), [NaN, NaN])
***** assert (class (hnrnd (1, 0)), "double")
***** assert (class (hnrnd (1, single (0))), "single")
***** assert (class (hnrnd (1, single ([0 0]))), "single")
***** assert (class (hnrnd (1, single (1))), "single")
***** assert (class (hnrnd (1, single ([1 1]))), "single")
***** assert (class (hnrnd (single (1), 1)), "single")
***** assert (class (hnrnd (single ([1 1]), 1)), "single")
***** error<hnrnd: function called with too few input arguments.> hnrnd ()
***** error<hnrnd: function called with too few input arguments.> hnrnd (1)
***** error<hnrnd: MU and SIGMA must be of common size or scalars.> ...
 hnrnd (ones (3), ones (2))
***** error<hnrnd: MU and SIGMA must be of common size or scalars.> ...
 hnrnd (ones (2), ones (3))
***** error<hnrnd: MU and SIGMA must not be complex.> hnrnd (i, 2, 3)
***** error<hnrnd: MU and SIGMA must not be complex.> hnrnd (1, i, 3)
***** error<hnrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 hnrnd (1, 2, -1)
***** error<hnrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 hnrnd (1, 2, 1.2)
***** error<hnrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 hnrnd (1, 2, ones (2))
***** error<hnrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 hnrnd (1, 2, [2 -1 2])
***** error<hnrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 hnrnd (1, 2, [2 0 2.5])
***** error<hnrnd: dimensions must be non-negative integers.> ...
 hnrnd (1, 2, 2, -1, 5)
***** error<hnrnd: dimensions must be non-negative integers.> ...
 hnrnd (1, 2, 2, 1.5, 5)
***** error<hnrnd: MU and SIGMA must be scalars or of size SZ.> ...
 hnrnd (2, ones (2), 3)
***** error<hnrnd: MU and SIGMA must be scalars or of size SZ.> ...
 hnrnd (2, ones (2), [3, 2])
***** error<hnrnd: MU and SIGMA must be scalars or of size SZ.> ...
 hnrnd (2, ones (2), 3, 2)
35 tests, 35 passed, 0 known failure, 0 skipped
[inst/dist_fun/logipdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/logipdf.m
***** demo
 ## Plot various PDFs from the logistic distribution
 x = -5:0.01:20;
 y1 = logipdf (x, 5, 2);
 y2 = logipdf (x, 9, 3);
 y3 = logipdf (x, 9, 4);
 y4 = logipdf (x, 6, 2);
 y5 = logipdf (x, 2, 1);
 plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-c", x, y5, "-m")
 grid on
 ylim ([0, 0.3])
 legend ({"μ = 5, σ = 2", "μ = 9, σ = 3", "μ = 9, σ = 4", ...
          "μ = 6, σ = 2", "μ = 2, σ = 1"}, "location", "northeast")
 title ("Logistic PDF")
 xlabel ("values in x")
 ylabel ("density")
***** shared x, y
 x = [-Inf -log(4) 0 log(4) Inf];
 y = [0, 0.16, 1/4, 0.16, 0];
***** assert (logipdf ([x, NaN], 0, 1), [y, NaN], eps)
***** assert (logipdf (x, 0, [-2, -1, 0, 1, 2]), [nan(1, 3), y([4:5])], eps)
***** assert (logipdf (single ([x, NaN]), 0, 1), single ([y, NaN]), eps ("single"))
***** assert (logipdf ([x, NaN], single (0), 1), single ([y, NaN]), eps ("single"))
***** assert (logipdf ([x, NaN], 0, single (1)), single ([y, NaN]), eps ("single"))
***** error<logipdf: function called with too few input arguments.> logipdf ()
***** error<logipdf: function called with too few input arguments.> logipdf (1)
***** error<logipdf: function called with too few input arguments.> ...
 logipdf (1, 2)
***** error<logipdf: X, MU, and SIGMA must be of common size or scalars.> ...
 logipdf (1, ones (2), ones (3))
***** error<logipdf: X, MU, and SIGMA must be of common size or scalars.> ...
 logipdf (ones (2), 1, ones (3))
***** error<logipdf: X, MU, and SIGMA must be of common size or scalars.> ...
 logipdf (ones (2), ones (3), 1)
***** error<logipdf: X, MU, and SIGMA must not be complex.> logipdf (i, 2, 3)
***** error<logipdf: X, MU, and SIGMA must not be complex.> logipdf (1, i, 3)
***** error<logipdf: X, MU, and SIGMA must not be complex.> logipdf (1, 2, i)
14 tests, 14 passed, 0 known failure, 0 skipped
[inst/dist_fun/burrinv.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/burrinv.m
***** demo
 ## Plot various iCDFs from the Burr type XII distribution
 p = 0.001:0.001:0.999;
 x1 = burrinv (p, 1, 1, 1);
 x2 = burrinv (p, 1, 1, 2);
 x3 = burrinv (p, 1, 1, 3);
 x4 = burrinv (p, 1, 2, 1);
 x5 = burrinv (p, 1, 3, 1);
 x6 = burrinv (p, 1, 0.5, 2);
 plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", ...
       p, x4, "-c", p, x5, "-m", p, x6, "-k")
 grid on
 ylim ([0, 5])
 legend ({"λ = 1, c = 1, k = 1", "λ = 1, c = 1, k = 2", ...
          "λ = 1, c = 1, k = 3", "λ = 1, c = 2, k = 1", ...
          "λ = 1, c = 3, k = 1", "λ = 1, c = 0.5, k = 2"}, ...
         "location", "northwest")
 title ("Burr type XII iCDF")
 xlabel ("probability")
 ylabel ("values in x")
***** shared p, y
 p = [-Inf, -1, 0, 1/2, 1, 2, Inf];
 y = [NaN, NaN, 0, 1 , Inf, NaN, NaN];
***** assert (burrinv (p, ones (1,7), ones (1,7), ones(1,7)), y, eps)
***** assert (burrinv (p, 1, 1, 1), y, eps)
***** assert (burrinv (p, [1, 1, 1, NaN, 1, 1, 1], 1, 1), [y(1:3), NaN, y(5:7)], eps)
***** assert (burrinv (p, 1, [1, 1, 1, NaN, 1, 1, 1], 1), [y(1:3), NaN, y(5:7)], eps)
***** assert (burrinv (p, 1, 1, [1, 1, 1, NaN, 1, 1, 1]), [y(1:3), NaN, y(5:7)], eps)
***** assert (burrinv ([p, NaN], 1, 1, 1), [y, NaN], eps)
***** assert (burrinv (single ([p, NaN]), 1, 1, 1), single ([y, NaN]), eps("single"))
***** assert (burrinv ([p, NaN], single (1), 1, 1), single ([y, NaN]), eps("single"))
***** assert (burrinv ([p, NaN], 1, single (1), 1), single ([y, NaN]), eps("single"))
***** assert (burrinv ([p, NaN], 1, 1, single (1)), single ([y, NaN]), eps("single"))
***** error<burrinv: function called with too few input arguments.> burrinv ()
***** error<burrinv: function called with too few input arguments.> burrinv (1)
***** error<burrinv: function called with too few input arguments.> burrinv (1, 2)
***** error<burrinv: function called with too few input arguments.> burrinv (1, 2, 3)
***** error<burrinv: function called with too many inputs> ...
 burrinv (1, 2, 3, 4, 5)
***** error<burrinv: P, LAMBDA, C, and K must be of common size or scalars.> ...
 burrinv (ones (3), ones (2), ones(2), ones(2))
***** error<burrinv: P, LAMBDA, C, and K must be of common size or scalars.> ...
 burrinv (ones (2), ones (3), ones(2), ones(2))
***** error<burrinv: P, LAMBDA, C, and K must be of common size or scalars.> ...
 burrinv (ones (2), ones (2), ones(3), ones(2))
***** error<burrinv: P, LAMBDA, C, and K must be of common size or scalars.> ...
 burrinv (ones (2), ones (2), ones(2), ones(3))
***** error<burrinv: P, LAMBDA, C, and K must not be complex.> burrinv (i, 2, 3, 4)
***** error<burrinv: P, LAMBDA, C, and K must not be complex.> burrinv (1, i, 3, 4)
***** error<burrinv: P, LAMBDA, C, and K must not be complex.> burrinv (1, 2, i, 4)
***** error<burrinv: P, LAMBDA, C, and K must not be complex.> burrinv (1, 2, 3, i)
23 tests, 23 passed, 0 known failure, 0 skipped
[inst/dist_fun/lognrnd.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/lognrnd.m
***** assert (size (lognrnd (1, 1)), [1 1])
***** assert (size (lognrnd (1, ones (2,1))), [2, 1])
***** assert (size (lognrnd (1, ones (2,2))), [2, 2])
***** assert (size (lognrnd (ones (2,1), 1)), [2, 1])
***** assert (size (lognrnd (ones (2,2), 1)), [2, 2])
***** assert (size (lognrnd (1, 1, 3)), [3, 3])
***** assert (size (lognrnd (1, 1, [4, 1])), [4, 1])
***** assert (size (lognrnd (1, 1, 4, 1)), [4, 1])
***** assert (size (lognrnd (1, 1, 4, 1, 5)), [4, 1, 5])
***** assert (size (lognrnd (1, 1, 0, 1)), [0, 1])
***** assert (size (lognrnd (1, 1, 1, 0)), [1, 0])
***** assert (size (lognrnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
***** assert (class (lognrnd (1, 1)), "double")
***** assert (class (lognrnd (1, single (1))), "single")
***** assert (class (lognrnd (1, single ([1, 1]))), "single")
***** assert (class (lognrnd (single (1), 1)), "single")
***** assert (class (lognrnd (single ([1, 1]), 1)), "single")
***** error<lognrnd: function called with too few input arguments.> lognrnd ()
***** error<lognrnd: function called with too few input arguments.> lognrnd (1)
***** error<lognrnd: MU and SIGMA must be of common size or scalars.> ...
 lognrnd (ones (3), ones (2))
***** error<lognrnd: MU and SIGMA must be of common size or scalars.> ...
 lognrnd (ones (2), ones (3))
***** error<lognrnd: MU and SIGMA must not be complex.> lognrnd (i, 2, 3)
***** error<lognrnd: MU and SIGMA must not be complex.> lognrnd (1, i, 3)
***** error<lognrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 lognrnd (1, 2, -1)
***** error<lognrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 lognrnd (1, 2, 1.2)
***** error<lognrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 lognrnd (1, 2, ones (2))
***** error<lognrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 lognrnd (1, 2, [2 -1 2])
***** error<lognrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 lognrnd (1, 2, [2 0 2.5])
***** error<lognrnd: dimensions must be non-negative integers.> ...
 lognrnd (1, 2, 2, -1, 5)
***** error<lognrnd: dimensions must be non-negative integers.> ...
 lognrnd (1, 2, 2, 1.5, 5)
***** error<lognrnd: MU and SIGMA must be scalars or of size SZ.> ...
 lognrnd (2, ones (2), 3)
***** error<lognrnd: MU and SIGMA must be scalars or of size SZ.> ...
 lognrnd (2, ones (2), [3, 2])
***** error<lognrnd: MU and SIGMA must be scalars or of size SZ.> ...
 lognrnd (2, ones (2), 3, 2)
33 tests, 33 passed, 0 known failure, 0 skipped
[inst/dist_fun/vmrnd.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/vmrnd.m
***** assert (size (vmrnd (1, 1)), [1 1])
***** assert (size (vmrnd (1, ones (2,1))), [2, 1])
***** assert (size (vmrnd (1, ones (2,2))), [2, 2])
***** assert (size (vmrnd (ones (2,1), 1)), [2, 1])
***** assert (size (vmrnd (ones (2,2), 1)), [2, 2])
***** assert (size (vmrnd (1, 1, 3)), [3, 3])
***** assert (size (vmrnd (1, 1, [4, 1])), [4, 1])
***** assert (size (vmrnd (1, 1, 4, 1)), [4, 1])
***** assert (size (vmrnd (1, 1, 4, 1, 5)), [4, 1, 5])
***** assert (size (vmrnd (1, 1, 0, 1)), [0, 1])
***** assert (size (vmrnd (1, 1, 1, 0)), [1, 0])
***** assert (size (vmrnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
***** assert (class (vmrnd (1, 1)), "double")
***** assert (class (vmrnd (1, single (1))), "single")
***** assert (class (vmrnd (1, single ([1, 1]))), "single")
***** assert (class (vmrnd (single (1), 1)), "single")
***** assert (class (vmrnd (single ([1, 1]), 1)), "single")
***** error<vmrnd: function called with too few input arguments.> vmrnd ()
***** error<vmrnd: function called with too few input arguments.> vmrnd (1)
***** error<vmrnd: MU and K must be of common size or scalars.> ...
 vmrnd (ones (3), ones (2))
***** error<vmrnd: MU and K must be of common size or scalars.> ...
 vmrnd (ones (2), ones (3))
***** error<vmrnd: MU and K must not be complex.> vmrnd (i, 2, 3)
***** error<vmrnd: MU and K must not be complex.> vmrnd (1, i, 3)
***** error<vmrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 vmrnd (1, 2, -1)
***** error<vmrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 vmrnd (1, 2, 1.2)
***** error<vmrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 vmrnd (1, 2, ones (2))
***** error<vmrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 vmrnd (1, 2, [2 -1 2])
***** error<vmrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 vmrnd (1, 2, [2 0 2.5])
***** error<vmrnd: dimensions must be non-negative integers.> ...
 vmrnd (1, 2, 2, -1, 5)
***** error<vmrnd: dimensions must be non-negative integers.> ...
 vmrnd (1, 2, 2, 1.5, 5)
***** error<vmrnd: MU and K must be scalars or of size SZ.> ...
 vmrnd (2, ones (2), 3)
***** error<vmrnd: MU and K must be scalars or of size SZ.> ...
 vmrnd (2, ones (2), [3, 2])
***** error<vmrnd: MU and K must be scalars or of size SZ.> ...
 vmrnd (2, ones (2), 3, 2)
33 tests, 33 passed, 0 known failure, 0 skipped
[inst/dist_fun/loglrnd.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/loglrnd.m
***** assert (size (loglrnd (1, 1)), [1 1])
***** assert (size (loglrnd (1, ones (2,1))), [2, 1])
***** assert (size (loglrnd (1, ones (2,2))), [2, 2])
***** assert (size (loglrnd (ones (2,1), 1)), [2, 1])
***** assert (size (loglrnd (ones (2,2), 1)), [2, 2])
***** assert (size (loglrnd (1, 1, 3)), [3, 3])
***** assert (size (loglrnd (1, 1, [4, 1])), [4, 1])
***** assert (size (loglrnd (1, 1, 4, 1)), [4, 1])
***** assert (size (loglrnd (1, 1, 4, 1, 5)), [4, 1, 5])
***** assert (size (loglrnd (1, 1, 0, 1)), [0, 1])
***** assert (size (loglrnd (1, 1, 1, 0)), [1, 0])
***** assert (size (loglrnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
***** assert (class (loglrnd (1, 1)), "double")
***** assert (class (loglrnd (1, single (1))), "single")
***** assert (class (loglrnd (1, single ([1, 1]))), "single")
***** assert (class (loglrnd (single (1), 1)), "single")
***** assert (class (loglrnd (single ([1, 1]), 1)), "single")
***** error<loglrnd: function called with too few input arguments.> loglrnd ()
***** error<loglrnd: function called with too few input arguments.> loglrnd (1)
***** error<loglrnd: MU and SIGMA must be of common size or scalars.> ...
 loglrnd (ones (3), ones (2))
***** error<loglrnd: MU and SIGMA must be of common size or scalars.> ...
 loglrnd (ones (2), ones (3))
***** error<loglrnd: MU and SIGMA must not be complex.> loglrnd (i, 2, 3)
***** error<loglrnd: MU and SIGMA must not be complex.> loglrnd (1, i, 3)
***** error<loglrnd: SZ must be mu scalar or mu row vector of non-negative integers.> ...
 loglrnd (1, 2, -1)
***** error<loglrnd: SZ must be mu scalar or mu row vector of non-negative integers.> ...
 loglrnd (1, 2, 1.2)
***** error<loglrnd: SZ must be mu scalar or mu row vector of non-negative integers.> ...
 loglrnd (1, 2, ones (2))
***** error<loglrnd: SZ must be mu scalar or mu row vector of non-negative integers.> ...
 loglrnd (1, 2, [2 -1 2])
***** error<loglrnd: SZ must be mu scalar or mu row vector of non-negative integers.> ...
 loglrnd (1, 2, [2 0 2.5])
***** error<loglrnd: dimensions must be non-negative integers.> ...
 loglrnd (1, 2, 2, -1, 5)
***** error<loglrnd: dimensions must be non-negative integers.> ...
 loglrnd (1, 2, 2, 1.5, 5)
***** error<loglrnd: MU and SIGMA must be scalars or of size SZ.> ...
 loglrnd (2, ones (2), 3)
***** error<loglrnd: MU and SIGMA must be scalars or of size SZ.> ...
 loglrnd (2, ones (2), [3, 2])
***** error<loglrnd: MU and SIGMA must be scalars or of size SZ.> ...
 loglrnd (2, ones (2), 3, 2)
33 tests, 33 passed, 0 known failure, 0 skipped
[inst/dist_fun/chi2rnd.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/chi2rnd.m
***** assert (size (chi2rnd (2)), [1, 1])
***** assert (size (chi2rnd (ones (2,1))), [2, 1])
***** assert (size (chi2rnd (ones (2,2))), [2, 2])
***** assert (size (chi2rnd (1, 3)), [3, 3])
***** assert (size (chi2rnd (1, [4 1])), [4, 1])
***** assert (size (chi2rnd (1, 4, 1)), [4, 1])
***** assert (size (chi2rnd (1, 4, 1)), [4, 1])
***** assert (size (chi2rnd (1, 4, 1, 5)), [4, 1, 5])
***** assert (size (chi2rnd (1, 0, 1)), [0, 1])
***** assert (size (chi2rnd (1, 1, 0)), [1, 0])
***** assert (size (chi2rnd (1, 1, 2, 0, 5)), [1, 2, 0, 5])
***** assert (class (chi2rnd (2)), "double")
***** assert (class (chi2rnd (single (2))), "single")
***** assert (class (chi2rnd (single ([2 2]))), "single")
***** error<chi2rnd: function called with too few input arguments.> chi2rnd ()
***** error<chi2rnd: DF must not be complex.> chi2rnd (i)
***** error<chi2rnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 chi2rnd (1, -1)
***** error<chi2rnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 chi2rnd (1, 1.2)
***** error<chi2rnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 chi2rnd (1, ones (2))
***** error<chi2rnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 chi2rnd (1, [2 -1 2])
***** error<chi2rnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 chi2rnd (1, [2 0 2.5])
***** error<chi2rnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 chi2rnd (ones (2), ones (2))
***** error<chi2rnd: dimensions must be non-negative integers.> ...
 chi2rnd (1, 2, -1, 5)
***** error<chi2rnd: dimensions must be non-negative integers.> ...
 chi2rnd (1, 2, 1.5, 5)
***** error<chi2rnd: DF must be scalar or of size SZ.> chi2rnd (ones (2,2), 3)
***** error<chi2rnd: DF must be scalar or of size SZ.> chi2rnd (ones (2,2), [3, 2])
***** error<chi2rnd: DF must be scalar or of size SZ.> chi2rnd (ones (2,2), 2, 3)
27 tests, 27 passed, 0 known failure, 0 skipped
[inst/dist_fun/invgrnd.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/invgrnd.m
***** assert (size (invgrnd (1, 1, 1)), [1, 1])
***** assert (size (invgrnd (1, 1, 2)), [2, 2])
***** assert (size (invgrnd (1, 1, [2, 1])), [2, 1])
***** assert (size (invgrnd (1, zeros (2, 2))), [2, 2])
***** assert (size (invgrnd (1, ones (2, 1))), [2, 1])
***** assert (size (invgrnd (1, ones (2, 2))), [2, 2])
***** assert (size (invgrnd (ones (2, 1), 1)), [2, 1])
***** assert (size (invgrnd (ones (2, 2), 1)), [2, 2])
***** assert (size (invgrnd (1, 1, 3)), [3, 3])
***** assert (size (invgrnd (1, 1, [4 1])), [4, 1])
***** assert (size (invgrnd (1, 1, 4, 1)), [4, 1])
***** test
 r =  invgrnd (1, [1, 0, -1]);
 assert (r([2:3]), [NaN, NaN])
***** assert (class (invgrnd (1, 0)), "double")
***** assert (class (invgrnd (1, single (0))), "single")
***** assert (class (invgrnd (1, single ([0 0]))), "single")
***** assert (class (invgrnd (1, single (1))), "single")
***** assert (class (invgrnd (1, single ([1 1]))), "single")
***** assert (class (invgrnd (single (1), 1)), "single")
***** assert (class (invgrnd (single ([1 1]), 1)), "single")
***** error<invgrnd: function called with too few input arguments.> invgrnd ()
***** error<invgrnd: function called with too few input arguments.> invgrnd (1)
***** error<invgrnd: MU and LAMBDA must be of common size or scalars.> ...
 invgrnd (ones (3), ones (2))
***** error<invgrnd: MU and LAMBDA must be of common size or scalars.> ...
 invgrnd (ones (2), ones (3))
***** error<invgrnd: MU and LAMBDA must not be complex.> invgrnd (i, 2, 3)
***** error<invgrnd: MU and LAMBDA must not be complex.> invgrnd (1, i, 3)
***** error<invgrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 invgrnd (1, 2, -1)
***** error<invgrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 invgrnd (1, 2, 1.2)
***** error<invgrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 invgrnd (1, 2, ones (2))
***** error<invgrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 invgrnd (1, 2, [2 -1 2])
***** error<invgrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 invgrnd (1, 2, [2 0 2.5])
***** error<invgrnd: dimensions must be non-negative integers.> ...
 invgrnd (1, 2, 2, -1, 5)
***** error<invgrnd: dimensions must be non-negative integers.> ...
 invgrnd (1, 2, 2, 1.5, 5)
***** error<invgrnd: MU and LAMBDA must be scalars or of size SZ.> ...
 invgrnd (2, ones (2), 3)
***** error<invgrnd: MU and LAMBDA must be scalars or of size SZ.> ...
 invgrnd (2, ones (2), [3, 2])
***** error<invgrnd: MU and LAMBDA must be scalars or of size SZ.> ...
 invgrnd (2, ones (2), 3, 2)
35 tests, 35 passed, 0 known failure, 0 skipped
[inst/dist_fun/betarnd.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/betarnd.m
***** assert (size (betarnd (2, 1/2)), [1 1])
***** assert (size (betarnd (2 * ones (2, 1), 1/2)), [2, 1])
***** assert (size (betarnd (2 * ones (2, 2), 1/2)), [2, 2])
***** assert (size (betarnd (2, 1/2 * ones (2, 1))), [2, 1])
***** assert (size (betarnd (1, 1/2 * ones (2, 2))), [2, 2])
***** assert (size (betarnd (ones (2, 1), 1)), [2, 1])
***** assert (size (betarnd (ones (2, 2), 1)), [2, 2])
***** assert (size (betarnd (2, 1/2, 3)), [3, 3])
***** assert (size (betarnd (1, 1, [4, 1])), [4, 1])
***** assert (size (betarnd (1, 1, 4, 1)), [4, 1])
***** assert (size (betarnd (1, 1, 4, 1, 5)), [4, 1, 5])
***** assert (size (betarnd (1, 1, 0, 1)), [0, 1])
***** assert (size (betarnd (1, 1, 1, 0)), [1, 0])
***** assert (size (betarnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
***** assert (class (betarnd (1, 1)), "double")
***** assert (class (betarnd (1, single (0))), "single")
***** assert (class (betarnd (1, single ([0, 0]))), "single")
***** assert (class (betarnd (1, single (1), 2)), "single")
***** assert (class (betarnd (1, single ([1, 1]), 1, 2)), "single")
***** assert (class (betarnd (single (1), 1, 2)), "single")
***** assert (class (betarnd (single ([1, 1]), 1, 1, 2)), "single")
***** error<betarnd: function called with too few input arguments.> betarnd ()
***** error<betarnd: function called with too few input arguments.> betarnd (1)
***** error<betarnd: A and B must be of common size or scalars.> ...
 betarnd (ones (3), ones (2))
***** error<betarnd: A and B must be of common size or scalars.> ...
 betarnd (ones (2), ones (3))
***** error<betarnd: A and B must not be complex.> betarnd (i, 2)
***** error<betarnd: A and B must not be complex.> betarnd (1, i)
***** error<betarnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 betarnd (1, 1/2, -1)
***** error<betarnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 betarnd (1, 1/2, 1.2)
***** error<betarnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 betarnd (1, 1/2, ones (2))
***** error<betarnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 betarnd (1, 1/2, [2 -1 2])
***** error<betarnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 betarnd (1, 1/2, [2 0 2.5])
***** error<betarnd: dimensions must be non-negative integers.> ...
 betarnd (1, 1/2, 2, -1, 5)
***** error<betarnd: dimensions must be non-negative integers.> ...
 betarnd (1, 1/2, 2, 1.5, 5)
***** error<betarnd: A and B must be scalars or of size SZ.> ...
 betarnd (2, 1/2 * ones (2), 3)
***** error<betarnd: A and B must be scalars or of size SZ.> ...
 betarnd (2, 1/2 * ones (2), [3, 2])
***** error<betarnd: A and B must be scalars or of size SZ.> ...
 betarnd (2, 1/2 * ones (2), 3, 2)
***** error<betarnd: A and B must be scalars or of size SZ.> ...
 betarnd (2 * ones (2), 1/2, 3)
***** error<betarnd: A and B must be scalars or of size SZ.> ...
 betarnd (2 * ones (2), 1/2, [3, 2])
***** error<betarnd: A and B must be scalars or of size SZ.> ...
 betarnd (2 * ones (2), 1/2, 3, 2)
40 tests, 40 passed, 0 known failure, 0 skipped
[inst/dist_fun/gevpdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/gevpdf.m
***** demo
 ## Plot various PDFs from the generalized extreme value distribution
 x = -1:0.001:10;
 y1 = gevpdf (x, 1, 1, 1);
 y2 = gevpdf (x, 0.5, 1, 1);
 y3 = gevpdf (x, 1, 1, 5);
 y4 = gevpdf (x, 1, 2, 5);
 y5 = gevpdf (x, 1, 5, 5);
 y6 = gevpdf (x, 1, 0.5, 5);
 plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", ...
       x, y4, "-c", x, y5, "-m", x, y6, "-k")
 grid on
 xlim ([-1, 10])
 ylim ([0, 1.1])
 legend ({"k = 1, σ = 1, μ = 1", "k = 0.5, σ = 1, μ = 1", ...
          "k = 1, σ = 1, μ = 5", "k = 1, σ = 2, μ = 5", ...
          "k = 1, σ = 5, μ = 5", "k = 1, σ = 0.5, μ = 5"}, ...
         "location", "northeast")
 title ("Generalized extreme value PDF")
 xlabel ("values in x")
 ylabel ("density")
***** test
 x = 0:0.5:2.5;
 sigma = 1:6;
 k = 1;
 mu = 0;
 y = gevpdf (x, k, sigma, mu);
 expected_y = [0.367879   0.143785   0.088569   0.063898   0.049953   0.040997];
 assert (y, expected_y, 0.001);
***** test
 x = -0.5:0.5:2.5;
 sigma = 0.5;
 k = 1;
 mu = 0;
 y = gevpdf (x, k, sigma, mu);
 expected_y = [0 0.735759   0.303265   0.159229   0.097350   0.065498   0.047027];
 assert (y, expected_y, 0.001);
***** test # check for continuity for k near 0
 x = 1;
 sigma = 0.5;
 k = -0.03:0.01:0.03;
 mu = 0;
 y = gevpdf (x, k, sigma, mu);
 expected_y = [0.23820   0.23764   0.23704   0.23641   0.23576   0.23508   0.23438];
 assert (y, expected_y, 0.001);
***** error<gevpdf: function called with too few input arguments.> gevpdf ()
***** error<gevpdf: function called with too few input arguments.> gevpdf (1)
***** error<gevpdf: function called with too few input arguments.> gevpdf (1, 2)
***** error<gevpdf: function called with too few input arguments.> gevpdf (1, 2, 3)
***** error<gevpdf: X, K, SIGMA, and MU must be of common size or scalars.> ...
 gevpdf (ones (3), ones (2), ones(2), ones(2))
***** error<gevpdf: X, K, SIGMA, and MU must be of common size or scalars.> ...
 gevpdf (ones (2), ones (3), ones(2), ones(2))
***** error<gevpdf: X, K, SIGMA, and MU must be of common size or scalars.> ...
 gevpdf (ones (2), ones (2), ones(3), ones(2))
***** error<gevpdf: X, K, SIGMA, and MU must be of common size or scalars.> ...
 gevpdf (ones (2), ones (2), ones(2), ones(3))
***** error<gevpdf: X, K, SIGMA, and MU must not be complex.> gevpdf (i, 2, 3, 4)
***** error<gevpdf: X, K, SIGMA, and MU must not be complex.> gevpdf (1, i, 3, 4)
***** error<gevpdf: X, K, SIGMA, and MU must not be complex.> gevpdf (1, 2, i, 4)
***** error<gevpdf: X, K, SIGMA, and MU must not be complex.> gevpdf (1, 2, 3, i)
15 tests, 15 passed, 0 known failure, 0 skipped
[inst/dist_fun/wblpdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/wblpdf.m
***** demo
 ## Plot various PDFs from the Weibul distribution
 x = 0:0.001:2.5;
 y1 = wblpdf (x, 1, 0.5);
 y2 = wblpdf (x, 1, 1);
 y3 = wblpdf (x, 1, 1.5);
 y4 = wblpdf (x, 1, 5);
 plot (x, y1, "-b", x, y2, "-r", x, y3, "-m", x, y4, "-g")
 grid on
 ylim ([0, 2.5])
 legend ({"λ = 5, k = 0.5", "λ = 9, k = 1", ...
          "λ = 6, k = 1.5", "λ = 2, k = 5"}, "location", "northeast")
 title ("Weibul PDF")
 xlabel ("values in x")
 ylabel ("density")
***** shared x,y
 x = [-1 0 0.5 1 Inf];
 y = [0, exp(-x(2:4)), NaN];
***** assert (wblpdf (x, ones (1,5), ones (1,5)), y)
***** assert (wblpdf (x, 1, ones (1,5)), y)
***** assert (wblpdf (x, ones (1,5), 1), y)
***** assert (wblpdf (x, [0 NaN Inf 1 1], 1), [NaN NaN NaN y(4:5)])
***** assert (wblpdf (x, 1, [0 NaN Inf 1 1]), [NaN NaN NaN y(4:5)])
***** assert (wblpdf ([x, NaN], 1, 1), [y, NaN])
***** assert (wblpdf (single ([x, NaN]), 1, 1), single ([y, NaN]))
***** assert (wblpdf ([x, NaN], single (1), 1), single ([y, NaN]))
***** assert (wblpdf ([x, NaN], 1, single (1)), single ([y, NaN]))
***** error wblpdf ()
***** error wblpdf (1,2,3,4)
***** error wblpdf (ones (3), ones (2), ones (2))
***** error wblpdf (ones (2), ones (3), ones (2))
***** error wblpdf (ones (2), ones (2), ones (3))
***** error wblpdf (i, 2, 2)
***** error wblpdf (2, i, 2)
***** error wblpdf (2, 2, i)
17 tests, 17 passed, 0 known failure, 0 skipped
[inst/dist_fun/evrnd.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/evrnd.m
***** assert (size (evrnd (1, 1)), [1 1])
***** assert (size (evrnd (1, ones (2,1))), [2, 1])
***** assert (size (evrnd (1, ones (2,2))), [2, 2])
***** assert (size (evrnd (ones (2,1), 1)), [2, 1])
***** assert (size (evrnd (ones (2,2), 1)), [2, 2])
***** assert (size (evrnd (1, 1, 3)), [3, 3])
***** assert (size (evrnd (1, 1, [4, 1])), [4, 1])
***** assert (size (evrnd (1, 1, 4, 1)), [4, 1])
***** assert (size (evrnd (1, 1, 4, 1, 5)), [4, 1, 5])
***** assert (size (evrnd (1, 1, 0, 1)), [0, 1])
***** assert (size (evrnd (1, 1, 1, 0)), [1, 0])
***** assert (size (evrnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
***** assert (class (evrnd (1, 1)), "double")
***** assert (class (evrnd (1, single (1))), "single")
***** assert (class (evrnd (1, single ([1, 1]))), "single")
***** assert (class (evrnd (single (1), 1)), "single")
***** assert (class (evrnd (single ([1, 1]), 1)), "single")
***** error<evrnd: function called with too few input arguments.> evrnd ()
***** error<evrnd: function called with too few input arguments.> evrnd (1)
***** error<evrnd: MU and SIGMA must be of common size or scalars.> ...
 evrnd (ones (3), ones (2))
***** error<evrnd: MU and SIGMA must be of common size or scalars.> ...
 evrnd (ones (2), ones (3))
***** error<evrnd: MU and SIGMA must not be complex.> evrnd (i, 2, 3)
***** error<evrnd: MU and SIGMA must not be complex.> evrnd (1, i, 3)
***** error<evrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 evrnd (1, 2, -1)
***** error<evrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 evrnd (1, 2, 1.2)
***** error<evrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 evrnd (1, 2, ones (2))
***** error<evrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 evrnd (1, 2, [2 -1 2])
***** error<evrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 evrnd (1, 2, [2 0 2.5])
***** error<evrnd: dimensions must be non-negative integers.> ...
 evrnd (1, 2, 2, -1, 5)
***** error<evrnd: dimensions must be non-negative integers.> ...
 evrnd (1, 2, 2, 1.5, 5)
***** error<evrnd: MU and SIGMA must be scalars or of size SZ.> ...
 evrnd (2, ones (2), 3)
***** error<evrnd: MU and SIGMA must be scalars or of size SZ.> ...
 evrnd (2, ones (2), [3, 2])
***** error<evrnd: MU and SIGMA must be scalars or of size SZ.> ...
 evrnd (2, ones (2), 3, 2)
33 tests, 33 passed, 0 known failure, 0 skipped
[inst/dist_fun/trnd.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/trnd.m
***** assert (size (trnd (2)), [1, 1])
***** assert (size (trnd (ones (2,1))), [2, 1])
***** assert (size (trnd (ones (2,2))), [2, 2])
***** assert (size (trnd (1, 3)), [3, 3])
***** assert (size (trnd (1, [4 1])), [4, 1])
***** assert (size (trnd (1, 4, 1)), [4, 1])
***** assert (size (trnd (1, 4, 1)), [4, 1])
***** assert (size (trnd (1, 4, 1, 5)), [4, 1, 5])
***** assert (size (trnd (1, 0, 1)), [0, 1])
***** assert (size (trnd (1, 1, 0)), [1, 0])
***** assert (size (trnd (1, 1, 2, 0, 5)), [1, 2, 0, 5])
***** assert (trnd (0, 1, 1), NaN)
***** assert (trnd ([0, 0, 0], [1, 3]), [NaN, NaN, NaN])
***** assert (class (trnd (2)), "double")
***** assert (class (trnd (single (2))), "single")
***** assert (class (trnd (single ([2 2]))), "single")
***** error<trnd: function called with too few input arguments.> trnd ()
***** error<trnd: DF must not be complex.> trnd (i)
***** error<trnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 trnd (1, -1)
***** error<trnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 trnd (1, 1.2)
***** error<trnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 trnd (1, ones (2))
***** error<trnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 trnd (1, [2 -1 2])
***** error<trnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 trnd (1, [2 0 2.5])
***** error<trnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 trnd (ones (2), ones (2))
***** error<trnd: dimensions must be non-negative integers.> ...
 trnd (1, 2, -1, 5)
***** error<trnd: dimensions must be non-negative integers.> ...
 trnd (1, 2, 1.5, 5)
***** error<trnd: DF must be scalar or of size SZ.> trnd (ones (2,2), 3)
***** error<trnd: DF must be scalar or of size SZ.> trnd (ones (2,2), [3, 2])
***** error<trnd: DF must be scalar or of size SZ.> trnd (ones (2,2), 2, 3)
29 tests, 29 passed, 0 known failure, 0 skipped
[inst/dist_fun/invgcdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/invgcdf.m
***** demo
 ## Plot various CDFs from the inverse Gaussian distribution
 x = 0:0.001:3;
 p1 = invgcdf (x, 1, 0.2);
 p2 = invgcdf (x, 1, 1);
 p3 = invgcdf (x, 1, 3);
 p4 = invgcdf (x, 3, 0.2);
 p5 = invgcdf (x, 3, 1);
 plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", x, p4, "-c", x, p5, "-y")
 grid on
 xlim ([0, 3])
 legend ({"μ = 1, σ = 0.2", "μ = 1, σ = 1", "μ = 1, σ = 3", ...
          "μ = 3, σ = 0.2", "μ = 3, σ = 1"}, "location", "southeast")
 title ("Inverse Gaussian CDF")
 xlabel ("values in x")
 ylabel ("probability")
***** shared x, p1, p1u, y2, y2u, y3, y3u
 x = [-Inf, -1, 0, 1/2, 1, Inf];
 p1 = [0, 0, 0, 0.3650, 0.6681, 1];
 p1u = [1, 1, 1, 0.6350, 0.3319, 0];
***** assert (invgcdf (x, ones (1,6), ones (1,6)), p1, 1e-4)
***** assert (invgcdf (x, 1, 1), p1, 1e-4)
***** assert (invgcdf (x, 1, ones (1,6)), p1, 1e-4)
***** assert (invgcdf (x, ones (1,6), 1), p1, 1e-4)
***** assert (invgcdf (x, 1, [1, 1, 1, NaN, 1, 1]), [p1(1:3), NaN, p1(5:6)], 1e-4)
***** assert (invgcdf (x, [1, 1, 1, NaN, 1, 1], 1), [p1(1:3), NaN, p1(5:6)], 1e-4)
***** assert (invgcdf ([x(1:3), NaN, x(5:6)], 1, 1), [p1(1:3), NaN, p1(5:6)], 1e-4)
***** assert (invgcdf (x, ones (1,6), ones (1,6), "upper"), p1u, 1e-4)
***** assert (invgcdf (x, 1, 1, "upper"), p1u, 1e-4)
***** assert (invgcdf (x, 1, ones (1,6), "upper"), p1u, 1e-4)
***** assert (invgcdf (x, ones (1,6), 1, "upper"), p1u, 1e-4)
***** assert (class (invgcdf (single ([x, NaN]), 1, 1)), "single")
***** assert (class (invgcdf ([x, NaN], 1, single (1))), "single")
***** assert (class (invgcdf ([x, NaN], single (1), 1)), "single")
***** error<invgcdf: function called with too few input arguments.> invgcdf ()
***** error<invgcdf: function called with too few input arguments.> invgcdf (1)
***** error<invgcdf: function called with too few input arguments.> invgcdf (1, 2)
***** error<invgcdf: invalid argument for upper tail.> invgcdf (1, 2, 3, "tail")
***** error<invgcdf: invalid argument for upper tail.> invgcdf (1, 2, 3, 5)
***** error<invgcdf: X, MU, and LAMBDA must be of common size or scalars.> ...
 invgcdf (ones (3), ones (2), ones(2))
***** error<invgcdf: X, MU, and LAMBDA must be of common size or scalars.> ...
 invgcdf (ones (2), ones (3), ones(2))
***** error<invgcdf: X, MU, and LAMBDA must be of common size or scalars.> ...
 invgcdf (ones (2), ones (2), ones(3))
***** error<invgcdf: X, MU, and LAMBDA must not be complex.> invgcdf (i, 2, 3)
***** error<invgcdf: X, MU, and LAMBDA must not be complex.> invgcdf (1, i, 3)
***** error<invgcdf: X, MU, and LAMBDA must not be complex.> invgcdf (1, 2, i)
25 tests, 25 passed, 0 known failure, 0 skipped
[inst/dist_fun/unifinv.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/unifinv.m
***** demo
 ## Plot various iCDFs from the continuous uniform distribution
 p = 0.001:0.001:0.999;
 x1 = unifinv (p, 2, 5);
 x2 = unifinv (p, 3, 9);
 plot (p, x1, "-b", p, x2, "-g")
 grid on
 xlim ([0, 1])
 ylim ([0, 10])
 legend ({"a = 2, b = 5", "a = 3, b = 9"}, "location", "northwest")
 title ("Continuous uniform iCDF")
 xlabel ("probability")
 ylabel ("values in x")
***** shared p
 p = [-1 0 0.5 1 2];
***** assert (unifinv (p, ones (1,5), 2*ones (1,5)), [NaN 1 1.5 2 NaN])
***** assert (unifinv (p, 0, 1), [NaN 1 1.5 2 NaN] - 1)
***** assert (unifinv (p, 1, 2*ones (1,5)), [NaN 1 1.5 2 NaN])
***** assert (unifinv (p, ones (1,5), 2), [NaN 1 1.5 2 NaN])
***** assert (unifinv (p, [1 2 NaN 1 1], 2), [NaN NaN NaN 2 NaN])
***** assert (unifinv (p, 1, 2*[1 0 NaN 1 1]), [NaN NaN NaN 2 NaN])
***** assert (unifinv ([p(1:2) NaN p(4:5)], 1, 2), [NaN 1 NaN 2 NaN])
***** assert (unifinv ([p, NaN], 1, 2), [NaN 1 1.5 2 NaN NaN])
***** assert (unifinv (single ([p, NaN]), 1, 2), single ([NaN 1 1.5 2 NaN NaN]))
***** assert (unifinv ([p, NaN], single (1), 2), single ([NaN 1 1.5 2 NaN NaN]))
***** assert (unifinv ([p, NaN], 1, single (2)), single ([NaN 1 1.5 2 NaN NaN]))
***** error<unifinv: function called with too few input arguments.> unifinv ()
***** error<unifinv: function called with too few input arguments.> unifinv (1, 2)
***** error<unifinv: P, A, and B must be of common size or scalars.> ...
 unifinv (ones (3), ones (2), ones (2))
***** error<unifinv: P, A, and B must be of common size or scalars.> ...
 unifinv (ones (2), ones (3), ones (2))
***** error<unifinv: P, A, and B must be of common size or scalars.> ...
 unifinv (ones (2), ones (2), ones (3))
***** error<unifinv: P, A, and B must not be complex.> unifinv (i, 2, 2)
***** error<unifinv: P, A, and B must not be complex.> unifinv (2, i, 2)
***** error<unifinv: P, A, and B must not be complex.> unifinv (2, 2, i)
19 tests, 19 passed, 0 known failure, 0 skipped
[inst/dist_fun/ricernd.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/ricernd.m
***** assert (size (ricernd (2, 1/2)), [1, 1])
***** assert (size (ricernd (2 * ones (2, 1), 1/2)), [2, 1])
***** assert (size (ricernd (2 * ones (2, 2), 1/2)), [2, 2])
***** assert (size (ricernd (2, 1/2 * ones (2, 1))), [2, 1])
***** assert (size (ricernd (1, 1/2 * ones (2, 2))), [2, 2])
***** assert (size (ricernd (ones (2, 1), 1)), [2, 1])
***** assert (size (ricernd (ones (2, 2), 1)), [2, 2])
***** assert (size (ricernd (2, 1/2, 3)), [3, 3])
***** assert (size (ricernd (1, 1, [4, 1])), [4, 1])
***** assert (size (ricernd (1, 1, 4, 1)), [4, 1])
***** assert (size (ricernd (1, 1, 4, 1, 5)), [4, 1, 5])
***** assert (size (ricernd (1, 1, 0, 1)), [0, 1])
***** assert (size (ricernd (1, 1, 1, 0)), [1, 0])
***** assert (size (ricernd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
***** assert (class (ricernd (1, 1)), "double")
***** assert (class (ricernd (1, single (0))), "single")
***** assert (class (ricernd (1, single ([0, 0]))), "single")
***** assert (class (ricernd (1, single (1), 2)), "single")
***** assert (class (ricernd (1, single ([1, 1]), 1, 2)), "single")
***** assert (class (ricernd (single (1), 1, 2)), "single")
***** assert (class (ricernd (single ([1, 1]), 1, 1, 2)), "single")
***** error<ricernd: function called with too few input arguments.> ricernd ()
***** error<ricernd: function called with too few input arguments.> ricernd (1)
***** error<ricernd: S and SIGMA must be of common size or scalars.> ...
 ricernd (ones (3), ones (2))
***** error<ricernd: S and SIGMA must be of common size or scalars.> ...
 ricernd (ones (2), ones (3))
***** error<ricernd: S and SIGMA must not be complex.> ricernd (i, 2)
***** error<ricernd: S and SIGMA must not be complex.> ricernd (1, i)
***** error<ricernd: SZ must be a scalar or a row vector of non-negative integers.> ...
 ricernd (1, 1/2, -1)
***** error<ricernd: SZ must be a scalar or a row vector of non-negative integers.> ...
 ricernd (1, 1/2, 1.2)
***** error<ricernd: SZ must be a scalar or a row vector of non-negative integers.> ...
 ricernd (1, 1/2, ones (2))
***** error<ricernd: SZ must be a scalar or a row vector of non-negative integers.> ...
 ricernd (1, 1/2, [2 -1 2])
***** error<ricernd: SZ must be a scalar or a row vector of non-negative integers.> ...
 ricernd (1, 1/2, [2 0 2.5])
***** error<ricernd: dimensions must be non-negative integers.> ...
 ricernd (1, 1/2, 2, -1, 5)
***** error<ricernd: dimensions must be non-negative integers.> ...
 ricernd (1, 1/2, 2, 1.5, 5)
***** error<ricernd: S and SIGMA must be scalars or of size SZ.> ...
 ricernd (2, 1/2 * ones (2), 3)
***** error<ricernd: S and SIGMA must be scalars or of size SZ.> ...
 ricernd (2, 1/2 * ones (2), [3, 2])
***** error<ricernd: S and SIGMA must be scalars or of size SZ.> ...
 ricernd (2, 1/2 * ones (2), 3, 2)
***** error<ricernd: S and SIGMA must be scalars or of size SZ.> ...
 ricernd (2 * ones (2), 1/2, 3)
***** error<ricernd: S and SIGMA must be scalars or of size SZ.> ...
 ricernd (2 * ones (2), 1/2, [3, 2])
***** error<ricernd: S and SIGMA must be scalars or of size SZ.> ...
 ricernd (2 * ones (2), 1/2, 3, 2)
40 tests, 40 passed, 0 known failure, 0 skipped
[inst/dist_fun/geocdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/geocdf.m
***** demo
 ## Plot various CDFs from the geometric distribution
 x = 0:10;
 p1 = geocdf (x, 0.2);
 p2 = geocdf (x, 0.5);
 p3 = geocdf (x, 0.7);
 plot (x, p1, "*b", x, p2, "*g", x, p3, "*r")
 grid on
 xlim ([0, 10])
 legend ({"ps = 0.2", "ps = 0.5", "ps = 0.7"}, "location", "southeast")
 title ("Geometric CDF")
 xlabel ("values in x (number of failures)")
 ylabel ("probability")
***** test
 p = geocdf ([1, 2, 3, 4], 0.25);
 assert (p(1), 0.4375000000, 1e-14);
 assert (p(2), 0.5781250000, 1e-14);
 assert (p(3), 0.6835937500, 1e-14);
 assert (p(4), 0.7626953125, 1e-14);
***** test
 p = geocdf ([1, 2, 3, 4], 0.25, "upper");
 assert (p(1), 0.5625000000, 1e-14);
 assert (p(2), 0.4218750000, 1e-14);
 assert (p(3), 0.3164062500, 1e-14);
 assert (p(4), 0.2373046875, 1e-14);
***** shared x, p
 x = [-1 0 1 Inf];
 p = [0 0.5 0.75 1];
***** assert (geocdf (x, 0.5*ones (1,4)), p)
***** assert (geocdf (x, 0.5), p)
***** assert (geocdf (x, 0.5*[-1 NaN 4 1]), [NaN NaN NaN p(4)])
***** assert (geocdf ([x(1:2) NaN x(4)], 0.5), [p(1:2) NaN p(4)])
***** assert (geocdf ([x, NaN], 0.5), [p, NaN])
***** assert (geocdf (single ([x, NaN]), 0.5), single ([p, NaN]))
***** assert (geocdf ([x, NaN], single (0.5)), single ([p, NaN]))
***** error<geocdf: function called with too few input arguments.> geocdf ()
***** error<geocdf: function called with too few input arguments.> geocdf (1)
***** error<geocdf: X and PS must be of common size or scalars.> ...
 geocdf (ones (3), ones (2))
***** error<geocdf: X and PS must be of common size or scalars.> ...
 geocdf (ones (2), ones (3))
***** error<geocdf: X and PS must not be complex.> geocdf (i, 2)
***** error<geocdf: X and PS must not be complex.> geocdf (2, i)
***** error<geocdf: invalid argument for upper tail.> geocdf (2, 3, "tail")
***** error<geocdf: invalid argument for upper tail.> geocdf (2, 3, 5)
17 tests, 17 passed, 0 known failure, 0 skipped
[inst/dist_fun/tcdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/tcdf.m
***** demo
 ## Plot various CDFs from the Student's T distribution
 x = -5:0.01:5;
 p1 = tcdf (x, 1);
 p2 = tcdf (x, 2);
 p3 = tcdf (x, 5);
 p4 = tcdf (x, Inf);
 plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", x, p4, "-m")
 grid on
 xlim ([-5, 5])
 ylim ([0, 1])
 legend ({"df = 1", "df = 2", ...
          "df = 5", 'df = \infty'}, "location", "southeast")
 title ("Student's T CDF")
 xlabel ("values in x")
 ylabel ("probability")
***** shared x,y
 x = [-Inf 0 1 Inf];
 y = [0 1/2 3/4 1];
***** assert (tcdf (x, ones (1,4)), y, eps)
***** assert (tcdf (x, 1), y, eps)
***** assert (tcdf (x, [0 1 NaN 1]), [NaN 1/2 NaN 1], eps)
***** assert (tcdf ([x(1:2) NaN x(4)], 1), [y(1:2) NaN y(4)], eps)
***** assert (tcdf (2, 3, "upper"), 0.0697, 1e-4)
***** assert (tcdf (205, 5, "upper"), 2.6206e-11, 1e-14)
***** assert (tcdf ([x, NaN], 1), [y, NaN], eps)
***** assert (tcdf (single ([x, NaN]), 1), single ([y, NaN]), eps ("single"))
***** assert (tcdf ([x, NaN], single (1)), single ([y, NaN]), eps ("single"))
***** error<tcdf: function called with too few input arguments.> tcdf ()
***** error<tcdf: function called with too few input arguments.> tcdf (1)
***** error<tcdf: invalid argument for upper tail.> tcdf (1, 2, "uper")
***** error<tcdf: invalid argument for upper tail.> tcdf (1, 2, 3)
***** error<tcdf: X and DF must be of common size or scalars.> ...
 tcdf (ones (3), ones (2))
***** error<tcdf: X and DF must be of common size or scalars.> ...
 tcdf (ones (3), ones (2))
***** error<tcdf: X and DF must be of common size or scalars.> ...
 tcdf (ones (3), ones (2), "upper")
***** error<tcdf: X and DF must not be complex.> tcdf (i, 2)
***** error<tcdf: X and DF must not be complex.> tcdf (2, i)
***** shared tol_rel
 tol_rel = 10 * eps;
***** assert (tcdf (10^(-10), 2.5), 0.50000000003618087, -tol_rel)
***** assert (tcdf (10^(-11), 2.5), 0.50000000000361809, -tol_rel)
***** assert (tcdf (10^(-12), 2.5), 0.50000000000036181, -tol_rel)
***** assert (tcdf (10^(-13), 2.5), 0.50000000000003618, -tol_rel)
***** assert (tcdf (10^(-14), 2.5), 0.50000000000000362, -tol_rel)
***** assert (tcdf (10^(-15), 2.5), 0.50000000000000036, -tol_rel)
***** assert (tcdf (10^(-16), 2.5), 0.50000000000000004, -tol_rel)
***** assert (tcdf (-10^1, 2.5), 2.2207478836537124e-03, -tol_rel)
***** assert (tcdf (-10^2, 2.5), 7.1916492116661878e-06, -tol_rel)
***** assert (tcdf (-10^3, 2.5), 2.2747463948307452e-08, -tol_rel)
***** assert (tcdf (-10^4, 2.5), 7.1933970159922115e-11, -tol_rel)
***** assert (tcdf (-10^5, 2.5), 2.2747519231756221e-13, -tol_rel)
30 tests, 30 passed, 0 known failure, 0 skipped
[inst/dist_fun/chi2pdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/chi2pdf.m
***** demo
 ## Plot various PDFs from the chi-squared distribution
 x = 0:0.01:8;
 y1 = chi2pdf (x, 1);
 y2 = chi2pdf (x, 2);
 y3 = chi2pdf (x, 3);
 y4 = chi2pdf (x, 4);
 y5 = chi2pdf (x, 6);
 y6 = chi2pdf (x, 9);
 plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", ...
       x, y4, "-c", x, y5, "-m", x, y6, "-y")
 grid on
 xlim ([0, 8])
 ylim ([0, 0.5])
 legend ({"df = 1", "df = 2", "df = 3", ...
          "df = 4", "df = 6", "df = 9"}, "location", "northeast")
 title ("Chi-squared PDF")
 xlabel ("values in x")
 ylabel ("density")
***** shared x, y
 x = [-1 0 0.5 1 Inf];
 y = [0, 1/2 * exp(-x(2:5)/2)];
***** assert (chi2pdf (x, 2*ones (1,5)), y)
***** assert (chi2pdf (x, 2), y)
***** assert (chi2pdf (x, 2*[1 0 NaN 1 1]), [y(1) NaN NaN y(4:5)])
***** assert (chi2pdf ([x, NaN], 2), [y, NaN])
***** assert (chi2pdf (single ([x, NaN]), 2), single ([y, NaN]))
***** assert (chi2pdf ([x, NaN], single (2)), single ([y, NaN]))
***** error<chi2pdf: function called with too few input arguments.> chi2pdf ()
***** error<chi2pdf: function called with too few input arguments.> chi2pdf (1)
***** error<chi2pdf: function called with too many inputs> chi2pdf (1,2,3)
***** error<chi2pdf: X and DF must be of common size or scalars.> ...
 chi2pdf (ones (3), ones (2))
***** error<chi2pdf: X and DF must be of common size or scalars.> ...
 chi2pdf (ones (2), ones (3))
***** error<chi2pdf: X and DF must not be complex.> chi2pdf (i, 2)
***** error<chi2pdf: X and DF must not be complex.> chi2pdf (2, i)
13 tests, 13 passed, 0 known failure, 0 skipped
[inst/dist_fun/vmpdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/vmpdf.m
***** demo
 ## Plot various PDFs from the von Mises distribution
 x1 = [-pi:0.1:pi];
 y1 = vmpdf (x1, 0, 0.5);
 y2 = vmpdf (x1, 0, 1);
 y3 = vmpdf (x1, 0, 2);
 y4 = vmpdf (x1, 0, 4);
 plot (x1, y1, "-r", x1, y2, "-g", x1, y3, "-b", x1, y4, "-c")
 grid on
 xlim ([-pi, pi])
 ylim ([0, 0.8])
 legend ({"μ = 0, k = 0.5", "μ = 0, k = 1", ...
          "μ = 0, k = 2", "μ = 0, k = 4"}, "location", "northwest")
 title ("Von Mises PDF")
 xlabel ("values in x")
 ylabel ("density")
***** shared x, y0, y1
 x = [-pi:pi/2:pi];
 y0 = [0.046245, 0.125708, 0.341710, 0.125708, 0.046245];
 y1 = [0.046245, 0.069817, 0.654958, 0.014082, 0.000039];
***** assert (vmpdf (x, 0, 1), y0, 1e-5)
***** assert (vmpdf (x, zeros (1,5), ones (1,5)), y0, 1e-6)
***** assert (vmpdf (x, 0, [1 2 3 4 5]), y1, 1e-6)
***** assert (isa (vmpdf (single (pi), 0, 1), "single"), true)
***** assert (isa (vmpdf (pi, single (0), 1), "single"), true)
***** assert (isa (vmpdf (pi, 0, single (1)), "single"), true)
***** error<vmpdf: function called with too few input arguments.> vmpdf ()
***** error<vmpdf: function called with too few input arguments.> vmpdf (1)
***** error<vmpdf: function called with too few input arguments.> vmpdf (1, 2)
***** error<vmpdf: X, MU, and K must be of common size or scalars.> ...
 vmpdf (ones (3), ones (2), ones (2))
***** error<vmpdf: X, MU, and K must be of common size or scalars.> ...
 vmpdf (ones (2), ones (3), ones (2))
***** error<vmpdf: X, MU, and K must be of common size or scalars.> ...
 vmpdf (ones (2), ones (2), ones (3))
***** error<vmpdf: X, MU, and K must not be complex.> vmpdf (i, 2, 2)
***** error<vmpdf: X, MU, and K must not be complex.> vmpdf (2, i, 2)
***** error<vmpdf: X, MU, and K must not be complex.> vmpdf (2, 2, i)
15 tests, 15 passed, 0 known failure, 0 skipped
[inst/dist_fun/normcdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/normcdf.m
***** demo
 ## Plot various CDFs from the normal distribution
 x = -5:0.01:5;
 p1 = normcdf (x, 0, 0.5);
 p2 = normcdf (x, 0, 1);
 p3 = normcdf (x, 0, 2);
 p4 = normcdf (x, -2, 0.8);
 plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", x, p4, "-c")
 grid on
 xlim ([-5, 5])
 legend ({"μ = 0, σ = 0.5", "μ = 0, σ = 1", ...
          "μ = 0, σ = 2", "μ = -2, σ = 0.8"}, "location", "southeast")
 title ("Normal CDF")
 xlabel ("values in x")
 ylabel ("probability")
***** shared x, y
 x = [-Inf 1 2 Inf];
 y = [0, 0.5, 1/2*(1+erf(1/sqrt(2))), 1];
***** assert (normcdf (x, ones (1,4), ones (1,4)), y)
***** assert (normcdf (x, 1, ones (1,4)), y)
***** assert (normcdf (x, ones (1,4), 1), y)
***** assert (normcdf (x, [0, -Inf, NaN, Inf], 1), [0, 1, NaN, NaN])
***** assert (normcdf (x, 1, [Inf, NaN, -1, 0]), [NaN, NaN, NaN, 1])
***** assert (normcdf ([x(1:2), NaN, x(4)], 1, 1), [y(1:2), NaN, y(4)])
***** assert (normcdf (x, "upper"), [1, 0.1587, 0.0228, 0], 1e-4)
***** assert (normcdf ([x, NaN], 1, 1), [y, NaN])
***** assert (normcdf (single ([x, NaN]), 1, 1), single ([y, NaN]), eps ("single"))
***** assert (normcdf ([x, NaN], single (1), 1), single ([y, NaN]), eps ("single"))
***** assert (normcdf ([x, NaN], 1, single (1)), single ([y, NaN]), eps ("single"))
***** error<normcdf: invalid number of input arguments.> normcdf ()
***** error<normcdf: invalid number of input arguments.> normcdf (1,2,3,4,5,6,7)
***** error<normcdf: invalid argument for upper tail.> normcdf (1, 2, 3, 4, "uper")
***** error<normcdf: X, MU, and SIGMA must be of common size or scalars.> ...
 normcdf (ones (3), ones (2), ones (2))
***** error<normcdf: invalid size of covariance matrix.> normcdf (2, 3, 4, [1, 2])
***** error<normcdf: covariance matrix is required for confidence bounds.> ...
 [p, plo, pup] = normcdf (1, 2, 3)
***** error<normcdf: invalid value for alpha.> [p, plo, pup] = ...
 normcdf (1, 2, 3, [1, 0; 0, 1], 0)
***** error<normcdf: invalid value for alpha.> [p, plo, pup] = ...
 normcdf (1, 2, 3, [1, 0; 0, 1], 1.22)
***** error<normcdf: invalid value for alpha.> [p, plo, pup] = ...
 normcdf (1, 2, 3, [1, 0; 0, 1], "alpha", "upper")
***** error<normcdf: X, MU, and SIGMA must not be complex.> normcdf (i, 2, 2)
***** error<normcdf: X, MU, and SIGMA must not be complex.> normcdf (2, i, 2)
***** error<normcdf: X, MU, and SIGMA must not be complex.> normcdf (2, 2, i)
***** error<normcdf: bad covariance matrix.> ...
 [p, plo, pup] =normcdf (1, 2, 3, [1, 0; 0, -inf], 0.04)
24 tests, 24 passed, 0 known failure, 0 skipped
[inst/dist_fun/wblinv.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/wblinv.m
***** demo
 ## Plot various iCDFs from the Weibull distribution
 p = 0.001:0.001:0.999;
 x1 = wblinv (p, 1, 0.5);
 x2 = wblinv (p, 1, 1);
 x3 = wblinv (p, 1, 1.5);
 x4 = wblinv (p, 1, 5);
 plot (p, x1, "-b", p, x2, "-r", p, x3, "-m", p, x4, "-g")
 ylim ([0, 2.5])
 grid on
 legend ({"λ = 1, k = 0.5", "λ = 1, k = 1",  ...
          "λ = 1, k = 1.5", "λ = 1, k = 5"}, "location", "northwest")
 title ("Weibull iCDF")
 xlabel ("probability")
 ylabel ("x")
***** shared p
 p = [-1 0 0.63212055882855778 1 2];
***** assert (wblinv (p, ones (1,5), ones (1,5)), [NaN 0 1 Inf NaN], eps)
***** assert (wblinv (p, 1, ones (1,5)), [NaN 0 1 Inf NaN], eps)
***** assert (wblinv (p, ones (1,5), 1), [NaN 0 1 Inf NaN], eps)
***** assert (wblinv (p, [1 -1 NaN Inf 1], 1), [NaN NaN NaN NaN NaN])
***** assert (wblinv (p, 1, [1 -1 NaN Inf 1]), [NaN NaN NaN NaN NaN])
***** assert (wblinv ([p(1:2) NaN p(4:5)], 1, 1), [NaN 0 NaN Inf NaN])
***** assert (wblinv ([p, NaN], 1, 1), [NaN 0 1 Inf NaN NaN], eps)
***** assert (wblinv (single ([p, NaN]), 1, 1), single ([NaN 0 1 Inf NaN NaN]), eps ("single"))
***** assert (wblinv ([p, NaN], single (1), 1), single ([NaN 0 1 Inf NaN NaN]), eps ("single"))
***** assert (wblinv ([p, NaN], 1, single (1)), single ([NaN 0 1 Inf NaN NaN]), eps ("single"))
***** error<wblinv: invalid number of input arguments.> wblinv ()
***** error<wblinv: invalid number of input arguments.> wblinv (1,2,3,4)
***** error<wblinv: P, LAMBDA, and K must be of common size or scalars.> ...
 wblinv (ones (3), ones (2), ones (2))
***** error<wblinv: P, LAMBDA, and K must be of common size or scalars.> ...
 wblinv (ones (2), ones (3), ones (2))
***** error<wblinv: P, LAMBDA, and K must be of common size or scalars.> ...
 wblinv (ones (2), ones (2), ones (3))
***** error<wblinv: P, LAMBDA, and K must not be complex.> wblinv (i, 2, 2)
***** error<wblinv: P, LAMBDA, and K must not be complex.> wblinv (2, i, 2)
***** error<wblinv: P, LAMBDA, and K must not be complex.> wblinv (2, 2, i)
18 tests, 18 passed, 0 known failure, 0 skipped
[inst/dist_fun/tlsinv.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/tlsinv.m
***** demo
 ## Plot various iCDFs from the location-scale Student's T distribution
 p = 0.001:0.001:0.999;
 x1 = tlsinv (p, 0, 1, 1);
 x2 = tlsinv (p, 0, 2, 2);
 x3 = tlsinv (p, 3, 2, 5);
 x4 = tlsinv (p, -1, 3, Inf);
 plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-m")
 grid on
 xlim ([0, 1])
 ylim ([-8, 8])
 legend ({"mu = 0, sigma = 1, nu = 1", "mu = 0, sigma = 2, nu = 2", ...
          "mu = 3, sigma = 2, nu = 5", 'mu = -1, sigma = 3, nu = \infty'}, ...
         "location", "southeast")
 title ("Location-scale Student's T iCDF")
 xlabel ("probability")
 ylabel ("values in x")
***** shared p
 p = [-1 0 0.5 1 2];
***** assert (tlsinv (p, 0, 1, ones (1,5)), [NaN -Inf 0 Inf NaN])
***** assert (tlsinv (p, 0, 1, 1), [NaN -Inf 0 Inf NaN], eps)
***** assert (tlsinv (p, 0, 1, [1 0 NaN 1 1]), [NaN NaN NaN Inf NaN], eps)
***** assert (tlsinv ([p(1:2) NaN p(4:5)], 0, 1, 1), [NaN -Inf NaN Inf NaN])
***** assert (class (tlsinv ([p, NaN], 0, 1, 1)), "double")
***** assert (class (tlsinv (single ([p, NaN]), 0, 1, 1)), "single")
***** assert (class (tlsinv ([p, NaN], single (0), 1, 1)), "single")
***** assert (class (tlsinv ([p, NaN], 0, single (1), 1)), "single")
***** assert (class (tlsinv ([p, NaN], 0, 1, single (1))), "single")
***** error<tlsinv: function called with too few input arguments.> tlsinv ()
***** error<tlsinv: function called with too few input arguments.> tlsinv (1)
***** error<tlsinv: function called with too few input arguments.> tlsinv (1, 2)
***** error<tlsinv: function called with too few input arguments.> tlsinv (1, 2, 3)
***** error<tlsinv: P, MU, SIGMA, and NU must be of common size or scalars.> ...
 tlsinv (ones (3), ones (2), 1, 1)
***** error<tlsinv: P, MU, SIGMA, and NU must be of common size or scalars.> ...
 tlsinv (ones (2), 1, ones (3), 1)
***** error<tlsinv: P, MU, SIGMA, and NU must be of common size or scalars.> ...
 tlsinv (ones (2), 1, 1, ones (3))
***** error<tlsinv: P, MU, SIGMA, and NU must not be complex.> tlsinv (i, 2, 3, 4)
***** error<tlsinv: P, MU, SIGMA, and NU must not be complex.> tlsinv (2, i, 3, 4)
***** error<tlsinv: P, MU, SIGMA, and NU must not be complex.> tlsinv (2, 2, i, 4)
***** error<tlsinv: P, MU, SIGMA, and NU must not be complex.> tlsinv (2, 2, 3, i)
20 tests, 20 passed, 0 known failure, 0 skipped
[inst/dist_fun/bisapdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/bisapdf.m
***** demo
 ## Plot various PDFs from the Birnbaum-Saunders distribution
 x = 0.01:0.01:4;
 y1 = bisapdf (x, 1, 0.5);
 y2 = bisapdf (x, 1, 1);
 y3 = bisapdf (x, 1, 2);
 y4 = bisapdf (x, 1, 5);
 y5 = bisapdf (x, 1, 10);
 plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-c", x, y5, "-m")
 grid on
 ylim ([0, 1.5])
 legend ({"β = 1 ,γ = 0.5", "β = 1, γ = 1", "β = 1, γ = 2", ...
          "β = 1, γ = 5", "β = 1, γ = 10"}, "location", "northeast")
 title ("Birnbaum-Saunders PDF")
 xlabel ("values in x")
 ylabel ("density")
***** demo
 ## Plot various PDFs from the Birnbaum-Saunders distribution
 x = 0.01:0.01:6;
 y1 = bisapdf (x, 1, 0.3);
 y2 = bisapdf (x, 2, 0.3);
 y3 = bisapdf (x, 1, 0.5);
 y4 = bisapdf (x, 3, 0.5);
 y5 = bisapdf (x, 5, 0.5);
 plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-c", x, y5, "-m")
 grid on
 ylim ([0, 1.5])
 legend ({"β = 1, γ = 0.3", "β = 2, γ = 0.3", "β = 1, γ = 0.5", ...
          "β = 3, γ = 0.5", "β = 5, γ = 0.5"}, "location", "northeast")
 title ("Birnbaum-Saunders CDF")
 xlabel ("values in x")
 ylabel ("density")
***** shared x, y
 x = [-1, 0, 1, 2, Inf];
 y = [0, 0, 0.3989422804014327, 0.1647717335503959, 0];
***** assert (bisapdf (x, ones (1,5), ones (1,5)), y, eps)
***** assert (bisapdf (x, 1, 1), y, eps)
***** assert (bisapdf (x, 1, ones (1,5)), y, eps)
***** assert (bisapdf (x, ones (1,5), 1), y, eps)
***** assert (bisapdf (x, 1, [1, 1, NaN, 1, 1]), [y(1:2), NaN, y(4:5)], eps)
***** assert (bisapdf (x, [1, 1, NaN, 1, 1], 1), [y(1:2), NaN, y(4:5)], eps)
***** assert (bisapdf ([x, NaN], 1, 1), [y, NaN], eps)
***** assert (bisapdf (single ([x, NaN]), 1, 1), single ([y, NaN]), eps ("single"))
***** assert (bisapdf ([x, NaN], 1, single (1)), single ([y, NaN]), eps ("single"))
***** assert (bisapdf ([x, NaN], single (1), 1), single ([y, NaN]), eps ("single"))
***** error<bisapdf: function called with too few input arguments.> bisapdf ()
***** error<bisapdf: function called with too few input arguments.> bisapdf (1)
***** error<bisapdf: function called with too few input arguments.> bisapdf (1, 2)
***** error<bisapdf: function called with too many inputs> bisapdf (1, 2, 3, 4)
***** error<bisapdf: X, BETA, and GAMMA must be of common size or scalars.> ...
 bisapdf (ones (3), ones (2), ones(2))
***** error<bisapdf: X, BETA, and GAMMA must be of common size or scalars.> ...
 bisapdf (ones (2), ones (3), ones(2))
***** error<bisapdf: X, BETA, and GAMMA must be of common size or scalars.> ...
 bisapdf (ones (2), ones (2), ones(3))
***** error<bisapdf: X, BETA, and GAMMA must not be complex.> bisapdf (i, 4, 3)
***** error<bisapdf: X, BETA, and GAMMA must not be complex.> bisapdf (1, i, 3)
***** error<bisapdf: X, BETA, and GAMMA must not be complex.> bisapdf (1, 4, i)
20 tests, 20 passed, 0 known failure, 0 skipped
[inst/dist_fun/unidcdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/unidcdf.m
***** demo
 ## Plot various CDFs from the discrete uniform distribution
 x = 0:10;
 p1 = unidcdf (x, 5);
 p2 = unidcdf (x, 9);
 plot (x, p1, "*b", x, p2, "*g")
 grid on
 xlim ([0, 10])
 ylim ([0, 1])
 legend ({"N = 5", "N = 9"}, "location", "southeast")
 title ("Discrete uniform CDF")
 xlabel ("values in x")
 ylabel ("probability")
***** shared x, y
 x = [0 1 2.5 10 11];
 y = [0, 0.1 0.2 1.0 1.0];
***** assert (unidcdf (x, 10*ones (1,5)), y)
***** assert (unidcdf (x, 10*ones (1,5), "upper"), 1 - y)
***** assert (unidcdf (x, 10), y)
***** assert (unidcdf (x, 10, "upper"), 1 - y)
***** assert (unidcdf (x, 10*[0 1 NaN 1 1]), [NaN 0.1 NaN y(4:5)])
***** assert (unidcdf ([x(1:2) NaN Inf x(5)], 10), [y(1:2) NaN 1 y(5)])
***** assert (unidcdf ([x, NaN], 10), [y, NaN])
***** assert (unidcdf (single ([x, NaN]), 10), single ([y, NaN]))
***** assert (unidcdf ([x, NaN], single (10)), single ([y, NaN]))
***** error<unidcdf: function called with too few input arguments.> unidcdf ()
***** error<unidcdf: function called with too few input arguments.> unidcdf (1)
***** error<unidcdf: invalid argument for upper tail.> unidcdf (1, 2, 3)
***** error<unidcdf: invalid argument for upper tail.> unidcdf (1, 2, "tail")
***** error<unidcdf: X and N must be of common size or scalars.> ...
 unidcdf (ones (3), ones (2))
***** error<unidcdf: X and N must be of common size or scalars.> ...
 unidcdf (ones (2), ones (3))
***** error<unidcdf: X and N must not be complex.> unidcdf (i, 2)
***** error<unidcdf: X and N must not be complex.> unidcdf (2, i)
17 tests, 17 passed, 0 known failure, 0 skipped
[inst/dist_fun/lognpdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/lognpdf.m
***** demo
 ## Plot various PDFs from the log-normal distribution
 x = 0:0.01:5;
 y1 = lognpdf (x, 0, 1);
 y2 = lognpdf (x, 0, 0.5);
 y3 = lognpdf (x, 0, 0.25);
 plot (x, y1, "-b", x, y2, "-g", x, y3, "-r")
 grid on
 ylim ([0, 2])
 legend ({"μ = 0, σ = 1", "μ = 0, σ = 0.5", "μ = 0, σ = 0.25"}, ...
          "location", "northeast")
 title ("Log-normal PDF")
 xlabel ("values in x")
 ylabel ("density")
***** shared x, y
 x = [-1 0 e Inf];
 y = [0, 0, 1/(e*sqrt(2*pi)) * exp(-1/2), 0];
***** assert (lognpdf (x, zeros (1,4), ones (1,4)), y, eps)
***** assert (lognpdf (x, 0, ones (1,4)), y, eps)
***** assert (lognpdf (x, zeros (1,4), 1), y, eps)
***** assert (lognpdf (x, [0 1 NaN 0], 1), [0 0 NaN y(4)], eps)
***** assert (lognpdf (x, 0, [0 NaN Inf 1]), [NaN NaN NaN y(4)], eps)
***** assert (lognpdf ([x, NaN], 0, 1), [y, NaN], eps)
***** assert (lognpdf (single ([x, NaN]), 0, 1), single ([y, NaN]), eps ("single"))
***** assert (lognpdf ([x, NaN], single (0), 1), single ([y, NaN]), eps ("single"))
***** assert (lognpdf ([x, NaN], 0, single (1)), single ([y, NaN]), eps ("single"))
***** error lognpdf ()
***** error lognpdf (1,2,3,4)
***** error lognpdf (ones (3), ones (2), ones (2))
***** error lognpdf (ones (2), ones (3), ones (2))
***** error lognpdf (ones (2), ones (2), ones (3))
***** error lognpdf (i, 2, 2)
***** error lognpdf (2, i, 2)
***** error lognpdf (2, 2, i)
17 tests, 17 passed, 0 known failure, 0 skipped
[inst/dist_fun/poisscdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/poisscdf.m
***** demo
 ## Plot various CDFs from the Poisson distribution
 x = 0:20;
 p1 = poisscdf (x, 1);
 p2 = poisscdf (x, 4);
 p3 = poisscdf (x, 10);
 plot (x, p1, "*b", x, p2, "*g", x, p3, "*r")
 grid on
 ylim ([0, 1])
 legend ({"λ = 1", "λ = 4", "λ = 10"}, "location", "southeast")
 title ("Poisson CDF")
 xlabel ("values in x (number of occurences)")
 ylabel ("probability")
***** shared x, y
 x = [-1 0 1 2 Inf];
 y = [0, gammainc(1, (x(2:4) +1), "upper"), 1];
***** assert (poisscdf (x, ones (1,5)), y)
***** assert (poisscdf (x, 1), y)
***** assert (poisscdf (x, [1 0 NaN 1 1]), [y(1) 1 NaN y(4:5)])
***** assert (poisscdf ([x(1:2) NaN Inf x(5)], 1), [y(1:2) NaN 1 y(5)])
***** assert (poisscdf ([x, NaN], 1), [y, NaN])
***** assert (poisscdf (single ([x, NaN]), 1), single ([y, NaN]), eps ("single"))
***** assert (poisscdf ([x, NaN], single (1)), single ([y, NaN]), eps ("single"))
***** error<poisscdf: function called with too few input arguments.> poisscdf ()
***** error<poisscdf: function called with too few input arguments.> poisscdf (1)
***** error<poisscdf: invalid argument for upper tail.> poisscdf (1, 2, 3)
***** error<poisscdf: invalid argument for upper tail.> poisscdf (1, 2, "tail")
***** error<poisscdf: X and LAMBDA must be of common size or scalars.> ...
 poisscdf (ones (3), ones (2))
***** error<poisscdf: X and LAMBDA must be of common size or scalars.> ...
 poisscdf (ones (2), ones (3))
***** error<poisscdf: X and LAMBDA must not be complex.> poisscdf (i, 2)
***** error<poisscdf: X and LAMBDA must not be complex.> poisscdf (2, i)
15 tests, 15 passed, 0 known failure, 0 skipped
[inst/dist_fun/bisarnd.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/bisarnd.m
***** assert (size (bisarnd (1, 1)), [1 1])
***** assert (size (bisarnd (1, ones (2,1))), [2, 1])
***** assert (size (bisarnd (1, ones (2,2))), [2, 2])
***** assert (size (bisarnd (ones (2,1), 1)), [2, 1])
***** assert (size (bisarnd (ones (2,2), 1)), [2, 2])
***** assert (size (bisarnd (1, 1, 3)), [3, 3])
***** assert (size (bisarnd (1, 1, [4, 1])), [4, 1])
***** assert (size (bisarnd (1, 1, 4, 1)), [4, 1])
***** assert (size (bisarnd (1, 1, 4, 1, 5)), [4, 1, 5])
***** assert (size (bisarnd (1, 1, 0, 1)), [0, 1])
***** assert (size (bisarnd (1, 1, 1, 0)), [1, 0])
***** assert (size (bisarnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
***** assert (class (bisarnd (1, 1)), "double")
***** assert (class (bisarnd (1, single (1))), "single")
***** assert (class (bisarnd (1, single ([1, 1]))), "single")
***** assert (class (bisarnd (single (1), 1)), "single")
***** assert (class (bisarnd (single ([1, 1]), 1)), "single")
***** error<bisarnd: function called with too few input arguments.> bisarnd ()
***** error<bisarnd: function called with too few input arguments.> bisarnd (1)
***** error<bisarnd: BETA and GAMMA must be of common size or scalars.> ...
 bisarnd (ones (3), ones (2))
***** error<bisarnd: BETA and GAMMA must be of common size or scalars.> ...
 bisarnd (ones (2), ones (3))
***** error<bisarnd: BETA and GAMMA must not be complex.> bisarnd (i, 2, 3)
***** error<bisarnd: BETA and GAMMA must not be complex.> bisarnd (1, i, 3)
***** error<bisarnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 bisarnd (1, 2, -1)
***** error<bisarnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 bisarnd (1, 2, 1.2)
***** error<bisarnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 bisarnd (1, 2, ones (2))
***** error<bisarnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 bisarnd (1, 2, [2 -1 2])
***** error<bisarnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 bisarnd (1, 2, [2 0 2.5])
***** error<bisarnd: dimensions must be non-negative integers.> ...
 bisarnd (1, 2, 2, -1, 5)
***** error<bisarnd: dimensions must be non-negative integers.> ...
 bisarnd (1, 2, 2, 1.5, 5)
***** error<bisarnd: BETA and GAMMA must be scalars or of size SZ.> ...
 bisarnd (2, ones (2), 3)
***** error<bisarnd: BETA and GAMMA must be scalars or of size SZ.> ...
 bisarnd (2, ones (2), [3, 2])
***** error<bisarnd: BETA and GAMMA must be scalars or of size SZ.> ...
 bisarnd (2, ones (2), 3, 2)
33 tests, 33 passed, 0 known failure, 0 skipped
[inst/dist_fun/normpdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/normpdf.m
***** demo
 ## Plot various PDFs from the normal distribution
 x = -5:0.01:5;
 y1 = normpdf (x, 0, 0.5);
 y2 = normpdf (x, 0, 1);
 y3 = normpdf (x, 0, 2);
 y4 = normpdf (x, -2, 0.8);
 plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-c")
 grid on
 xlim ([-5, 5])
 ylim ([0, 0.9])
 legend ({"μ = 0, σ = 0.5", "μ = 0, σ = 1", ...
          "μ = 0, σ = 2", "μ = -2, σ = 0.8"}, "location", "northeast")
 title ("Normal PDF")
 xlabel ("values in x")
 ylabel ("density")
***** shared x, y
 x = [-Inf, 1, 2, Inf];
 y = 1 / sqrt (2 * pi) * exp (-(x - 1) .^ 2 / 2);
***** assert (normpdf (x, ones (1,4), ones (1,4)), y, eps)
***** assert (normpdf (x, 1, ones (1,4)), y, eps)
***** assert (normpdf (x, ones (1,4), 1), y, eps)
***** assert (normpdf (x, [0 -Inf NaN Inf], 1), [y(1) NaN NaN NaN], eps)
***** assert (normpdf (x, 1, [Inf NaN -1 0]), [NaN NaN NaN NaN], eps)
***** assert (normpdf ([x, NaN], 1, 1), [y, NaN], eps)
***** assert (normpdf (single ([x, NaN]), 1, 1), single ([y, NaN]), eps ("single"))
***** assert (normpdf ([x, NaN], single (1), 1), single ([y, NaN]), eps ("single"))
***** assert (normpdf ([x, NaN], 1, single (1)), single ([y, NaN]), eps ("single"))
***** error<normpdf: function called with too few input arguments.> normpdf ()
***** error<normpdf: X, MU, and SIGMA must be of common size or scalars.> ...
 normpdf (ones (3), ones (2), ones (2))
***** error<normpdf: X, MU, and SIGMA must be of common size or scalars.> ...
 normpdf (ones (2), ones (3), ones (2))
***** error<normpdf: X, MU, and SIGMA must be of common size or scalars.> ...
 normpdf (ones (2), ones (2), ones (3))
***** error<normpdf: X, MU, and SIGMA must not be complex.> normpdf (i, 2, 2)
***** error<normpdf: X, MU, and SIGMA must not be complex.> normpdf (2, i, 2)
***** error<normpdf: X, MU, and SIGMA must not be complex.> normpdf (2, 2, i)
16 tests, 16 passed, 0 known failure, 0 skipped
[inst/dist_fun/invgpdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/invgpdf.m
***** demo
 ## Plot various PDFs from the inverse Gaussian distribution
 x = 0:0.001:3;
 y1 = invgpdf (x, 1, 0.2);
 y2 = invgpdf (x, 1, 1);
 y3 = invgpdf (x, 1, 3);
 y4 = invgpdf (x, 3, 0.2);
 y5 = invgpdf (x, 3, 1);
 plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-c", x, y5, "-y")
 grid on
 xlim ([0, 3])
 ylim ([0, 3])
 legend ({"μ = 1, σ = 0.2", "μ = 1, σ = 1", "μ = 1, σ = 3", ...
          "μ = 3, σ = 0.2", "μ = 3, σ = 1"}, "location", "northeast")
 title ("Inverse Gaussian PDF")
 xlabel ("values in x")
 ylabel ("density")
***** shared x, y
 x = [-Inf, -1, 0, 1/2, 1, Inf];
 y = [0, 0, 0, 0.8788, 0.3989, 0];
***** assert (invgpdf ([x, NaN], 1, 1), [y, NaN], 1e-4)
***** assert (invgpdf (x, 1, [-2, -1, 0, 1, 1, 1]), [nan(1,3), y([4:6])], 1e-4)
***** assert (class (hncdf (single ([x, NaN]), 1, 1)), "single")
***** assert (class (hncdf ([x, NaN], 1, single (1))), "single")
***** assert (class (hncdf ([x, NaN], single (1), 1)), "single")
***** error<invgpdf: function called with too few input arguments.> invgpdf ()
***** error<invgpdf: function called with too few input arguments.> invgpdf (1)
***** error<invgpdf: function called with too few input arguments.> invgpdf (1, 2)
***** error<invgpdf: X, MU, and LAMBDA must be of common size or scalars.> ...
 invgpdf (1, ones (2), ones (3))
***** error<invgpdf: X, MU, and LAMBDA must be of common size or scalars.> ...
 invgpdf (ones (2), 1, ones (3))
***** error<invgpdf: X, MU, and LAMBDA must be of common size or scalars.> ...
 invgpdf (ones (2), ones (3), 1)
***** error<invgpdf: X, MU, and LAMBDA must not be complex.> invgpdf (i, 2, 3)
***** error<invgpdf: X, MU, and LAMBDA must not be complex.> invgpdf (1, i, 3)
***** error<invgpdf: X, MU, and LAMBDA must not be complex.> invgpdf (1, 2, i)
14 tests, 14 passed, 0 known failure, 0 skipped
[inst/dist_fun/expinv.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/expinv.m
***** demo
 ## Plot various iCDFs from the exponential distribution
 p = 0.001:0.001:0.999;
 x1 = expinv (p, 2/3);
 x2 = expinv (p, 1.0);
 x3 = expinv (p, 2.0);
 plot (p, x1, "-b", p, x2, "-g", p, x3, "-r")
 grid on
 ylim ([0, 5])
 legend ({"μ = 2/3", "μ = 1", "μ = 2"}, "location", "northwest")
 title ("Exponential iCDF")
 xlabel ("probability")
 ylabel ("values in x")
***** shared p
 p = [-1 0 0.3934693402873666 1 2];
***** assert (expinv (p, 2*ones (1,5)), [NaN 0 1 Inf NaN], eps)
***** assert (expinv (p, 2), [NaN 0 1 Inf NaN], eps)
***** assert (expinv (p, 2*[1 0 NaN 1 1]), [NaN NaN NaN Inf NaN], eps)
***** assert (expinv ([p(1:2) NaN p(4:5)], 2), [NaN 0 NaN Inf NaN], eps)
***** assert (expinv ([p, NaN], 2), [NaN 0 1 Inf NaN NaN], eps)
***** assert (expinv (single ([p, NaN]), 2), single ([NaN 0 1 Inf NaN NaN]), eps)
***** assert (expinv ([p, NaN], single (2)), single ([NaN 0 1 Inf NaN NaN]), eps)
***** error<expinv: invalid number of input arguments.> expinv ()
***** error<expinv: invalid number of input arguments.> expinv (1, 2 ,3 ,4 ,5)
***** error<expinv: P and MU must be of common size or scalars.> ...
 expinv (ones (3), ones (2))
***** error<expinv: invalid size of variance, PCOV must be a scalar.> ...
 expinv (2, 3, [1, 2])
***** error<expinv: variance, PCOV, is required for confidence bounds.> ...
 [x, xlo, xup] = expinv (1, 2)
***** error<expinv: invalid value for alpha.> [x, xlo, xup] = ...
 expinv (1, 2, 3, 0)
***** error<expinv: invalid value for alpha.> [x, xlo, xup] = ...
 expinv (1, 2, 3, 1.22)
***** error<expinv: invalid value for alpha.> [x, xlo, xup] = ...
 expinv (1, 2, 3, [0.05, 0.1])
***** error<expinv: P and MU must not be complex.> expinv (i, 2)
***** error<expinv: P and MU must not be complex.> expinv (2, i)
***** error<expinv: variance, PCOV, cannot be negative.> ...
 [x, xlo, xup] = expinv (1, 2, -1, 0.04)
18 tests, 18 passed, 0 known failure, 0 skipped
[inst/dist_fun/unifpdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/unifpdf.m
***** demo
 ## Plot various PDFs from the continuous uniform distribution
 x = 0:0.001:10;
 y1 = unifpdf (x, 2, 5);
 y2 = unifpdf (x, 3, 9);
 plot (x, y1, "-b", x, y2, "-g")
 grid on
 xlim ([0, 10])
 ylim ([0, 0.4])
 legend ({"a = 2, b = 5", "a = 3, b = 9"}, "location", "northeast")
 title ("Continuous uniform PDF")
 xlabel ("values in x")
 ylabel ("density")
***** shared x, y
 x = [-1 0 0.5 1 2] + 1;
 y = [0 1 1 1 0];
***** assert (unifpdf (x, ones (1,5), 2*ones (1,5)), y)
***** assert (unifpdf (x, 1, 2*ones (1,5)), y)
***** assert (unifpdf (x, ones (1,5), 2), y)
***** assert (unifpdf (x, [2 NaN 1 1 1], 2), [NaN NaN y(3:5)])
***** assert (unifpdf (x, 1, 2*[0 NaN 1 1 1]), [NaN NaN y(3:5)])
***** assert (unifpdf ([x, NaN], 1, 2), [y, NaN])
***** assert (unifpdf (x, 0, 1), [1 1 0 0 0])
***** assert (unifpdf (single ([x, NaN]), 1, 2), single ([y, NaN]))
***** assert (unifpdf (single ([x, NaN]), single (1), 2), single ([y, NaN]))
***** assert (unifpdf ([x, NaN], 1, single (2)), single ([y, NaN]))
***** error<unifpdf: function called with too few input arguments.> unifpdf ()
***** error<unifpdf: function called with too few input arguments.> unifpdf (1)
***** error<unifpdf: function called with too few input arguments.> unifpdf (1, 2)
***** error<unifpdf: X, A, and B must be of common size or scalars.> ...
 unifpdf (ones (3), ones (2), ones (2))
***** error<unifpdf: X, A, and B must be of common size or scalars.> ...
 unifpdf (ones (2), ones (3), ones (2))
***** error<unifpdf: X, A, and B must be of common size or scalars.> ...
 unifpdf (ones (2), ones (2), ones (3))
***** error<unifpdf: X, A, and B must not be complex.> unifpdf (i, 2, 2)
***** error<unifpdf: X, A, and B must not be complex.> unifpdf (2, i, 2)
***** error<unifpdf: X, A, and B must not be complex.> unifpdf (2, 2, i)
19 tests, 19 passed, 0 known failure, 0 skipped
[inst/dist_fun/tlsrnd.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/tlsrnd.m
***** assert (size (tlsrnd (1, 2, 3)), [1, 1])
***** assert (size (tlsrnd (ones (2,1), 2, 3)), [2, 1])
***** assert (size (tlsrnd (ones (2,2), 2, 3)), [2, 2])
***** assert (size (tlsrnd (1, 2, 3, 3)), [3, 3])
***** assert (size (tlsrnd (1, 2, 3, [4 1])), [4, 1])
***** assert (size (tlsrnd (1, 2, 3, 4, 1)), [4, 1])
***** assert (size (tlsrnd (1, 2, 3, 4, 1)), [4, 1])
***** assert (size (tlsrnd (1, 2, 3, 4, 1, 5)), [4, 1, 5])
***** assert (size (tlsrnd (1, 2, 3, 0, 1)), [0, 1])
***** assert (size (tlsrnd (1, 2, 3, 1, 0)), [1, 0])
***** assert (size (tlsrnd (1, 2, 3, 1, 2, 0, 5)), [1, 2, 0, 5])
***** assert (tlsrnd (1, 2, 0, 1, 1), NaN)
***** assert (tlsrnd (1, 2, [0, 0, 0], [1, 3]), [NaN, NaN, NaN])
***** assert (class (tlsrnd (1, 2, 3)), "double")
***** assert (class (tlsrnd (single (1), 2, 3)), "single")
***** assert (class (tlsrnd (single ([1, 1]), 2, 3)), "single")
***** assert (class (tlsrnd (1, single (2), 3)), "single")
***** assert (class (tlsrnd (1, single ([2, 2]), 3)), "single")
***** assert (class (tlsrnd (1, 2, single (3))), "single")
***** assert (class (tlsrnd (1, 2, single ([3, 3]))), "single")
***** error<tlsrnd: function called with too few input arguments.> tlsrnd ()
***** error<tlsrnd: function called with too few input arguments.> tlsrnd (1)
***** error<tlsrnd: function called with too few input arguments.> tlsrnd (1, 2)
***** error<tlsrnd: MU, SIGMA, and NU must be of common size or scalars.> ...
 tlsrnd (ones (3), ones (2), 1)
***** error<tlsrnd: MU, SIGMA, and NU must be of common size or scalars.> ...
 tlsrnd (ones (2), 1, ones (3))
***** error<tlsrnd: MU, SIGMA, and NU must be of common size or scalars.> ...
 tlsrnd (1, ones (2), ones (3))
***** error<tlsrnd: MU, SIGMA, and NU must not be complex.> tlsrnd (i, 2, 3)
***** error<tlsrnd: MU, SIGMA, and NU must not be complex.> tlsrnd (1, i, 3)
***** error<tlsrnd: MU, SIGMA, and NU must not be complex.> tlsrnd (1, 2, i)
***** error<tlsrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 tlsrnd (1, 2, 3, -1)
***** error<tlsrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 tlsrnd (1, 2, 3, 1.2)
***** error<tlsrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 tlsrnd (1, 2, 3, ones (2))
***** error<tlsrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 tlsrnd (1, 2, 3, [2 -1 2])
***** error<tlsrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 tlsrnd (1, 2, 3, [2 0 2.5])
***** error<tlsrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 tlsrnd (ones (2), 2, 3, ones (2))
***** error<tlsrnd: dimensions must be non-negative integers.> ...
 tlsrnd (1, 2, 3, 2, -1, 5)
***** error<tlsrnd: dimensions must be non-negative integers.> ...
 tlsrnd (1, 2, 3, 2, 1.5, 5)
***** error<tlsrnd: MU, SIGMA, and NU must be scalar or of size SZ.> ...
 tlsrnd (ones (2,2), 2, 3, 3)
***** error<tlsrnd: MU, SIGMA, and NU must be scalar or of size SZ.> ...
 tlsrnd (1, ones (2,2), 3, 3)
***** error<tlsrnd: MU, SIGMA, and NU must be scalar or of size SZ.> ...
 tlsrnd (1, 2, ones (2,2), 3)
***** error<tlsrnd: MU, SIGMA, and NU must be scalar or of size SZ.> ...
 tlsrnd (1, 2, ones (2,2), [3, 3])
***** error<tlsrnd: MU, SIGMA, and NU must be scalar or of size SZ.> ...
 tlsrnd (1, 2, ones (2,2), 2, 3)
42 tests, 42 passed, 0 known failure, 0 skipped
[inst/dist_fun/ncx2inv.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/ncx2inv.m
***** demo
 ## Plot various iCDFs from the noncentral chi-squared distribution
 p = 0.001:0.001:0.999;
 x1 = ncx2inv (p, 2, 1);
 x2 = ncx2inv (p, 2, 2);
 x3 = ncx2inv (p, 2, 3);
 x4 = ncx2inv (p, 4, 1);
 x5 = ncx2inv (p, 4, 2);
 x6 = ncx2inv (p, 4, 3);
 plot (p, x1, "-r", p, x2, "-g", p, x3, "-k", ...
       p, x4, "-m", p, x5, "-c", p, x6, "-y")
 grid on
 ylim ([0, 10])
 legend ({"df = 2, λ = 1", "df = 2, λ = 2", ...
          "df = 2, λ = 3", "df = 4, λ = 1", ...
          "df = 4, λ = 2", "df = 4, λ = 3"}, "location", "northwest")
 title ("Noncentral chi-squared iCDF")
 xlabel ("probability")
 ylabel ("values in x")
***** demo
 ## Compare the noncentral chi-squared CDF with LAMBDA = 2 to the
 ## chi-squared CDF with the same number of degrees of freedom (4).

 p = 0.001:0.001:0.999;
 x1 = ncx2inv (p, 4, 2);
 x2 = chi2inv (p, 4);
 plot (p, x1, "-", p, x2, "-");
 grid on
 ylim ([0, 10])
 legend ({"Noncentral χ^2(4,2)", "χ^2(4)"}, "location", "northwest")
 title ("Noncentral chi-squared vs chi-squared quantile functions")
 xlabel ("probability")
 ylabel ("values in x")
***** test
 x = [0,0.3443,0.7226,1.1440,1.6220,2.1770,2.8436,3.6854,4.8447,6.7701,Inf];
 assert (ncx2inv ([0:0.1:1], 2, 1), x, 1e-4);
***** test
 x = [0,0.8295,1.6001,2.3708,3.1785,4.0598,5.0644,6.2765,7.8763,10.4199,Inf];
 assert (ncx2inv ([0:0.1:1], 2, 3), x, 1e-4);
***** test
 x = [0,0.5417,1.3483,2.1796,3.0516,4.0003,5.0777,6.3726,8.0748,10.7686,Inf];
 assert (ncx2inv ([0:0.1:1], 1, 4), x, 1e-4);
***** test
 x = [0.1808, 0.6456, 1.1842, 1.7650, 2.3760, 3.0105];
 assert (ncx2inv (0.05, [1, 2, 3, 4, 5, 6], 4), x, 1e-4);
***** test
 x = [0.4887, 0.6699, 0.9012, 1.1842, 1.5164, 1.8927];
 assert (ncx2inv (0.05, 3, [1, 2, 3, 4, 5, 6]), x, 1e-4);
***** test
 x = [1.3941, 1.6824, 2.0103, 2.3760, NaN, 3.2087];
 assert (ncx2inv (0.05, 5, [1, 2, 3, 4, -1, 6]), x, 1e-4);
***** test
 assert (ncx2inv (0.996, 5, 8), 35.51298862765576, 2e-13);
***** error<ncx2inv: function called with too few input arguments.> ncx2inv ()
***** error<ncx2inv: function called with too few input arguments.> ncx2inv (1)
***** error<ncx2inv: function called with too few input arguments.> ncx2inv (1, 2)
***** error<ncx2inv: P, DF, and LAMBDA must be of common size or scalars.> ...
 ncx2inv (ones (3), ones (2), ones (2))
***** error<ncx2inv: P, DF, and LAMBDA must be of common size or scalars.> ...
 ncx2inv (ones (2), ones (3), ones (2))
***** error<ncx2inv: P, DF, and LAMBDA must be of common size or scalars.> ...
 ncx2inv (ones (2), ones (2), ones (3))
***** error<ncx2inv: P, DF, and LAMBDA must not be complex.> ncx2inv (i, 2, 2)
***** error<ncx2inv: P, DF, and LAMBDA must not be complex.> ncx2inv (2, i, 2)
***** error<ncx2inv: P, DF, and LAMBDA must not be complex.> ncx2inv (2, 2, i)
16 tests, 16 passed, 0 known failure, 0 skipped
[inst/dist_fun/hninv.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/hninv.m
***** demo
 ## Plot various iCDFs from the half-normal distribution
 p = 0.001:0.001:0.999;
 x1 = hninv (p, 0, 1);
 x2 = hninv (p, 0, 2);
 x3 = hninv (p, 0, 3);
 x4 = hninv (p, 0, 5);
 plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-c")
 grid on
 ylim ([0, 10])
 legend ({"μ = 0, σ = 1", "μ = 0, σ = 2", ...
          "μ = 0, σ = 3", "μ = 0, σ = 5"}, "location", "northwest")
 title ("Half-normal iCDF")
 xlabel ("probability")
 ylabel ("x")
***** shared p, x
 p = [0, 0.3829, 0.6827, 1];
 x = [0, 1/2, 1, Inf];
***** assert (hninv (p, 0, 1), x, 1e-4);
***** assert (hninv (p, 5, 1), x + 5, 1e-4);
***** assert (hninv (p, 0, ones (1,4)), x, 1e-4);
***** assert (hninv (p, 0, [-1, 0, 1, 1]), [NaN, NaN, x(3:4)], 1e-4)
***** assert (class (hninv (single ([p, NaN]), 0, 1)), "single")
***** assert (class (hninv ([p, NaN], single (0), 1)), "single")
***** assert (class (hninv ([p, NaN], 0, single (1))), "single")
***** error<hninv: function called with too few input arguments.> hninv (1)
***** error<hninv: function called with too few input arguments.> hninv (1, 2)
***** error<hninv: P, MU, and SIGMA must be of common size or scalars.> ...
 hninv (1, ones (2), ones (3))
***** error<hninv: P, MU, and SIGMA must be of common size or scalars.> ...
 hninv (ones (2), 1, ones (3))
***** error<hninv: P, MU, and SIGMA must be of common size or scalars.> ...
 hninv (ones (2), ones (3), 1)
***** error<hninv: P, MU, and SIGMA must not be complex.> hninv (i, 2, 3)
***** error<hninv: P, MU, and SIGMA must not be complex.> hninv (1, i, 3)
***** error<hninv: P, MU, and SIGMA must not be complex.> hninv (1, 2, i)
15 tests, 15 passed, 0 known failure, 0 skipped
[inst/dist_fun/gpinv.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/gpinv.m
***** demo
 ## Plot various iCDFs from the generalized Pareto distribution
 p = 0.001:0.001:0.999;
 x1 = gpinv (p, 1, 1, 0);
 x2 = gpinv (p, 5, 1, 0);
 x3 = gpinv (p, 20, 1, 0);
 x4 = gpinv (p, 1, 2, 0);
 x5 = gpinv (p, 5, 2, 0);
 x6 = gpinv (p, 20, 2, 0);
 plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", ...
       p, x4, "-c", p, x5, "-m", p, x6, "-k")
 grid on
 ylim ([0, 5])
 legend ({"k = 1, σ = 1, θ = 0", "k = 5, σ = 1, θ = 0", ...
          "k = 20, σ = 1, θ = 0", "k = 1, σ = 2, θ = 0", ...
          "k = 5, σ = 2, θ = 0", "k = 20, σ = 2, θ = 0"}, ...
         "location", "southeast")
 title ("Generalized Pareto iCDF")
 xlabel ("probability")
 ylabel ("values in x")
***** shared p, y1, y2, y3
 p = [-1, 0, 1/2, 1, 2];
 y1 = [NaN, 0, 0.6931471805599453, Inf, NaN];
 y2 = [NaN, 0, 1, Inf, NaN];
 y3 = [NaN, 0, 1/2, 1, NaN];
***** assert (gpinv (p, zeros (1,5), ones (1,5), zeros (1,5)), y1)
***** assert (gpinv (p, 0, 1, zeros (1,5)), y1)
***** assert (gpinv (p, 0, ones (1,5), 0), y1)
***** assert (gpinv (p, zeros (1,5), 1, 0), y1)
***** assert (gpinv (p, 0, 1, 0), y1)
***** assert (gpinv (p, 0, 1, [0, 0, NaN, 0, 0]), [y1(1:2), NaN, y1(4:5)])
***** assert (gpinv (p, 0, [1, 1, NaN, 1, 1], 0), [y1(1:2), NaN, y1(4:5)])
***** assert (gpinv (p, [0, 0, NaN, 0, 0], 1, 0), [y1(1:2), NaN, y1(4:5)])
***** assert (gpinv ([p(1:2), NaN, p(4:5)], 0, 1, 0), [y1(1:2), NaN, y1(4:5)])
***** assert (gpinv (p, ones (1,5), ones (1,5), zeros (1,5)), y2)
***** assert (gpinv (p, 1, 1, zeros (1,5)), y2)
***** assert (gpinv (p, 1, ones (1,5), 0), y2)
***** assert (gpinv (p, ones (1,5), 1, 0), y2)
***** assert (gpinv (p, 1, 1, 0), y2)
***** assert (gpinv (p, 1, 1, [0, 0, NaN, 0, 0]), [y2(1:2), NaN, y2(4:5)])
***** assert (gpinv (p, 1, [1, 1, NaN, 1, 1], 0), [y2(1:2), NaN, y2(4:5)])
***** assert (gpinv (p, [1, 1, NaN, 1, 1], 1, 0), [y2(1:2), NaN, y2(4:5)])
***** assert (gpinv ([p(1:2), NaN, p(4:5)], 1, 1, 0), [y2(1:2), NaN, y2(4:5)])
***** assert (gpinv (p, -ones (1,5), ones (1,5), zeros (1,5)), y3)
***** assert (gpinv (p, -1, 1, zeros (1,5)), y3)
***** assert (gpinv (p, -1, ones (1,5), 0), y3)
***** assert (gpinv (p, -ones (1,5), 1, 0), y3)
***** assert (gpinv (p, -1, 1, 0), y3)
***** assert (gpinv (p, -1, 1, [0, 0, NaN, 0, 0]), [y3(1:2), NaN, y3(4:5)])
***** assert (gpinv (p, -1, [1, 1, NaN, 1, 1], 0), [y3(1:2), NaN, y3(4:5)])
***** assert (gpinv (p, -[1, 1, NaN, 1, 1], 1, 0), [y3(1:2), NaN, y3(4:5)])
***** assert (gpinv ([p(1:2), NaN, p(4:5)], -1, 1, 0), [y3(1:2), NaN, y3(4:5)])
***** assert (gpinv (single ([p, NaN]), 0, 1, 0), single ([y1, NaN]))
***** assert (gpinv ([p, NaN], 0, 1, single (0)), single ([y1, NaN]))
***** assert (gpinv ([p, NaN], 0, single (1), 0), single ([y1, NaN]))
***** assert (gpinv ([p, NaN], single (0), 1, 0), single ([y1, NaN]))
***** assert (gpinv (single ([p, NaN]), 1, 1, 0), single ([y2, NaN]))
***** assert (gpinv ([p, NaN], 1, 1, single (0)), single ([y2, NaN]))
***** assert (gpinv ([p, NaN], 1, single (1), 0), single ([y2, NaN]))
***** assert (gpinv ([p, NaN], single (1), 1, 0), single ([y2, NaN]))
***** assert (gpinv (single ([p, NaN]), -1, 1, 0), single ([y3, NaN]))
***** assert (gpinv ([p, NaN], -1, 1, single (0)), single ([y3, NaN]))
***** assert (gpinv ([p, NaN], -1, single (1), 0), single ([y3, NaN]))
***** assert (gpinv ([p, NaN], single (-1), 1, 0), single ([y3, NaN]))
***** error<gpinv: function called with too few input arguments.> gpinv ()
***** error<gpinv: function called with too few input arguments.> gpinv (1)
***** error<gpinv: function called with too few input arguments.> gpinv (1, 2)
***** error<gpinv: function called with too few input arguments.> gpinv (1, 2, 3)
***** error<gpinv: P, K, SIGMA, and THETA must be of common size or scalars.> ...
 gpinv (ones (3), ones (2), ones(2), ones(2))
***** error<gpinv: P, K, SIGMA, and THETA must be of common size or scalars.> ...
 gpinv (ones (2), ones (3), ones(2), ones(2))
***** error<gpinv: P, K, SIGMA, and THETA must be of common size or scalars.> ...
 gpinv (ones (2), ones (2), ones(3), ones(2))
***** error<gpinv: P, K, SIGMA, and THETA must be of common size or scalars.> ...
 gpinv (ones (2), ones (2), ones(2), ones(3))
***** error<gpinv: P, K, SIGMA, and THETA must not be complex.> gpinv (i, 2, 3, 4)
***** error<gpinv: P, K, SIGMA, and THETA must not be complex.> gpinv (1, i, 3, 4)
***** error<gpinv: P, K, SIGMA, and THETA must not be complex.> gpinv (1, 2, i, 4)
***** error<gpinv: P, K, SIGMA, and THETA must not be complex.> gpinv (1, 2, 3, i)
51 tests, 51 passed, 0 known failure, 0 skipped
[inst/dist_fun/tripdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/tripdf.m
***** demo
 ## Plot various CDFs from the triangular distribution
 x = 0.001:0.001:10;
 y1 = tripdf (x, 3, 4, 6);
 y2 = tripdf (x, 1, 2, 5);
 y3 = tripdf (x, 2, 3, 9);
 y4 = tripdf (x, 2, 5, 9);
 plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-c")
 grid on
 xlim ([0, 10])
 legend ({"a = 3, b = 4, c = 6", "a = 1, b = 2, c = 5", ...
          "a = 2, b = 3, c = 9", "a = 2, b = 5, c = 9"}, ...
         "location", "northeast")
 title ("Triangular CDF")
 xlabel ("values in x")
 ylabel ("probability")
***** shared x, y, deps
 x = [-1, 0, 0.1, 0.5, 0.9, 1, 2] + 1;
 y = [0, 0, 0.4, 2, 0.4, 0, 0];
 deps = 2*eps;
***** assert (tripdf (x, ones (1,7), 1.5*ones (1,7), 2*ones (1,7)), y, deps)
***** assert (tripdf (x, 1*ones (1,7), 1.5, 2), y, deps)
***** assert (tripdf (x, 1, 1.5, 2*ones (1,7)), y, deps)
***** assert (tripdf (x, 1, 1.5*ones (1,7), 2), y, deps)
***** assert (tripdf (x, 1, 1.5, 2), y, deps)
***** assert (tripdf (x, [1, 1, NaN, 1, 1, 1, 1], 1.5, 2), [y(1:2), NaN, y(4:7)], deps)
***** assert (tripdf (x, 1, 1.5, 2*[1, 1, NaN, 1, 1, 1, 1]), [y(1:2), NaN, y(4:7)], deps)
***** assert (tripdf (x, 1, 1.5*[1, 1, NaN, 1, 1, 1, 1], 2), [y(1:2), NaN, y(4:7)], deps)
***** assert (tripdf ([x, NaN], 1, 1.5, 2), [y, NaN], deps)
***** assert (tripdf (single ([x, NaN]), 1, 1.5, 2), single ([y, NaN]), eps("single"))
***** assert (tripdf ([x, NaN], single (1), 1.5, 2), single ([y, NaN]), eps("single"))
***** assert (tripdf ([x, NaN], 1, 1.5, single (2)), single ([y, NaN]), eps("single"))
***** assert (tripdf ([x, NaN], 1, single (1.5), 2), single ([y, NaN]), eps("single"))
***** error<tripdf: function called with too few input arguments.> tripdf ()
***** error<tripdf: function called with too few input arguments.> tripdf (1)
***** error<tripdf: function called with too few input arguments.> tripdf (1, 2)
***** error<tripdf: function called with too few input arguments.> tripdf (1, 2, 3)
***** error<tripdf: function called with too many inputs> ...
 tripdf (1, 2, 3, 4, 5)
***** error<tripdf: X, A, B, and C must be of common size or scalars.> ...
 tripdf (ones (3), ones (2), ones(2), ones(2))
***** error<tripdf: X, A, B, and C must be of common size or scalars.> ...
 tripdf (ones (2), ones (3), ones(2), ones(2))
***** error<tripdf: X, A, B, and C must be of common size or scalars.> ...
 tripdf (ones (2), ones (2), ones(3), ones(2))
***** error<tripdf: X, A, B, and C must be of common size or scalars.> ...
 tripdf (ones (2), ones (2), ones(2), ones(3))
***** error<tripdf: X, A, B, and C must not be complex.> tripdf (i, 2, 3, 4)
***** error<tripdf: X, A, B, and C must not be complex.> tripdf (1, i, 3, 4)
***** error<tripdf: X, A, B, and C must not be complex.> tripdf (1, 2, i, 4)
***** error<tripdf: X, A, B, and C must not be complex.> tripdf (1, 2, 3, i)
26 tests, 26 passed, 0 known failure, 0 skipped
[inst/dist_fun/hygecdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/hygecdf.m
***** demo
 ## Plot various CDFs from the hypergeometric distribution
 x = 0:60;
 p1 = hygecdf (x, 500, 50, 100);
 p2 = hygecdf (x, 500, 60, 200);
 p3 = hygecdf (x, 500, 70, 300);
 plot (x, p1, "*b", x, p2, "*g", x, p3, "*r")
 grid on
 xlim ([0, 60])
 legend ({"m = 500, k = 50, n = 100", "m = 500, k = 60, n = 200", ...
          "m = 500, k = 70, n = 300"}, "location", "southeast")
 title ("Hypergeometric CDF")
 xlabel ("values in x (number of successes)")
 ylabel ("probability")
***** shared x, y
 x = [-1 0 1 2 3];
 y = [0 1/6 5/6 1 1];
***** assert (hygecdf (x, 4*ones (1,5), 2, 2), y, 5*eps)
***** assert (hygecdf (x, 4, 2*ones (1,5), 2), y, 5*eps)
***** assert (hygecdf (x, 4, 2, 2*ones (1,5)), y, 5*eps)
***** assert (hygecdf (x, 4*[1 -1 NaN 1.1 1], 2, 2), [y(1) NaN NaN NaN y(5)], 5*eps)
***** assert (hygecdf (x, 4*[1 -1 NaN 1.1 1], 2, 2, "upper"), ...
 [y(5) NaN NaN NaN y(1)], 5*eps)
***** assert (hygecdf (x, 4, 2*[1 -1 NaN 1.1 1], 2), [y(1) NaN NaN NaN y(5)], 5*eps)
***** assert (hygecdf (x, 4, 2*[1 -1 NaN 1.1 1], 2, "upper"), ...
 [y(5) NaN NaN NaN y(1)], 5*eps)
***** assert (hygecdf (x, 4, 5, 2), [NaN NaN NaN NaN NaN])
***** assert (hygecdf (x, 4, 2, 2*[1 -1 NaN 1.1 1]), [y(1) NaN NaN NaN y(5)], 5*eps)
***** assert (hygecdf (x, 4, 2, 2*[1 -1 NaN 1.1 1], "upper"), ...
 [y(5) NaN NaN NaN y(1)], 5*eps)
***** assert (hygecdf (x, 4, 2, 5), [NaN NaN NaN NaN NaN])
***** assert (hygecdf ([x(1:2) NaN x(4:5)], 4, 2, 2), [y(1:2) NaN y(4:5)], 5*eps)
***** test
 p = hygecdf (x, 10, [1 2 3 4 5], 2, "upper");
 assert (p, [1, 34/90, 2/30, 0, 0], 10*eps);
***** test
 p = hygecdf (2*x, 10, [1 2 3 4 5], 2, "upper");
 assert (p, [1, 34/90, 0, 0, 0], 10*eps);
***** assert (hygecdf ([x, NaN], 4, 2, 2), [y, NaN], 5*eps)
***** assert (hygecdf (single ([x, NaN]), 4, 2, 2), single ([y, NaN]), ...
 eps ("single"))
***** assert (hygecdf ([x, NaN], single (4), 2, 2), single ([y, NaN]), ...
 eps ("single"))
***** assert (hygecdf ([x, NaN], 4, single (2), 2), single ([y, NaN]), ...
 eps ("single"))
***** assert (hygecdf ([x, NaN], 4, 2, single (2)), single ([y, NaN]), ...
 eps ("single"))
***** error<hygecdf: function called with too few input arguments.> hygecdf ()
***** error<hygecdf: function called with too few input arguments.> hygecdf (1)
***** error<hygecdf: function called with too few input arguments.> hygecdf (1,2)
***** error<hygecdf: function called with too few input arguments.> hygecdf (1,2,3)
***** error<hygecdf: invalid argument for upper tail.> hygecdf (1,2,3,4,5)
***** error<hygecdf: invalid argument for upper tail.> hygecdf (1,2,3,4,"uper")
***** error<hygecdf: X, T, k, and N must be of common size or scalars.> ...
 hygecdf (ones (2), ones (3), 1, 1)
***** error<hygecdf: X, T, k, and N must be of common size or scalars.> ...
 hygecdf (1, ones (2), ones (3), 1)
***** error<hygecdf: X, T, k, and N must be of common size or scalars.> ...
 hygecdf (1, 1, ones (2), ones (3))
***** error<hygecdf: X, T, k, and N must not be complex.> hygecdf (i, 2, 2, 2)
***** error<hygecdf: X, T, k, and N must not be complex.> hygecdf (2, i, 2, 2)
***** error<hygecdf: X, T, k, and N must not be complex.> hygecdf (2, 2, i, 2)
***** error<hygecdf: X, T, k, and N must not be complex.> hygecdf (2, 2, 2, i)
32 tests, 32 passed, 0 known failure, 0 skipped
[inst/dist_fun/chi2cdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/chi2cdf.m
***** demo
 ## Plot various CDFs from the chi-squared distribution
 x = 0:0.01:8;
 p1 = chi2cdf (x, 1);
 p2 = chi2cdf (x, 2);
 p3 = chi2cdf (x, 3);
 p4 = chi2cdf (x, 4);
 p5 = chi2cdf (x, 6);
 p6 = chi2cdf (x, 9);
 plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", ...
       x, p4, "-c", x, p5, "-m", x, p6, "-y")
 grid on
 xlim ([0, 8])
 legend ({"df = 1", "df = 2", "df = 3", ...
          "df = 4", "df = 6", "df = 9"}, "location", "southeast")
 title ("Chi-squared CDF")
 xlabel ("values in x")
 ylabel ("probability")
***** shared x, p, u
 x = [-1, 0, 0.5, 1, 2];
 p = [0, (1 - exp (-x(2:end) / 2))];
 u = [1, 0, NaN, 0.3934693402873666, 0.6321205588285577];
***** assert (chi2cdf (x, 2 * ones (1,5)), p, eps)
***** assert (chi2cdf (x, 2), p, eps)
***** assert (chi2cdf (x, 2 * [1, 0, NaN, 1, 1]), [p(1), 1, NaN, p(4:5)], eps)
***** assert (chi2cdf (x, 2 * [1, 0, NaN, 1, 1], "upper"), ...
                        [p(1), 1, NaN, u(4:5)], eps)
***** assert (chi2cdf ([x(1:2), NaN, x(4:5)], 2), [p(1:2), NaN, p(4:5)], eps)
***** assert (chi2cdf ([x, NaN], 2), [p, NaN], eps)
***** assert (chi2cdf (single ([x, NaN]), 2), single ([p, NaN]), eps ("single"))
***** assert (chi2cdf ([x, NaN], single (2)), single ([p, NaN]), eps ("single"))
***** error<chi2cdf: function called with too few input arguments.> chi2cdf ()
***** error<chi2cdf: function called with too few input arguments.> chi2cdf (1)
***** error<chi2cdf: function called with too many inputs> chi2cdf (1, 2, 3, 4)
***** error<chi2cdf: invalid argument for upper tail.> chi2cdf (1, 2, 3)
***** error<chi2cdf: invalid argument for upper tail.> chi2cdf (1, 2, "uper")
***** error<chi2cdf: X and DF must be of common size or scalars.> ...
 chi2cdf (ones (3), ones (2))
***** error<chi2cdf: X and DF must be of common size or scalars.> ...
 chi2cdf (ones (2), ones (3))
***** error<chi2cdf: X and DF must not be complex.> chi2cdf (i, 2)
***** error<chi2cdf: X and DF must not be complex.> chi2cdf (2, i)
17 tests, 17 passed, 0 known failure, 0 skipped
[inst/dist_fun/iwishrnd.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/iwishrnd.m
***** assert(size (iwishrnd (1,2,1)), [1, 1]);
***** assert(size (iwishrnd ([],2,1)), [1, 1]);
***** assert(size (iwishrnd ([3 1; 1 3], 2.00001, [], 1)), [2, 2]);
***** assert(size (iwishrnd (eye(2), 2, [], 3)), [2, 2, 3]);
***** error iwishrnd ()
***** error iwishrnd (1)
***** error iwishrnd ([-3 1; 1 3],1)
***** error iwishrnd ([1; 1],1)
8 tests, 8 passed, 0 known failure, 0 skipped
[inst/dist_fun/fcdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/fcdf.m
***** demo
 ## Plot various CDFs from the F distribution
 x = 0.01:0.01:4;
 p1 = fcdf (x, 1, 2);
 p2 = fcdf (x, 2, 1);
 p3 = fcdf (x, 5, 2);
 p4 = fcdf (x, 10, 1);
 p5 = fcdf (x, 100, 100);
 plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", x, p4, "-c", x, p5, "-m")
 grid on
 legend ({"df1 = 1, df2 = 2", "df1 = 2, df2 = 1", ...
          "df1 = 5, df2 = 2", "df1 = 10, df2 = 1", ...
          "df1 = 100, df2 = 100"}, "location", "southeast")
 title ("F CDF")
 xlabel ("values in x")
 ylabel ("probability")
***** shared x, y
 x = [-1, 0, 0.5, 1, 2, Inf];
 y = [0, 0, 1/3, 1/2, 2/3, 1];
***** assert (fcdf (x, 2*ones (1,6), 2*ones (1,6)), y, eps)
***** assert (fcdf (x, 2, 2*ones (1,6)), y, eps)
***** assert (fcdf (x, 2*ones (1,6), 2), y, eps)
***** assert (fcdf (x, [0 NaN Inf 2 2 2], 2), [NaN NaN 0.1353352832366127 y(4:6)], eps)
***** assert (fcdf (x, 2, [0 NaN Inf 2 2 2]), [NaN NaN 0.3934693402873666 y(4:6)], eps)
***** assert (fcdf ([x(1:2) NaN x(4:6)], 2, 2), [y(1:2) NaN y(4:6)], eps)
***** assert (fcdf ([x, NaN], 2, 2), [y, NaN], eps)
***** assert (fcdf (single ([x, NaN]), 2, 2), single ([y, NaN]), eps ("single"))
***** assert (fcdf ([x, NaN], single (2), 2), single ([y, NaN]), eps ("single"))
***** assert (fcdf ([x, NaN], 2, single (2)), single ([y, NaN]), eps ("single"))
***** error<fcdf: function called with too few input arguments.> fcdf ()
***** error<fcdf: function called with too few input arguments.> fcdf (1)
***** error<fcdf: function called with too few input arguments.> fcdf (1, 2)
***** error<fcdf: invalid argument for upper tail.> fcdf (1, 2, 3, 4)
***** error<fcdf: invalid argument for upper tail.> fcdf (1, 2, 3, "tail")
***** error<fcdf: X, DF1, and DF2 must be of common size or scalars.> ...
 fcdf (ones (3), ones (2), ones (2))
***** error<fcdf: X, DF1, and DF2 must be of common size or scalars.> ...
 fcdf (ones (2), ones (3), ones (2))
***** error<fcdf: X, DF1, and DF2 must be of common size or scalars.> ...
 fcdf (ones (2), ones (2), ones (3))
***** error<fcdf: X, DF1, and DF2 must not be complex.> fcdf (i, 2, 2)
***** error<fcdf: X, DF1, and DF2 must not be complex.> fcdf (2, i, 2)
***** error<fcdf: X, DF1, and DF2 must not be complex.> fcdf (2, 2, i)
21 tests, 21 passed, 0 known failure, 0 skipped
[inst/dist_fun/nctcdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/nctcdf.m
***** demo
 ## Plot various CDFs from the noncentral Τ distribution
 x = -5:0.01:5;
 p1 = nctcdf (x, 1, 0);
 p2 = nctcdf (x, 4, 0);
 p3 = nctcdf (x, 1, 2);
 p4 = nctcdf (x, 4, 2);
 plot (x, p1, "-r", x, p2, "-g", x, p3, "-k", x, p4, "-m")
 grid on
 xlim ([-5, 5])
 legend ({"df = 1, μ = 0", "df = 4, μ = 0", ...
          "df = 1, μ = 2", "df = 4, μ = 2"}, "location", "southeast")
 title ("Noncentral Τ CDF")
 xlabel ("values in x")
 ylabel ("probability")
***** demo
 ## Compare the noncentral T CDF with MU = 1 to the T CDF
 ## with the same number of degrees of freedom (10).

 x = -5:0.1:5;
 p1 = nctcdf (x, 10, 1);
 p2 = tcdf (x, 10);
 plot (x, p1, "-", x, p2, "-")
 grid on
 xlim ([-5, 5])
 legend ({"Noncentral T(10,1)", "T(10)"}, "location", "southeast")
 title ("Noncentral T vs T CDFs")
 xlabel ("values in x")
 ylabel ("probability")
***** test
 x = -2:0.1:2;
 p = nctcdf (x, 10, 1);
 assert (p(1), 0.003302485766631558, 1e-14);
 assert (p(2), 0.004084668193532631, 1e-14);
 assert (p(3), 0.005052800319478737, 1e-14);
 assert (p(41), 0.8076115625303751, 1e-14);
***** test
 p = nctcdf (12, 10, 3);
 assert (p, 0.9997719343243797, 1e-14);
***** test
 p = nctcdf (2, 3, 2);
 assert (p, 0.4430757822176028, 1e-14);
***** test
 p = nctcdf (2, 3, 2, "upper");
 assert (p, 0.5569242177823971, 1e-14);
***** test
 p = nctcdf ([3, 6], 3, 2, "upper");
 assert (p, [0.3199728259444777, 0.07064855592441913], 1e-14);
***** error<nctcdf: function called with too few input arguments.> nctcdf ()
***** error<nctcdf: function called with too few input arguments.> nctcdf (1)
***** error<nctcdf: function called with too few input arguments.> nctcdf (1, 2)
***** error<nctcdf: invalid argument for upper tail.> nctcdf (1, 2, 3, "tail")
***** error<nctcdf: invalid argument for upper tail.> nctcdf (1, 2, 3, 4)
***** error<nctcdf: X, DF, and MU must be of common size or scalars.> ...
 nctcdf (ones (3), ones (2), ones (2))
***** error<nctcdf: X, DF, and MU must be of common size or scalars.> ...
 nctcdf (ones (2), ones (3), ones (2))
***** error<nctcdf: X, DF, and MU must be of common size or scalars.> ...
 nctcdf (ones (2), ones (2), ones (3))
***** error<nctcdf: X, DF, and MU must not be complex.> nctcdf (i, 2, 2)
***** error<nctcdf: X, DF, and MU must not be complex.> nctcdf (2, i, 2)
***** error<nctcdf: X, DF, and MU must not be complex.> nctcdf (2, 2, i)
16 tests, 16 passed, 0 known failure, 0 skipped
[inst/dist_fun/unifcdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/unifcdf.m
***** demo
 ## Plot various CDFs from the continuous uniform distribution
 x = 0:0.1:10;
 p1 = unifcdf (x, 2, 5);
 p2 = unifcdf (x, 3, 9);
 plot (x, p1, "-b", x, p2, "-g")
 grid on
 xlim ([0, 10])
 ylim ([0, 1])
 legend ({"a = 2, b = 5", "a = 3, b = 9"}, "location", "southeast")
 title ("Continuous uniform CDF")
 xlabel ("values in x")
 ylabel ("probability")
***** shared x, y
 x = [-1 0 0.5 1 2] + 1;
 y = [0 0 0.5 1 1];
***** assert (unifcdf (x, ones (1,5), 2*ones (1,5)), y)
***** assert (unifcdf (x, ones (1,5), 2*ones (1,5), "upper"), 1 - y)
***** assert (unifcdf (x, 1, 2*ones (1,5)), y)
***** assert (unifcdf (x, 1, 2*ones (1,5), "upper"), 1 - y)
***** assert (unifcdf (x, ones (1,5), 2), y)
***** assert (unifcdf (x, ones (1,5), 2, "upper"), 1 - y)
***** assert (unifcdf (x, [2 1 NaN 1 1], 2), [NaN 0 NaN 1 1])
***** assert (unifcdf (x, [2 1 NaN 1 1], 2, "upper"), 1 - [NaN 0 NaN 1 1])
***** assert (unifcdf (x, 1, 2*[0 1 NaN 1 1]), [NaN 0 NaN 1 1])
***** assert (unifcdf (x, 1, 2*[0 1 NaN 1 1], "upper"), 1 - [NaN 0 NaN 1 1])
***** assert (unifcdf ([x(1:2) NaN x(4:5)], 1, 2), [y(1:2) NaN y(4:5)])
***** assert (unifcdf ([x(1:2) NaN x(4:5)], 1, 2, "upper"), 1 - [y(1:2) NaN y(4:5)])
***** assert (unifcdf ([x, NaN], 1, 2), [y, NaN])
***** assert (unifcdf (single ([x, NaN]), 1, 2), single ([y, NaN]))
***** assert (unifcdf ([x, NaN], single (1), 2), single ([y, NaN]))
***** assert (unifcdf ([x, NaN], 1, single (2)), single ([y, NaN]))
***** error<unifcdf: function called with too few input arguments.> unifcdf ()
***** error<unifcdf: function called with too few input arguments.> unifcdf (1)
***** error<unifcdf: function called with too few input arguments.> unifcdf (1, 2)
***** error<unifcdf: invalid argument for upper tail.> unifcdf (1, 2, 3, 4)
***** error<unifcdf: invalid argument for upper tail.> unifcdf (1, 2, 3, "tail")
***** error<unifcdf: X, A, and B must be of common size or scalars.> ...
 unifcdf (ones (3), ones (2), ones (2))
***** error<unifcdf: X, A, and B must be of common size or scalars.> ...
 unifcdf (ones (2), ones (3), ones (2))
***** error<unifcdf: X, A, and B must be of common size or scalars.> ...
 unifcdf (ones (2), ones (2), ones (3))
***** error<unifcdf: X, A, and B must not be complex.> unifcdf (i, 2, 2)
***** error<unifcdf: X, A, and B must not be complex.> unifcdf (2, i, 2)
***** error<unifcdf: X, A, and B must not be complex.> unifcdf (2, 2, i)
27 tests, 27 passed, 0 known failure, 0 skipped
[inst/dist_fun/raylrnd.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/raylrnd.m
***** assert (size (raylrnd (2)), [1, 1])
***** assert (size (raylrnd (ones (2,1))), [2, 1])
***** assert (size (raylrnd (ones (2,2))), [2, 2])
***** assert (size (raylrnd (1, 3)), [3, 3])
***** assert (size (raylrnd (1, [4 1])), [4, 1])
***** assert (size (raylrnd (1, 4, 1)), [4, 1])
***** assert (size (raylrnd (1, 4, 1)), [4, 1])
***** assert (size (raylrnd (1, 4, 1, 5)), [4, 1, 5])
***** assert (size (raylrnd (1, 0, 1)), [0, 1])
***** assert (size (raylrnd (1, 1, 0)), [1, 0])
***** assert (size (raylrnd (1, 1, 2, 0, 5)), [1, 2, 0, 5])
***** assert (raylrnd (0, 1, 1), NaN)
***** assert (raylrnd ([0, 0, 0], [1, 3]), [NaN, NaN, NaN])
***** assert (class (raylrnd (2)), "double")
***** assert (class (raylrnd (single (2))), "single")
***** assert (class (raylrnd (single ([2 2]))), "single")
***** error<raylrnd: function called with too few input arguments.> raylrnd ()
***** error<raylrnd: SIGMA must not be complex.> raylrnd (i)
***** error<raylrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 raylrnd (1, -1)
***** error<raylrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 raylrnd (1, 1.2)
***** error<raylrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 raylrnd (1, ones (2))
***** error<raylrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 raylrnd (1, [2 -1 2])
***** error<raylrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 raylrnd (1, [2 0 2.5])
***** error<raylrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 raylrnd (ones (2), ones (2))
***** error<raylrnd: dimensions must be non-negative integers.> ...
 raylrnd (1, 2, -1, 5)
***** error<raylrnd: dimensions must be non-negative integers.> ...
 raylrnd (1, 2, 1.5, 5)
***** error<raylrnd: SIGMA must be scalar or of size SZ.> raylrnd (ones (2,2), 3)
***** error<raylrnd: SIGMA must be scalar or of size SZ.> raylrnd (ones (2,2), [3, 2])
***** error<raylrnd: SIGMA must be scalar or of size SZ.> raylrnd (ones (2,2), 2, 3)
29 tests, 29 passed, 0 known failure, 0 skipped
[inst/dist_fun/geopdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/geopdf.m
***** demo
 ## Plot various PDFs from the geometric distribution
 x = 0:10;
 y1 = geopdf (x, 0.2);
 y2 = geopdf (x, 0.5);
 y3 = geopdf (x, 0.7);
 plot (x, y1, "*b", x, y2, "*g", x, y3, "*r")
 grid on
 ylim ([0, 0.8])
 legend ({"ps = 0.2", "ps = 0.5", "ps = 0.7"}, "location", "northeast")
 title ("Geometric PDF")
 xlabel ("values in x (number of failures)")
 ylabel ("density")
***** shared x, y
 x = [-1 0 1 Inf];
 y = [0, 1/2, 1/4, NaN];
***** assert (geopdf (x, 0.5*ones (1,4)), y)
***** assert (geopdf (x, 0.5), y)
***** assert (geopdf (x, 0.5*[-1 NaN 4 1]), [NaN NaN NaN y(4)])
***** assert (geopdf ([x, NaN], 0.5), [y, NaN])
***** assert (geopdf (single ([x, NaN]), 0.5), single ([y, NaN]), 5*eps ("single"))
***** assert (geopdf ([x, NaN], single (0.5)), single ([y, NaN]), 5*eps ("single"))
***** error geopdf ()
***** error geopdf (1)
***** error geopdf (1,2,3)
***** error geopdf (ones (3), ones (2))
***** error geopdf (ones (2), ones (3))
***** error geopdf (i, 2)
***** error geopdf (2, i)
13 tests, 13 passed, 0 known failure, 0 skipped
[inst/dist_fun/gumbelinv.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/gumbelinv.m
***** demo
 ## Plot various iCDFs from the Gumbel distribution
 p = 0.001:0.001:0.999;
 x1 = gumbelinv (p, 0.5, 2);
 x2 = gumbelinv (p, 1.0, 2);
 x3 = gumbelinv (p, 1.5, 3);
 x4 = gumbelinv (p, 3.0, 4);
 plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-c")
 grid on
 ylim ([-5, 20])
 legend ({"μ = 0.5, β = 2", "μ = 1.0, β = 2", ...
          "μ = 1.5, β = 3", "μ = 3.0, β = 4"}, "location", "northwest")
 title ("Gumbel iCDF")
 xlabel ("probability")
 ylabel ("values in x")
***** shared p, x
 p = [0, 0.05, 0.5 0.95];
 x = [-Inf, -1.0972, 0.3665, 2.9702];
***** assert (gumbelinv (p), x, 1e-4)
***** assert (gumbelinv (p, zeros (1,4), ones (1,4)), x, 1e-4)
***** assert (gumbelinv (p, 0, ones (1,4)), x, 1e-4)
***** assert (gumbelinv (p, zeros (1,4), 1), x, 1e-4)
***** assert (gumbelinv (p, [0, -Inf, NaN, Inf], 1), [-Inf, -Inf, NaN, Inf], 1e-4)
***** assert (gumbelinv (p, 0, [Inf, NaN, -1, 0]), [-Inf, NaN, NaN, NaN], 1e-4)
***** assert (gumbelinv ([p(1:2), NaN, p(4)], 0, 1), [x(1:2), NaN, x(4)], 1e-4)
***** assert (gumbelinv ([p, NaN], 0, 1), [x, NaN], 1e-4)
***** assert (gumbelinv (single ([p, NaN]), 0, 1), single ([x, NaN]), 1e-4)
***** assert (gumbelinv ([p, NaN], single (0), 1), single ([x, NaN]), 1e-4)
***** assert (gumbelinv ([p, NaN], 0, single (1)), single ([x, NaN]), 1e-4)
 p = [0.05, 0.5, 0.95];
 x = gumbelinv(p);
***** assert (gumbelcdf(x), p, 1e-4)
***** error<gumbelinv: invalid number of input arguments.> gumbelinv ()
***** error gumbelinv (1,2,3,4,5,6)
***** error<gumbelinv: P, MU, and BETA must be of common size or scalars.> ...
 gumbelinv (ones (3), ones (2), ones (2))
***** error<gumbelinv: invalid size of covariance matrix.> ...
 [p, plo, pup] = gumbelinv (2, 3, 4, [1, 2])
***** error<gumbelinv: covariance matrix is required for confidence bounds.> ...
 [p, plo, pup] = gumbelinv (1, 2, 3)
***** error<gumbelinv: invalid value for alpha.> [p, plo, pup] = ...
 gumbelinv (1, 2, 3, [1, 0; 0, 1], 0)
***** error<gumbelinv: invalid value for alpha.> [p, plo, pup] = ...
 gumbelinv (1, 2, 3, [1, 0; 0, 1], 1.22)
***** error<gumbelinv: P, MU, and BETA must not be complex.> gumbelinv (i, 2, 2)
***** error<gumbelinv: P, MU, and BETA must not be complex.> gumbelinv (2, i, 2)
***** error<gumbelinv: P, MU, and BETA must not be complex.> gumbelinv (2, 2, i)
***** error<gumbelinv: bad covariance matrix.> ...
 [p, plo, pup] = gumbelinv (1, 2, 3, [-1, 10; -Inf, -Inf], 0.04)
23 tests, 23 passed, 0 known failure, 0 skipped
[inst/dist_fun/unidrnd.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/unidrnd.m
***** assert (size (unidrnd (2)), [1, 1])
***** assert (size (unidrnd (ones (2,1))), [2, 1])
***** assert (size (unidrnd (ones (2,2))), [2, 2])
***** assert (size (unidrnd (1, 3)), [3, 3])
***** assert (size (unidrnd (1, [4 1])), [4, 1])
***** assert (size (unidrnd (1, 4, 1)), [4, 1])
***** assert (size (unidrnd (1, 4, 1)), [4, 1])
***** assert (size (unidrnd (1, 4, 1, 5)), [4, 1, 5])
***** assert (size (unidrnd (1, 0, 1)), [0, 1])
***** assert (size (unidrnd (1, 1, 0)), [1, 0])
***** assert (size (unidrnd (1, 1, 2, 0, 5)), [1, 2, 0, 5])
***** assert (unidrnd (0, 1, 1), NaN)
***** assert (unidrnd ([0, 0, 0], [1, 3]), [NaN, NaN, NaN])
***** assert (class (unidrnd (2)), "double")
***** assert (class (unidrnd (single (2))), "single")
***** assert (class (unidrnd (single ([2 2]))), "single")
***** error<unidrnd: function called with too few input arguments.> unidrnd ()
***** error<unidrnd: N must not be complex.> unidrnd (i)
***** error<unidrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 unidrnd (1, -1)
***** error<unidrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 unidrnd (1, 1.2)
***** error<unidrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 unidrnd (1, ones (2))
***** error<unidrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 unidrnd (1, [2 -1 2])
***** error<unidrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 unidrnd (1, [2 0 2.5])
***** error<unidrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 unidrnd (ones (2), ones (2))
***** error<unidrnd: dimensions must be non-negative integers.> ...
 unidrnd (1, 2, -1, 5)
***** error<unidrnd: dimensions must be non-negative integers.> ...
 unidrnd (1, 2, 1.5, 5)
***** error<unidrnd: N must be scalar or of size SZ.> unidrnd (ones (2,2), 3)
***** error<unidrnd: N must be scalar or of size SZ.> unidrnd (ones (2,2), [3, 2])
***** error<unidrnd: N must be scalar or of size SZ.> unidrnd (ones (2,2), 2, 3)
29 tests, 29 passed, 0 known failure, 0 skipped
[inst/dist_fun/nakainv.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/nakainv.m
***** demo
 ## Plot various iCDFs from the Nakagami distribution
 p = 0.001:0.001:0.999;
 x1 = nakainv (p, 0.5, 1);
 x2 = nakainv (p, 1, 1);
 x3 = nakainv (p, 1, 2);
 x4 = nakainv (p, 1, 3);
 x5 = nakainv (p, 2, 1);
 x6 = nakainv (p, 2, 2);
 x7 = nakainv (p, 5, 1);
 plot (p, x1, "-r", p, x2, "-g", p, x3, "-y", p, x4, "-m", ...
       p, x5, "-k", p, x6, "-b", p, x7, "-c")
 grid on
 ylim ([0, 3])
 legend ({"μ = 0.5, ω = 1", "μ = 1, ω = 1", "μ = 1, ω = 2", ...
          "μ = 1, ω = 3", "μ = 2, ω = 1", "μ = 2, ω = 2", ...
          "μ = 5, ω = 1"}, "location", "northwest")
 title ("Nakagami iCDF")
 xlabel ("probability")
 ylabel ("values in x")
***** shared p, y
 p = [-Inf, -1, 0, 1/2, 1, 2, Inf];
 y = [NaN, NaN, 0, 0.83255461115769769, Inf, NaN, NaN];
***** assert (nakainv (p, ones (1,7), ones (1,7)), y, eps)
***** assert (nakainv (p, 1, 1), y, eps)
***** assert (nakainv (p, [1, 1, 1, NaN, 1, 1, 1], 1), [y(1:3), NaN, y(5:7)], eps)
***** assert (nakainv (p, 1, [1, 1, 1, NaN, 1, 1, 1]), [y(1:3), NaN, y(5:7)], eps)
***** assert (nakainv ([p, NaN], 1, 1), [y, NaN], eps)
***** assert (nakainv (single ([p, NaN]), 1, 1), single ([y, NaN]))
***** assert (nakainv ([p, NaN], single (1), 1), single ([y, NaN]))
***** assert (nakainv ([p, NaN], 1, single (1)), single ([y, NaN]))
***** error<nakainv: function called with too few input arguments.> nakainv ()
***** error<nakainv: function called with too few input arguments.> nakainv (1)
***** error<nakainv: function called with too few input arguments.> nakainv (1, 2)
***** error<nakainv: P, MU, and OMEGA must be of common size or scalars.> ...
 nakainv (ones (3), ones (2), ones(2))
***** error<nakainv: P, MU, and OMEGA must be of common size or scalars.> ...
 nakainv (ones (2), ones (3), ones(2))
***** error<nakainv: P, MU, and OMEGA must be of common size or scalars.> ...
 nakainv (ones (2), ones (2), ones(3))
***** error<nakainv: P, MU, and OMEGA must not be complex.> nakainv (i, 4, 3)
***** error<nakainv: P, MU, and OMEGA must not be complex.> nakainv (1, i, 3)
***** error<nakainv: P, MU, and OMEGA must not be complex.> nakainv (1, 4, i)
17 tests, 17 passed, 0 known failure, 0 skipped
[inst/dist_fun/bisacdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/bisacdf.m
***** demo
 ## Plot various CDFs from the Birnbaum-Saunders distribution
 x = 0.01:0.01:4;
 p1 = bisacdf (x, 1, 0.5);
 p2 = bisacdf (x, 1, 1);
 p3 = bisacdf (x, 1, 2);
 p4 = bisacdf (x, 1, 5);
 p5 = bisacdf (x, 1, 10);
 plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", x, p4, "-c", x, p5, "-m")
 grid on
 legend ({"β = 1, γ = 0.5", "β = 1, γ = 1", "β = 1, γ = 2", ...
          "β = 1, γ = 5", "β = 1, γ = 10"}, "location", "southeast")
 title ("Birnbaum-Saunders CDF")
 xlabel ("values in x")
 ylabel ("probability")
***** demo
 ## Plot various CDFs from the Birnbaum-Saunders distribution
 x = 0.01:0.01:6;
 p1 = bisacdf (x, 1, 0.3);
 p2 = bisacdf (x, 2, 0.3);
 p3 = bisacdf (x, 1, 0.5);
 p4 = bisacdf (x, 3, 0.5);
 p5 = bisacdf (x, 5, 0.5);
 plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", x, p4, "-c", x, p5, "-m")
 grid on
 legend ({"β = 1, γ = 0.3", "β = 2, γ = 0.3", "β = 1, γ = 0.5", ...
          "β = 3, γ = 0.5", "β = 5, γ = 0.5"}, "location", "southeast")
 title ("Birnbaum-Saunders CDF")
 xlabel ("values in x")
 ylabel ("probability")
***** shared x, y
 x = [-1, 0, 1, 2, Inf];
 y = [0, 0, 1/2, 0.76024993890652337, 1];
***** assert (bisacdf (x, ones (1,5), ones (1,5)), y, eps)
***** assert (bisacdf (x, 1, 1), y, eps)
***** assert (bisacdf (x, 1, ones (1,5)), y, eps)
***** assert (bisacdf (x, ones (1,5), 1), y, eps)
***** assert (bisacdf (x, 1, 1), y, eps)
***** assert (bisacdf (x, 1, [1, 1, NaN, 1, 1]), [y(1:2), NaN, y(4:5)], eps)
***** assert (bisacdf (x, [1, 1, NaN, 1, 1], 1), [y(1:2), NaN, y(4:5)], eps)
***** assert (bisacdf ([x, NaN], 1, 1), [y, NaN], eps)
***** assert (bisacdf (single ([x, NaN]), 1, 1), single ([y, NaN]), eps ("single"))
***** assert (bisacdf ([x, NaN], 1, single (1)), single ([y, NaN]), eps ("single"))
***** assert (bisacdf ([x, NaN], single (1), 1), single ([y, NaN]), eps ("single"))
***** error<bisacdf: function called with too few input arguments.> bisacdf ()
***** error<bisacdf: function called with too few input arguments.> bisacdf (1)
***** error<bisacdf: function called with too few input arguments.> bisacdf (1, 2)
***** error<bisacdf: function called with too many inputs> ...
 bisacdf (1, 2, 3, 4, 5)
***** error<bisacdf: invalid argument for upper tail.> bisacdf (1, 2, 3, "tail")
***** error<bisacdf: invalid argument for upper tail.> bisacdf (1, 2, 3, 4)
***** error<bisacdf: X, BETA, and GAMMA must be of common size or scalars.> ...
 bisacdf (ones (3), ones (2), ones(2))
***** error<bisacdf: X, BETA, and GAMMA must be of common size or scalars.> ...
 bisacdf (ones (2), ones (3), ones(2))
***** error<bisacdf: X, BETA, and GAMMA must be of common size or scalars.> ...
 bisacdf (ones (2), ones (2), ones(3))
***** error<bisacdf: X, BETA, and GAMMA must not be complex.> bisacdf (i, 4, 3)
***** error<bisacdf: X, BETA, and GAMMA must not be complex.> bisacdf (1, i, 3)
***** error<bisacdf: X, BETA, and GAMMA must not be complex.> bisacdf (1, 4, i)
23 tests, 23 passed, 0 known failure, 0 skipped
[inst/dist_fun/iwishpdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/iwishpdf.m
***** assert(iwishpdf(4, 3, 3.1), 0.04226595, 1E-7);
***** assert(iwishpdf([2 -0.3;-0.3 4], [1 0.3;0.3 1], 4), 1.60166e-05, 1E-10);
***** assert(iwishpdf([6 2 5; 2 10 -5; 5 -5 25], ...
 [9 5 5; 5 10 -8; 5 -8 22], 5.1), 4.946831e-12, 1E-17);
***** error iwishpdf ()
***** error iwishpdf (1, 2)
***** error iwishpdf (1, 2, 0)
6 tests, 6 passed, 0 known failure, 0 skipped
[inst/dist_fun/gevinv.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/gevinv.m
***** demo
 ## Plot various iCDFs from the generalized extreme value distribution
 p = 0.001:0.001:0.999;
 x1 = gevinv (p, 1, 1, 1);
 x2 = gevinv (p, 0.5, 1, 1);
 x3 = gevinv (p, 1, 1, 5);
 x4 = gevinv (p, 1, 2, 5);
 x5 = gevinv (p, 1, 5, 5);
 x6 = gevinv (p, 1, 0.5, 5);
 plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", ...
       p, x4, "-c", p, x5, "-m", p, x6, "-k")
 grid on
 ylim ([-1, 10])
 legend ({"k = 1, σ = 1, μ = 1", "k = 0.5, σ = 1, μ = 1", ...
          "k = 1, σ = 1, μ = 5", "k = 1, σ = 2, μ = 5", ...
          "k = 1, σ = 5, μ = 5", "k = 1, σ = 0.5, μ = 5"}, ...
         "location", "northwest")
 title ("Generalized extreme value iCDF")
 xlabel ("probability")
 ylabel ("values in x")
***** test
 p = 0.1:0.1:0.9;
 k = 0;
 sigma = 1;
 mu = 0;
 x = gevinv (p, k, sigma, mu);
 c = gevcdf(x, k, sigma, mu);
 assert (c, p, 0.001);
***** test
 p = 0.1:0.1:0.9;
 k = 1;
 sigma = 1;
 mu = 0;
 x = gevinv (p, k, sigma, mu);
 c = gevcdf(x, k, sigma, mu);
 assert (c, p, 0.001);
***** test
 p = 0.1:0.1:0.9;
 k = 0.3;
 sigma = 1;
 mu = 0;
 x = gevinv (p, k, sigma, mu);
 c = gevcdf(x, k, sigma, mu);
 assert (c, p, 0.001);
***** error<gevinv: function called with too few input arguments.> gevinv ()
***** error<gevinv: function called with too few input arguments.> gevinv (1)
***** error<gevinv: function called with too few input arguments.> gevinv (1, 2)
***** error<gevinv: function called with too few input arguments.> gevinv (1, 2, 3)
***** error<gevinv: P, K, SIGMA, and MU must be of common size or scalars.> ...
 gevinv (ones (3), ones (2), ones(2), ones(2))
***** error<gevinv: P, K, SIGMA, and MU must be of common size or scalars.> ...
 gevinv (ones (2), ones (3), ones(2), ones(2))
***** error<gevinv: P, K, SIGMA, and MU must be of common size or scalars.> ...
 gevinv (ones (2), ones (2), ones(3), ones(2))
***** error<gevinv: P, K, SIGMA, and MU must be of common size or scalars.> ...
 gevinv (ones (2), ones (2), ones(2), ones(3))
***** error<gevinv: P, K, SIGMA, and MU must not be complex.> gevinv (i, 2, 3, 4)
***** error<gevinv: P, K, SIGMA, and MU must not be complex.> gevinv (1, i, 3, 4)
***** error<gevinv: P, K, SIGMA, and MU must not be complex.> gevinv (1, 2, i, 4)
***** error<gevinv: P, K, SIGMA, and MU must not be complex.> gevinv (1, 2, 3, i)
15 tests, 15 passed, 0 known failure, 0 skipped
[inst/dist_fun/logirnd.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/logirnd.m
***** assert (size (logirnd (1, 1)), [1 1])
***** assert (size (logirnd (1, ones (2,1))), [2, 1])
***** assert (size (logirnd (1, ones (2,2))), [2, 2])
***** assert (size (logirnd (ones (2,1), 1)), [2, 1])
***** assert (size (logirnd (ones (2,2), 1)), [2, 2])
***** assert (size (logirnd (1, 1, 3)), [3, 3])
***** assert (size (logirnd (1, 1, [4, 1])), [4, 1])
***** assert (size (logirnd (1, 1, 4, 1)), [4, 1])
***** assert (size (logirnd (1, 1, 4, 1, 5)), [4, 1, 5])
***** assert (size (logirnd (1, 1, 0, 1)), [0, 1])
***** assert (size (logirnd (1, 1, 1, 0)), [1, 0])
***** assert (size (logirnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
***** assert (class (logirnd (1, 1)), "double")
***** assert (class (logirnd (1, single (1))), "single")
***** assert (class (logirnd (1, single ([1, 1]))), "single")
***** assert (class (logirnd (single (1), 1)), "single")
***** assert (class (logirnd (single ([1, 1]), 1)), "single")
***** error<logirnd: function called with too few input arguments.> logirnd ()
***** error<logirnd: function called with too few input arguments.> logirnd (1)
***** error<logirnd: MU and SIGMA must be of common size or scalars.> ...
 logirnd (ones (3), ones (2))
***** error<logirnd: MU and SIGMA must be of common size or scalars.> ...
 logirnd (ones (2), ones (3))
***** error<logirnd: MU and SIGMA must not be complex.> logirnd (i, 2, 3)
***** error<logirnd: MU and SIGMA must not be complex.> logirnd (1, i, 3)
***** error<logirnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 logirnd (1, 2, -1)
***** error<logirnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 logirnd (1, 2, 1.2)
***** error<logirnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 logirnd (1, 2, ones (2))
***** error<logirnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 logirnd (1, 2, [2 -1 2])
***** error<logirnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 logirnd (1, 2, [2 0 2.5])
***** error<logirnd: dimensions must be non-negative integers.> ...
 logirnd (1, 2, 2, -1, 5)
***** error<logirnd: dimensions must be non-negative integers.> ...
 logirnd (1, 2, 2, 1.5, 5)
***** error<logirnd: MU and SIGMA must be scalars or of size SZ.> ...
 logirnd (2, ones (2), 3)
***** error<logirnd: MU and SIGMA must be scalars or of size SZ.> ...
 logirnd (2, ones (2), [3, 2])
***** error<logirnd: MU and SIGMA must be scalars or of size SZ.> ...
 logirnd (2, ones (2), 3, 2)
33 tests, 33 passed, 0 known failure, 0 skipped
[inst/dist_fun/wishrnd.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/wishrnd.m
***** assert(size (wishrnd (1,2)), [1, 1]);
***** assert(size (wishrnd (1,2,[])), [1, 1]);
***** assert(size (wishrnd (1,2,1)), [1, 1]);
***** assert(size (wishrnd ([],2,1)), [1, 1]);
***** assert(size (wishrnd ([3 1; 1 3], 2.00001, [], 1)), [2, 2]);
***** assert(size (wishrnd (eye(2), 2, [], 3)), [2, 2, 3]);
***** error wishrnd ()
***** error wishrnd (1)
***** error wishrnd ([1; 1], 2)
9 tests, 9 passed, 0 known failure, 0 skipped
[inst/dist_fun/ncfpdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/ncfpdf.m
***** demo
 ## Plot various PDFs from the noncentral F distribution
 x = 0:0.01:5;
 y1 = ncfpdf (x, 2, 5, 1);
 y2 = ncfpdf (x, 2, 5, 2);
 y3 = ncfpdf (x, 5, 10, 1);
 y4 = ncfpdf (x, 10, 20, 10);
 plot (x, y1, "-r", x, y2, "-g", x, y3, "-k", x, y4, "-m")
 grid on
 xlim ([0, 5])
 ylim ([0, 0.8])
 legend ({"df1 = 2, df2 = 5, λ = 1", "df1 = 2, df2 = 5, λ = 2", ...
          "df1 = 5, df2 = 10, λ = 1", "df1 = 10, df2 = 20, λ = 10"}, ...
         "location", "northeast")
 title ("Noncentral F PDF")
 xlabel ("values in x")
 ylabel ("density")
***** demo
 ## Compare the noncentral F PDF with LAMBDA = 10 to the F PDF with the
 ## same number of numerator and denominator degrees of freedom (5, 20)

 x = 0.01:0.1:10.01;
 y1 = ncfpdf (x, 5, 20, 10);
 y2 = fpdf (x, 5, 20);
 plot (x, y1, "-", x, y2, "-");
 grid on
 xlim ([0, 10])
 ylim ([0, 0.8])
 legend ({"Noncentral F(5,20,10)", "F(5,20)"}, "location", "northeast")
 title ("Noncentral F vs F PDFs")
 xlabel ("values in x")
 ylabel ("density")
***** shared x1, df1, df2, lambda
 x1 = [-Inf, 2, NaN, 4, Inf];
 df1 = [2, 0, -1, 1, 4];
 df2 = [2, 4, 5, 6, 8];
 lambda = [1, NaN, 3, -1, 2];
***** assert (ncfpdf (x1, df1, df2, lambda), [0, NaN, NaN, NaN, NaN]);
***** assert (ncfpdf (x1, df1, df2, 1), [0, NaN, NaN, ...
                                   0.05607937264237208, NaN], 1e-14);
***** assert (ncfpdf (x1, df1, df2, 3), [0, NaN, NaN, ...
                                   0.080125760971946518, NaN], 1e-14);
***** assert (ncfpdf (x1, df1, df2, 2), [0, NaN, NaN, ...
                                   0.0715902008258656, NaN], 1e-14);
***** assert (ncfpdf (x1, 3, 5, lambda), [0, NaN, NaN, NaN, NaN]);
***** assert (ncfpdf (2, df1, df2, lambda), [0.1254046999837947, NaN, NaN, ...
                                      NaN, 0.2152571783045893], 1e-14);
***** assert (ncfpdf (4, df1, df2, lambda), [0.05067089541001374, NaN, NaN, ...
                                      NaN, 0.05560846335398539], 1e-14);
***** error<ncfpdf: function called with too few input arguments.> ncfpdf ()
***** error<ncfpdf: function called with too few input arguments.> ncfpdf (1)
***** error<ncfpdf: function called with too few input arguments.> ncfpdf (1, 2)
***** error<ncfpdf: function called with too few input arguments.> ncfpdf (1, 2, 3)
***** error<ncfpdf: X, DF1, DF2, and LAMBDA must be of common size or scalars.> ...
 ncfpdf (ones (3), ones (2), ones (2), ones (2))
***** error<ncfpdf: X, DF1, DF2, and LAMBDA must be of common size or scalars.> ...
 ncfpdf (ones (2), ones (3), ones (2), ones (2))
***** error<ncfpdf: X, DF1, DF2, and LAMBDA must be of common size or scalars.> ...
 ncfpdf (ones (2), ones (2), ones (3), ones (2))
***** error<ncfpdf: X, DF1, DF2, and LAMBDA must be of common size or scalars.> ...
 ncfpdf (ones (2), ones (2), ones (2), ones (3))
***** error<ncfpdf: X, DF1, DF2, and LAMBDA must not be complex.> ncfpdf (i, 2, 2, 2)
***** error<ncfpdf: X, DF1, DF2, and LAMBDA must not be complex.> ncfpdf (2, i, 2, 2)
***** error<ncfpdf: X, DF1, DF2, and LAMBDA must not be complex.> ncfpdf (2, 2, i, 2)
***** error<ncfpdf: X, DF1, DF2, and LAMBDA must not be complex.> ncfpdf (2, 2, 2, i)
19 tests, 19 passed, 0 known failure, 0 skipped
[inst/dist_fun/chi2inv.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/chi2inv.m
***** demo
 ## Plot various iCDFs from the chi-squared distribution
 p = 0.001:0.001:0.999;
 x1 = chi2inv (p, 1);
 x2 = chi2inv (p, 2);
 x3 = chi2inv (p, 3);
 x4 = chi2inv (p, 4);
 x5 = chi2inv (p, 6);
 x6 = chi2inv (p, 9);
 plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", ...
       p, x4, "-c", p, x5, "-m", p, x6, "-y")
 grid on
 ylim ([0, 8])
 legend ({"df = 1", "df = 2", "df = 3", ...
          "df = 4", "df = 6", "df = 9"}, "location", "northwest")
 title ("Chi-squared iCDF")
 xlabel ("probability")
 ylabel ("values in x")
***** shared p
 p = [-1 0 0.3934693402873666 1 2];
***** assert (chi2inv (p, 2*ones (1,5)), [NaN 0 1 Inf NaN], 5*eps)
***** assert (chi2inv (p, 2), [NaN 0 1 Inf NaN], 5*eps)
***** assert (chi2inv (p, 2*[0 1 NaN 1 1]), [NaN 0 NaN Inf NaN], 5*eps)
***** assert (chi2inv ([p(1:2) NaN p(4:5)], 2), [NaN 0 NaN Inf NaN], 5*eps)
***** assert (chi2inv ([p, NaN], 2), [NaN 0 1 Inf NaN NaN], 5*eps)
***** assert (chi2inv (single ([p, NaN]), 2), single ([NaN 0 1 Inf NaN NaN]), 5*eps ("single"))
***** assert (chi2inv ([p, NaN], single (2)), single ([NaN 0 1 Inf NaN NaN]), 5*eps ("single"))
***** error<chi2inv: function called with too few input arguments.> chi2inv ()
***** error<chi2inv: function called with too few input arguments.> chi2inv (1)
***** error<chi2inv: function called with too many inputs> chi2inv (1,2,3)
***** error<chi2inv: P and DF must be of common size or scalars.> ...
 chi2inv (ones (3), ones (2))
***** error<chi2inv: P and DF must be of common size or scalars.> ...
 chi2inv (ones (2), ones (3))
***** error<chi2inv: P and DF must not be complex.> chi2inv (i, 2)
***** error<chi2inv: P and DF must not be complex.> chi2inv (2, i)
14 tests, 14 passed, 0 known failure, 0 skipped
[inst/dist_fun/expcdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/expcdf.m
***** demo
 ## Plot various CDFs from the exponential distribution
 x = 0:0.01:5;
 p1 = expcdf (x, 2/3);
 p2 = expcdf (x, 1.0);
 p3 = expcdf (x, 2.0);
 plot (x, p1, "-b", x, p2, "-g", x, p3, "-r")
 grid on
 legend ({"μ = 2/3", "μ = 1", "μ = 2"}, "location", "southeast")
 title ("Exponential CDF")
 xlabel ("values in x")
 ylabel ("probability")
***** shared x, p
 x = [-1 0 0.5 1 Inf];
 p = [0, 1 - exp(-x(2:end)/2)];
***** assert (expcdf (x, 2 * ones (1, 5)), p, 1e-16)
***** assert (expcdf (x, 2), p, 1e-16)
***** assert (expcdf (x, 2 * [1, 0, NaN, 1, 1]), [0, NaN, NaN, p(4:5)], 1e-16)
***** assert (expcdf ([x, NaN], 2), [p, NaN], 1e-16)
***** assert (expcdf (single ([x, NaN]), 2), single ([p, NaN]))
***** assert (expcdf ([x, NaN], single (2)), single ([p, NaN]))
***** test
 [p, plo, pup] = expcdf (1, 2, 3);
 assert (p, 0.39346934028737, 1e-14);
 assert (plo, 0.08751307220484, 1e-14);
 assert (pup, 0.93476821257933, 1e-14);
***** test
 [p, plo, pup] = expcdf (1, 2, 2, 0.1);
 assert (p, 0.39346934028737, 1e-14);
 assert (plo, 0.14466318041675, 1e-14);
 assert (pup, 0.79808291849140, 1e-14);
***** test
 [p, plo, pup] = expcdf (1, 2, 2, 0.1, "upper");
 assert (p, 0.60653065971263, 1e-14);
 assert (plo, 0.20191708150860, 1e-14);
 assert (pup, 0.85533681958325, 1e-14);
***** error<expcdf: invalid number of input arguments.> expcdf ()
***** error<expcdf: invalid number of input arguments.> expcdf (1, 2 ,3 ,4 ,5, 6)
***** error<expcdf: invalid argument for upper tail.> expcdf (1, 2, 3, 4, "uper")
***** error<expcdf: X and MU must be of common size or scalars.> ...
 expcdf (ones (3), ones (2))
***** error<expcdf: invalid size of variance, PCOV must be a scalar.> ...
 expcdf (2, 3, [1, 2])
***** error<expcdf: variance, PCOV, is required for confidence bounds.> ...
 [p, plo, pup] = expcdf (1, 2)
***** error<expcdf: invalid value for alpha.> [p, plo, pup] = ...
 expcdf (1, 2, 3, 0)
***** error<expcdf: invalid value for alpha.> [p, plo, pup] = ...
 expcdf (1, 2, 3, 1.22)
***** error<expcdf: invalid value for alpha.> [p, plo, pup] = ...
 expcdf (1, 2, 3, "alpha", "upper")
***** error<expcdf: X and MU must not be complex.> expcdf (i, 2)
***** error<expcdf: X and MU must not be complex.> expcdf (2, i)
***** error<expcdf: variance, PCOV, cannot be negative.> ...
 [p, plo, pup] = expcdf (1, 2, -1, 0.04)
21 tests, 21 passed, 0 known failure, 0 skipped
[inst/dist_fun/gpcdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/gpcdf.m
***** demo
 ## Plot various CDFs from the generalized Pareto distribution
 x = 0:0.001:5;
 p1 = gpcdf (x, 1, 1, 0);
 p2 = gpcdf (x, 5, 1, 0);
 p3 = gpcdf (x, 20, 1, 0);
 p4 = gpcdf (x, 1, 2, 0);
 p5 = gpcdf (x, 5, 2, 0);
 p6 = gpcdf (x, 20, 2, 0);
 plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", ...
       x, p4, "-c", x, p5, "-m", x, p6, "-k")
 grid on
 xlim ([0, 5])
 legend ({"k = 1, σ = 1, θ = 0", "k = 5, σ = 1, θ = 0", ...
          "k = 20, σ = 1, θ = 0", "k = 1, σ = 2, θ = 0", ...
          "k = 5, σ = 2, θ = 0", "k = 20, σ = 2, θ = 0"}, ...
         "location", "northwest")
 title ("Generalized Pareto CDF")
 xlabel ("values in x")
 ylabel ("probability")
***** shared x, y1, y1u, y2, y2u, y3, y3u
 x = [-Inf, -1, 0, 1/2, 1, Inf];
 y1 = [0, 0, 0, 0.3934693402873666, 0.6321205588285577, 1];
 y1u = [1, 1, 1, 0.6065306597126334, 0.3678794411714423, 0];
 y2 = [0, 0, 0, 1/3, 1/2, 1];
 y2u = [1, 1, 1, 2/3, 1/2, 0];
 y3 = [0, 0, 0, 1/2, 1, 1];
 y3u = [1, 1, 1, 1/2, 0, 0];
***** assert (gpcdf (x, zeros (1,6), ones (1,6), zeros (1,6)), y1, eps)
***** assert (gpcdf (x, 0, 1, zeros (1,6)), y1, eps)
***** assert (gpcdf (x, 0, ones (1,6), 0), y1, eps)
***** assert (gpcdf (x, zeros (1,6), 1, 0), y1, eps)
***** assert (gpcdf (x, 0, 1, 0), y1, eps)
***** assert (gpcdf (x, 0, 1, [0, 0, 0, NaN, 0, 0]), [y1(1:3), NaN, y1(5:6)], eps)
***** assert (gpcdf (x, 0, [1, 1, 1, NaN, 1, 1], 0), [y1(1:3), NaN, y1(5:6)], eps)
***** assert (gpcdf (x, [0, 0, 0, NaN, 0, 0], 1, 0), [y1(1:3), NaN, y1(5:6)], eps)
***** assert (gpcdf ([x(1:3), NaN, x(5:6)], 0, 1, 0), [y1(1:3), NaN, y1(5:6)], eps)
***** assert (gpcdf (x, zeros (1,6), ones (1,6), zeros (1,6), "upper"), y1u, eps)
***** assert (gpcdf (x, 0, 1, zeros (1,6), "upper"), y1u, eps)
***** assert (gpcdf (x, 0, ones (1,6), 0, "upper"), y1u, eps)
***** assert (gpcdf (x, zeros (1,6), 1, 0, "upper"), y1u, eps)
***** assert (gpcdf (x, 0, 1, 0, "upper"), y1u, eps)
***** assert (gpcdf (x, ones (1,6), ones (1,6), zeros (1,6)), y2, eps)
***** assert (gpcdf (x, 1, 1, zeros (1,6)), y2, eps)
***** assert (gpcdf (x, 1, ones (1,6), 0), y2, eps)
***** assert (gpcdf (x, ones (1,6), 1, 0), y2, eps)
***** assert (gpcdf (x, 1, 1, 0), y2, eps)
***** assert (gpcdf (x, 1, 1, [0, 0, 0, NaN, 0, 0]), [y2(1:3), NaN, y2(5:6)], eps)
***** assert (gpcdf (x, 1, [1, 1, 1, NaN, 1, 1], 0), [y2(1:3), NaN, y2(5:6)], eps)
***** assert (gpcdf (x, [1, 1, 1, NaN, 1, 1], 1, 0), [y2(1:3), NaN, y2(5:6)], eps)
***** assert (gpcdf ([x(1:3), NaN, x(5:6)], 1, 1, 0), [y2(1:3), NaN, y2(5:6)], eps)
***** assert (gpcdf (x, ones (1,6), ones (1,6), zeros (1,6), "upper"), y2u, eps)
***** assert (gpcdf (x, 1, 1, zeros (1,6), "upper"), y2u, eps)
***** assert (gpcdf (x, 1, ones (1,6), 0, "upper"), y2u, eps)
***** assert (gpcdf (x, ones (1,6), 1, 0, "upper"), y2u, eps)
***** assert (gpcdf (x, 1, 1, 0, "upper"), y2u, eps)
***** assert (gpcdf (x, 1, 1, [0, 0, 0, NaN, 0, 0], "upper"), ...
                        [y2u(1:3), NaN, y2u(5:6)], eps)
***** assert (gpcdf (x, 1, [1, 1, 1, NaN, 1, 1], 0, "upper"), ...
                        [y2u(1:3), NaN, y2u(5:6)], eps)
***** assert (gpcdf (x, [1, 1, 1, NaN, 1, 1], 1, 0, "upper"), ...
                        [y2u(1:3), NaN, y2u(5:6)], eps)
***** assert (gpcdf ([x(1:3), NaN, x(5:6)], 1, 1, 0, "upper"), ...
                        [y2u(1:3), NaN, y2u(5:6)], eps)
***** assert (gpcdf (x, -ones (1,6), ones (1,6), zeros (1,6)), y3, eps)
***** assert (gpcdf (x, -1, 1, zeros (1,6)), y3, eps)
***** assert (gpcdf (x, -1, ones (1,6), 0), y3, eps)
***** assert (gpcdf (x, -ones (1,6), 1, 0), y3, eps)
***** assert (gpcdf (x, -1, 1, 0), y3, eps)
***** assert (gpcdf (x, -1, 1, [0, 0, 0, NaN, 0, 0]), [y3(1:3), NaN, y3(5:6)], eps)
***** assert (gpcdf (x, -1, [1, 1, 1, NaN, 1, 1], 0), [y3(1:3), NaN, y3(5:6)], eps)
***** assert (gpcdf (x, [-1, -1, -1, NaN, -1, -1], 1, 0), [y3(1:3), NaN, y3(5:6)], eps)
***** assert (gpcdf ([x(1:3), NaN, x(5:6)], -1, 1, 0), [y3(1:3), NaN, y3(5:6)], eps)
***** assert (gpcdf (x, -ones (1,6), ones (1,6), zeros (1,6), "upper"), y3u, eps)
***** assert (gpcdf (x, -1, 1, zeros (1,6), "upper"), y3u, eps)
***** assert (gpcdf (x, -1, ones (1,6), 0, "upper"), y3u, eps)
***** assert (gpcdf (x, -ones (1,6), 1, 0, "upper"), y3u, eps)
***** assert (gpcdf (x, -1, 1, 0, "upper"), y3u, eps)
***** assert (gpcdf (x, -1, 1, [0, 0, 0, NaN, 0, 0], "upper"), ...
                         [y3u(1:3), NaN, y3u(5:6)], eps)
***** assert (gpcdf (x, -1, [1, 1, 1, NaN, 1, 1], 0, "upper"), ...
                          [y3u(1:3), NaN, y3u(5:6)], eps)
***** assert (gpcdf (x, [-1, -1, -1, NaN, -1, -1], 1, 0, "upper"), ...
                          [y3u(1:3), NaN, y3u(5:6)], eps)
***** assert (gpcdf ([x(1:3), NaN, x(5:6)], -1, 1, 0, "upper"), ...
                          [y3u(1:3), NaN, y3u(5:6)], eps)
***** assert (gpcdf (single ([x, NaN]), 0, 1, 0), single ([y1, NaN]), eps("single"))
***** assert (gpcdf ([x, NaN], 0, 1, single (0)), single ([y1, NaN]), eps("single"))
***** assert (gpcdf ([x, NaN], 0, single (1), 0), single ([y1, NaN]), eps("single"))
***** assert (gpcdf ([x, NaN], single (0), 1, 0), single ([y1, NaN]), eps("single"))
***** assert (gpcdf (single ([x, NaN]), 1, 1, 0), single ([y2, NaN]), eps("single"))
***** assert (gpcdf ([x, NaN], 1, 1, single (0)), single ([y2, NaN]), eps("single"))
***** assert (gpcdf ([x, NaN], 1, single (1), 0), single ([y2, NaN]), eps("single"))
***** assert (gpcdf ([x, NaN], single (1), 1, 0), single ([y2, NaN]), eps("single"))
***** assert (gpcdf (single ([x, NaN]), -1, 1, 0), single ([y3, NaN]), eps("single"))
***** assert (gpcdf ([x, NaN], -1, 1, single (0)), single ([y3, NaN]), eps("single"))
***** assert (gpcdf ([x, NaN], -1, single (1), 0), single ([y3, NaN]), eps("single"))
***** assert (gpcdf ([x, NaN], single (-1), 1, 0), single ([y3, NaN]), eps("single"))
***** error<gpcdf: function called with too few input arguments.> gpcdf ()
***** error<gpcdf: function called with too few input arguments.> gpcdf (1)
***** error<gpcdf: function called with too few input arguments.> gpcdf (1, 2)
***** error<gpcdf: function called with too few input arguments.> gpcdf (1, 2, 3)
***** error<gpcdf: invalid argument for upper tail.> gpcdf (1, 2, 3, 4, "tail")
***** error<gpcdf: invalid argument for upper tail.> gpcdf (1, 2, 3, 4, 5)
***** error<gpcdf: X, K, SIGMA, and THETA must be of common size or scalars.> ...
 gpcdf (ones (3), ones (2), ones(2), ones(2))
***** error<gpcdf: X, K, SIGMA, and THETA must be of common size or scalars.> ...
 gpcdf (ones (2), ones (3), ones(2), ones(2))
***** error<gpcdf: X, K, SIGMA, and THETA must be of common size or scalars.> ...
 gpcdf (ones (2), ones (2), ones(3), ones(2))
***** error<gpcdf: X, K, SIGMA, and THETA must be of common size or scalars.> ...
 gpcdf (ones (2), ones (2), ones(2), ones(3))
***** error<gpcdf: X, K, SIGMA, and THETA must not be complex.> gpcdf (i, 2, 3, 4)
***** error<gpcdf: X, K, SIGMA, and THETA must not be complex.> gpcdf (1, i, 3, 4)
***** error<gpcdf: X, K, SIGMA, and THETA must not be complex.> gpcdf (1, 2, i, 4)
***** error<gpcdf: X, K, SIGMA, and THETA must not be complex.> gpcdf (1, 2, 3, i)
76 tests, 76 passed, 0 known failure, 0 skipped
[inst/dist_fun/burrpdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/burrpdf.m
***** demo
 ## Plot various PDFs from the Burr type XII distribution
 x = 0.001:0.001:3;
 y1 = burrpdf (x, 1, 1, 1);
 y2 = burrpdf (x, 1, 1, 2);
 y3 = burrpdf (x, 1, 1, 3);
 y4 = burrpdf (x, 1, 2, 1);
 y5 = burrpdf (x, 1, 3, 1);
 y6 = burrpdf (x, 1, 0.5, 2);
 plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", ...
       x, y4, "-c", x, y5, "-m", x, y6, "-k")
 grid on
 ylim ([0, 2])
 legend ({"λ = 1, c = 1, k = 1", "λ = 1, c = 1, k = 2", ...
          "λ = 1, c = 1, k = 3", "λ = 1, c = 2, k = 1", ...
          "λ = 1, c = 3, k = 1", "λ = 1, c = 0.5, k = 2"}, ...
         "location", "northeast")
 title ("Burr type XII PDF")
 xlabel ("values in x")
 ylabel ("density")
***** shared x, y
 x = [-1, 0, 1, 2, Inf];
 y = [0, 1, 1/4, 1/9, 0];
***** assert (burrpdf (x, ones(1,5), ones (1,5), ones (1,5)), y)
***** assert (burrpdf (x, 1, 1, 1), y)
***** assert (burrpdf (x, [1, 1, NaN, 1, 1], 1, 1), [y(1:2), NaN, y(4:5)])
***** assert (burrpdf (x, 1, [1, 1, NaN, 1, 1], 1), [y(1:2), NaN, y(4:5)])
***** assert (burrpdf (x, 1, 1, [1, 1, NaN, 1, 1]), [y(1:2), NaN, y(4:5)])
***** assert (burrpdf ([x, NaN], 1, 1, 1), [y, NaN])
***** assert (burrpdf (single ([x, NaN]), 1, 1, 1), single ([y, NaN]))
***** assert (burrpdf ([x, NaN], single (1), 1, 1), single ([y, NaN]))
***** assert (burrpdf ([x, NaN], 1, single (1), 1), single ([y, NaN]))
***** assert (burrpdf ([x, NaN], 1, 1, single (1)), single ([y, NaN]))
***** error<burrpdf: function called with too few input arguments.> burrpdf ()
***** error<burrpdf: function called with too few input arguments.> burrpdf (1)
***** error<burrpdf: function called with too few input arguments.> burrpdf (1, 2)
***** error<burrpdf: function called with too few input arguments.> burrpdf (1, 2, 3)
***** error<burrpdf: function called with too many inputs> ...
 burrpdf (1, 2, 3, 4, 5)
***** error<burrpdf: X, LAMBDA, C, and K must be of common size or scalars.> ...
 burrpdf (ones (3), ones (2), ones(2), ones(2))
***** error<burrpdf: X, LAMBDA, C, and K must be of common size or scalars.> ...
 burrpdf (ones (2), ones (3), ones(2), ones(2))
***** error<burrpdf: X, LAMBDA, C, and K must be of common size or scalars.> ...
 burrpdf (ones (2), ones (2), ones(3), ones(2))
***** error<burrpdf: X, LAMBDA, C, and K must be of common size or scalars.> ...
 burrpdf (ones (2), ones (2), ones(2), ones(3))
***** error<burrpdf: X, LAMBDA, C, and K must not be complex.> burrpdf (i, 2, 3, 4)
***** error<burrpdf: X, LAMBDA, C, and K must not be complex.> burrpdf (1, i, 3, 4)
***** error<burrpdf: X, LAMBDA, C, and K must not be complex.> burrpdf (1, 2, i, 4)
***** error<burrpdf: X, LAMBDA, C, and K must not be complex.> burrpdf (1, 2, 3, i)
23 tests, 23 passed, 0 known failure, 0 skipped
[inst/dist_fun/invginv.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/invginv.m
***** demo
 ## Plot various iCDFs from the inverse Gaussian distribution
 p = 0.001:0.001:0.999;
 x1 = invginv (p, 1, 0.2);
 x2 = invginv (p, 1, 1);
 x3 = invginv (p, 1, 3);
 x4 = invginv (p, 3, 0.2);
 x5 = invginv (p, 3, 1);
 plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-c", p, x5, "-y")
 grid on
 ylim ([0, 3])
 legend ({"μ = 1, σ = 0.2", "μ = 1, σ = 1", "μ = 1, σ = 3", ...
          "μ = 3, σ = 0.2", "μ = 3, σ = 1"}, "location", "northwest")
 title ("Inverse Gaussian iCDF")
 xlabel ("probability")
 ylabel ("x")
***** shared p, x
 p = [0, 0.3829, 0.6827, 1];
 x = [0, 0.5207, 1.0376, Inf];
***** assert (invginv (p, 1, 1), x, 1e-4);
***** assert (invginv (p, 1, ones (1,4)), x, 1e-4);
***** assert (invginv (p, 1, [-1, 0, 1, 1]), [NaN, NaN, x(3:4)], 1e-4)
***** assert (invginv (p, [-1, 0, 1, 1], 1), [NaN, NaN, x(3:4)], 1e-4)
***** assert (class (invginv (single ([p, NaN]), 0, 1)), "single")
***** assert (class (invginv ([p, NaN], single (0), 1)), "single")
***** assert (class (invginv ([p, NaN], 0, single (1))), "single")
***** error<invginv: function called with too few input arguments.> invginv (1)
***** error<invginv: function called with too few input arguments.> invginv (1, 2)
***** error<invginv: P, MU, and LAMBDA must be of common size or scalars.> ...
 invginv (1, ones (2), ones (3))
***** error<invginv: P, MU, and LAMBDA must be of common size or scalars.> ...
 invginv (ones (2), 1, ones (3))
***** error<invginv: P, MU, and LAMBDA must be of common size or scalars.> ...
 invginv (ones (2), ones (3), 1)
***** error<invginv: P, MU, and LAMBDA must not be complex.> invginv (i, 2, 3)
***** error<invginv: P, MU, and LAMBDA must not be complex.> invginv (1, i, 3)
***** error<invginv: P, MU, and LAMBDA must not be complex.> invginv (1, 2, i)
15 tests, 15 passed, 0 known failure, 0 skipped
[inst/dist_fun/hncdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/hncdf.m
***** demo
 ## Plot various CDFs from the half-normal distribution
 x = 0:0.001:10;
 p1 = hncdf (x, 0, 1);
 p2 = hncdf (x, 0, 2);
 p3 = hncdf (x, 0, 3);
 p4 = hncdf (x, 0, 5);
 plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", x, p4, "-c")
 grid on
 xlim ([0, 10])
 legend ({"μ = 0, σ = 1", "μ = 0, σ = 2", ...
          "μ = 0, σ = 3", "μ = 0, σ = 5"}, "location", "southeast")
 title ("Half-normal CDF")
 xlabel ("values in x")
 ylabel ("probability")
***** demo
 ## Plot half-normal against normal cumulative distribution function
 x = -5:0.001:5;
 p1 = hncdf (x, 0, 1);
 p2 = normcdf (x);
 plot (x, p1, "-b", x, p2, "-g")
 grid on
 xlim ([-5, 5])
 legend ({"half-normal with μ = 0, σ = 1", ...
          "standart normal (μ = 0, σ = 1)"}, "location", "southeast")
 title ("Half-normal against standard normal CDF")
 xlabel ("values in x")
 ylabel ("probability")
***** shared x, p1, p1u, y2, y2u, y3, y3u
 x = [-Inf, -1, 0, 1/2, 1, Inf];
 p1 = [0, 0, 0, 0.3829, 0.6827, 1];
 p1u = [1, 1, 1, 0.6171, 0.3173, 0];
***** assert (hncdf (x, zeros (1,6), ones (1,6)), p1, 1e-4)
***** assert (hncdf (x, 0, 1), p1, 1e-4)
***** assert (hncdf (x, 0, ones (1,6)), p1, 1e-4)
***** assert (hncdf (x, zeros (1,6), 1), p1, 1e-4)
***** assert (hncdf (x, 0, [1, 1, 1, NaN, 1, 1]), [p1(1:3), NaN, p1(5:6)], 1e-4)
***** assert (hncdf (x, [0, 0, 0, NaN, 0, 0], 1), [p1(1:3), NaN, p1(5:6)], 1e-4)
***** assert (hncdf ([x(1:3), NaN, x(5:6)], 0, 1), [p1(1:3), NaN, p1(5:6)], 1e-4)
***** assert (hncdf (x, zeros (1,6), ones (1,6), "upper"), p1u, 1e-4)
***** assert (hncdf (x, 0, 1, "upper"), p1u, 1e-4)
***** assert (hncdf (x, 0, ones (1,6), "upper"), p1u, 1e-4)
***** assert (hncdf (x, zeros (1,6), 1, "upper"), p1u, 1e-4)
***** assert (class (hncdf (single ([x, NaN]), 0, 1)), "single")
***** assert (class (hncdf ([x, NaN], 0, single (1))), "single")
***** assert (class (hncdf ([x, NaN], single (0), 1)), "single")
***** error<hncdf: function called with too few input arguments.> hncdf ()
***** error<hncdf: function called with too few input arguments.> hncdf (1)
***** error<hncdf: function called with too few input arguments.> hncdf (1, 2)
***** error<hncdf: invalid argument for upper tail.> hncdf (1, 2, 3, "tail")
***** error<hncdf: invalid argument for upper tail.> hncdf (1, 2, 3, 5)
***** error<hncdf: X, MU, and SIGMA must be of common size or scalars.> ...
 hncdf (ones (3), ones (2), ones(2))
***** error<hncdf: X, MU, and SIGMA must be of common size or scalars.> ...
 hncdf (ones (2), ones (3), ones(2))
***** error<hncdf: X, MU, and SIGMA must be of common size or scalars.> ...
 hncdf (ones (2), ones (2), ones(3))
***** error<hncdf: X, MU, and SIGMA must not be complex.> hncdf (i, 2, 3)
***** error<hncdf: X, MU, and SIGMA must not be complex.> hncdf (1, i, 3)
***** error<hncdf: X, MU, and SIGMA must not be complex.> hncdf (1, 2, i)
25 tests, 25 passed, 0 known failure, 0 skipped
[inst/dist_fun/cauchypdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/cauchypdf.m
***** demo
 ## Plot various PDFs from the Cauchy distribution
 x = -5:0.01:5;
 y1 = cauchypdf (x, 0, 0.5);
 y2 = cauchypdf (x, 0, 1);
 y3 = cauchypdf (x, 0, 2);
 y4 = cauchypdf (x, -2, 1);
 plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-c")
 grid on
 xlim ([-5, 5])
 ylim ([0, 0.7])
 legend ({"x0 = 0, γ = 0.5", "x0 = 0, γ = 1", ...
          "x0 = 0, γ = 2", "x0 = -2, γ = 1"}, "location", "northeast")
 title ("Cauchy PDF")
 xlabel ("values in x")
 ylabel ("density")
***** shared x, y
 x = [-1 0 0.5 1 2];
 y = 1/pi * ( 2 ./ ((x-1).^2 + 2^2) );
***** assert (cauchypdf (x, ones (1,5), 2*ones (1,5)), y)
***** assert (cauchypdf (x, 1, 2*ones (1,5)), y)
***** assert (cauchypdf (x, ones (1,5), 2), y)
***** assert (cauchypdf (x, [-Inf 1 NaN 1 Inf], 2), [NaN y(2) NaN y(4) NaN])
***** assert (cauchypdf (x, 1, 2*[0 1 NaN 1 Inf]), [NaN y(2) NaN y(4) NaN])
***** assert (cauchypdf ([x, NaN], 1, 2), [y, NaN])
***** assert (cauchypdf (single ([x, NaN]), 1, 2), single ([y, NaN]), eps ("single"))
***** assert (cauchypdf ([x, NaN], single (1), 2), single ([y, NaN]), eps ("single"))
***** assert (cauchypdf ([x, NaN], 1, single (2)), single ([y, NaN]), eps ("single"))
***** test
 x = rand (10, 1);
 assert (cauchypdf (x, 0, 1), tpdf (x, 1), eps);
***** error<cauchypdf: function called with too few input arguments.> cauchypdf ()
***** error<cauchypdf: function called with too few input arguments.> cauchypdf (1)
***** error<cauchypdf: function called with too few input arguments.> ...
 cauchypdf (1, 2)
***** error<cauchypdf: function called with too many inputs> cauchypdf (1, 2, 3, 4)
***** error<cauchypdf: X, X0, and GAMMA must be of common size or scalars.> ...
 cauchypdf (ones (3), ones (2), ones(2))
***** error<cauchypdf: X, X0, and GAMMA must be of common size or scalars.> ...
 cauchypdf (ones (2), ones (3), ones(2))
***** error<cauchypdf: X, X0, and GAMMA must be of common size or scalars.> ...
 cauchypdf (ones (2), ones (2), ones(3))
***** error<cauchypdf: X, X0, and GAMMA must not be complex.> cauchypdf (i, 4, 3)
***** error<cauchypdf: X, X0, and GAMMA must not be complex.> cauchypdf (1, i, 3)
***** error<cauchypdf: X, X0, and GAMMA must not be complex.> cauchypdf (1, 4, i)
20 tests, 20 passed, 0 known failure, 0 skipped
[inst/dist_fun/evinv.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/evinv.m
***** demo
 ## Plot various iCDFs from the extreme value distribution
 p = 0.001:0.001:0.999;
 x1 = evinv (p, 0.5, 2);
 x2 = evinv (p, 1.0, 2);
 x3 = evinv (p, 1.5, 3);
 x4 = evinv (p, 3.0, 4);
 plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-c")
 grid on
 ylim ([-10, 10])
 legend ({"μ = 0.5, σ = 2", "μ = 1.0, σ = 2", ...
          "μ = 1.5, σ = 3", "μ = 3.0, σ = 4"}, "location", "northwest")
 title ("Extreme value iCDF")
 xlabel ("probability")
 ylabel ("values in x")
***** shared p, x
 p = [0, 0.05, 0.5 0.95];
 x = [-Inf, -2.9702, -0.3665, 1.0972];
***** assert (evinv (p), x, 1e-4)
***** assert (evinv (p, zeros (1,4), ones (1,4)), x, 1e-4)
***** assert (evinv (p, 0, ones (1,4)), x, 1e-4)
***** assert (evinv (p, zeros (1,4), 1), x, 1e-4)
***** assert (evinv (p, [0, -Inf, NaN, Inf], 1), [-Inf, -Inf, NaN, Inf], 1e-4)
***** assert (evinv (p, 0, [Inf, NaN, -1, 0]), [-Inf, NaN, NaN, NaN], 1e-4)
***** assert (evinv ([p(1:2), NaN, p(4)], 0, 1), [x(1:2), NaN, x(4)], 1e-4)
***** assert (evinv ([p, NaN], 0, 1), [x, NaN], 1e-4)
***** assert (evinv (single ([p, NaN]), 0, 1), single ([x, NaN]), 1e-4)
***** assert (evinv ([p, NaN], single (0), 1), single ([x, NaN]), 1e-4)
***** assert (evinv ([p, NaN], 0, single (1)), single ([x, NaN]), 1e-4)
***** error<evinv: invalid number of input arguments.> evinv ()
***** error evinv (1,2,3,4,5,6)
***** error<evinv: P, MU, and SIGMA must be of common size or scalars.> ...
 evinv (ones (3), ones (2), ones (2))
***** error<evinv: invalid size of covariance matrix.> ...
 [p, plo, pup] = evinv (2, 3, 4, [1, 2])
***** error<evinv: covariance matrix is required for confidence bounds.> ...
 [p, plo, pup] = evinv (1, 2, 3)
***** error<evinv: invalid value for alpha.> [p, plo, pup] = ...
 evinv (1, 2, 3, [1, 0; 0, 1], 0)
***** error<evinv: invalid value for alpha.> [p, plo, pup] = ...
 evinv (1, 2, 3, [1, 0; 0, 1], 1.22)
***** error<evinv: P, MU, and SIGMA must not be complex.> evinv (i, 2, 2)
***** error<evinv: P, MU, and SIGMA must not be complex.> evinv (2, i, 2)
***** error<evinv: P, MU, and SIGMA must not be complex.> evinv (2, 2, i)
***** error<evinv: bad covariance matrix.> ...
 [p, plo, pup] = evinv (1, 2, 3, [-1, -10; -Inf, -Inf], 0.04)
22 tests, 22 passed, 0 known failure, 0 skipped
[inst/dist_fun/wblrnd.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/wblrnd.m
***** assert (size (wblrnd (1, 1)), [1 1])
***** assert (size (wblrnd (1, ones (2,1))), [2, 1])
***** assert (size (wblrnd (1, ones (2,2))), [2, 2])
***** assert (size (wblrnd (ones (2,1), 1)), [2, 1])
***** assert (size (wblrnd (ones (2,2), 1)), [2, 2])
***** assert (size (wblrnd (1, 1, 3)), [3, 3])
***** assert (size (wblrnd (1, 1, [4, 1])), [4, 1])
***** assert (size (wblrnd (1, 1, 4, 1)), [4, 1])
***** assert (size (wblrnd (1, 1, 4, 1, 5)), [4, 1, 5])
***** assert (size (wblrnd (1, 1, 0, 1)), [0, 1])
***** assert (size (wblrnd (1, 1, 1, 0)), [1, 0])
***** assert (size (wblrnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
***** assert (class (wblrnd (1, 1)), "double")
***** assert (class (wblrnd (1, single (1))), "single")
***** assert (class (wblrnd (1, single ([1, 1]))), "single")
***** assert (class (wblrnd (single (1), 1)), "single")
***** assert (class (wblrnd (single ([1, 1]), 1)), "single")
***** error<wblrnd: function called with too few input arguments.> wblrnd ()
***** error<wblrnd: function called with too few input arguments.> wblrnd (1)
***** error<wblrnd: LAMBDA and K must be of common size or scalars.> ...
 wblrnd (ones (3), ones (2))
***** error<wblrnd: LAMBDA and K must be of common size or scalars.> ...
 wblrnd (ones (2), ones (3))
***** error<wblrnd: LAMBDA and K must not be complex.> wblrnd (i, 2, 3)
***** error<wblrnd: LAMBDA and K must not be complex.> wblrnd (1, i, 3)
***** error<wblrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 wblrnd (1, 2, -1)
***** error<wblrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 wblrnd (1, 2, 1.2)
***** error<wblrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 wblrnd (1, 2, ones (2))
***** error<wblrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 wblrnd (1, 2, [2 -1 2])
***** error<wblrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 wblrnd (1, 2, [2 0 2.5])
***** error<wblrnd: dimensions must be non-negative integers.> ...
 wblrnd (1, 2, 2, -1, 5)
***** error<wblrnd: dimensions must be non-negative integers.> ...
 wblrnd (1, 2, 2, 1.5, 5)
***** error<wblrnd: LAMBDA and K must be scalar or of size SZ.> ...
 wblrnd (2, ones (2), 3)
***** error<wblrnd: LAMBDA and K must be scalar or of size SZ.> ...
 wblrnd (2, ones (2), [3, 2])
***** error<wblrnd: LAMBDA and K must be scalar or of size SZ.> ...
 wblrnd (2, ones (2), 3, 2)
33 tests, 33 passed, 0 known failure, 0 skipped
[inst/dist_fun/loglpdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/loglpdf.m
***** demo
 ## Plot various PDFs from the log-logistic distribution
 x = 0.001:0.001:2;
 y1 = loglpdf (x, log (1), 1/0.5);
 y2 = loglpdf (x, log (1), 1);
 y3 = loglpdf (x, log (1), 1/2);
 y4 = loglpdf (x, log (1), 1/4);
 y5 = loglpdf (x, log (1), 1/8);
 plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-c", x, y5, "-m")
 grid on
 ylim ([0,3])
 legend ({"σ = 2 (β = 0.5)", "σ = 1 (β = 1)", "σ = 0.5 (β = 2)", ...
          "σ = 0.25 (β = 4)", "σ = 0.125 (β = 8)"}, "location", "northeast")
 title ("Log-logistic PDF")
 xlabel ("values in x")
 ylabel ("density")
 text (0.1, 2.8, "μ = 0 (α = 1), values of σ (β) as shown in legend")
***** shared out1, out2
 out1 = [0, 0, 1, 0.2500, 0.1111, 0.0625, 0.0400, 0.0278, 0];
 out2 = [0, 0, 0.0811, 0.0416, 0.0278, 0.0207, 0.0165, 0];
***** assert (loglpdf ([-1,0,realmin,1:5,Inf], 0, 1), out1, 1e-4)
***** assert (loglpdf ([-1,0,realmin,1:5,Inf], 0, 1), out1, 1e-4)
***** assert (loglpdf ([-1:5,Inf], 1, 3), out2, 1e-4)
***** assert (class (loglpdf (single (1), 2, 3)), "single")
***** assert (class (loglpdf (1, single (2), 3)), "single")
***** assert (class (loglpdf (1, 2, single (3))), "single")
***** error<loglpdf: function called with too few input arguments.> loglpdf (1)
***** error<loglpdf: function called with too few input arguments.> loglpdf (1, 2)
***** error<loglpdf: X, MU, and SIGMA must be of common size or scalars.> ...
 loglpdf (1, ones (2), ones (3))
***** error<loglpdf: X, MU, and SIGMA must be of common size or scalars.> ...
 loglpdf (ones (2), 1, ones (3))
***** error<loglpdf: X, MU, and SIGMA must be of common size or scalars.> ...
 loglpdf (ones (2), ones (3), 1)
***** error<loglpdf: X, MU, and SIGMA must not be complex.> loglpdf (i, 2, 3)
***** error<loglpdf: X, MU, and SIGMA must not be complex.> loglpdf (1, i, 3)
***** error<loglpdf: X, MU, and SIGMA must not be complex.> loglpdf (1, 2, i)
14 tests, 14 passed, 0 known failure, 0 skipped
[inst/dist_fun/copulapdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/copulapdf.m
***** test
 x = [0.2:0.2:0.6; 0.2:0.2:0.6];
 theta = [1; 2];
 y = copulapdf ("Clayton", x, theta);
 expected_p = [0.9872; 0.7295];
 assert (y, expected_p, 0.001);
***** test
 x = [0.2:0.2:0.6; 0.2:0.2:0.6];
 y = copulapdf ("Gumbel", x, 2);
 expected_p = [0.9468; 0.9468];
 assert (y, expected_p, 0.001);
***** test
 x = [0.2, 0.6; 0.2, 0.6];
 theta = [1; 2];
 y = copulapdf ("Frank", x, theta);
 expected_p = [0.9378; 0.8678];
 assert (y, expected_p, 0.001);
***** test
 x = [0.2, 0.6; 0.2, 0.6];
 theta = [0.3; 0.7];
 y = copulapdf ("AMH", x, theta);
 expected_p = [0.9540; 0.8577];
 assert (y, expected_p, 0.001);
4 tests, 4 passed, 0 known failure, 0 skipped
[inst/dist_fun/burrrnd.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/burrrnd.m
***** assert (size (burrrnd (1, 1, 1)), [1 1])
***** assert (size (burrrnd (ones (2,1), 1, 1)), [2, 1])
***** assert (size (burrrnd (ones (2,2), 1, 1)), [2, 2])
***** assert (size (burrrnd (1, ones (2,1), 1)), [2, 1])
***** assert (size (burrrnd (1, ones (2,2), 1)), [2, 2])
***** assert (size (burrrnd (1, 1, ones (2,1))), [2, 1])
***** assert (size (burrrnd (1, 1, ones (2,2))), [2, 2])
***** assert (size (burrrnd (1, 1, 1, 3)), [3, 3])
***** assert (size (burrrnd (1, 1, 1, [4 1])), [4, 1])
***** assert (size (burrrnd (1, 1, 1, 4, 1)), [4, 1])
***** assert (class (burrrnd (1,1,1)), "double")
***** assert (class (burrrnd (single (1),1,1)), "single")
***** assert (class (burrrnd (single ([1 1]),1,1)), "single")
***** assert (class (burrrnd (1,single (1),1)), "single")
***** assert (class (burrrnd (1,single ([1 1]),1)), "single")
***** assert (class (burrrnd (1,1,single (1))), "single")
***** assert (class (burrrnd (1,1,single ([1 1]))), "single")
***** error<burrrnd: function called with too few input arguments.> burrrnd ()
***** error<burrrnd: function called with too few input arguments.> burrrnd (1)
***** error<burrrnd: function called with too few input arguments.> burrrnd (1, 2)
***** error<burrrnd: LAMBDA, C, and K must be of common size or scalars.> ...
 burrrnd (ones (3), ones (2), ones (2))
***** error<burrrnd: LAMBDA, C, and K must be of common size or scalars.> ...
 burrrnd (ones (2), ones (3), ones (2))
***** error<burrrnd: LAMBDA, C, and K must be of common size or scalars.> ...
 burrrnd (ones (2), ones (2), ones (3))
***** error<burrrnd: LAMBDA, C, and K must not be complex.> burrrnd (i, 2, 3)
***** error<burrrnd: LAMBDA, C, and K must not be complex.> burrrnd (1, i, 3)
***** error<burrrnd: LAMBDA, C, and K must not be complex.> burrrnd (1, 2, i)
***** error<burrrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 burrrnd (1, 2, 3, -1)
***** error<burrrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 burrrnd (1, 2, 3, 1.2)
***** error<burrrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 burrrnd (1, 2, 3, ones (2))
***** error<burrrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 burrrnd (1, 2, 3, [2 -1 2])
***** error<burrrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 burrrnd (1, 2, 3, [2 0 2.5])
***** error<burrrnd: dimensions must be non-negative integers.> ...
 burrrnd (1, 2, 3, 2, -1, 5)
***** error<burrrnd: dimensions must be non-negative integers.> ...
 burrrnd (1, 2, 3, 2, 1.5, 5)
***** error<burrrnd: LAMBDA, C, and K must be scalars or of size SZ.> ...
 burrrnd (2, ones (2), 2, 3)
***** error<burrrnd: LAMBDA, C, and K must be scalars or of size SZ.> ...
 burrrnd (2, ones (2), 2, [3, 2])
***** error<burrrnd: LAMBDA, C, and K must be scalars or of size SZ.> ...
 burrrnd (2, ones (2), 2, 3, 2)
36 tests, 36 passed, 0 known failure, 0 skipped
[inst/dist_fun/plpdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/plpdf.m
***** demo
 ## Plot various PDFs from the Piecewise linear distribution
 data = 0:0.01:10;
 x1 = [0, 1, 3, 4, 7, 10];
 Fx1 = [0, 0.2, 0.5, 0.6, 0.7, 1];
 x2 = [0, 2, 5, 6, 7, 8];
 Fx2 = [0, 0.1, 0.3, 0.6, 0.9, 1];
 y1 = plpdf (data, x1, Fx1);
 y2 = plpdf (data, x2, Fx2);
 plot (data, y1, "-b", data, y2, "g")
 grid on
 ylim ([0, 0.6])
 xlim ([0, 10])
 legend ({"x1, Fx1", "x2, Fx2"}, "location", "northeast")
 title ("Piecewise linear CDF")
 xlabel ("values in data")
 ylabel ("density")
***** shared x, Fx
 x = [0, 1, 3, 4, 7, 10];
 Fx = [0, 0.2, 0.5, 0.6, 0.7, 1];
***** assert (plpdf (0.5, x, Fx), 0.2, eps);
***** assert (plpdf (1.5, x, Fx), 0.15, eps);
***** assert (plpdf (3.5, x, Fx), 0.1, eps);
***** assert (plpdf (5, x, Fx), 0.1/3, eps);
***** assert (plpdf (8, x, Fx), 0.1, eps);
***** error<plpdf: function called with too few input arguments.> plpdf ()
***** error<plpdf: function called with too few input arguments.> plpdf (1)
***** error<plpdf: function called with too few input arguments.> plpdf (1, 2)
***** error<plpdf: X and FX must be vectors of equal size.> ...
 plpdf (1, [0, 1, 2], [0, 1])
***** error<plpdf: X and FX must be at least two-elements long.> ...
 plpdf (1, [0], [1])
***** error<plpdf: FX must be bounded in the range> ...
 plpdf (1, [0, 1, 2], [0, 1, 1.5])
***** error<plpdf: FX must be bounded in the range> ...
 plpdf (1, [0, 1, 2], [0, i, 1])
***** error<plpdf: DATA, X, and FX must not be complex.> ...
 plpdf (i, [0, 1, 2], [0, 0.5, 1])
***** error<plpdf: DATA, X, and FX must not be complex.> ...
 plpdf (1, [0, i, 2], [0, 0.5, 1])
***** error<plpdf: DATA, X, and FX must not be complex.> ...
 plpdf (1, [0, 1, 2], [0, 0.5i, 1])
15 tests, 15 passed, 0 known failure, 0 skipped
[inst/dist_fun/unifrnd.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/unifrnd.m
***** assert (size (unifrnd (1, 1)), [1 1])
***** assert (size (unifrnd (1, ones (2,1))), [2, 1])
***** assert (size (unifrnd (1, ones (2,2))), [2, 2])
***** assert (size (unifrnd (ones (2,1), 1)), [2, 1])
***** assert (size (unifrnd (ones (2,2), 1)), [2, 2])
***** assert (size (unifrnd (1, 1, 3)), [3, 3])
***** assert (size (unifrnd (1, 1, [4, 1])), [4, 1])
***** assert (size (unifrnd (1, 1, 4, 1)), [4, 1])
***** assert (size (unifrnd (1, 1, 4, 1, 5)), [4, 1, 5])
***** assert (size (unifrnd (1, 1, 0, 1)), [0, 1])
***** assert (size (unifrnd (1, 1, 1, 0)), [1, 0])
***** assert (size (unifrnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
***** assert (class (unifrnd (1, 1)), "double")
***** assert (class (unifrnd (1, single (1))), "single")
***** assert (class (unifrnd (1, single ([1, 1]))), "single")
***** assert (class (unifrnd (single (1), 1)), "single")
***** assert (class (unifrnd (single ([1, 1]), 1)), "single")
***** error<unifrnd: function called with too few input arguments.> unifrnd ()
***** error<unifrnd: function called with too few input arguments.> unifrnd (1)
***** error<unifrnd: A and B must be of common size or scalars.> ...
 unifrnd (ones (3), ones (2))
***** error<unifrnd: A and B must be of common size or scalars.> ...
 unifrnd (ones (2), ones (3))
***** error<unifrnd: A and B must not be complex.> unifrnd (i, 2, 3)
***** error<unifrnd: A and B must not be complex.> unifrnd (1, i, 3)
***** error<unifrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 unifrnd (1, 2, -1)
***** error<unifrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 unifrnd (1, 2, 1.2)
***** error<unifrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 unifrnd (1, 2, ones (2))
***** error<unifrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 unifrnd (1, 2, [2 -1 2])
***** error<unifrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 unifrnd (1, 2, [2 0 2.5])
***** error<unifrnd: dimensions must be non-negative integers.> ...
 unifrnd (1, 2, 2, -1, 5)
***** error<unifrnd: dimensions must be non-negative integers.> ...
 unifrnd (1, 2, 2, 1.5, 5)
***** error<unifrnd: A and B must be scalars or of size SZ.> ...
 unifrnd (2, ones (2), 3)
***** error<unifrnd: A and B must be scalars or of size SZ.> ...
 unifrnd (2, ones (2), [3, 2])
***** error<unifrnd: A and B must be scalars or of size SZ.> ...
 unifrnd (2, ones (2), 3, 2)
33 tests, 33 passed, 0 known failure, 0 skipped
[inst/dist_fun/cauchyinv.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/cauchyinv.m
***** demo
 ## Plot various iCDFs from the Cauchy distribution
 p = 0.001:0.001:0.999;
 x1 = cauchyinv (p, 0, 0.5);
 x2 = cauchyinv (p, 0, 1);
 x3 = cauchyinv (p, 0, 2);
 x4 = cauchyinv (p, -2, 1);
 plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-c")
 grid on
 ylim ([-5, 5])
 legend ({"x0 = 0, γ = 0.5", "x0 = 0, γ = 1", ...
          "x0 = 0, γ = 2", "x0 = -2, γ = 1"}, "location", "northwest")
 title ("Cauchy iCDF")
 xlabel ("probability")
 ylabel ("values in x")
***** shared p
 p = [-1 0 0.5 1 2];
***** assert (cauchyinv (p, ones (1,5), 2 * ones (1,5)), [NaN -Inf 1 Inf NaN], eps)
***** assert (cauchyinv (p, 1, 2 * ones (1,5)), [NaN -Inf 1 Inf NaN], eps)
***** assert (cauchyinv (p, ones (1,5), 2), [NaN -Inf 1 Inf NaN], eps)
***** assert (cauchyinv (p, [1 -Inf NaN Inf 1], 2), [NaN NaN NaN NaN NaN])
***** assert (cauchyinv (p, 1, 2 * [1 0 NaN Inf 1]), [NaN NaN NaN NaN NaN])
***** assert (cauchyinv ([p(1:2) NaN p(4:5)], 1, 2), [NaN -Inf NaN Inf NaN])
***** assert (cauchyinv ([p, NaN], 1, 2), [NaN -Inf 1 Inf NaN NaN], eps)
***** assert (cauchyinv (single ([p, NaN]), 1, 2), ...
 single ([NaN -Inf 1 Inf NaN NaN]), eps ("single"))
***** assert (cauchyinv ([p, NaN], single (1), 2), ...
 single ([NaN -Inf 1 Inf NaN NaN]), eps ("single"))
***** assert (cauchyinv ([p, NaN], 1, single (2)), ...
 single ([NaN -Inf 1 Inf NaN NaN]), eps ("single"))
***** error<cauchyinv: function called with too few input arguments.> cauchyinv ()
***** error<cauchyinv: function called with too few input arguments.> cauchyinv (1)
***** error<cauchyinv: function called with too few input arguments.> ...
 cauchyinv (1, 2)
***** error<cauchyinv: function called with too many inputs> cauchyinv (1, 2, 3, 4)
***** error<cauchyinv: P, X0, and GAMMA must be of common size or scalars.> ...
 cauchyinv (ones (3), ones (2), ones(2))
***** error<cauchyinv: P, X0, and GAMMA must be of common size or scalars.> ...
 cauchyinv (ones (2), ones (3), ones(2))
***** error<cauchyinv: P, X0, and GAMMA must be of common size or scalars.> ...
 cauchyinv (ones (2), ones (2), ones(3))
***** error<cauchyinv: P, X0, and GAMMA must not be complex.> cauchyinv (i, 4, 3)
***** error<cauchyinv: P, X0, and GAMMA must not be complex.> cauchyinv (1, i, 3)
***** error<cauchyinv: P, X0, and GAMMA must not be complex.> cauchyinv (1, 4, i)
20 tests, 20 passed, 0 known failure, 0 skipped
[inst/dist_fun/vminv.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/vminv.m
***** demo
 ## Plot various iCDFs from the von Mises distribution
 p1 = [0,0.005,0.01:0.01:0.1,0.15,0.2:0.1:0.8,0.85,0.9:0.01:0.99,0.995,1];
 x1 = vminv (p1, 0, 0.5);
 x2 = vminv (p1, 0, 1);
 x3 = vminv (p1, 0, 2);
 x4 = vminv (p1, 0, 4);
 plot (p1, x1, "-r", p1, x2, "-g", p1, x3, "-b", p1, x4, "-c")
 grid on
 ylim ([-pi, pi])
 legend ({"μ = 0, k = 0.5", "μ = 0, k = 1", ...
          "μ = 0, k = 2", "μ = 0, k = 4"}, "location", "northwest")
 title ("Von Mises iCDF")
 xlabel ("probability")
 ylabel ("values in x")
***** shared x, p0, p1
 x = [-pi:pi/2:pi];
 p0 = [0, 0.10975, 0.5, 0.89025, 1];
 p1 = [0, 0.03752, 0.5, 0.99622, 1];
***** assert (vminv (p0, 0, 1), x, 5e-5)
***** assert (vminv (p0, zeros (1,5), ones (1,5)), x, 5e-5)
***** assert (vminv (p1, 0, [1 2 3 4 5]), x, [5e-5, 5e-4, 5e-5, 5e-4, 5e-5])
***** error<vminv: function called with too few input arguments.> vminv ()
***** error<vminv: function called with too few input arguments.> vminv (1)
***** error<vminv: function called with too few input arguments.> vminv (1, 2)
***** error<vminv: P, MU, and K must be of common size or scalars.> ...
 vminv (ones (3), ones (2), ones (2))
***** error<vminv: P, MU, and K must be of common size or scalars.> ...
 vminv (ones (2), ones (3), ones (2))
***** error<vminv: P, MU, and K must be of common size or scalars.> ...
 vminv (ones (2), ones (2), ones (3))
***** error<vminv: P, MU, and K must not be complex.> vminv (i, 2, 2)
***** error<vminv: P, MU, and K must not be complex.> vminv (2, i, 2)
***** error<vminv: P, MU, and K must not be complex.> vminv (2, 2, i)
12 tests, 12 passed, 0 known failure, 0 skipped
[inst/dist_fun/nbincdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/nbincdf.m
***** demo
 ## Plot various CDFs from the negative binomial distribution
 x = 0:50;
 p1 = nbincdf (x, 2, 0.15);
 p2 = nbincdf (x, 5, 0.2);
 p3 = nbincdf (x, 4, 0.4);
 p4 = nbincdf (x, 10, 0.3);
 plot (x, p1, "*r", x, p2, "*g", x, p3, "*k", x, p4, "*m")
 grid on
 xlim ([0, 40])
 legend ({"r = 2, ps = 0.15", "r = 5, ps = 0.2", "r = 4, p = 0.4", ...
          "r = 10, ps = 0.3"}, "location", "southeast")
 title ("Negative binomial CDF")
 xlabel ("values in x (number of failures)")
 ylabel ("probability")
***** shared x, y
 x = [-1 0 1 2 Inf];
 y = [0 1/2 3/4 7/8 1];
***** assert (nbincdf (x, ones (1,5), 0.5*ones (1,5)), y)
***** assert (nbincdf (x, 1, 0.5*ones (1,5)), y)
***** assert (nbincdf (x, ones (1,5), 0.5), y)
***** assert (nbincdf (x, ones (1,5), 0.5, "upper"), 1 - y, eps)
***** assert (nbincdf ([x(1:3) 0 x(5)], [0 1 NaN 1.5 Inf], 0.5), ...
 [NaN 1/2 NaN nbinpdf(0,1.5,0.5) NaN], eps)
***** assert (nbincdf (x, 1, 0.5*[-1 NaN 4 1 1]), [NaN NaN NaN y(4:5)])
***** assert (nbincdf ([x(1:2) NaN x(4:5)], 1, 0.5), [y(1:2) NaN y(4:5)])
***** assert (nbincdf ([x, NaN], 1, 0.5), [y, NaN])
***** assert (nbincdf (single ([x, NaN]), 1, 0.5), single ([y, NaN]))
***** assert (nbincdf ([x, NaN], single (1), 0.5), single ([y, NaN]))
***** assert (nbincdf ([x, NaN], 1, single (0.5)), single ([y, NaN]))
***** error<nbincdf: function called with too few input arguments.> nbincdf ()
***** error<nbincdf: function called with too few input arguments.> nbincdf (1)
***** error<nbincdf: function called with too few input arguments.> nbincdf (1, 2)
***** error<nbincdf: invalid argument for upper tail.> nbincdf (1, 2, 3, 4)
***** error<nbincdf: invalid argument for upper tail.> nbincdf (1, 2, 3, "some")
***** error<nbincdf: X, R, and PS must be of common size or scalars.> ...
 nbincdf (ones (3), ones (2), ones (2))
***** error<nbincdf: X, R, and PS must be of common size or scalars.> ...
 nbincdf (ones (2), ones (3), ones (2))
***** error<nbincdf: X, R, and PS must be of common size or scalars.> ...
 nbincdf (ones (2), ones (2), ones (3))
***** error<nbincdf: X, R, and PS must not be complex.> nbincdf (i, 2, 2)
***** error<nbincdf: X, R, and PS must not be complex.> nbincdf (2, i, 2)
***** error<nbincdf: X, R, and PS must not be complex.> nbincdf (2, 2, i)
22 tests, 22 passed, 0 known failure, 0 skipped
[inst/dist_fun/jsucdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/jsucdf.m
***** error jsucdf ()
***** error jsucdf (1, 2, 3, 4)
***** error<jsucdf: X, ALPHA1, and ALPHA2 must be of common size or scalars.> ...
 jsucdf (1, ones (2), ones (3))
3 tests, 3 passed, 0 known failure, 0 skipped
[inst/dist_fun/wishpdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/wishpdf.m
***** assert(wishpdf(4, 3, 3.1), 0.07702496, 1E-7);
***** assert(wishpdf([2 -0.3;-0.3 4], [1 0.3;0.3 1], 4), 0.004529741, 1E-7);
***** assert(wishpdf([6 2 5; 2 10 -5; 5 -5 25], [9 5 5; 5 10 -8; 5 -8 22], 5.1), 4.474865e-10, 1E-15);
***** error wishpdf ()
***** error wishpdf (1, 2)
***** error wishpdf (1, 2, 0)
***** error wishpdf (1, 2)
7 tests, 7 passed, 0 known failure, 0 skipped
[inst/dist_fun/logninv.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/logninv.m
***** demo
 ## Plot various iCDFs from the log-normal distribution
 p = 0.001:0.001:0.999;
 x1 = logninv (p, 0, 1);
 x2 = logninv (p, 0, 0.5);
 x3 = logninv (p, 0, 0.25);
 plot (p, x1, "-b", p, x2, "-g", p, x3, "-r")
 grid on
 ylim ([0, 3])
 legend ({"μ = 0, σ = 1", "μ = 0, σ = 0.5", "μ = 0, σ = 0.25"}, ...
         "location", "northwest")
 title ("Log-normal iCDF")
 xlabel ("probability")
 ylabel ("values in x")
***** shared p
 p = [-1 0 0.5 1 2];
***** assert (logninv (p, ones (1,5), ones (1,5)), [NaN 0 e Inf NaN])
***** assert (logninv (p, 1, ones (1,5)), [NaN 0 e Inf NaN])
***** assert (logninv (p, ones (1,5), 1), [NaN 0 e Inf NaN])
***** assert (logninv (p, [1 1 NaN 0 1], 1), [NaN 0 NaN Inf NaN])
***** assert (logninv (p, 1, [1 0 NaN Inf 1]), [NaN NaN NaN NaN NaN])
***** assert (logninv ([p(1:2) NaN p(4:5)], 1, 2), [NaN 0 NaN Inf NaN])
***** assert (logninv ([p, NaN], 1, 1), [NaN 0 e Inf NaN NaN])
***** assert (logninv (single ([p, NaN]), 1, 1), single ([NaN 0 e Inf NaN NaN]))
***** assert (logninv ([p, NaN], single (1), 1), single ([NaN 0 e Inf NaN NaN]))
***** assert (logninv ([p, NaN], 1, single (1)), single ([NaN 0 e Inf NaN NaN]))
***** error logninv ()
***** error logninv (1,2,3,4)
***** error logninv (ones (3), ones (2), ones (2))
***** error logninv (ones (2), ones (3), ones (2))
***** error logninv (ones (2), ones (2), ones (3))
***** error logninv (i, 2, 2)
***** error logninv (2, i, 2)
***** error logninv (2, 2, i)
18 tests, 18 passed, 0 known failure, 0 skipped
[inst/dist_fun/gumbelrnd.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/gumbelrnd.m
***** assert (size (gumbelrnd (1, 1)), [1 1])
***** assert (size (gumbelrnd (1, ones (2,1))), [2, 1])
***** assert (size (gumbelrnd (1, ones (2,2))), [2, 2])
***** assert (size (gumbelrnd (ones (2,1), 1)), [2, 1])
***** assert (size (gumbelrnd (ones (2,2), 1)), [2, 2])
***** assert (size (gumbelrnd (1, 1, 3)), [3, 3])
***** assert (size (gumbelrnd (1, 1, [4, 1])), [4, 1])
***** assert (size (gumbelrnd (1, 1, 4, 1)), [4, 1])
***** assert (size (gumbelrnd (1, 1, 4, 1, 5)), [4, 1, 5])
***** assert (size (gumbelrnd (1, 1, 0, 1)), [0, 1])
***** assert (size (gumbelrnd (1, 1, 1, 0)), [1, 0])
***** assert (size (gumbelrnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
***** assert (class (gumbelrnd (1, 1)), "double")
***** assert (class (gumbelrnd (1, single (1))), "single")
***** assert (class (gumbelrnd (1, single ([1, 1]))), "single")
***** assert (class (gumbelrnd (single (1), 1)), "single")
***** assert (class (gumbelrnd (single ([1, 1]), 1)), "single")
***** error<gumbelrnd: function called with too few input arguments.> gumbelrnd ()
***** error<gumbelrnd: function called with too few input arguments.> gumbelrnd (1)
***** error<gumbelrnd: MU and BETA must be of common size or scalars.> ...
 gumbelrnd (ones (3), ones (2))
***** error<gumbelrnd: MU and BETA must be of common size or scalars.> ...
 gumbelrnd (ones (2), ones (3))
***** error<gumbelrnd: MU and BETA must not be complex.> gumbelrnd (i, 2, 3)
***** error<gumbelrnd: MU and BETA must not be complex.> gumbelrnd (1, i, 3)
***** error<gumbelrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 gumbelrnd (1, 2, -1)
***** error<gumbelrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 gumbelrnd (1, 2, 1.2)
***** error<gumbelrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 gumbelrnd (1, 2, ones (2))
***** error<gumbelrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 gumbelrnd (1, 2, [2 -1 2])
***** error<gumbelrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 gumbelrnd (1, 2, [2 0 2.5])
***** error<gumbelrnd: dimensions must be non-negative integers.> ...
 gumbelrnd (1, 2, 2, -1, 5)
***** error<gumbelrnd: dimensions must be non-negative integers.> ...
 gumbelrnd (1, 2, 2, 1.5, 5)
***** error<gumbelrnd: MU and BETA must be scalars or of size SZ.> ...
 gumbelrnd (2, ones (2), 3)
***** error<gumbelrnd: MU and BETA must be scalars or of size SZ.> ...
 gumbelrnd (2, ones (2), [3, 2])
***** error<gumbelrnd: MU and BETA must be scalars or of size SZ.> ...
 gumbelrnd (2, ones (2), 3, 2)
33 tests, 33 passed, 0 known failure, 0 skipped
[inst/dist_fun/raylinv.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/raylinv.m
***** demo
 ## Plot various iCDFs from the Rayleigh distribution
 p = 0.001:0.001:0.999;
 x1 = raylinv (p, 0.5);
 x2 = raylinv (p, 1);
 x3 = raylinv (p, 2);
 x4 = raylinv (p, 3);
 x5 = raylinv (p, 4);
 plot (p, x1, "-b", p, x2, "g", p, x3, "-r", p, x4, "-m", p, x5, "-k")
 grid on
 ylim ([0, 10])
 legend ({"σ = 0,5", "σ = 1", "σ = 2", ...
          "σ = 3", "σ = 4"}, "location", "northwest")
 title ("Rayleigh iCDF")
 xlabel ("probability")
 ylabel ("values in x")
***** test
 p = 0:0.1:0.5;
 sigma = 1:6;
 x = raylinv (p, sigma);
 expected_x = [0.0000, 0.9181, 2.0041, 3.3784, 5.0538, 7.0645];
 assert (x, expected_x, 0.001);
***** test
 p = 0:0.1:0.5;
 x = raylinv (p, 0.5);
 expected_x = [0.0000, 0.2295, 0.3340, 0.4223, 0.5054, 0.5887];
 assert (x, expected_x, 0.001);
***** error<raylinv: function called with too few input arguments.> raylinv ()
***** error<raylinv: function called with too few input arguments.> raylinv (1)
***** error<raylinv: P and SIGMA must be of common size or scalars.> ...
 raylinv (ones (3), ones (2))
***** error<raylinv: P and SIGMA must be of common size or scalars.> ...
 raylinv (ones (2), ones (3))
***** error<raylinv: P and SIGMA must not be complex.> raylinv (i, 2)
***** error<raylinv: P and SIGMA must not be complex.> raylinv (2, i)
8 tests, 8 passed, 0 known failure, 0 skipped
[inst/dist_fun/evpdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/evpdf.m
***** demo
 ## Plot various PDFs from the Extreme value distribution
 x = -10:0.001:10;
 y1 = evpdf (x, 0.5, 2);
 y2 = evpdf (x, 1.0, 2);
 y3 = evpdf (x, 1.5, 3);
 y4 = evpdf (x, 3.0, 4);
 plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-c")
 grid on
 ylim ([0, 0.2])
 legend ({"μ = 0.5, σ = 2", "μ = 1.0, σ = 2", ...
          "μ = 1.5, σ = 3", "μ = 3.0, σ = 4"}, "location", "northeast")
 title ("Extreme value PDF")
 xlabel ("values in x")
 ylabel ("density")
***** shared x, y0, y1
 x = [-5, 0, 1, 2, 3];
 y0 = [0.0067, 0.3679, 0.1794, 0.0046, 0];
 y1 = [0.0025, 0.2546, 0.3679, 0.1794, 0.0046];
***** assert (evpdf (x), y0, 1e-4)
***** assert (evpdf (x, zeros (1,5), ones (1,5)), y0, 1e-4)
***** assert (evpdf (x, ones (1,5), ones (1,5)), y1, 1e-4)
***** error<evpdf: function called with too few input arguments.> evpdf ()
***** error<evpdf: X, MU, and SIGMA must be of common size or scalars.> ...
 evpdf (ones (3), ones (2), ones (2))
***** error<evpdf: X, MU, and SIGMA must not be complex.> evpdf (i, 2, 2)
***** error<evpdf: X, MU, and SIGMA must not be complex.> evpdf (2, i, 2)
***** error<evpdf: X, MU, and SIGMA must not be complex.> evpdf (2, 2, i)
8 tests, 8 passed, 0 known failure, 0 skipped
[inst/dist_fun/laplacernd.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/laplacernd.m
***** assert (size (laplacernd (1, 1)), [1 1])
***** assert (size (laplacernd (1, ones (2,1))), [2, 1])
***** assert (size (laplacernd (1, ones (2,2))), [2, 2])
***** assert (size (laplacernd (ones (2,1), 1)), [2, 1])
***** assert (size (laplacernd (ones (2,2), 1)), [2, 2])
***** assert (size (laplacernd (1, 1, 3)), [3, 3])
***** assert (size (laplacernd (1, 1, [4, 1])), [4, 1])
***** assert (size (laplacernd (1, 1, 4, 1)), [4, 1])
***** assert (size (laplacernd (1, 1, 4, 1, 5)), [4, 1, 5])
***** assert (size (laplacernd (1, 1, 0, 1)), [0, 1])
***** assert (size (laplacernd (1, 1, 1, 0)), [1, 0])
***** assert (size (laplacernd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
***** assert (class (laplacernd (1, 1)), "double")
***** assert (class (laplacernd (1, single (1))), "single")
***** assert (class (laplacernd (1, single ([1, 1]))), "single")
***** assert (class (laplacernd (single (1), 1)), "single")
***** assert (class (laplacernd (single ([1, 1]), 1)), "single")
***** error<laplacernd: function called with too few input arguments.> laplacernd ()
***** error<laplacernd: function called with too few input arguments.> laplacernd (1)
***** error<laplacernd: MU and BETA must be of common size or scalars.> ...
 laplacernd (ones (3), ones (2))
***** error<laplacernd: MU and BETA must be of common size or scalars.> ...
 laplacernd (ones (2), ones (3))
***** error<laplacernd: MU and BETA must not be complex.> laplacernd (i, 2, 3)
***** error<laplacernd: MU and BETA must not be complex.> laplacernd (1, i, 3)
***** error<laplacernd: SZ must be a scalar or a row vector of non-negative integers.> ...
 laplacernd (1, 2, -1)
***** error<laplacernd: SZ must be a scalar or a row vector of non-negative integers.> ...
 laplacernd (1, 2, 1.2)
***** error<laplacernd: SZ must be a scalar or a row vector of non-negative integers.> ...
 laplacernd (1, 2, ones (2))
***** error<laplacernd: SZ must be a scalar or a row vector of non-negative integers.> ...
 laplacernd (1, 2, [2 -1 2])
***** error<laplacernd: SZ must be a scalar or a row vector of non-negative integers.> ...
 laplacernd (1, 2, [2 0 2.5])
***** error<laplacernd: dimensions must be non-negative integers.> ...
 laplacernd (1, 2, 2, -1, 5)
***** error<laplacernd: dimensions must be non-negative integers.> ...
 laplacernd (1, 2, 2, 1.5, 5)
***** error<laplacernd: MU and BETA must be scalars or of size SZ.> ...
 laplacernd (2, ones (2), 3)
***** error<laplacernd: MU and BETA must be scalars or of size SZ.> ...
 laplacernd (2, ones (2), [3, 2])
***** error<laplacernd: MU and BETA must be scalars or of size SZ.> ...
 laplacernd (2, ones (2), 3, 2)
33 tests, 33 passed, 0 known failure, 0 skipped
[inst/dist_fun/binornd.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/binornd.m
***** assert (size (binornd (2, 1/2)), [1 1])
***** assert (size (binornd (2 * ones (2, 1), 1/2)), [2, 1])
***** assert (size (binornd (2 * ones (2, 2), 1/2)), [2, 2])
***** assert (size (binornd (2, 1/2 * ones (2, 1))), [2, 1])
***** assert (size (binornd (1, 1/2 * ones (2, 2))), [2, 2])
***** assert (size (binornd (ones (2, 1), 1)), [2, 1])
***** assert (size (binornd (ones (2, 2), 1)), [2, 2])
***** assert (size (binornd (2, 1/2, 3)), [3, 3])
***** assert (size (binornd (1, 1, [4, 1])), [4, 1])
***** assert (size (binornd (1, 1, 4, 1)), [4, 1])
***** assert (size (binornd (1, 1, 4, 1, 5)), [4, 1, 5])
***** assert (size (binornd (1, 1, 0, 1)), [0, 1])
***** assert (size (binornd (1, 1, 1, 0)), [1, 0])
***** assert (size (binornd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
***** assert (class (binornd (1, 1)), "double")
***** assert (class (binornd (1, single (0))), "single")
***** assert (class (binornd (1, single ([0, 0]))), "single")
***** assert (class (binornd (1, single (1), 2)), "single")
***** assert (class (binornd (1, single ([1, 1]), 1, 2)), "single")
***** assert (class (binornd (single (1), 1, 2)), "single")
***** assert (class (binornd (single ([1, 1]), 1, 1, 2)), "single")
***** error<binornd: function called with too few input arguments.> binornd ()
***** error<binornd: function called with too few input arguments.> binornd (1)
***** error<binornd: N and PS must be of common size or scalars.> ...
 binornd (ones (3), ones (2))
***** error<binornd: N and PS must be of common size or scalars.> ...
 binornd (ones (2), ones (3))
***** error<binornd: N and PS must not be complex.> binornd (i, 2)
***** error<binornd: N and PS must not be complex.> binornd (1, i)
***** error<binornd: SZ must be a scalar or a row vector of non-negative integers.> ...
 binornd (1, 1/2, -1)
***** error<binornd: SZ must be a scalar or a row vector of non-negative integers.> ...
 binornd (1, 1/2, 1.2)
***** error<binornd: SZ must be a scalar or a row vector of non-negative integers.> ...
 binornd (1, 1/2, ones (2))
***** error<binornd: SZ must be a scalar or a row vector of non-negative integers.> ...
 binornd (1, 1/2, [2 -1 2])
***** error<binornd: SZ must be a scalar or a row vector of non-negative integers.> ...
 binornd (1, 1/2, [2 0 2.5])
***** error<binornd: dimensions must be non-negative integers.> ...
 binornd (1, 1/2, 2, -1, 5)
***** error<binornd: dimensions must be non-negative integers.> ...
 binornd (1, 1/2, 2, 1.5, 5)
***** error<binornd: N and PS must be scalars or of size SZ.> ...
 binornd (2, 1/2 * ones (2), 3)
***** error<binornd: N and PS must be scalars or of size SZ.> ...
 binornd (2, 1/2 * ones (2), [3, 2])
***** error<binornd: N and PS must be scalars or of size SZ.> ...
 binornd (2, 1/2 * ones (2), 3, 2)
***** error<binornd: N and PS must be scalars or of size SZ.> ...
 binornd (2 * ones (2), 1/2, 3)
***** error<binornd: N and PS must be scalars or of size SZ.> ...
 binornd (2 * ones (2), 1/2, [3, 2])
***** error<binornd: N and PS must be scalars or of size SZ.> ...
 binornd (2 * ones (2), 1/2, 3, 2)
40 tests, 40 passed, 0 known failure, 0 skipped
[inst/dist_fun/nbinpdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/nbinpdf.m
***** demo
 ## Plot various PDFs from the negative binomial distribution
 x = 0:40;
 y1 = nbinpdf (x, 2, 0.15);
 y2 = nbinpdf (x, 5, 0.2);
 y3 = nbinpdf (x, 4, 0.4);
 y4 = nbinpdf (x, 10, 0.3);
 plot (x, y1, "*r", x, y2, "*g", x, y3, "*k", x, y4, "*m")
 grid on
 xlim ([0, 40])
 ylim ([0, 0.12])
 legend ({"r = 2, ps = 0.15", "r = 5, ps = 0.2", "r = 4, p = 0.4", ...
          "r = 10, ps = 0.3"}, "location", "northeast")
 title ("Negative binomial PDF")
 xlabel ("values in x (number of failures)")
 ylabel ("density")
***** shared x, y
 x = [-1 0 1 2 Inf];
 y = [0 1/2 1/4 1/8 NaN];
***** assert (nbinpdf (x, ones (1,5), 0.5*ones (1,5)), y)
***** assert (nbinpdf (x, 1, 0.5*ones (1,5)), y)
***** assert (nbinpdf (x, ones (1,5), 0.5), y)
***** assert (nbinpdf (x, [0 1 NaN 1.5 Inf], 0.5), [NaN 1/2 NaN 1.875*0.5^1.5/4 NaN], eps)
***** assert (nbinpdf (x, 1, 0.5*[-1 NaN 4 1 1]), [NaN NaN NaN y(4:5)])
***** assert (nbinpdf ([x, NaN], 1, 0.5), [y, NaN])
***** assert (nbinpdf (single ([x, NaN]), 1, 0.5), single ([y, NaN]))
***** assert (nbinpdf ([x, NaN], single (1), 0.5), single ([y, NaN]))
***** assert (nbinpdf ([x, NaN], 1, single (0.5)), single ([y, NaN]))
***** error<nbinpdf: function called with too few input arguments.> nbinpdf ()
***** error<nbinpdf: function called with too few input arguments.> nbinpdf (1)
***** error<nbinpdf: function called with too few input arguments.> nbinpdf (1, 2)
***** error<nbinpdf: X, R, and PS must be of common size or scalars.> ...
 nbinpdf (ones (3), ones (2), ones (2))
***** error<nbinpdf: X, R, and PS must be of common size or scalars.> ...
 nbinpdf (ones (2), ones (3), ones (2))
***** error<nbinpdf: X, R, and PS must be of common size or scalars.> ...
 nbinpdf (ones (2), ones (2), ones (3))
***** error<nbinpdf: X, R, and PS must not be complex.> nbinpdf (i, 2, 2)
***** error<nbinpdf: X, R, and PS must not be complex.> nbinpdf (2, i, 2)
***** error<nbinpdf: X, R, and PS must not be complex.> nbinpdf (2, 2, i)
18 tests, 18 passed, 0 known failure, 0 skipped
[inst/dist_fun/norminv.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/norminv.m
***** demo
 ## Plot various iCDFs from the normal distribution
 p = 0.001:0.001:0.999;
 x1 = norminv (p, 0, 0.5);
 x2 = norminv (p, 0, 1);
 x3 = norminv (p, 0, 2);
 x4 = norminv (p, -2, 0.8);
 plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-c")
 grid on
 ylim ([-5, 5])
 legend ({"μ = 0, σ = 0.5", "μ = 0, σ = 1", ...
          "μ = 0, σ = 2", "μ = -2, σ = 0.8"}, "location", "northwest")
 title ("Normal iCDF")
 xlabel ("probability")
 ylabel ("values in x")
***** shared p
 p = [-1 0 0.5 1 2];
***** assert (norminv (p, ones (1,5), ones (1,5)), [NaN -Inf 1 Inf NaN])
***** assert (norminv (p, 1, ones (1,5)), [NaN -Inf 1 Inf NaN])
***** assert (norminv (p, ones (1,5), 1), [NaN -Inf 1 Inf NaN])
***** assert (norminv (p, [1 -Inf NaN Inf 1], 1), [NaN NaN NaN NaN NaN])
***** assert (norminv (p, 1, [1 0 NaN Inf 1]), [NaN NaN NaN NaN NaN])
***** assert (norminv ([p(1:2) NaN p(4:5)], 1, 1), [NaN -Inf NaN Inf NaN])
***** assert (norminv (p), probit (p))
***** assert (norminv (0.31254), probit (0.31254))
***** assert (norminv ([p, NaN], 1, 1), [NaN -Inf 1 Inf NaN NaN])
***** assert (norminv (single ([p, NaN]), 1, 1), single ([NaN -Inf 1 Inf NaN NaN]))
***** assert (norminv ([p, NaN], single (1), 1), single ([NaN -Inf 1 Inf NaN NaN]))
***** assert (norminv ([p, NaN], 1, single (1)), single ([NaN -Inf 1 Inf NaN NaN]))
***** error<norminv: function called with too few input arguments.> norminv ()
***** error<norminv: P, MU, and SIGMA must be of common size or scalars.> ...
 norminv (ones (3), ones (2), ones (2))
***** error<norminv: P, MU, and SIGMA must be of common size or scalars.> ...
 norminv (ones (2), ones (3), ones (2))
***** error<norminv: P, MU, and SIGMA must be of common size or scalars.> ...
 norminv (ones (2), ones (2), ones (3))
***** error<norminv: P, MU, and SIGMA must not be complex.> norminv (i, 2, 2)
***** error<norminv: P, MU, and SIGMA must not be complex.> norminv (2, i, 2)
***** error<norminv: P, MU, and SIGMA must not be complex.> norminv (2, 2, i)
19 tests, 19 passed, 0 known failure, 0 skipped
[inst/dist_fun/tlspdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/tlspdf.m
***** demo
 ## Plot various PDFs from the Student's T distribution
 x = -8:0.01:8;
 y1 = tlspdf (x, 0, 1, 1);
 y2 = tlspdf (x, 0, 2, 2);
 y3 = tlspdf (x, 3, 2, 5);
 y4 = tlspdf (x, -1, 3, Inf);
 plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-m")
 grid on
 xlim ([-8, 8])
 ylim ([0, 0.41])
 legend ({"mu = 0, sigma = 1, nu = 1", "mu = 0, sigma = 2, nu = 2", ...
          "mu = 3, sigma = 2, nu = 5", 'mu = -1, sigma = 3, nu = \infty'}, ...
         "location", "northwest")
 title ("Location-scale Student's T PDF")
 xlabel ("values in x")
 ylabel ("density")
***** test
 x = rand (10,1);
 y = 1./(pi * (1 + x.^2));
 assert (tlspdf (x, 0, 1, 1), y, 5*eps);
 assert (tlspdf (x+5, 5, 1, 1), y, 5*eps);
 assert (tlspdf (x.*2, 0, 2, 1), y./2, 5*eps);
***** shared x, y
 x = [-Inf 0 0.5 1 Inf];
 y = 1./(pi * (1 + x.^2));
***** assert (tlspdf (x, 0, 1, ones (1,5)), y, eps)
***** assert (tlspdf (x, 0, 1, 1), y, eps)
***** assert (tlspdf (x, 0, 1, [0 NaN 1 1 1]), [NaN NaN y(3:5)], eps)
***** assert (tlspdf (x, 0, 1, Inf), normpdf (x))
***** assert (class (tlspdf ([x, NaN], 1, 1, 1)), "double")
***** assert (class (tlspdf (single ([x, NaN]), 1, 1, 1)), "single")
***** assert (class (tlspdf ([x, NaN], single (1), 1, 1)), "single")
***** assert (class (tlspdf ([x, NaN], 1, single (1), 1)), "single")
***** assert (class (tlspdf ([x, NaN], 1, 1, single (1))), "single")
***** error<tlspdf: function called with too few input arguments.> tlspdf ()
***** error<tlspdf: function called with too few input arguments.> tlspdf (1)
***** error<tlspdf: function called with too few input arguments.> tlspdf (1, 2)
***** error<tlspdf: function called with too few input arguments.> tlspdf (1, 2, 3)
***** error<tlspdf: X, MU, SIGMA, and NU must be of common size or scalars.> ...
 tlspdf (ones (3), ones (2), 1, 1)
***** error<tlspdf: X, MU, SIGMA, and NU must be of common size or scalars.> ...
 tlspdf (ones (2), 1, ones (3), 1)
***** error<tlspdf: X, MU, SIGMA, and NU must be of common size or scalars.> ...
 tlspdf (ones (2), 1, 1, ones (3))
***** error<tlspdf: X, MU, SIGMA, and NU must not be complex.> tlspdf (i, 2, 1, 1)
***** error<tlspdf: X, MU, SIGMA, and NU must not be complex.> tlspdf (2, i, 1, 1)
***** error<tlspdf: X, MU, SIGMA, and NU must not be complex.> tlspdf (2, 1, i, 1)
***** error<tlspdf: X, MU, SIGMA, and NU must not be complex.> tlspdf (2, 1, 1, i)
21 tests, 21 passed, 0 known failure, 0 skipped
[inst/dist_fun/tpdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/tpdf.m
***** demo
 ## Plot various PDFs from the Student's T distribution
 x = -5:0.01:5;
 y1 = tpdf (x, 1);
 y2 = tpdf (x, 2);
 y3 = tpdf (x, 5);
 y4 = tpdf (x, Inf);
 plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-m")
 grid on
 xlim ([-5, 5])
 ylim ([0, 0.41])
 legend ({"df = 1", "df = 2", ...
          "df = 5", 'df = \infty'}, "location", "northeast")
 title ("Student's T PDF")
 xlabel ("values in x")
 ylabel ("density")
***** test
 x = rand (10,1);
 y = 1./(pi * (1 + x.^2));
 assert (tpdf (x, 1), y, 5*eps);
***** shared x, y
 x = [-Inf 0 0.5 1 Inf];
 y = 1./(pi * (1 + x.^2));
***** assert (tpdf (x, ones (1,5)), y, eps)
***** assert (tpdf (x, 1), y, eps)
***** assert (tpdf (x, [0 NaN 1 1 1]), [NaN NaN y(3:5)], eps)
***** assert (tpdf (x, Inf), normpdf (x))
***** assert (tpdf ([x, NaN], 1), [y, NaN], eps)
***** assert (tpdf (single ([x, NaN]), 1), single ([y, NaN]), eps ("single"))
***** assert (tpdf ([x, NaN], single (1)), single ([y, NaN]), eps ("single"))
***** error<tpdf: function called with too few input arguments.> tpdf ()
***** error<tpdf: function called with too few input arguments.> tpdf (1)
***** error<tpdf: X and DF must be of common size or scalars.> ...
 tpdf (ones (3), ones (2))
***** error<tpdf: X and DF must be of common size or scalars.> ...
 tpdf (ones (2), ones (3))
***** error<tpdf: X and DF must not be complex.> tpdf (i, 2)
***** error<tpdf: X and DF must not be complex.> tpdf (2, i)
14 tests, 14 passed, 0 known failure, 0 skipped
[inst/dist_fun/normrnd.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/normrnd.m
***** assert (size (normrnd (1, 1)), [1 1])
***** assert (size (normrnd (1, ones (2,1))), [2, 1])
***** assert (size (normrnd (1, ones (2,2))), [2, 2])
***** assert (size (normrnd (ones (2,1), 1)), [2, 1])
***** assert (size (normrnd (ones (2,2), 1)), [2, 2])
***** assert (size (normrnd (1, 1, 3)), [3, 3])
***** assert (size (normrnd (1, 1, [4, 1])), [4, 1])
***** assert (size (normrnd (1, 1, 4, 1)), [4, 1])
***** assert (size (normrnd (1, 1, 4, 1, 5)), [4, 1, 5])
***** assert (size (normrnd (1, 1, 0, 1)), [0, 1])
***** assert (size (normrnd (1, 1, 1, 0)), [1, 0])
***** assert (size (normrnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
***** assert (class (normrnd (1, 1)), "double")
***** assert (class (normrnd (1, single (1))), "single")
***** assert (class (normrnd (1, single ([1, 1]))), "single")
***** assert (class (normrnd (single (1), 1)), "single")
***** assert (class (normrnd (single ([1, 1]), 1)), "single")
***** error<normrnd: function called with too few input arguments.> normrnd ()
***** error<normrnd: function called with too few input arguments.> normrnd (1)
***** error<normrnd: MU and SIGMA must be of common size or scalars.> ...
 normrnd (ones (3), ones (2))
***** error<normrnd: MU and SIGMA must be of common size or scalars.> ...
 normrnd (ones (2), ones (3))
***** error<normrnd: MU and SIGMA must not be complex.> normrnd (i, 2, 3)
***** error<normrnd: MU and SIGMA must not be complex.> normrnd (1, i, 3)
***** error<normrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 normrnd (1, 2, -1)
***** error<normrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 normrnd (1, 2, 1.2)
***** error<normrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 normrnd (1, 2, ones (2))
***** error<normrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 normrnd (1, 2, [2 -1 2])
***** error<normrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 normrnd (1, 2, [2 0 2.5])
***** error<normrnd: dimensions must be non-negative integers.> ...
 normrnd (1, 2, 2, -1, 5)
***** error<normrnd: dimensions must be non-negative integers.> ...
 normrnd (1, 2, 2, 1.5, 5)
***** error<normrnd: MU and SIGMA must be scalars or of size SZ.> ...
 normrnd (2, ones (2), 3)
***** error<normrnd: MU and SIGMA must be scalars or of size SZ.> ...
 normrnd (2, ones (2), [3, 2])
***** error<normrnd: MU and SIGMA must be scalars or of size SZ.> ...
 normrnd (2, ones (2), 3, 2)
33 tests, 33 passed, 0 known failure, 0 skipped
[inst/dist_fun/plrnd.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/plrnd.m
***** shared x, Fx
 x = [0, 1, 3, 4, 7, 10];
 Fx = [0, 0.2, 0.5, 0.6, 0.7, 1];
***** assert (size (plrnd (x, Fx)), [1, 1])
***** assert (size (plrnd (x, Fx, 3)), [3, 3])
***** assert (size (plrnd (x, Fx, [4, 1])), [4, 1])
***** assert (size (plrnd (x, Fx, 4, 1)), [4, 1])
***** assert (size (plrnd (x, Fx, 4, 1, 5)), [4, 1, 5])
***** assert (size (plrnd (x, Fx, 0, 1)), [0, 1])
***** assert (size (plrnd (x, Fx, 1, 0)), [1, 0])
***** assert (size (plrnd (x, Fx, 1, 2, 0, 5)), [1, 2, 0, 5])
***** assert (class (plrnd (x, Fx)), "double")
***** assert (class (plrnd (x, single (Fx))), "single")
***** assert (class (plrnd (single (x), Fx)), "single")
***** error<plrnd: function called with too few input arguments.> plrnd ()
***** error<plrnd: function called with too few input arguments.> plrnd (1)
***** error<plrnd: X and FX must be vectors of equal size.> ...
 plrnd ([0, 1, 2], [0, 1])
***** error<plrnd: X and FX must be at least two-elements long.> ...
 plrnd ([0], [1])
***** error<plrnd: FX must be bounded in the range> ...
 plrnd ([0, 1, 2], [0, 1, 1.5])
***** error<plrnd: FX must be bounded in the range> ...
 plrnd ([0, 1, 2], [0, i, 1])
***** error<plrnd: X and FX must not be complex.> ...
 plrnd ([0, i, 2], [0, 0.5, 1])
***** error<plrnd: X and FX must not be complex.> ...
 plrnd ([0, i, 2], [0, 0.5i, 1])
***** error<plrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 plrnd (x, Fx, -1)
***** error<plrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 plrnd (x, Fx, 1.2)
***** error<plrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 plrnd (x, Fx, ones (2))
***** error<plrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 plrnd (x, Fx, [2 -1 2])
***** error<plrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 plrnd (x, Fx, [2 0 2.5])
***** error<plrnd: dimensions must be non-negative integers.> ...
 plrnd (x, Fx, 2, -1, 5)
***** error<plrnd: dimensions must be non-negative integers.> ...
 plrnd (x, Fx, 2, 1.5, 5)
26 tests, 26 passed, 0 known failure, 0 skipped
[inst/dist_fun/vmcdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/vmcdf.m
***** demo
 ## Plot various CDFs from the von Mises distribution
 x1 = [-pi:0.1:pi];
 p1 = vmcdf (x1, 0, 0.5);
 p2 = vmcdf (x1, 0, 1);
 p3 = vmcdf (x1, 0, 2);
 p4 = vmcdf (x1, 0, 4);
 plot (x1, p1, "-r", x1, p2, "-g", x1, p3, "-b", x1, p4, "-c")
 grid on
 xlim ([-pi, pi])
 legend ({"μ = 0, k = 0.5", "μ = 0, k = 1", ...
          "μ = 0, k = 2", "μ = 0, k = 4"}, "location", "northwest")
 title ("Von Mises CDF")
 xlabel ("values in x")
 ylabel ("probability")
***** shared x, p0, p1
 x = [-pi:pi/2:pi];
 p0 = [0, 0.10975, 0.5, 0.89025, 1];
 p1 = [0, 0.03752, 0.5, 0.99622, 1];
***** assert (vmcdf (x, 0, 1), p0, 1e-5)
***** assert (vmcdf (x, 0, 1, "upper"), 1 - p0, 1e-5)
***** assert (vmcdf (x, zeros (1,5), ones (1,5)), p0, 1e-5)
***** assert (vmcdf (x, zeros (1,5), ones (1,5), "upper"), 1 - p0, 1e-5)
***** assert (vmcdf (x, 0, [1 2 3 4 5]), p1, 1e-5)
***** assert (vmcdf (x, 0, [1 2 3 4 5], "upper"), 1 - p1, 1e-5)
***** assert (isa (vmcdf (single (pi), 0, 1), "single"), true)
***** assert (isa (vmcdf (pi, single (0), 1), "single"), true)
***** assert (isa (vmcdf (pi, 0, single (1)), "single"), true)
***** error<vmcdf: function called with too few input arguments.> vmcdf ()
***** error<vmcdf: function called with too few input arguments.> vmcdf (1)
***** error<vmcdf: function called with too few input arguments.> vmcdf (1, 2)
***** error<vmcdf: invalid argument for upper tail.> vmcdf (1, 2, 3, "tail")
***** error<vmcdf: invalid argument for upper tail.> vmcdf (1, 2, 3, 4)
***** error<vmcdf: X, MU, and K must be of common size or scalars.> ...
 vmcdf (ones (3), ones (2), ones (2))
***** error<vmcdf: X, MU, and K must be of common size or scalars.> ...
 vmcdf (ones (2), ones (3), ones (2))
***** error<vmcdf: X, MU, and K must be of common size or scalars.> ...
 vmcdf (ones (2), ones (2), ones (3))
***** error<vmcdf: X, MU, and K must not be complex.> vmcdf (i, 2, 2)
***** error<vmcdf: X, MU, and K must not be complex.> vmcdf (2, i, 2)
***** error<vmcdf: X, MU, and K must not be complex.> vmcdf (2, 2, i)
20 tests, 20 passed, 0 known failure, 0 skipped
[inst/dist_fun/gprnd.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/gprnd.m
***** assert (size (gprnd (0, 1, 0)), [1, 1])
***** assert (size (gprnd (0, 1, zeros (2,1))), [2, 1])
***** assert (size (gprnd (0, 1, zeros (2,2))), [2, 2])
***** assert (size (gprnd (0, ones (2,1), 0)), [2, 1])
***** assert (size (gprnd (0, ones (2,2), 0)), [2, 2])
***** assert (size (gprnd (zeros (2,1), 1, 0)), [2, 1])
***** assert (size (gprnd (zeros (2,2), 1, 0)), [2, 2])
***** assert (size (gprnd (0, 1, 0, 3)), [3, 3])
***** assert (size (gprnd (0, 1, 0, [4 1])), [4, 1])
***** assert (size (gprnd (0, 1, 0, 4, 1)), [4, 1])
***** assert (size (gprnd (1,1,0)), [1, 1])
***** assert (size (gprnd (1, 1, zeros (2,1))), [2, 1])
***** assert (size (gprnd (1, 1, zeros (2,2))), [2, 2])
***** assert (size (gprnd (1, ones (2,1), 0)), [2, 1])
***** assert (size (gprnd (1, ones (2,2), 0)), [2, 2])
***** assert (size (gprnd (ones (2,1), 1, 0)), [2, 1])
***** assert (size (gprnd (ones (2,2), 1, 0)), [2, 2])
***** assert (size (gprnd (1, 1, 0, 3)), [3, 3])
***** assert (size (gprnd (1, 1, 0, [4 1])), [4, 1])
***** assert (size (gprnd (1, 1, 0, 4, 1)), [4, 1])
***** assert (size (gprnd (-1, 1, 0)), [1, 1])
***** assert (size (gprnd (-1, 1, zeros (2,1))), [2, 1])
***** assert (size (gprnd (1, -1, zeros (2,2))), [2, 2])
***** assert (size (gprnd (-1, ones (2,1), 0)), [2, 1])
***** assert (size (gprnd (-1, ones (2,2), 0)), [2, 2])
***** assert (size (gprnd (-ones (2,1), 1, 0)), [2, 1])
***** assert (size (gprnd (-ones (2,2), 1, 0)), [2, 2])
***** assert (size (gprnd (-1, 1, 0, 3)), [3, 3])
***** assert (size (gprnd (-1, 1, 0, [4, 1])), [4, 1])
***** assert (size (gprnd (-1, 1, 0, 4, 1)), [4, 1])
***** assert (class (gprnd (0, 1, 0)), "double")
***** assert (class (gprnd (0, 1, single (0))), "single")
***** assert (class (gprnd (0, 1, single ([0, 0]))), "single")
***** assert (class (gprnd (0, single (1),0)), "single")
***** assert (class (gprnd (0, single ([1, 1]),0)), "single")
***** assert (class (gprnd (single (0), 1, 0)), "single")
***** assert (class (gprnd (single ([0, 0]), 1, 0)), "single")
***** error<gprnd: function called with too few input arguments.> gprnd ()
***** error<gprnd: function called with too few input arguments.> gprnd (1)
***** error<gprnd: function called with too few input arguments.> gprnd (1, 2)
***** error<gprnd: K, SIGMA, and THETA must be of common size or scalars.> ...
 gprnd (ones (3), ones (2), ones (2))
***** error<gprnd: K, SIGMA, and THETA must be of common size or scalars.> ...
 gprnd (ones (2), ones (3), ones (2))
***** error<gprnd: K, SIGMA, and THETA must be of common size or scalars.> ...
 gprnd (ones (2), ones (2), ones (3))
***** error<gprnd: K, SIGMA, and THETA must not be complex.> gprnd (i, 2, 3)
***** error<gprnd: K, SIGMA, and THETA must not be complex.> gprnd (1, i, 3)
***** error<gprnd: K, SIGMA, and THETA must not be complex.> gprnd (1, 2, i)
***** error<gprnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 gprnd (1, 2, 3, -1)
***** error<gprnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 gprnd (1, 2, 3, 1.2)
***** error<gprnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 gprnd (1, 2, 3, ones (2))
***** error<gprnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 gprnd (1, 2, 3, [2 -1 2])
***** error<gprnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 gprnd (1, 2, 3, [2 0 2.5])
***** error<gprnd: dimensions must be non-negative integers.> ...
 gprnd (1, 2, 3, 2, -1, 5)
***** error<gprnd: dimensions must be non-negative integers.> ...
 gprnd (1, 2, 3, 2, 1.5, 5)
***** error<gprnd: K, SIGMA, and THETA must be scalars or of size SZ.> ...
 gprnd (2, ones (2), 2, 3)
***** error<gprnd: K, SIGMA, and THETA must be scalars or of size SZ.> ...
 gprnd (2, ones (2), 2, [3, 2])
***** error<gprnd: K, SIGMA, and THETA must be scalars or of size SZ.> ...
 gprnd (2, ones (2), 2, 3, 2)
56 tests, 56 passed, 0 known failure, 0 skipped
[inst/dist_fun/ncx2pdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/ncx2pdf.m
***** demo
 ## Plot various PDFs from the noncentral chi-squared distribution
 x = 0:0.1:10;
 y1 = ncx2pdf (x, 2, 1);
 y2 = ncx2pdf (x, 2, 2);
 y3 = ncx2pdf (x, 2, 3);
 y4 = ncx2pdf (x, 4, 1);
 y5 = ncx2pdf (x, 4, 2);
 y6 = ncx2pdf (x, 4, 3);
 plot (x, y1, "-r", x, y2, "-g", x, y3, "-k", ...
       x, y4, "-m", x, y5, "-c", x, y6, "-y")
 grid on
 xlim ([0, 10])
 ylim ([0, 0.32])
 legend ({"df = 2, λ = 1", "df = 2, λ = 2", ...
          "df = 2, λ = 3", "df = 4, λ = 1", ...
          "df = 4, λ = 2", "df = 4, λ = 3"}, "location", "northeast")
 title ("Noncentral chi-squared PDF")
 xlabel ("values in x")
 ylabel ("density")
***** demo
 ## Compare the noncentral chi-squared PDF with LAMBDA = 2 to the
 ## chi-squared PDF with the same number of degrees of freedom (4).

 x = 0:0.1:10;
 y1 = ncx2pdf (x, 4, 2);
 y2 = chi2pdf (x, 4);
 plot (x, y1, "-", x, y2, "-");
 grid on
 xlim ([0, 10])
 ylim ([0, 0.32])
 legend ({"Noncentral T(10,1)", "T(10)"}, "location", "northwest")
 title ("Noncentral chi-squared vs chi-squared PDFs")
 xlabel ("values in x")
 ylabel ("density")
***** shared x1, df, d1
 x1 = [-Inf, 2, NaN, 4, Inf];
 df = [2, 0, -1, 1, 4];
 d1 = [1, NaN, 3, -1, 2];
***** assert (ncx2pdf (x1, df, d1), [0, NaN, NaN, NaN, 0]);
***** assert (ncx2pdf (x1, df, 1), [0, 0.07093996461786045, NaN, ...
                              0.06160064323277038, 0], 1e-14);
***** assert (ncx2pdf (x1, df, 3), [0, 0.1208364909271113, NaN, ...
                              0.09631299762429098, 0], 1e-14);
***** assert (ncx2pdf (x1, df, 2), [0, 0.1076346446244688, NaN, ...
                              0.08430464047296625, 0], 1e-14);
***** assert (ncx2pdf (x1, 2, d1), [0, NaN, NaN, NaN, 0]);
***** assert (ncx2pdf (2, df, d1), [0.1747201674611283, NaN, NaN, ...
                              NaN, 0.1076346446244688], 1e-14);
***** assert (ncx2pdf (4, df, d1), [0.09355987820265799, NaN, NaN, ...
                              NaN, 0.1192317192431485], 1e-14);
***** error<ncx2pdf: function called with too few input arguments.> ncx2pdf ()
***** error<ncx2pdf: function called with too few input arguments.> ncx2pdf (1)
***** error<ncx2pdf: function called with too few input arguments.> ncx2pdf (1, 2)
***** error<ncx2pdf: X, DF, and LAMBDA must be of common size or scalars.> ...
 ncx2pdf (ones (3), ones (2), ones (2))
***** error<ncx2pdf: X, DF, and LAMBDA must be of common size or scalars.> ...
 ncx2pdf (ones (2), ones (3), ones (2))
***** error<ncx2pdf: X, DF, and LAMBDA must be of common size or scalars.> ...
 ncx2pdf (ones (2), ones (2), ones (3))
***** error<ncx2pdf: X, DF, and LAMBDA must not be complex.> ncx2pdf (i, 2, 2)
***** error<ncx2pdf: X, DF, and LAMBDA must not be complex.> ncx2pdf (2, i, 2)
***** error<ncx2pdf: X, DF, and LAMBDA must not be complex.> ncx2pdf (2, 2, i)
16 tests, 16 passed, 0 known failure, 0 skipped
[inst/dist_fun/poissinv.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/poissinv.m
***** demo
 ## Plot various iCDFs from the Poisson distribution
 p = 0.001:0.001:0.999;
 x1 = poissinv (p, 13);
 x2 = poissinv (p, 4);
 x3 = poissinv (p, 10);
 plot (p, x1, "-b", p, x2, "-g", p, x3, "-r")
 grid on
 ylim ([0, 20])
 legend ({"λ = 1", "λ = 4", "λ = 10"}, "location", "northwest")
 title ("Poisson iCDF")
 xlabel ("probability")
 ylabel ("values in x (number of occurences)")
***** shared p
 p = [-1 0 0.5 1 2];
***** assert (poissinv (p, ones (1,5)), [NaN 0 1 Inf NaN])
***** assert (poissinv (p, 1), [NaN 0 1 Inf NaN])
***** assert (poissinv (p, [1 0 NaN 1 1]), [NaN NaN NaN Inf NaN])
***** assert (poissinv ([p(1:2) NaN p(4:5)], 1), [NaN 0 NaN Inf NaN])
***** assert (poissinv ([p, NaN], 1), [NaN 0 1 Inf NaN NaN])
***** assert (poissinv (single ([p, NaN]), 1), single ([NaN 0 1 Inf NaN NaN]))
***** assert (poissinv ([p, NaN], single (1)), single ([NaN 0 1 Inf NaN NaN]))
***** error<poissinv: function called with too few input arguments.> poissinv ()
***** error<poissinv: function called with too few input arguments.> poissinv (1)
***** error<poissinv: P and LAMBDA must be of common size or scalars.> ...
 poissinv (ones (3), ones (2))
***** error<poissinv: P and LAMBDA must be of common size or scalars.> ...
 poissinv (ones (2), ones (3))
***** error<poissinv: P and LAMBDA must not be complex.> poissinv (i, 2)
***** error<poissinv: P and LAMBDA must not be complex.> poissinv (2, i)
13 tests, 13 passed, 0 known failure, 0 skipped
[inst/dist_fun/nctpdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/nctpdf.m
***** demo
 ## Plot various PDFs from the noncentral T distribution
 x = -5:0.01:10;
 y1 = nctpdf (x, 1, 0);
 y2 = nctpdf (x, 4, 0);
 y3 = nctpdf (x, 1, 2);
 y4 = nctpdf (x, 4, 2);
 plot (x, y1, "-r", x, y2, "-g", x, y3, "-k", x, y4, "-m")
 grid on
 xlim ([-5, 10])
 ylim ([0, 0.4])
 legend ({"df = 1, μ = 0", "df = 4, μ = 0", ...
          "df = 1, μ = 2", "df = 4, μ = 2"}, "location", "northeast")
 title ("Noncentral T PDF")
 xlabel ("values in x")
 ylabel ("density")
***** demo
 ## Compare the noncentral T PDF with MU = 1 to the T PDF
 ## with the same number of degrees of freedom (10).

 x = -5:0.1:5;
 y1 = nctpdf (x, 10, 1);
 y2 = tpdf (x, 10);
 plot (x, y1, "-", x, y2, "-");
 grid on
 xlim ([-5, 5])
 ylim ([0, 0.4])
 legend ({"Noncentral χ^2(4,2)", "χ^2(4)"}, "location", "northwest")
 title ("Noncentral T vs T PDFs")
 xlabel ("values in x")
 ylabel ("density")
***** shared x1, df, mu
 x1 = [-Inf, 2, NaN, 4, Inf];
 df = [2, 0, -1, 1, 4];
 mu = [1, NaN, 3, -1, 2];
***** assert (nctpdf (x1, df, mu), [0, NaN, NaN, 0.00401787561306999, 0], 1e-14);
***** assert (nctpdf (x1, df, 1), [0, NaN, NaN, 0.0482312135423008, 0], 1e-14);
***** assert (nctpdf (x1, df, 3), [0, NaN, NaN, 0.1048493126401585, 0], 1e-14);
***** assert (nctpdf (x1, df, 2), [0, NaN, NaN, 0.08137377919890307, 0], 1e-14);
***** assert (nctpdf (x1, 3, mu), [0, NaN, NaN, 0.001185305171654381, 0], 1e-14);
***** assert (nctpdf (2, df, mu), [0.1791097459405861, NaN, NaN, ...
                             0.0146500727180389, 0.3082302682110299], 1e-14);
***** assert (nctpdf (4, df, mu), [0.04467929612254971, NaN, NaN, ...
                             0.00401787561306999, 0.0972086534042828], 1e-14);
***** error<nctpdf: function called with too few input arguments.> nctpdf ()
***** error<nctpdf: function called with too few input arguments.> nctpdf (1)
***** error<nctpdf: function called with too few input arguments.> nctpdf (1, 2)
***** error<nctpdf: X, DF, and MU must be of common size or scalars.> ...
 nctpdf (ones (3), ones (2), ones (2))
***** error<nctpdf: X, DF, and MU must be of common size or scalars.> ...
 nctpdf (ones (2), ones (3), ones (2))
***** error<nctpdf: X, DF, and MU must be of common size or scalars.> ...
 nctpdf (ones (2), ones (2), ones (3))
***** error<nctpdf: X, DF, and MU must not be complex.> nctpdf (i, 2, 2)
***** error<nctpdf: X, DF, and MU must not be complex.> nctpdf (2, i, 2)
***** error<nctpdf: X, DF, and MU must not be complex.> nctpdf (2, 2, i)
16 tests, 16 passed, 0 known failure, 0 skipped
[inst/dist_fun/cauchycdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/cauchycdf.m
***** demo
 ## Plot various CDFs from the Cauchy distribution
 x = -5:0.01:5;
 p1 = cauchycdf (x, 0, 0.5);
 p2 = cauchycdf (x, 0, 1);
 p3 = cauchycdf (x, 0, 2);
 p4 = cauchycdf (x, -2, 1);
 plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", x, p4, "-c")
 grid on
 xlim ([-5, 5])
 legend ({"x0 = 0, γ = 0.5", "x0 = 0, γ = 1", ...
          "x0 = 0, γ = 2", "x0 = -2, γ = 1"}, "location", "southeast")
 title ("Cauchy CDF")
 xlabel ("values in x")
 ylabel ("probability")
***** shared x, y
 x = [-1 0 0.5 1 2];
 y = 1/pi * atan ((x-1) / 2) + 1/2;
***** assert (cauchycdf (x, ones (1,5), 2*ones (1,5)), y)
***** assert (cauchycdf (x, 1, 2*ones (1,5)), y)
***** assert (cauchycdf (x, ones (1,5), 2), y)
***** assert (cauchycdf (x, [-Inf 1 NaN 1 Inf], 2), [NaN y(2) NaN y(4) NaN])
***** assert (cauchycdf (x, 1, 2*[0 1 NaN 1 Inf]), [NaN y(2) NaN y(4) NaN])
***** assert (cauchycdf ([x(1:2) NaN x(4:5)], 1, 2), [y(1:2) NaN y(4:5)])
***** assert (cauchycdf ([x, NaN], 1, 2), [y, NaN])
***** assert (cauchycdf (single ([x, NaN]), 1, 2), single ([y, NaN]), eps ("single"))
***** assert (cauchycdf ([x, NaN], single (1), 2), single ([y, NaN]), eps ("single"))
***** assert (cauchycdf ([x, NaN], 1, single (2)), single ([y, NaN]), eps ("single"))
***** error<cauchycdf: function called with too few input arguments.> cauchycdf ()
***** error<cauchycdf: function called with too few input arguments.> cauchycdf (1)
***** error<cauchycdf: function called with too few input arguments.> ...
 cauchycdf (1, 2)
***** error<cauchycdf: function called with too many inputs> ...
 cauchycdf (1, 2, 3, 4, 5)
***** error<cauchycdf: invalid argument for upper tail.> cauchycdf (1, 2, 3, "tail")
***** error<cauchycdf: invalid argument for upper tail.> cauchycdf (1, 2, 3, 4)
***** error<cauchycdf: X, X0, and GAMMA must be of common size or scalars.> ...
 cauchycdf (ones (3), ones (2), ones (2))
***** error<cauchycdf: X, X0, and GAMMA must be of common size or scalars.> ...
 cauchycdf (ones (2), ones (3), ones (2))
***** error<cauchycdf: X, X0, and GAMMA must be of common size or scalars.> ...
 cauchycdf (ones (2), ones (2), ones (3))
***** error<cauchycdf: X, X0, and GAMMA must not be complex.> cauchycdf (i, 2, 2)
***** error<cauchycdf: X, X0, and GAMMA must not be complex.> cauchycdf (2, i, 2)
***** error<cauchycdf: X, X0, and GAMMA must not be complex.> cauchycdf (2, 2, i)
22 tests, 22 passed, 0 known failure, 0 skipped
[inst/dist_fun/binocdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/binocdf.m
***** demo
 ## Plot various CDFs from the binomial distribution
 x = 0:40;
 p1 = binocdf (x, 20, 0.5);
 p2 = binocdf (x, 20, 0.7);
 p3 = binocdf (x, 40, 0.5);
 plot (x, p1, "*b", x, p2, "*g", x, p3, "*r")
 grid on
 legend ({"n = 20, ps = 0.5", "n = 20, ps = 0.7", ...
          "n = 40, ps = 0.5"}, "location", "southeast")
 title ("Binomial CDF")
 xlabel ("values in x (number of successes)")
 ylabel ("probability")
***** shared x, p, p1
 x = [-1 0 1 2 3];
 p = [0 1/4 3/4 1 1];
 p1 = 1 - p;
***** assert (binocdf (x, 2 * ones (1, 5), 0.5 * ones (1, 5)), p, eps)
***** assert (binocdf (x, 2, 0.5 * ones (1, 5)), p, eps)
***** assert (binocdf (x, 2 * ones (1, 5), 0.5), p, eps)
***** assert (binocdf (x, 2 * [0 -1 NaN 1.1 1], 0.5), [0 NaN NaN NaN 1])
***** assert (binocdf (x, 2, 0.5 * [0 -1 NaN 3 1]), [0 NaN NaN NaN 1])
***** assert (binocdf ([x(1:2) NaN x(4:5)], 2, 0.5), [p(1:2) NaN p(4:5)], eps)
***** assert (binocdf (99, 100, 0.1, "upper"), 1e-100, 1e-112);
***** assert (binocdf (x, 2 * ones (1, 5), 0.5*ones (1,5), "upper"), p1, eps)
***** assert (binocdf (x, 2, 0.5 * ones (1, 5), "upper"), p1, eps)
***** assert (binocdf (x, 2 * ones (1, 5), 0.5, "upper"), p1, eps)
***** assert (binocdf (x, 2 * [0 -1 NaN 1.1 1], 0.5, "upper"), [1 NaN NaN NaN 0])
***** assert (binocdf (x, 2, 0.5 * [0 -1 NaN 3 1], "upper"), [1 NaN NaN NaN 0])
***** assert (binocdf ([x(1:2) NaN x(4:5)], 2, 0.5, "upper"), [p1(1:2) NaN p1(4:5)])
***** assert (binocdf ([x, NaN], 2, 0.5), [p, NaN], eps)
***** assert (binocdf (single ([x, NaN]), 2, 0.5), single ([p, NaN]))
***** assert (binocdf ([x, NaN], single (2), 0.5), single ([p, NaN]))
***** assert (binocdf ([x, NaN], 2, single (0.5)), single ([p, NaN]))
***** error<binocdf: function called with too few input arguments.> binocdf ()
***** error<binocdf: function called with too few input arguments.> binocdf (1)
***** error<binocdf: function called with too few input arguments.> binocdf (1, 2)
***** error<binocdf: function called with too many inputs> binocdf (1, 2, 3, 4, 5)
***** error<binocdf: invalid argument for upper tail.> binocdf (1, 2, 3, "tail")
***** error<binocdf: invalid argument for upper tail.> binocdf (1, 2, 3, 4)
***** error<binocdf: X, N, and PS must be of common size or scalars.> ...
 binocdf (ones (3), ones (2), ones (2))
***** error<binocdf: X, N, and PS must be of common size or scalars.> ...
 binocdf (ones (2), ones (3), ones (2))
***** error<binocdf: X, N, and PS must be of common size or scalars.> ...
 binocdf (ones (2), ones (2), ones (3))
***** error<binocdf: X, N, and PS must not be complex.> binocdf (i, 2, 2)
***** error<binocdf: X, N, and PS must not be complex.> binocdf (2, i, 2)
***** error<binocdf: X, N, and PS must not be complex.> binocdf (2, 2, i)
29 tests, 29 passed, 0 known failure, 0 skipped
[inst/dist_fun/ncfcdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/ncfcdf.m
***** demo
 ## Plot various CDFs from the noncentral F distribution
 x = 0:0.01:5;
 p1 = ncfcdf (x, 2, 5, 1);
 p2 = ncfcdf (x, 2, 5, 2);
 p3 = ncfcdf (x, 5, 10, 1);
 p4 = ncfcdf (x, 10, 20, 10);
 plot (x, p1, "-r", x, p2, "-g", x, p3, "-k", x, p4, "-m")
 grid on
 xlim ([0, 5])
 legend ({"df1 = 2, df2 = 5, λ = 1", "df1 = 2, df2 = 5, λ = 2", ...
          "df1 = 5, df2 = 10, λ = 1", "df1 = 10, df2 = 20, λ = 10"}, ...
         "location", "southeast")
 title ("Noncentral F CDF")
 xlabel ("values in x")
 ylabel ("probability")
***** demo
 ## Compare the noncentral F CDF with LAMBDA = 10 to the F CDF with the
 ## same number of numerator and denominator degrees of freedom (5, 20)

 x = 0.01:0.1:10.01;
 p1 = ncfcdf (x, 5, 20, 10);
 p2 = fcdf (x, 5, 20);
 plot (x, p1, "-", x, p2, "-");
 grid on
 xlim ([0, 10])
 legend ({"Noncentral F(5,20,10)", "F(5,20)"}, "location", "southeast")
 title ("Noncentral F vs F CDFs")
 xlabel ("values in x")
 ylabel ("probability")
***** test
 x = -2:0.1:2;
 p = ncfcdf (x, 10, 1, 3);
 assert (p([1:21]), zeros (1, 21), 1e-76);
 assert (p(22), 0.004530737275319753, 1e-14);
 assert (p(30), 0.255842099135669, 1e-14);
 assert (p(41), 0.4379890998457305, 1e-14);
***** test
 p = ncfcdf (12, 10, 3, 2);
 assert (p, 0.9582287900447416, 1e-14);
***** test
 p = ncfcdf (2, 3, 2, 1);
 assert (p, 0.5731985522994989, 1e-14);
***** test
 p = ncfcdf (2, 3, 2, 1, "upper");
 assert (p, 0.4268014477004823, 1e-14);
***** test
 p = ncfcdf ([3, 6], 3, 2, 5, "upper");
 assert (p, [0.530248523596927, 0.3350482341323044], 1e-14);
***** error<ncfcdf: function called with too few input arguments.> ncfcdf ()
***** error<ncfcdf: function called with too few input arguments.> ncfcdf (1)
***** error<ncfcdf: function called with too few input arguments.> ncfcdf (1, 2)
***** error<ncfcdf: function called with too few input arguments.> ncfcdf (1, 2, 3)
***** error<ncfcdf: invalid argument for upper tail.> ncfcdf (1, 2, 3, 4, "tail")
***** error<ncfcdf: invalid argument for upper tail.> ncfcdf (1, 2, 3, 4, 5)
***** error<ncfcdf: X, DF1, DF2, and LAMBDA must be of common size or scalars.> ...
 ncfcdf (ones (3), ones (2), ones (2), ones (2))
***** error<ncfcdf: X, DF1, DF2, and LAMBDA must be of common size or scalars.> ...
 ncfcdf (ones (2), ones (3), ones (2), ones (2))
***** error<ncfcdf: X, DF1, DF2, and LAMBDA must be of common size or scalars.> ...
 ncfcdf (ones (2), ones (2), ones (3), ones (2))
***** error<ncfcdf: X, DF1, DF2, and LAMBDA must be of common size or scalars.> ...
 ncfcdf (ones (2), ones (2), ones (2), ones (3))
***** error<ncfcdf: X, DF1, DF2, and LAMBDA must not be complex.> ncfcdf (i, 2, 2, 2)
***** error<ncfcdf: X, DF1, DF2, and LAMBDA must not be complex.> ncfcdf (2, i, 2, 2)
***** error<ncfcdf: X, DF1, DF2, and LAMBDA must not be complex.> ncfcdf (2, 2, i, 2)
***** error<ncfcdf: X, DF1, DF2, and LAMBDA must not be complex.> ncfcdf (2, 2, 2, i)
19 tests, 19 passed, 0 known failure, 0 skipped
[inst/dist_fun/gumbelcdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/gumbelcdf.m
***** demo
 ## Plot various CDFs from the Gumbel distribution
 x = -5:0.01:20;
 p1 = gumbelcdf (x, 0.5, 2);
 p2 = gumbelcdf (x, 1.0, 2);
 p3 = gumbelcdf (x, 1.5, 3);
 p4 = gumbelcdf (x, 3.0, 4);
 plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", x, p4, "-c")
 grid on
 legend ({"μ = 0.5, β = 2", "μ = 1.0, β = 2", ...
          "μ = 1.5, β = 3", "μ = 3.0, β = 4"}, "location", "southeast")
 title ("Gumbel CDF")
 xlabel ("values in x")
 ylabel ("probability")
***** shared x, y
 x = [-Inf, 1, 2, Inf];
 y = [0, 0.3679, 0.6922, 1];
***** assert (gumbelcdf (x, ones (1,4), ones (1,4)), y, 1e-4)
***** assert (gumbelcdf (x, 1, ones (1,4)), y, 1e-4)
***** assert (gumbelcdf (x, ones (1,4), 1), y, 1e-4)
***** assert (gumbelcdf (x, [0, -Inf, NaN, Inf], 1), [0, 1, NaN, NaN], 1e-4)
***** assert (gumbelcdf (x, 1, [Inf, NaN, -1, 0]), [NaN, NaN, NaN, NaN], 1e-4)
***** assert (gumbelcdf ([x(1:2), NaN, x(4)], 1, 1), [y(1:2), NaN, y(4)], 1e-4)
***** assert (gumbelcdf (x, "upper"), [1, 0.3078, 0.1266, 0], 1e-4)
***** assert (gumbelcdf ([x, NaN], 1, 1), [y, NaN], 1e-4)
***** assert (gumbelcdf (single ([x, NaN]), 1, 1), single ([y, NaN]), 1e-4)
***** assert (gumbelcdf ([x, NaN], single (1), 1), single ([y, NaN]), 1e-4)
***** assert (gumbelcdf ([x, NaN], 1, single (1)), single ([y, NaN]), 1e-4)
***** error<gumbelcdf: invalid number of input arguments.> gumbelcdf ()
***** error<gumbelcdf: invalid number of input arguments.> gumbelcdf (1,2,3,4,5,6,7)
***** error<gumbelcdf: invalid argument for upper tail.> gumbelcdf (1, 2, 3, 4, "uper")
***** error<gumbelcdf: X, MU, and BETA must be of common size or scalars.> ...
 gumbelcdf (ones (3), ones (2), ones (2))
***** error<gumbelcdf: invalid size of covariance matrix.> gumbelcdf (2, 3, 4, [1, 2])
***** error<gumbelcdf: covariance matrix is required for confidence bounds.> ...
 [p, plo, pup] = gumbelcdf (1, 2, 3)
***** error<gumbelcdf: invalid value for alpha.> [p, plo, pup] = ...
 gumbelcdf (1, 2, 3, [1, 0; 0, 1], 0)
***** error<gumbelcdf: invalid value for alpha.> [p, plo, pup] = ...
 gumbelcdf (1, 2, 3, [1, 0; 0, 1], 1.22)
***** error<gumbelcdf: invalid value for alpha.> [p, plo, pup] = ...
 gumbelcdf (1, 2, 3, [1, 0; 0, 1], "alpha", "upper")
***** error<gumbelcdf: X, MU, and BETA must not be complex.> gumbelcdf (i, 2, 2)
***** error<gumbelcdf: X, MU, and BETA must not be complex.> gumbelcdf (2, i, 2)
***** error<gumbelcdf: X, MU, and BETA must not be complex.> gumbelcdf (2, 2, i)
***** error<gumbelcdf: bad covariance matrix.> ...
 [p, plo, pup] = gumbelcdf (1, 2, 3, [1, 0; 0, -inf], 0.04)
24 tests, 24 passed, 0 known failure, 0 skipped
[inst/dist_fun/gaminv.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/gaminv.m
***** demo
 ## Plot various iCDFs from the Gamma distribution
 p = 0.001:0.001:0.999;
 x1 = gaminv (p, 1, 2);
 x2 = gaminv (p, 2, 2);
 x3 = gaminv (p, 3, 2);
 x4 = gaminv (p, 5, 1);
 x5 = gaminv (p, 9, 0.5);
 x6 = gaminv (p, 7.5, 1);
 x7 = gaminv (p, 0.5, 1);
 plot (p, x1, "-r", p, x2, "-g", p, x3, "-y", p, x4, "-m", ...
       p, x5, "-k", p, x6, "-b", p, x7, "-c")
 ylim ([0, 20])
 grid on
 legend ({"α = 1, β = 2", "α = 2, β = 2", "α = 3, β = 2", ...
          "α = 5, β = 1", "α = 9, β = 0.5", "α = 7.5, β = 1", ...
          "α = 0.5, β = 1"}, "location", "northwest")
 title ("Gamma iCDF")
 xlabel ("probability")
 ylabel ("x")
***** shared p
 p = [-1 0 0.63212055882855778 1 2];
***** assert (gaminv (p, ones (1,5), ones (1,5)), [NaN 0 1 Inf NaN], eps)
***** assert (gaminv (p, 1, ones (1,5)), [NaN 0 1 Inf NaN], eps)
***** assert (gaminv (p, ones (1,5), 1), [NaN 0 1 Inf NaN], eps)
***** assert (gaminv (p, [1 -Inf NaN Inf 1], 1), [NaN NaN NaN NaN NaN])
***** assert (gaminv (p, 1, [1 -Inf NaN Inf 1]), [NaN NaN NaN NaN NaN])
***** assert (gaminv ([p(1:2) NaN p(4:5)], 1, 1), [NaN 0 NaN Inf NaN])
***** assert (gaminv ([p(1:2) NaN p(4:5)], 1, 1), [NaN 0 NaN Inf NaN])
***** assert (gaminv (1e-16, 1, 1), 1e-16, eps)
***** assert (gaminv (1e-16, 1, 2), 2e-16, eps)
***** assert (gaminv (1e-20, 3, 5), 1.957434012161815e-06, eps)
***** assert (gaminv (1e-15, 1, 1), 1e-15, eps)
***** assert (gaminv (1e-35, 1, 1), 1e-35, eps)
***** assert (gaminv ([p, NaN], 1, 1), [NaN 0 1 Inf NaN NaN], eps)
***** assert (gaminv (single ([p, NaN]), 1, 1), single ([NaN 0 1 Inf NaN NaN]), ...
 eps ("single"))
***** assert (gaminv ([p, NaN], single (1), 1), single ([NaN 0 1 Inf NaN NaN]), ...
 eps ("single"))
***** assert (gaminv ([p, NaN], 1, single (1)), single ([NaN 0 1 Inf NaN NaN]), ...
 eps ("single"))
***** error<gaminv: function called with too few input arguments.> gaminv ()
***** error<gaminv: function called with too few input arguments.> gaminv (1)
***** error<gaminv: function called with too few input arguments.> gaminv (1,2)
***** error<gaminv: P, Α, and Β must be of common size or scalars.> ...
 gaminv (ones (3), ones (2), ones (2))
***** error<gaminv: P, Α, and Β must be of common size or scalars.> ...
 gaminv (ones (2), ones (3), ones (2))
***** error<gaminv: P, Α, and Β must be of common size or scalars.> ...
 gaminv (ones (2), ones (2), ones (3))
***** error<gaminv: P, Α, and Β must not be complex.> gaminv (i, 2, 2)
***** error<gaminv: P, Α, and Β must not be complex.> gaminv (2, i, 2)
***** error<gaminv: P, Α, and Β must not be complex.> gaminv (2, 2, i)
25 tests, 25 passed, 0 known failure, 0 skipped
[inst/dist_fun/ricepdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/ricepdf.m
***** demo
 ## Plot various PDFs from the Rician distribution
 x = 0:0.01:8;
 y1 = ricepdf (x, 0, 1);
 y2 = ricepdf (x, 0.5, 1);
 y3 = ricepdf (x, 1, 1);
 y4 = ricepdf (x, 2, 1);
 y5 = ricepdf (x, 4, 1);
 plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-m", x, y5, "-k")
 grid on
 ylim ([0, 0.65])
 xlim ([0, 8])
 legend ({"s = 0, σ = 1", "s = 0.5, σ = 1", "s = 1, σ = 1", ...
          "s = 2, σ = 1", "s = 4, σ = 1"}, "location", "northeast")
 title ("Rician PDF")
 xlabel ("values in x")
 ylabel ("density")
***** shared x, y
 x = [-1 0 0.5 1 2];
 y = [0 0 0.1073 0.1978 0.2846];
***** assert (ricepdf (x, ones (1, 5), 2 * ones (1, 5)), y, 1e-4)
***** assert (ricepdf (x, 1, 2 * ones (1, 5)), y, 1e-4)
***** assert (ricepdf (x, ones (1, 5), 2), y, 1e-4)
***** assert (ricepdf (x, [0 NaN 1 1 1], 2), [0 NaN y(3:5)], 1e-4)
***** assert (ricepdf (x, 1, 2 * [0 NaN 1 1 1]), [0 NaN y(3:5)], 1e-4)
***** assert (ricepdf ([x, NaN], 1, 2), [y, NaN], 1e-4)
***** assert (ricepdf (single ([x, NaN]), 1, 2), single ([y, NaN]), 1e-4)
***** assert (ricepdf ([x, NaN], single (1), 2), single ([y, NaN]), 1e-4)
***** assert (ricepdf ([x, NaN], 1, single (2)), single ([y, NaN]), 1e-4)
***** error<ricepdf: function called with too few input arguments.> ricepdf ()
***** error<ricepdf: function called with too few input arguments.> ricepdf (1)
***** error<ricepdf: function called with too few input arguments.> ricepdf (1,2)
***** error<ricepdf: function called with too many inputs> ricepdf (1,2,3,4)
***** error<ricepdf: X, S, and SIGMA must be of common size or scalars.> ...
 ricepdf (ones (3), ones (2), ones (2))
***** error<ricepdf: X, S, and SIGMA must be of common size or scalars.> ...
 ricepdf (ones (2), ones (3), ones (2))
***** error<ricepdf: X, S, and SIGMA must be of common size or scalars.> ...
 ricepdf (ones (2), ones (2), ones (3))
***** error<ricepdf: X, S, and SIGMA must not be complex.> ricepdf (i, 2, 2)
***** error<ricepdf: X, S, and SIGMA must not be complex.> ricepdf (2, i, 2)
***** error<ricepdf: X, S, and SIGMA must not be complex.> ricepdf (2, 2, i)
19 tests, 19 passed, 0 known failure, 0 skipped
[inst/dist_fun/tlscdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/tlscdf.m
***** demo
 ## Plot various CDFs from the location-scale Student's T distribution
 x = -8:0.01:8;
 p1 = tlscdf (x, 0, 1, 1);
 p2 = tlscdf (x, 0, 2, 2);
 p3 = tlscdf (x, 3, 2, 5);
 p4 = tlscdf (x, -1, 3, Inf);
 plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", x, p4, "-m")
 grid on
 xlim ([-8, 8])
 ylim ([0, 1])
 legend ({"mu = 0, sigma = 1, nu = 1", "mu = 0, sigma = 2, nu = 2", ...
          "mu = 3, sigma = 2, nu = 5", 'mu = -1, sigma = 3, nu = \infty'}, ...
         "location", "northwest")
 title ("Location-scale Student's T CDF")
 xlabel ("values in x")
 ylabel ("probability")
***** shared x,y
 x = [-Inf 0 1 Inf];
 y = [0 1/2 3/4 1];
***** assert (tlscdf (x, 0, 1, ones (1,4)), y, eps)
***** assert (tlscdf (x, 0, 1, 1), y, eps)
***** assert (tlscdf (x, 0, 1, [0 1 NaN 1]), [NaN 1/2 NaN 1], eps)
***** assert (tlscdf ([x(1:2) NaN x(4)], 0, 1, 1), [y(1:2) NaN y(4)], eps)
***** assert (tlscdf (2, 0, 1, 3, "upper"), 0.0697, 1e-4)
***** assert (tlscdf (205, 0, 1, 5, "upper"), 2.6206e-11, 1e-14)
***** assert (tlscdf ([x, NaN], 0, 1, 1), [y, NaN], eps)
***** assert (tlscdf (single ([x, NaN]), 0, 1, 1), single ([y, NaN]), eps ("single"))
***** assert (tlscdf ([x, NaN], single (0), 1, 1), single ([y, NaN]), eps ("single"))
***** assert (tlscdf ([x, NaN], 0, single (1), 1), single ([y, NaN]), eps ("single"))
***** assert (tlscdf ([x, NaN], 0, 1, single (1)), single ([y, NaN]), eps ("single"))
***** error<tlscdf: function called with too few input arguments.> tlscdf ()
***** error<tlscdf: function called with too few input arguments.> tlscdf (1)
***** error<tlscdf: function called with too few input arguments.> tlscdf (1, 2)
***** error<tlscdf: function called with too few input arguments.> tlscdf (1, 2, 3)
***** error<tlscdf: invalid argument for upper tail.> tlscdf (1, 2, 3, 4, "uper")
***** error<tlscdf: invalid argument for upper tail.> tlscdf (1, 2, 3, 4, 5)
***** error<tlscdf: X, MU, SIGMA, and NU must be of common size or scalars.> ...
 tlscdf (ones (3), ones (2), 1, 1)
***** error<tlscdf: X, MU, SIGMA, and NU must be of common size or scalars.> ...
 tlscdf (ones (3), 1, ones (2), 1)
***** error<tlscdf: X, MU, SIGMA, and NU must be of common size or scalars.> ...
 tlscdf (ones (3), 1, 1, ones (2))
***** error<tlscdf: X, MU, SIGMA, and NU must be of common size or scalars.> ...
 tlscdf (ones (3), ones (2), 1, 1, "upper")
***** error<tlscdf: X, MU, SIGMA, and NU must be of common size or scalars.> ...
 tlscdf (ones (3), 1, ones (2), 1, "upper")
***** error<tlscdf: X, MU, SIGMA, and NU must be of common size or scalars.> ...
 tlscdf (ones (3), 1, 1, ones (2), "upper")
***** error<tlscdf: X, MU, SIGMA, and NU must not be complex.> tlscdf (i, 2, 1, 1)
***** error<tlscdf: X, MU, SIGMA, and NU must not be complex.> tlscdf (2, i, 1, 1)
***** error<tlscdf: X, MU, SIGMA, and NU must not be complex.> tlscdf (2, 1, i, 1)
***** error<tlscdf: X, MU, SIGMA, and NU must not be complex.> tlscdf (2, 1, 1, i)
27 tests, 27 passed, 0 known failure, 0 skipped
[inst/dist_fun/hnpdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/hnpdf.m
***** demo
 ## Plot various PDFs from the half-normal distribution
 x = 0:0.001:10;
 y1 = hnpdf (x, 0, 1);
 y2 = hnpdf (x, 0, 2);
 y3 = hnpdf (x, 0, 3);
 y4 = hnpdf (x, 0, 5);
 plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-c")
 grid on
 xlim ([0, 10])
 ylim ([0, 0.9])
 legend ({"μ = 0, σ = 1", "μ = 0, σ = 2", ...
          "μ = 0, σ = 3", "μ = 0, σ = 5"}, "location", "northeast")
 title ("Half-normal PDF")
 xlabel ("values in x")
 ylabel ("density")
***** demo
 ## Plot half-normal against normal probability density function
 x = -5:0.001:5;
 y1 = hnpdf (x, 0, 1);
 y2 = normpdf (x);
 plot (x, y1, "-b", x, y2, "-g")
 grid on
 xlim ([-5, 5])
 ylim ([0, 0.9])
 legend ({"half-normal with μ = 0, σ = 1", ...
          "standart normal (μ = 0, σ = 1)"}, "location", "northeast")
 title ("Half-normal against standard normal PDF")
 xlabel ("values in x")
 ylabel ("density")
***** shared x, y
 x = [-Inf, -1, 0, 1/2, 1, Inf];
 y = [0, 0, 0.7979, 0.7041, 0.4839, 0];
***** assert (hnpdf ([x, NaN], 0, 1), [y, NaN], 1e-4)
***** assert (hnpdf (x, 0, [-2, -1, 0, 1, 1, 1]), [nan(1,3), y([4:6])], 1e-4)
***** assert (class (hncdf (single ([x, NaN]), 0, 1)), "single")
***** assert (class (hncdf ([x, NaN], 0, single (1))), "single")
***** assert (class (hncdf ([x, NaN], single (0), 1)), "single")
***** error<hnpdf: function called with too few input arguments.> hnpdf ()
***** error<hnpdf: function called with too few input arguments.> hnpdf (1)
***** error<hnpdf: function called with too few input arguments.> hnpdf (1, 2)
***** error<hnpdf: X, MU, and SIGMA must be of common size or scalars.> ...
 hnpdf (1, ones (2), ones (3))
***** error<hnpdf: X, MU, and SIGMA must be of common size or scalars.> ...
 hnpdf (ones (2), 1, ones (3))
***** error<hnpdf: X, MU, and SIGMA must be of common size or scalars.> ...
 hnpdf (ones (2), ones (3), 1)
***** error<hnpdf: X, MU, and SIGMA must not be complex.> hnpdf (i, 2, 3)
***** error<hnpdf: X, MU, and SIGMA must not be complex.> hnpdf (1, i, 3)
***** error<hnpdf: X, MU, and SIGMA must not be complex.> hnpdf (1, 2, i)
14 tests, 14 passed, 0 known failure, 0 skipped
[inst/dist_fun/gampdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/gampdf.m
***** demo
 ## Plot various PDFs from the Gamma distribution
 x = 0:0.01:20;
 y1 = gampdf (x, 1, 2);
 y2 = gampdf (x, 2, 2);
 y3 = gampdf (x, 3, 2);
 y4 = gampdf (x, 5, 1);
 y5 = gampdf (x, 9, 0.5);
 y6 = gampdf (x, 7.5, 1);
 y7 = gampdf (x, 0.5, 1);
 plot (x, y1, "-r", x, y2, "-g", x, y3, "-y", x, y4, "-m", ...
       x, y5, "-k", x, y6, "-b", x, y7, "-c")
 grid on
 ylim ([0,0.5])
 legend ({"α = 1, β = 2", "α = 2, β = 2", "α = 3, β = 2", ...
          "α = 5, β = 1", "α = 9, β = 0.5", "α = 7.5, β = 1", ...
          "α = 0.5, β = 1"}, "location", "northeast")
 title ("Gamma PDF")
 xlabel ("values in x")
 ylabel ("density")
***** shared x, y
 x = [-1 0 0.5 1 Inf];
 y = [0 exp(-x(2:end))];
***** assert (gampdf (x, ones (1,5), ones (1,5)), y)
***** assert (gampdf (x, 1, ones (1,5)), y)
***** assert (gampdf (x, ones (1,5), 1), y)
***** assert (gampdf (x, [0 -Inf NaN Inf 1], 1), [NaN NaN NaN NaN y(5)])
***** assert (gampdf (x, 1, [0 -Inf NaN Inf 1]), [NaN NaN NaN 0 y(5)])
***** assert (gampdf ([x, NaN], 1, 1), [y, NaN])
***** assert (gampdf (single ([x, NaN]), 1, 1), single ([y, NaN]))
***** assert (gampdf ([x, NaN], single (1), 1), single ([y, NaN]))
***** assert (gampdf ([x, NaN], 1, single (1)), single ([y, NaN]))
***** error<gampdf: function called with too few input arguments.> gampdf ()
***** error<gampdf: function called with too few input arguments.> gampdf (1)
***** error<gampdf: function called with too few input arguments.> gampdf (1,2)
***** error<gampdf: X, A, and B must be of common size or scalars.> ...
 gampdf (ones (3), ones (2), ones (2))
***** error<gampdf: X, A, and B must be of common size or scalars.> ...
 gampdf (ones (2), ones (3), ones (2))
***** error<gampdf: X, A, and B must be of common size or scalars.> ...
 gampdf (ones (2), ones (2), ones (3))
***** error<gampdf: X, A, and B must not be complex.> gampdf (i, 2, 2)
***** error<gampdf: X, A, and B must not be complex.> gampdf (2, i, 2)
***** error<gampdf: X, A, and B must not be complex.> gampdf (2, 2, i)
18 tests, 18 passed, 0 known failure, 0 skipped
[inst/dist_fun/hygernd.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/hygernd.m
***** assert (size (hygernd (4,2,2)), [1, 1])
***** assert (size (hygernd (4*ones (2,1), 2,2)), [2, 1])
***** assert (size (hygernd (4*ones (2,2), 2,2)), [2, 2])
***** assert (size (hygernd (4, 2*ones (2,1), 2)), [2, 1])
***** assert (size (hygernd (4, 2*ones (2,2), 2)), [2, 2])
***** assert (size (hygernd (4, 2, 2*ones (2,1))), [2, 1])
***** assert (size (hygernd (4, 2, 2*ones (2,2))), [2, 2])
***** assert (size (hygernd (4, 2, 2, 3)), [3, 3])
***** assert (size (hygernd (4, 2, 2, [4 1])), [4, 1])
***** assert (size (hygernd (4, 2, 2, 4, 1)), [4, 1])
***** assert (class (hygernd (4,2,2)), "double")
***** assert (class (hygernd (single (4),2,2)), "single")
***** assert (class (hygernd (single ([4 4]),2,2)), "single")
***** assert (class (hygernd (4,single (2),2)), "single")
***** assert (class (hygernd (4,single ([2 2]),2)), "single")
***** assert (class (hygernd (4,2,single (2))), "single")
***** assert (class (hygernd (4,2,single ([2 2]))), "single")
***** error<hygernd: function called with too few input arguments.> hygernd ()
***** error<hygernd: function called with too few input arguments.> hygernd (1)
***** error<hygernd: function called with too few input arguments.> hygernd (1, 2)
***** error<hygernd: T, M, and N must be of common size or scalars.> ...
 hygernd (ones (3), ones (2), ones (2))
***** error<hygernd: T, M, and N must be of common size or scalars.> ...
 hygernd (ones (2), ones (3), ones (2))
***** error<hygernd: T, M, and N must be of common size or scalars.> ...
 hygernd (ones (2), ones (2), ones (3))
***** error<hygernd: T, M, and N must not be complex.> hygernd (i, 2, 3)
***** error<hygernd: T, M, and N must not be complex.> hygernd (1, i, 3)
***** error<hygernd: T, M, and N must not be complex.> hygernd (1, 2, i)
***** error<hygernd: SZ must be a scalar or a row vector of non-negative integers.> ...
 hygernd (1, 2, 3, -1)
***** error<hygernd: SZ must be a scalar or a row vector of non-negative integers.> ...
 hygernd (1, 2, 3, 1.2)
***** error<hygernd: SZ must be a scalar or a row vector of non-negative integers.> ...
 hygernd (1, 2, 3, ones (2))
***** error<hygernd: SZ must be a scalar or a row vector of non-negative integers.> ...
 hygernd (1, 2, 3, [2 -1 2])
***** error<hygernd: SZ must be a scalar or a row vector of non-negative integers.> ...
 hygernd (1, 2, 3, [2 0 2.5])
***** error<hygernd: dimensions must be non-negative integers.> ...
 hygernd (1, 2, 3, 2, -1, 5)
***** error<hygernd: dimensions must be non-negative integers.> ...
 hygernd (1, 2, 3, 2, 1.5, 5)
***** error<hygernd: T, M, and N must be scalars or of size SZ.> ...
 hygernd (2, ones (2), 2, 3)
***** error<hygernd: T, M, and N must be scalars or of size SZ.> ...
 hygernd (2, ones (2), 2, [3, 2])
***** error<hygernd: T, M, and N must be scalars or of size SZ.> ...
 hygernd (2, ones (2), 2, 3, 2)
36 tests, 36 passed, 0 known failure, 0 skipped
[inst/dist_fun/finv.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/finv.m
***** demo
 ## Plot various iCDFs from the F distribution
 p = 0.001:0.001:0.999;
 x1 = finv (p, 1, 1);
 x2 = finv (p, 2, 1);
 x3 = finv (p, 5, 2);
 x4 = finv (p, 10, 1);
 x5 = finv (p, 100, 100);
 plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-c", p, x5, "-m")
 grid on
 ylim ([0, 4])
 legend ({"df1 = 1, df2 = 2", "df1 = 2, df2 = 1", ...
          "df1 = 5, df2 = 2", "df1 = 10, df2 = 1", ...
          "df1 = 100, df2 = 100"}, "location", "northwest")
 title ("F iCDF")
 xlabel ("probability")
 ylabel ("values in x")
***** shared p
 p = [-1 0 0.5 1 2];
***** assert (finv (p, 2*ones (1,5), 2*ones (1,5)), [NaN 0 1 Inf NaN])
***** assert (finv (p, 2, 2*ones (1,5)), [NaN 0 1 Inf NaN])
***** assert (finv (p, 2*ones (1,5), 2), [NaN 0 1 Inf NaN])
***** assert (finv (p, [2 -Inf NaN Inf 2], 2), [NaN NaN NaN NaN NaN])
***** assert (finv (p, 2, [2 -Inf NaN Inf 2]), [NaN NaN NaN NaN NaN])
***** assert (finv ([p(1:2) NaN p(4:5)], 2, 2), [NaN 0 NaN Inf NaN])
***** assert (finv (0.025, 10, 1e6), 0.3247, 1e-4)
***** assert (finv (0.025, 10, 1e7), 0.3247, 1e-4)
***** assert (finv (0.025, 10, 1e10), 0.3247, 1e-4)
***** assert (finv (0.025, 10, 1e255), 0.3247, 1e-4)
***** assert (finv (0.025, 10, Inf), 0.3247, 1e-4)
***** assert (finv ([p, NaN], 2, 2), [NaN 0 1 Inf NaN NaN])
***** assert (finv (single ([p, NaN]), 2, 2), single ([NaN 0 1 Inf NaN NaN]))
***** assert (finv ([p, NaN], single (2), 2), single ([NaN 0 1 Inf NaN NaN]))
***** assert (finv ([p, NaN], 2, single (2)), single ([NaN 0 1 Inf NaN NaN]))
***** error<finv: function called with too few input arguments.> finv ()
***** error<finv: function called with too few input arguments.> finv (1)
***** error<finv: function called with too few input arguments.> finv (1,2)
***** error<finv: P, DF1, and DF2 must be of common size or scalars.> ...
 finv (ones (3), ones (2), ones (2))
***** error<finv: P, DF1, and DF2 must be of common size or scalars.> ...
 finv (ones (2), ones (3), ones (2))
***** error<finv: P, DF1, and DF2 must be of common size or scalars.> ...
 finv (ones (2), ones (2), ones (3))
***** error<finv: P, DF1, and DF2 must not be complex.> finv (i, 2, 2)
***** error<finv: P, DF1, and DF2 must not be complex.> finv (2, i, 2)
***** error<finv: P, DF1, and DF2 must not be complex.> finv (2, 2, i)
24 tests, 24 passed, 0 known failure, 0 skipped
[inst/dist_fun/exprnd.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/exprnd.m
***** assert (size (exprnd (2)), [1, 1])
***** assert (size (exprnd (ones (2,1))), [2, 1])
***** assert (size (exprnd (ones (2,2))), [2, 2])
***** assert (size (exprnd (1, 3)), [3, 3])
***** assert (size (exprnd (1, [4 1])), [4, 1])
***** assert (size (exprnd (1, 4, 1)), [4, 1])
***** assert (size (exprnd (1, 4, 1)), [4, 1])
***** assert (size (exprnd (1, 4, 1, 5)), [4, 1, 5])
***** assert (size (exprnd (1, 0, 1)), [0, 1])
***** assert (size (exprnd (1, 1, 0)), [1, 0])
***** assert (size (exprnd (1, 1, 2, 0, 5)), [1, 2, 0, 5])
***** assert (class (exprnd (2)), "double")
***** assert (class (exprnd (single (2))), "single")
***** assert (class (exprnd (single ([2 2]))), "single")
***** error<exprnd: function called with too few input arguments.> exprnd ()
***** error<exprnd: MU must not be complex.> exprnd (i)
***** error<exprnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 exprnd (1, -1)
***** error<exprnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 exprnd (1, 1.2)
***** error<exprnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 exprnd (1, ones (2))
***** error<exprnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 exprnd (1, [2 -1 2])
***** error<exprnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 exprnd (1, [2 0 2.5])
***** error<exprnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 exprnd (ones (2), ones (2))
***** error<exprnd: dimensions must be non-negative integers.> ...
 exprnd (1, 2, -1, 5)
***** error<exprnd: dimensions must be non-negative integers.> ...
 exprnd (1, 2, 1.5, 5)
***** error<exprnd: MU must be scalar or of size SZ.> exprnd (ones (2,2), 3)
***** error<exprnd: MU must be scalar or of size SZ.> exprnd (ones (2,2), [3, 2])
***** error<exprnd: MU must be scalar or of size SZ.> exprnd (ones (2,2), 2, 3)
27 tests, 27 passed, 0 known failure, 0 skipped
[inst/dist_fun/gumbelpdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/gumbelpdf.m
***** demo
 ## Plot various PDFs from the Extreme value distribution
 x = -5:0.001:20;
 y1 = gumbelpdf (x, 0.5, 2);
 y2 = gumbelpdf (x, 1.0, 2);
 y3 = gumbelpdf (x, 1.5, 3);
 y4 = gumbelpdf (x, 3.0, 4);
 plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-c")
 grid on
 ylim ([0, 0.2])
 legend ({"μ = 0.5, β = 2", "μ = 1.0, β = 2", ...
          "μ = 1.5, β = 3", "μ = 3.0, β = 4"}, "location", "northeast")
 title ("Extreme value PDF")
 xlabel ("values in x")
 ylabel ("density")
***** shared x, y0, y1
 x = [-5, 0, 1, 2, 3];
 y0 = [0, 0.3679, 0.2547, 0.1182, 0.0474];
 y1 = [0, 0.1794, 0.3679, 0.2547, 0.1182];
***** assert (gumbelpdf (x), y0, 1e-4)
***** assert (gumbelpdf (x, zeros (1,5), ones (1,5)), y0, 1e-4)
***** assert (gumbelpdf (x, ones (1,5), ones (1,5)), y1, 1e-4)
***** error<gumbelpdf: too few input arguments.> gumbelpdf ()
***** error<gumbelpdf: X, MU, and BETA must be of common size or scalars.> ...
 gumbelpdf (ones (3), ones (2), ones (2))
***** error<gumbelpdf: X, MU, and BETA must not be complex.> gumbelpdf (i, 2, 2)
***** error<gumbelpdf: X, MU, and BETA must not be complex.> gumbelpdf (2, i, 2)
***** error<gumbelpdf: X, MU, and BETA must not be complex.> gumbelpdf (2, 2, i)
8 tests, 8 passed, 0 known failure, 0 skipped
[inst/dist_fun/mnrnd.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/mnrnd.m
***** test
 n = 10;
 pk = [0.2, 0.5, 0.3];
 r = mnrnd (n, pk);
 assert (size (r), size (pk));
 assert (all (r >= 0));
 assert (all (round (r) == r));
 assert (sum (r) == n);
***** test
 n = 10 * ones (3, 1);
 pk = [0.2, 0.5, 0.3];
 r = mnrnd (n, pk);
 assert (size (r), [length(n), length(pk)]);
 assert (all (r >= 0));
 assert (all (round (r) == r));
 assert (all (sum (r, 2) == n));
***** test
 n = (1:2)';
 pk = [0.2, 0.5, 0.3; 0.1, 0.1, 0.8];
 r = mnrnd (n, pk);
 assert (size (r), size (pk));
 assert (all (r >= 0));
 assert (all (round (r) == r));
 assert (all (sum (r, 2) == n));
3 tests, 3 passed, 0 known failure, 0 skipped
[inst/dist_fun/wienrnd.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/wienrnd.m
***** error wienrnd (0)
***** error wienrnd (1, 3, -50)
***** error wienrnd (5, 0)
***** error wienrnd (0.4, 3, 5)
***** error wienrnd ([1 4], 3, 5)
5 tests, 5 passed, 0 known failure, 0 skipped
[inst/dist_fun/cauchyrnd.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/cauchyrnd.m
***** assert (size (cauchyrnd (1, 1)), [1 1])
***** assert (size (cauchyrnd (1, ones (2,1))), [2, 1])
***** assert (size (cauchyrnd (1, ones (2,2))), [2, 2])
***** assert (size (cauchyrnd (ones (2,1), 1)), [2, 1])
***** assert (size (cauchyrnd (ones (2,2), 1)), [2, 2])
***** assert (size (cauchyrnd (1, 1, 3)), [3, 3])
***** assert (size (cauchyrnd (1, 1, [4, 1])), [4, 1])
***** assert (size (cauchyrnd (1, 1, 4, 1)), [4, 1])
***** assert (size (cauchyrnd (1, 1, 4, 1, 5)), [4, 1, 5])
***** assert (size (cauchyrnd (1, 1, 0, 1)), [0, 1])
***** assert (size (cauchyrnd (1, 1, 1, 0)), [1, 0])
***** assert (size (cauchyrnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
***** assert (class (cauchyrnd (1, 1)), "double")
***** assert (class (cauchyrnd (1, single (1))), "single")
***** assert (class (cauchyrnd (1, single ([1, 1]))), "single")
***** assert (class (cauchyrnd (single (1), 1)), "single")
***** assert (class (cauchyrnd (single ([1, 1]), 1)), "single")
***** error<cauchyrnd: function called with too few input arguments.> cauchyrnd ()
***** error<cauchyrnd: function called with too few input arguments.> cauchyrnd (1)
***** error<cauchyrnd: X0 and GAMMA must be of common size or scalars.> ...
 cauchyrnd (ones (3), ones (2))
***** error<cauchyrnd: X0 and GAMMA must be of common size or scalars.> ...
 cauchyrnd (ones (2), ones (3))
***** error<cauchyrnd: X0 and GAMMA must not be complex.> cauchyrnd (i, 2, 3)
***** error<cauchyrnd: X0 and GAMMA must not be complex.> cauchyrnd (1, i, 3)
***** error<cauchyrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 cauchyrnd (1, 2, -1)
***** error<cauchyrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 cauchyrnd (1, 2, 1.2)
***** error<cauchyrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 cauchyrnd (1, 2, ones (2))
***** error<cauchyrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 cauchyrnd (1, 2, [2 -1 2])
***** error<cauchyrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 cauchyrnd (1, 2, [2 0 2.5])
***** error<cauchyrnd: dimensions must be non-negative integers.> ...
 cauchyrnd (1, 2, 2, -1, 5)
***** error<cauchyrnd: dimensions must be non-negative integers.> ...
 cauchyrnd (1, 2, 2, 1.5, 5)
***** error<cauchyrnd: X0 and GAMMA must be scalars or of size SZ.> ...
 cauchyrnd (2, ones (2), 3)
***** error<cauchyrnd: X0 and GAMMA must be scalars or of size SZ.> ...
 cauchyrnd (2, ones (2), [3, 2])
***** error<cauchyrnd: X0 and GAMMA must be scalars or of size SZ.> ...
 cauchyrnd (2, ones (2), 3, 2)
33 tests, 33 passed, 0 known failure, 0 skipped
[inst/dist_fun/logicdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/logicdf.m
***** demo
 ## Plot various CDFs from the logistic distribution
 x = -5:0.01:20;
 p1 = logicdf (x, 5, 2);
 p2 = logicdf (x, 9, 3);
 p3 = logicdf (x, 9, 4);
 p4 = logicdf (x, 6, 2);
 p5 = logicdf (x, 2, 1);
 plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", x, p4, "-c", x, p5, "-m")
 grid on
 legend ({"μ = 5, σ = 2", "μ = 9, σ = 3", "μ = 9, σ = 4", ...
          "μ = 6, σ = 2", "μ = 2, σ = 1"}, "location", "southeast")
 title ("Logistic CDF")
 xlabel ("values in x")
 ylabel ("probability")
***** shared x, y
 x = [-Inf -log(3) 0 log(3) Inf];
 y = [0, 1/4, 1/2, 3/4, 1];
***** assert (logicdf ([x, NaN], 0, 1), [y, NaN], eps)
***** assert (logicdf (x, 0, [-2, -1, 0, 1, 2]), [nan(1, 3), 0.75, 1])
***** assert (logicdf (single ([x, NaN]), 0, 1), single ([y, NaN]), eps ("single"))
***** assert (logicdf ([x, NaN], single (0), 1), single ([y, NaN]), eps ("single"))
***** assert (logicdf ([x, NaN], 0, single (1)), single ([y, NaN]), eps ("single"))
***** error<logicdf: function called with too few input arguments.> logicdf ()
***** error<logicdf: function called with too few input arguments.> logicdf (1)
***** error<logicdf: function called with too few input arguments.> ...
 logicdf (1, 2)
***** error<logicdf: invalid argument for upper tail.> logicdf (1, 2, 3, "tail")
***** error<logicdf: invalid argument for upper tail.> logicdf (1, 2, 3, 4)
***** error<logicdf: X, MU, and SIGMA must be of common size or scalars.> ...
 logicdf (1, ones (2), ones (3))
***** error<logicdf: X, MU, and SIGMA must be of common size or scalars.> ...
 logicdf (ones (2), 1, ones (3))
***** error<logicdf: X, MU, and SIGMA must be of common size or scalars.> ...
 logicdf (ones (2), ones (3), 1)
***** error<logicdf: X, MU, and SIGMA must not be complex.> logicdf (i, 2, 3)
***** error<logicdf: X, MU, and SIGMA must not be complex.> logicdf (1, i, 3)
***** error<logicdf: X, MU, and SIGMA must not be complex.> logicdf (1, 2, i)
16 tests, 16 passed, 0 known failure, 0 skipped
[inst/dist_fun/mnpdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/mnpdf.m
***** test
 x = [1, 4, 2];
 pk = [0.2, 0.5, 0.3];
 y = mnpdf (x, pk);
 assert (y, 0.11812, 0.001);
***** test
 x = [1, 4, 2; 1, 0, 9];
 pk = [0.2, 0.5, 0.3; 0.1, 0.1, 0.8];
 y = mnpdf (x, pk);
 assert (y, [0.11812; 0.13422], 0.001);
2 tests, 2 passed, 0 known failure, 0 skipped
[inst/dist_fun/poisspdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/poisspdf.m
***** demo
 ## Plot various PDFs from the Poisson distribution
 x = 0:20;
 y1 = poisspdf (x, 1);
 y2 = poisspdf (x, 4);
 y3 = poisspdf (x, 10);
 plot (x, y1, "*b", x, y2, "*g", x, y3, "*r")
 grid on
 ylim ([0, 0.4])
 legend ({"λ = 1", "λ = 4", "λ = 10"}, "location", "northeast")
 title ("Poisson PDF")
 xlabel ("values in x (number of occurences)")
 ylabel ("density")
***** shared x, y
 x = [-1 0 1 2 Inf];
 y = [0, exp(-1)*[1 1 0.5], 0];
***** assert (poisspdf (x, ones (1,5)), y, eps)
***** assert (poisspdf (x, 1), y, eps)
***** assert (poisspdf (x, [1 0 NaN 1 1]), [y(1) NaN NaN y(4:5)], eps)
***** assert (poisspdf ([x, NaN], 1), [y, NaN], eps)
***** assert (poisspdf (single ([x, NaN]), 1), single ([y, NaN]), eps ("single"))
***** assert (poisspdf ([x, NaN], single (1)), single ([y, NaN]), eps ("single"))
***** error<poisspdf: function called with too few input arguments.> poisspdf ()
***** error<poisspdf: function called with too few input arguments.> poisspdf (1)
***** error<poisspdf: X and LAMBDA must be of common size or scalars.> ...
 poisspdf (ones (3), ones (2))
***** error<poisspdf: X and LAMBDA must be of common size or scalars.> ...
 poisspdf (ones (2), ones (3))
***** error<poisspdf: X and LAMBDA must not be complex.> poisspdf (i, 2)
***** error<poisspdf: X and LAMBDA must not be complex.> poisspdf (2, i)
12 tests, 12 passed, 0 known failure, 0 skipped
[inst/dist_fun/loglcdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/loglcdf.m
***** demo
 ## Plot various CDFs from the log-logistic distribution
 x = 0:0.001:2;
 p1 = loglcdf (x, log (1), 1/0.5);
 p2 = loglcdf (x, log (1), 1);
 p3 = loglcdf (x, log (1), 1/2);
 p4 = loglcdf (x, log (1), 1/4);
 p5 = loglcdf (x, log (1), 1/8);
 plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", x, p4, "-c", x, p5, "-m")
 legend ({"σ = 2 (β = 0.5)", "σ = 1 (β = 1)", "σ = 0.5 (β = 2)", ...
          "σ = 0.25 (β = 4)", "σ = 0.125 (β = 8)"}, "location", "northwest")
 grid on
 title ("Log-logistic CDF")
 xlabel ("values in x")
 ylabel ("probability")
 text (0.05, 0.64, "μ = 0 (α = 1), values of σ (β) as shown in legend")
***** shared out1, out2
 out1 = [0, 0.5, 0.66666667, 0.75, 0.8, 0.83333333];
 out2 = [0, 0.4174, 0.4745, 0.5082, 0.5321, 0.5506];
***** assert (loglcdf ([0:5], 0, 1), out1, 1e-8)
***** assert (loglcdf ([0:5], 0, 1, "upper"), 1 - out1, 1e-8)
***** assert (loglcdf ([0:5], 0, 1), out1, 1e-8)
***** assert (loglcdf ([0:5], 0, 1, "upper"), 1 - out1, 1e-8)
***** assert (loglcdf ([0:5], 1, 3), out2, 1e-4)
***** assert (loglcdf ([0:5], 1, 3, "upper"), 1 - out2, 1e-4)
***** assert (class (loglcdf (single (1), 2, 3)), "single")
***** assert (class (loglcdf (1, single (2), 3)), "single")
***** assert (class (loglcdf (1, 2, single (3))), "single")
***** error<loglcdf: function called with too few input arguments.> loglcdf (1)
***** error<loglcdf: function called with too few input arguments.> loglcdf (1, 2)
***** error<loglcdf: invalid argument for upper tail.> ...
 loglcdf (1, 2, 3, 4)
***** error<loglcdf: invalid argument for upper tail.> ...
 loglcdf (1, 2, 3, "uper")
***** error<loglcdf: X, MU, and SIGMA must be of common size or scalars.> ...
 loglcdf (1, ones (2), ones (3))
***** error<loglcdf: X, MU, and SIGMA must be of common size or scalars.> ...
 loglcdf (1, ones (2), ones (3), "upper")
***** error<loglcdf: X, MU, and SIGMA must be of common size or scalars.> ...
 loglcdf (ones (2), 1, ones (3))
***** error<loglcdf: X, MU, and SIGMA must be of common size or scalars.> ...
 loglcdf (ones (2), 1, ones (3), "upper")
***** error<loglcdf: X, MU, and SIGMA must be of common size or scalars.> ...
 loglcdf (ones (2), ones (3), 1)
***** error<loglcdf: X, MU, and SIGMA must be of common size or scalars.> ...
 loglcdf (ones (2), ones (3), 1, "upper")
***** error<loglcdf: X, MU, and SIGMA must not be complex.> loglcdf (i, 2, 3)
***** error<loglcdf: X, MU, and SIGMA must not be complex.> loglcdf (i, 2, 3, "upper")
***** error<loglcdf: X, MU, and SIGMA must not be complex.> loglcdf (1, i, 3)
***** error<loglcdf: X, MU, and SIGMA must not be complex.> loglcdf (1, i, 3, "upper")
***** error<loglcdf: X, MU, and SIGMA must not be complex.> loglcdf (1, 2, i)
***** error<loglcdf: X, MU, and SIGMA must not be complex.> loglcdf (1, 2, i, "upper")
25 tests, 25 passed, 0 known failure, 0 skipped
[inst/dist_fun/gevrnd.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/gevrnd.m
***** assert(size (gevrnd (1,2,1)), [1, 1]);
***** assert(size (gevrnd (ones(2,1), 2, 1)), [2, 1]);
***** assert(size (gevrnd (ones(2,2), 2, 1)), [2, 2]);
***** assert(size (gevrnd (1, 2*ones(2,1), 1)), [2, 1]);
***** assert(size (gevrnd (1, 2*ones(2,2), 1)), [2, 2]);
***** assert(size (gevrnd (1, 2, 1, 3)), [3, 3]);
***** assert(size (gevrnd (1, 2, 1, [4 1])), [4, 1]);
***** assert(size (gevrnd (1, 2, 1, 4, 1)), [4, 1]);
***** assert (class (gevrnd (1,1,1)), "double")
***** assert (class (gevrnd (single (1),1,1)), "single")
***** assert (class (gevrnd (single ([1 1]),1,1)), "single")
***** assert (class (gevrnd (1,single (1),1)), "single")
***** assert (class (gevrnd (1,single ([1 1]),1)), "single")
***** assert (class (gevrnd (1,1,single (1))), "single")
***** assert (class (gevrnd (1,1,single ([1 1]))), "single")
***** error<gevrnd: function called with too few input arguments.> gevrnd ()
***** error<gevrnd: function called with too few input arguments.> gevrnd (1)
***** error<gevrnd: function called with too few input arguments.> gevrnd (1, 2)
***** error<gevrnd: K, SIGMA, and MU must be of common size or scalars.> ...
 gevrnd (ones (3), ones (2), ones (2))
***** error<gevrnd: K, SIGMA, and MU must be of common size or scalars.> ...
 gevrnd (ones (2), ones (3), ones (2))
***** error<gevrnd: K, SIGMA, and MU must be of common size or scalars.> ...
 gevrnd (ones (2), ones (2), ones (3))
***** error<gevrnd: K, SIGMA, and MU must not be complex.> gevrnd (i, 2, 3)
***** error<gevrnd: K, SIGMA, and MU must not be complex.> gevrnd (1, i, 3)
***** error<gevrnd: K, SIGMA, and MU must not be complex.> gevrnd (1, 2, i)
***** error<gevrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 gevrnd (1, 2, 3, -1)
***** error<gevrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 gevrnd (1, 2, 3, 1.2)
***** error<gevrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 gevrnd (1, 2, 3, ones (2))
***** error<gevrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 gevrnd (1, 2, 3, [2 -1 2])
***** error<gevrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 gevrnd (1, 2, 3, [2 0 2.5])
***** error<gevrnd: dimensions must be non-negative integers.> ...
 gevrnd (1, 2, 3, 2, -1, 5)
***** error<gevrnd: dimensions must be non-negative integers.> ...
 gevrnd (1, 2, 3, 2, 1.5, 5)
***** error<gevrnd: K, SIGMA, and MU must be scalars or of size SZ.> ...
 gevrnd (2, ones (2), 2, 3)
***** error<gevrnd: K, SIGMA, and MU must be scalars or of size SZ.> ...
 gevrnd (2, ones (2), 2, [3, 2])
***** error<gevrnd: K, SIGMA, and MU must be scalars or of size SZ.> ...
 gevrnd (2, ones (2), 2, 3, 2)
34 tests, 34 passed, 0 known failure, 0 skipped
[inst/dist_fun/nbininv.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/nbininv.m
***** demo
 ## Plot various iCDFs from the negative binomial distribution
 p = 0.001:0.001:0.999;
 x1 = nbininv (p, 2, 0.15);
 x2 = nbininv (p, 5, 0.2);
 x3 = nbininv (p, 4, 0.4);
 x4 = nbininv (p, 10, 0.3);
 plot (p, x1, "-r", p, x2, "-g", p, x3, "-k", p, x4, "-m")
 grid on
 ylim ([0, 40])
 legend ({"r = 2, ps = 0.15", "r = 5, ps = 0.2", "r = 4, p = 0.4", ...
          "r = 10, ps = 0.3"}, "location", "northwest")
 title ("Negative binomial iCDF")
 xlabel ("probability")
 ylabel ("values in x (number of failures)")
***** shared p
 p = [-1 0 3/4 1 2];
***** assert (nbininv (p, ones (1,5), 0.5*ones (1,5)), [NaN 0 1 Inf NaN])
***** assert (nbininv (p, 1, 0.5*ones (1,5)), [NaN 0 1 Inf NaN])
***** assert (nbininv (p, ones (1,5), 0.5), [NaN 0 1 Inf NaN])
***** assert (nbininv (p, [1 0 NaN Inf 1], 0.5), [NaN NaN NaN NaN NaN])
***** assert (nbininv (p, [1 0 1.5 Inf 1], 0.5), [NaN NaN 2 NaN NaN])
***** assert (nbininv (p, 1, 0.5*[1 -Inf NaN Inf 1]), [NaN NaN NaN NaN NaN])
***** assert (nbininv ([p(1:2) NaN p(4:5)], 1, 0.5), [NaN 0 NaN Inf NaN])
***** assert (nbininv ([p, NaN], 1, 0.5), [NaN 0 1 Inf NaN NaN])
***** assert (nbininv (single ([p, NaN]), 1, 0.5), single ([NaN 0 1 Inf NaN NaN]))
***** assert (nbininv ([p, NaN], single (1), 0.5), single ([NaN 0 1 Inf NaN NaN]))
***** assert (nbininv ([p, NaN], 1, single (0.5)), single ([NaN 0 1 Inf NaN NaN]))
***** shared y, tol
 y = magic (3) + 1;
 tol = 1;
***** assert (nbininv (nbincdf (1:10, 3, 0.1), 3, 0.1), 1:10, tol)
***** assert (nbininv (nbincdf (1:10, 3./(1:10), 0.1), 3./(1:10), 0.1), 1:10, tol)
***** assert (nbininv (nbincdf (y, 3./y, 1./y), 3./y, 1./y), y, tol)
***** error<nbininv: function called with too few input arguments.> nbininv ()
***** error<nbininv: function called with too few input arguments.> nbininv (1)
***** error<nbininv: function called with too few input arguments.> nbininv (1, 2)
***** error<nbininv: P, R, and PS must be of common size or scalars.> ...
 nbininv (ones (3), ones (2), ones (2))
***** error<nbininv: P, R, and PS must be of common size or scalars.> ...
 nbininv (ones (2), ones (3), ones (2))
***** error<nbininv: P, R, and PS must be of common size or scalars.> ...
 nbininv (ones (2), ones (2), ones (3))
***** error<nbininv: P, R, and PS must not be complex.> nbininv (i, 2, 2)
***** error<nbininv: P, R, and PS must not be complex.> nbininv (2, i, 2)
***** error<nbininv: P, R, and PS must not be complex.> nbininv (2, 2, i)
23 tests, 23 passed, 0 known failure, 0 skipped
[inst/dist_fun/binoinv.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/binoinv.m
***** demo
 ## Plot various iCDFs from the binomial distribution
 p = 0.001:0.001:0.999;
 x1 = binoinv (p, 20, 0.5);
 x2 = binoinv (p, 20, 0.7);
 x3 = binoinv (p, 40, 0.5);
 plot (p, x1, "-b", p, x2, "-g", p, x3, "-r")
 grid on
 legend ({"n = 20, ps = 0.5", "n = 20, ps = 0.7", ...
          "n = 40, ps = 0.5"}, "location", "southeast")
 title ("Binomial iCDF")
 xlabel ("probability")
 ylabel ("values in x (number of successes)")
***** shared p
 p = [-1 0 0.5 1 2];
***** assert (binoinv (p, 2*ones (1,5), 0.5*ones (1,5)), [NaN 0 1 2 NaN])
***** assert (binoinv (p, 2, 0.5*ones (1,5)), [NaN 0 1 2 NaN])
***** assert (binoinv (p, 2*ones (1,5), 0.5), [NaN 0 1 2 NaN])
***** assert (binoinv (p, 2*[0 -1 NaN 1.1 1], 0.5), [NaN NaN NaN NaN NaN])
***** assert (binoinv (p, 2, 0.5*[0 -1 NaN 3 1]), [NaN NaN NaN NaN NaN])
***** assert (binoinv ([p(1:2) NaN p(4:5)], 2, 0.5), [NaN 0 NaN 2 NaN])
***** assert (binoinv ([p, NaN], 2, 0.5), [NaN 0 1 2 NaN NaN])
***** assert (binoinv (single ([p, NaN]), 2, 0.5), single ([NaN 0 1 2 NaN NaN]))
***** assert (binoinv ([p, NaN], single (2), 0.5), single ([NaN 0 1 2 NaN NaN]))
***** assert (binoinv ([p, NaN], 2, single (0.5)), single ([NaN 0 1 2 NaN NaN]))
***** shared x, tol
 x = magic (3) + 1;
 tol = 1;
***** assert (binoinv (binocdf (1:10, 11, 0.1), 11, 0.1), 1:10, tol)
***** assert (binoinv (binocdf (1:10, 2*(1:10), 0.1), 2*(1:10), 0.1), 1:10, tol)
***** assert (binoinv (binocdf (x, 2*x, 1./x), 2*x, 1./x), x, tol)
***** error<binoinv: function called with too few input arguments.> binoinv ()
***** error<binoinv: function called with too few input arguments.> binoinv (1)
***** error<binoinv: function called with too few input arguments.> binoinv (1,2)
***** error<binoinv: function called with too many inputs> binoinv (1,2,3,4)
***** error<binoinv: P, N, and PS must be of common size or scalars.> ...
 binoinv (ones (3), ones (2), ones (2))
***** error<binoinv: P, N, and PS must be of common size or scalars.> ...
 binoinv (ones (2), ones (3), ones (2))
***** error<binoinv: P, N, and PS must be of common size or scalars.> ...
 binoinv (ones (2), ones (2), ones (3))
***** error<binoinv: P, N, and PS must not be complex.> binoinv (i, 2, 2)
***** error<binoinv: P, N, and PS must not be complex.> binoinv (2, i, 2)
***** error<binoinv: P, N, and PS must not be complex.> binoinv (2, 2, i)
23 tests, 23 passed, 0 known failure, 0 skipped
[inst/dist_fun/laplacecdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/laplacecdf.m
***** demo
 ## Plot various CDFs from the Laplace distribution
 x = -10:0.01:10;
 p1 = laplacecdf (x, 0, 1);
 p2 = laplacecdf (x, 0, 2);
 p3 = laplacecdf (x, 0, 4);
 p4 = laplacecdf (x, -5, 4);
 plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", x, p4, "-c")
 grid on
 xlim ([-10, 10])
 legend ({"μ = 0, β = 1", "μ = 0, β = 2", ...
          "μ = 0, β = 4", "μ = -5, β = 4"}, "location", "southeast")
 title ("Laplace CDF")
 xlabel ("values in x")
 ylabel ("probability")
***** shared x, y
 x = [-Inf, -log(2), 0, log(2), Inf];
 y = [0, 1/4, 1/2, 3/4, 1];
***** assert (laplacecdf ([x, NaN], 0, 1), [y, NaN])
***** assert (laplacecdf (x, 0, [-2, -1, 0, 1, 2]), [nan(1, 3), 0.75, 1])
***** assert (laplacecdf (single ([x, NaN]), 0, 1), single ([y, NaN]), eps ("single"))
***** assert (laplacecdf ([x, NaN], single (0), 1), single ([y, NaN]), eps ("single"))
***** assert (laplacecdf ([x, NaN], 0, single (1)), single ([y, NaN]), eps ("single"))
***** error<laplacecdf: function called with too few input arguments.> laplacecdf ()
***** error<laplacecdf: function called with too few input arguments.> laplacecdf (1)
***** error<laplacecdf: function called with too few input arguments.> ...
 laplacecdf (1, 2)
***** error<laplacecdf: function called with too many inputs> ...
 laplacecdf (1, 2, 3, 4, 5)
***** error<laplacecdf: invalid argument for upper tail.> laplacecdf (1, 2, 3, "tail")
***** error<laplacecdf: invalid argument for upper tail.> laplacecdf (1, 2, 3, 4)
***** error<laplacecdf: X, MU, and BETA must be of common size or scalars.> ...
 laplacecdf (ones (3), ones (2), ones (2))
***** error<laplacecdf: X, MU, and BETA must be of common size or scalars.> ...
 laplacecdf (ones (2), ones (3), ones (2))
***** error<laplacecdf: X, MU, and BETA must be of common size or scalars.> ...
 laplacecdf (ones (2), ones (2), ones (3))
***** error<laplacecdf: X, MU, and BETA must not be complex.> laplacecdf (i, 2, 2)
***** error<laplacecdf: X, MU, and BETA must not be complex.> laplacecdf (2, i, 2)
***** error<laplacecdf: X, MU, and BETA must not be complex.> laplacecdf (2, 2, i)
17 tests, 17 passed, 0 known failure, 0 skipped
[inst/dist_fun/wblcdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/wblcdf.m
***** demo
 ## Plot various CDFs from the Weibull distribution
 x = 0:0.001:2.5;
 p1 = wblcdf (x, 1, 0.5);
 p2 = wblcdf (x, 1, 1);
 p3 = wblcdf (x, 1, 1.5);
 p4 = wblcdf (x, 1, 5);
 plot (x, p1, "-b", x, p2, "-r", x, p3, "-m", x, p4, "-g")
 grid on
 legend ({"λ = 1, k = 0.5", "λ = 1, k = 1", ...
          "λ = 1, k = 1.5", "λ = 1, k = 5"}, "location", "southeast")
 title ("Weibull CDF")
 xlabel ("values in x")
 ylabel ("probability")
***** shared x, y
 x = [-1 0 0.5 1 Inf];
 y = [0, 1-exp(-x(2:4)), 1];
***** assert (wblcdf (x, ones (1,5), ones (1,5)), y, 1e-16)
***** assert (wblcdf (x, ones (1,5), ones (1,5), "upper"), 1 - y)
***** assert (wblcdf (x, "upper"), 1 - y)
***** assert (wblcdf (x, 1, ones (1,5)), y, 1e-16)
***** assert (wblcdf (x, ones (1,5), 1), y, 1e-16)
***** assert (wblcdf (x, [0 1 NaN Inf 1], 1), [NaN 0 NaN 0 1])
***** assert (wblcdf (x, [0 1 NaN Inf 1], 1, "upper"), 1 - [NaN 0 NaN 0 1])
***** assert (wblcdf (x, 1, [0 1 NaN Inf 1]), [NaN 0 NaN y(4:5)])
***** assert (wblcdf (x, 1, [0 1 NaN Inf 1], "upper"), 1 - [NaN 0 NaN y(4:5)])
***** assert (wblcdf ([x(1:2) NaN x(4:5)], 1, 1), [y(1:2) NaN y(4:5)])
***** assert (wblcdf ([x(1:2) NaN x(4:5)], 1, 1, "upper"), 1 - [y(1:2) NaN y(4:5)])
***** assert (wblcdf ([x, NaN], 1, 1), [y, NaN], 1e-16)
***** assert (wblcdf (single ([x, NaN]), 1, 1), single ([y, NaN]))
***** assert (wblcdf ([x, NaN], single (1), 1), single ([y, NaN]))
***** assert (wblcdf ([x, NaN], 1, single (1)), single ([y, NaN]))
***** error<wblcdf: invalid number of input arguments.> wblcdf ()
***** error<wblcdf: invalid number of input arguments.> wblcdf (1,2,3,4,5,6,7)
***** error<wblcdf: invalid argument for upper tail.> wblcdf (1, 2, 3, 4, "uper")
***** error<wblcdf: X, LAMBDA, and K must be of common size or scalars.> ...
 wblcdf (ones (3), ones (2), ones (2))
***** error<wblcdf: invalid size of covariance matrix.> wblcdf (2, 3, 4, [1, 2])
***** error<wblcdf: covariance matrix is required for confidence bounds.> ...
 [p, plo, pup] = wblcdf (1, 2, 3)
***** error<wblcdf: invalid value for alpha.> [p, plo, pup] = ...
 wblcdf (1, 2, 3, [1, 0; 0, 1], 0)
***** error<wblcdf: invalid value for alpha.> [p, plo, pup] = ...
 wblcdf (1, 2, 3, [1, 0; 0, 1], 1.22)
***** error<wblcdf: invalid value for alpha.> [p, plo, pup] = ...
 wblcdf (1, 2, 3, [1, 0; 0, 1], "alpha", "upper")
***** error<wblcdf: X, LAMBDA, and K must not be complex.> wblcdf (i, 2, 2)
***** error<wblcdf: X, LAMBDA, and K must not be complex.> wblcdf (2, i, 2)
***** error<wblcdf: X, LAMBDA, and K must not be complex.> wblcdf (2, 2, i)
***** error<wblcdf: bad covariance matrix.> ...
 [p, plo, pup] =wblcdf (1, 2, 3, [1, 0; 0, -inf], 0.04)
28 tests, 28 passed, 0 known failure, 0 skipped
[inst/dist_fun/geoinv.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/geoinv.m
***** demo
 ## Plot various iCDFs from the geometric distribution
 p = 0.001:0.001:0.999;
 x1 = geoinv (p, 0.2);
 x2 = geoinv (p, 0.5);
 x3 = geoinv (p, 0.7);
 plot (p, x1, "-b", p, x2, "-g", p, x3, "-r")
 grid on
 ylim ([0, 10])
 legend ({"ps = 0.2", "ps = 0.5", "ps = 0.7"}, "location", "northwest")
 title ("Geometric iCDF")
 xlabel ("probability")
 ylabel ("values in x (number of failures)")
***** shared p
 p = [-1 0 0.75 1 2];
***** assert (geoinv (p, 0.5*ones (1,5)), [NaN 0 1 Inf NaN])
***** assert (geoinv (p, 0.5), [NaN 0 1 Inf NaN])
***** assert (geoinv (p, 0.5*[1 -1 NaN 4 1]), [NaN NaN NaN NaN NaN])
***** assert (geoinv ([p(1:2) NaN p(4:5)], 0.5), [NaN 0 NaN Inf NaN])
***** assert (geoinv ([p, NaN], 0.5), [NaN 0 1 Inf NaN NaN])
***** assert (geoinv (single ([p, NaN]), 0.5), single ([NaN 0 1 Inf NaN NaN]))
***** assert (geoinv ([p, NaN], single (0.5)), single ([NaN 0 1 Inf NaN NaN]))
***** error<geoinv: function called with too few input arguments.> geoinv ()
***** error<geoinv: function called with too few input arguments.> geoinv (1)
***** error<geoinv: P and PS must be of common size or scalars.> ...
 geoinv (ones (3), ones (2))
***** error<geoinv: P and PS must be of common size or scalars.> ...
 geoinv (ones (2), ones (3))
***** error<geoinv: P and PS must not be complex.> ...
 geoinv (i, 2)
***** error<geoinv: P and PS must not be complex.> ...
 geoinv (2, i)
13 tests, 13 passed, 0 known failure, 0 skipped
[inst/dist_fun/gevcdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/gevcdf.m
***** demo
 ## Plot various CDFs from the generalized extreme value distribution
 x = -1:0.001:10;
 p1 = gevcdf (x, 1, 1, 1);
 p2 = gevcdf (x, 0.5, 1, 1);
 p3 = gevcdf (x, 1, 1, 5);
 p4 = gevcdf (x, 1, 2, 5);
 p5 = gevcdf (x, 1, 5, 5);
 p6 = gevcdf (x, 1, 0.5, 5);
 plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", ...
       x, p4, "-c", x, p5, "-m", x, p6, "-k")
 grid on
 xlim ([-1, 10])
 legend ({"k = 1, σ = 1, μ = 1", "k = 0.5, σ = 1, μ = 1", ...
          "k = 1, σ = 1, μ = 5", "k = 1, σ = 2, μ = 5", ...
          "k = 1, σ = 5, μ = 5", "k = 1, σ = 0.5, μ = 5"}, ...
         "location", "southeast")
 title ("Generalized extreme value CDF")
 xlabel ("values in x")
 ylabel ("probability")
***** test
 x = 0:0.5:2.5;
 sigma = 1:6;
 k = 1;
 mu = 0;
 p = gevcdf (x, k, sigma, mu);
 expected_p = [0.36788, 0.44933, 0.47237, 0.48323, 0.48954, 0.49367];
 assert (p, expected_p, 0.001);
***** test
 x = -0.5:0.5:2.5;
 sigma = 0.5;
 k = 1;
 mu = 0;
 p = gevcdf (x, k, sigma, mu);
 expected_p = [0, 0.36788, 0.60653, 0.71653, 0.77880, 0.81873, 0.84648];
 assert (p, expected_p, 0.001);
***** test # check for continuity for k near 0
 x = 1;
 sigma = 0.5;
 k = -0.03:0.01:0.03;
 mu = 0;
 p = gevcdf (x, k, sigma, mu);
 expected_p = [0.88062, 0.87820, 0.87580, 0.87342, 0.87107, 0.86874, 0.86643];
 assert (p, expected_p, 0.001);
***** error<gevcdf: function called with too few input arguments.> gevcdf ()
***** error<gevcdf: function called with too few input arguments.> gevcdf (1)
***** error<gevcdf: function called with too few input arguments.> gevcdf (1, 2)
***** error<gevcdf: function called with too few input arguments.> gevcdf (1, 2, 3)
***** error<gevcdf: function called with too many inputs> ...
 gevcdf (1, 2, 3, 4, 5, 6)
***** error<gevcdf: invalid argument for upper tail.> gevcdf (1, 2, 3, 4, "tail")
***** error<gevcdf: invalid argument for upper tail.> gevcdf (1, 2, 3, 4, 5)
***** error<gevcdf: X, K, SIGMA, and MU must be of common size or scalars.> ...
 gevcdf (ones (3), ones (2), ones(2), ones(2))
***** error<gevcdf: X, K, SIGMA, and MU must be of common size or scalars.> ...
 gevcdf (ones (2), ones (3), ones(2), ones(2))
***** error<gevcdf: X, K, SIGMA, and MU must be of common size or scalars.> ...
 gevcdf (ones (2), ones (2), ones(3), ones(2))
***** error<gevcdf: X, K, SIGMA, and MU must be of common size or scalars.> ...
 gevcdf (ones (2), ones (2), ones(2), ones(3))
***** error<gevcdf: X, K, SIGMA, and MU must not be complex.> gevcdf (i, 2, 3, 4)
***** error<gevcdf: X, K, SIGMA, and MU must not be complex.> gevcdf (1, i, 3, 4)
***** error<gevcdf: X, K, SIGMA, and MU must not be complex.> gevcdf (1, 2, i, 4)
***** error<gevcdf: X, K, SIGMA, and MU must not be complex.> gevcdf (1, 2, 3, i)
18 tests, 18 passed, 0 known failure, 0 skipped
[inst/dist_fun/ncx2cdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/ncx2cdf.m
***** demo
 ## Plot various CDFs from the noncentral chi-squared distribution
 x = 0:0.1:10;
 p1 = ncx2cdf (x, 2, 1);
 p2 = ncx2cdf (x, 2, 2);
 p3 = ncx2cdf (x, 2, 3);
 p4 = ncx2cdf (x, 4, 1);
 p5 = ncx2cdf (x, 4, 2);
 p6 = ncx2cdf (x, 4, 3);
 plot (x, p1, "-r", x, p2, "-g", x, p3, "-k", ...
       x, p4, "-m", x, p5, "-c", x, p6, "-y")
 grid on
 xlim ([0, 10])
 legend ({"df = 2, λ = 1", "df = 2, λ = 2", ...
          "df = 2, λ = 3", "df = 4, λ = 1", ...
          "df = 4, λ = 2", "df = 4, λ = 3"}, "location", "southeast")
 title ("Noncentral chi-squared CDF")
 xlabel ("values in x")
 ylabel ("probability")
***** demo
 ## Compare the noncentral chi-squared CDF with LAMBDA = 2 to the
 ## chi-squared CDF with the same number of degrees of freedom (4).

 x = 0:0.1:10;
 p1 = ncx2cdf (x, 4, 2);
 p2 = chi2cdf (x, 4);
 plot (x, p1, "-", x, p2, "-")
 grid on
 xlim ([0, 10])
 legend ({"Noncentral χ^2(4,2)", "χ^2(4)"}, "location", "northwest")
 title ("Noncentral chi-squared vs chi-squared CDFs")
 xlabel ("values in x")
 ylabel ("probability")
***** test
 x = -2:0.1:2;
 p = ncx2cdf (x, 10, 1);
 assert (p([1:21]), zeros (1, 21), 3e-84);
 assert (p(22), 1.521400636466575e-09, 1e-14);
 assert (p(30), 6.665480510026046e-05, 1e-14);
 assert (p(41), 0.002406447308399836, 1e-14);
***** test
 p = ncx2cdf (12, 10, 3);
 assert (p, 0.4845555602398649, 1e-14);
***** test
 p = ncx2cdf (2, 3, 2);
 assert (p, 0.2207330870741212, 1e-14);
***** test
 p = ncx2cdf (2, 3, 2, "upper");
 assert (p, 0.7792669129258789, 1e-14);
***** test
 p = ncx2cdf ([3, 6], 3, 2, "upper");
 assert (p, [0.6423318186400054, 0.3152299878943012], 1e-14);
***** error<ncx2cdf: function called with too few input arguments.> ncx2cdf ()
***** error<ncx2cdf: function called with too few input arguments.> ncx2cdf (1)
***** error<ncx2cdf: function called with too few input arguments.> ncx2cdf (1, 2)
***** error<ncx2cdf: invalid argument for upper tail.> ncx2cdf (1, 2, 3, "tail")
***** error<ncx2cdf: invalid argument for upper tail.> ncx2cdf (1, 2, 3, 4)
***** error<ncx2cdf: X, DF, and LAMBDA must be of common size or scalars.> ...
 ncx2cdf (ones (3), ones (2), ones (2))
***** error<ncx2cdf: X, DF, and LAMBDA must be of common size or scalars.> ...
 ncx2cdf (ones (2), ones (3), ones (2))
***** error<ncx2cdf: X, DF, and LAMBDA must be of common size or scalars.> ...
 ncx2cdf (ones (2), ones (2), ones (3))
***** error<ncx2cdf: X, DF, and LAMBDA must not be complex.> ncx2cdf (i, 2, 2)
***** error<ncx2cdf: X, DF, and LAMBDA must not be complex.> ncx2cdf (2, i, 2)
***** error<ncx2cdf: X, DF, and LAMBDA must not be complex.> ncx2cdf (2, 2, i)
16 tests, 16 passed, 0 known failure, 0 skipped
[inst/dist_fun/hygepdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/hygepdf.m
***** demo
 ## Plot various PDFs from the hypergeometric distribution
 x = 0:60;
 y1 = hygepdf (x, 500, 50, 100);
 y2 = hygepdf (x, 500, 60, 200);
 y3 = hygepdf (x, 500, 70, 300);
 plot (x, y1, "*b", x, y2, "*g", x, y3, "*r")
 grid on
 xlim ([0, 60])
 ylim ([0, 0.18])
 legend ({"m = 500, k = 50, μ = 100", "m = 500, k = 60, μ = 200", ...
          "m = 500, k = 70, μ = 300"}, "location", "northeast")
 title ("Hypergeometric PDF")
 xlabel ("values in x (number of successes)")
 ylabel ("density")
***** shared x, y
 x = [-1 0 1 2 3];
 y = [0 1/6 4/6 1/6 0];
***** assert (hygepdf (x, 4*ones (1,5), 2, 2), y, eps)
***** assert (hygepdf (x, 4, 2*ones (1,5), 2), y, eps)
***** assert (hygepdf (x, 4, 2, 2*ones (1,5)), y, eps)
***** assert (hygepdf (x, 4*[1 -1 NaN 1.1 1], 2, 2), [0 NaN NaN NaN 0], eps)
***** assert (hygepdf (x, 4, 2*[1 -1 NaN 1.1 1], 2), [0 NaN NaN NaN 0], eps)
***** assert (hygepdf (x, 4, 5, 2), [NaN NaN NaN NaN NaN], eps)
***** assert (hygepdf (x, 4, 2, 2*[1 -1 NaN 1.1 1]), [0 NaN NaN NaN 0], eps)
***** assert (hygepdf (x, 4, 2, 5), [NaN NaN NaN NaN NaN], eps)
***** assert (hygepdf ([x, NaN], 4, 2, 2), [y, NaN], eps)
***** assert (hygepdf (single ([x, NaN]), 4, 2, 2), single ([y, NaN]), eps("single"))
***** assert (hygepdf ([x, NaN], single (4), 2, 2), single ([y, NaN]), eps("single"))
***** assert (hygepdf ([x, NaN], 4, single (2), 2), single ([y, NaN]), eps("single"))
***** assert (hygepdf ([x, NaN], 4, 2, single (2)), single ([y, NaN]), eps("single"))
***** test
 z = zeros(3,5);
 z([4,5,6,8,9,12]) = [1, 0.5, 1/6, 0.5, 2/3, 1/6];
 assert (hygepdf (x, 4, [0, 1, 2], 2, "vectorexpand"), z, eps);
 assert (hygepdf (x, 4, [0, 1, 2]', 2, "vectorexpand"), z, eps);
 assert (hygepdf (x', 4, [0, 1, 2], 2, "vectorexpand"), z, eps);
 assert (hygepdf (2, 4, [0 ,1, 2], 2, "vectorexpand"), z(:,4), eps);
 assert (hygepdf (x, 4, 1, 2, "vectorexpand"), z(2,:), eps);
 assert (hygepdf ([NaN, x], 4, [0 1 2]', 2, "vectorexpand"), [NaN(3,1), z], eps);
***** error<hygepdf: function called with too few input arguments.> hygepdf ()
***** error<hygepdf: function called with too few input arguments.> hygepdf (1)
***** error<hygepdf: function called with too few input arguments.> hygepdf (1,2)
***** error<hygepdf: function called with too few input arguments.> hygepdf (1,2,3)
***** error<hygepdf: X, T, M, and N must be of common size or scalars.> ...
 hygepdf (1, ones (3), ones (2), ones (2))
***** error<hygepdf: X, T, M, and N must be of common size or scalars.> ...
 hygepdf (1, ones (2), ones (3), ones (2))
***** error<hygepdf: X, T, M, and N must be of common size or scalars.> ...
 hygepdf (1, ones (2), ones (2), ones (3))
***** error<hygepdf: X, T, M, and N must not be complex.> hygepdf (i, 2, 2, 2)
***** error<hygepdf: X, T, M, and N must not be complex.> hygepdf (2, i, 2, 2)
***** error<hygepdf: X, T, M, and N must not be complex.> hygepdf (2, 2, i, 2)
***** error<hygepdf: X, T, M, and N must not be complex.> hygepdf (2, 2, 2, i)
25 tests, 25 passed, 0 known failure, 0 skipped
[inst/dist_fun/ncfinv.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/ncfinv.m
***** demo
 ## Plot various iCDFs from the noncentral F distribution
 p = 0.001:0.001:0.999;
 x1 = ncfinv (p, 2, 5, 1);
 x2 = ncfinv (p, 2, 5, 2);
 x3 = ncfinv (p, 5, 10, 1);
 x4 = ncfinv (p, 10, 20, 10);
 plot (p, x1, "-r", p, x2, "-g", p, x3, "-k", p, x4, "-m")
 grid on
 ylim ([0, 5])
 legend ({"df1 = 2, df2 = 5, λ = 1", "df1 = 2, df2 = 5, λ = 2", ...
          "df1 = 5, df2 = 10, λ = 1", "df1 = 10, df2 = 20, λ = 10"}, ...
         "location", "northwest")
 title ("Noncentral F iCDF")
 xlabel ("probability")
 ylabel ("values in x")
***** demo
 ## Compare the noncentral F iCDF with LAMBDA = 10 to the F iCDF with the
 ## same number of numerator and denominator degrees of freedom (5, 20)

 p = 0.001:0.001:0.999;
 x1 = ncfinv (p, 5, 20, 10);
 x2 = finv (p, 5, 20);
 plot (p, x1, "-", p, x2, "-");
 grid on
 ylim ([0, 10])
 legend ({"Noncentral F(5,20,10)", "F(5,20)"}, "location", "northwest")
 title ("Noncentral F vs F quantile functions")
 xlabel ("probability")
 ylabel ("values in x")
***** test
 x = [0,0.1775,0.3864,0.6395,0.9564,1.3712,1.9471,2.8215,4.3679,8.1865,Inf];
 assert (ncfinv ([0:0.1:1], 2, 3, 1), x, 1e-4);
***** test
 x = [0,0.7492,1.3539,2.0025,2.7658,3.7278,5.0324,6.9826,10.3955,18.7665,Inf];
 assert (ncfinv ([0:0.1:1], 2, 3, 5), x, 1e-4);
***** test
 x = [0,0.2890,0.8632,1.5653,2.4088,3.4594,4.8442,6.8286,10.0983,17.3736,Inf];
 assert (ncfinv ([0:0.1:1], 1, 4, 3), x, 1e-4);
***** test
 x = [0.078410, 0.212716, 0.288618, 0.335752, 0.367963, 0.391460];
 assert (ncfinv (0.05, [1, 2, 3, 4, 5, 6], 10, 3), x, 1e-6);
***** test
 x = [0.2574, 0.2966, 0.3188, 0.3331, 0.3432, 0.3507];
 assert (ncfinv (0.05, 5, [1, 2, 3, 4, 5, 6], 3), x, 1e-4);
***** test
 x = [1.6090, 1.8113, 1.9215, 1.9911, NaN, 2.0742];
 assert (ncfinv (0.05, 1, [1, 2, 3, 4, -1, 6], 10), x, 1e-4);
***** test
 assert (ncfinv (0.996, 3, 5, 8), 58.0912074080671, 2e-13);
***** error<ncfinv: function called with too few input arguments.> ncfinv ()
***** error<ncfinv: function called with too few input arguments.> ncfinv (1)
***** error<ncfinv: function called with too few input arguments.> ncfinv (1, 2)
***** error<ncfinv: function called with too few input arguments.> ncfinv (1, 2, 3)
***** error<ncfinv: P, DF1, DF2, and LAMBDA must be of common size or scalars.> ...
 ncfinv (ones (3), ones (2), ones (2), ones (2))
***** error<ncfinv: P, DF1, DF2, and LAMBDA must be of common size or scalars.> ...
 ncfinv (ones (2), ones (3), ones (2), ones (2))
***** error<ncfinv: P, DF1, DF2, and LAMBDA must be of common size or scalars.> ...
 ncfinv (ones (2), ones (2), ones (3), ones (2))
***** error<ncfinv: P, DF1, DF2, and LAMBDA must be of common size or scalars.> ...
 ncfinv (ones (2), ones (2), ones (2), ones (3))
***** error<ncfinv: P, DF1, DF2, and LAMBDA must not be complex.> ncfinv (i, 2, 2, 2)
***** error<ncfinv: P, DF1, DF2, and LAMBDA must not be complex.> ncfinv (2, i, 2, 2)
***** error<ncfinv: P, DF1, DF2, and LAMBDA must not be complex.> ncfinv (2, 2, i, 2)
***** error<ncfinv: P, DF1, DF2, and LAMBDA must not be complex.> ncfinv (2, 2, 2, i)
19 tests, 19 passed, 0 known failure, 0 skipped
[inst/dist_fun/frnd.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/frnd.m
***** assert (size (frnd (1, 1)), [1 1])
***** assert (size (frnd (1, ones (2,1))), [2, 1])
***** assert (size (frnd (1, ones (2,2))), [2, 2])
***** assert (size (frnd (ones (2,1), 1)), [2, 1])
***** assert (size (frnd (ones (2,2), 1)), [2, 2])
***** assert (size (frnd (1, 1, 3)), [3, 3])
***** assert (size (frnd (1, 1, [4, 1])), [4, 1])
***** assert (size (frnd (1, 1, 4, 1)), [4, 1])
***** assert (size (frnd (1, 1, 4, 1, 5)), [4, 1, 5])
***** assert (size (frnd (1, 1, 0, 1)), [0, 1])
***** assert (size (frnd (1, 1, 1, 0)), [1, 0])
***** assert (size (frnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
***** assert (class (frnd (1, 1)), "double")
***** assert (class (frnd (1, single (1))), "single")
***** assert (class (frnd (1, single ([1, 1]))), "single")
***** assert (class (frnd (single (1), 1)), "single")
***** assert (class (frnd (single ([1, 1]), 1)), "single")
***** error<frnd: function called with too few input arguments.> frnd ()
***** error<frnd: function called with too few input arguments.> frnd (1)
***** error<frnd: DF1 and DF2 must be of common size or scalars.> ...
 frnd (ones (3), ones (2))
***** error<frnd: DF1 and DF2 must be of common size or scalars.> ...
 frnd (ones (2), ones (3))
***** error<frnd: DF1 and DF2 must not be complex.> frnd (i, 2, 3)
***** error<frnd: DF1 and DF2 must not be complex.> frnd (1, i, 3)
***** error<frnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 frnd (1, 2, -1)
***** error<frnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 frnd (1, 2, 1.2)
***** error<frnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 frnd (1, 2, ones (2))
***** error<frnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 frnd (1, 2, [2 -1 2])
***** error<frnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 frnd (1, 2, [2 0 2.5])
***** error<frnd: dimensions must be non-negative integers.> ...
 frnd (1, 2, 2, -1, 5)
***** error<frnd: dimensions must be non-negative integers.> ...
 frnd (1, 2, 2, 1.5, 5)
***** error<frnd: DF1 and DF2 must be scalars or of size SZ.> ...
 frnd (2, ones (2), 3)
***** error<frnd: DF1 and DF2 must be scalars or of size SZ.> ...
 frnd (2, ones (2), [3, 2])
***** error<frnd: DF1 and DF2 must be scalars or of size SZ.> ...
 frnd (2, ones (2), 3, 2)
33 tests, 33 passed, 0 known failure, 0 skipped
[inst/dist_fun/nakapdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/nakapdf.m
***** demo
 ## Plot various PDFs from the Nakagami distribution
 x = 0:0.01:3;
 y1 = nakapdf (x, 0.5, 1);
 y2 = nakapdf (x, 1, 1);
 y3 = nakapdf (x, 1, 2);
 y4 = nakapdf (x, 1, 3);
 y5 = nakapdf (x, 2, 1);
 y6 = nakapdf (x, 2, 2);
 y7 = nakapdf (x, 5, 1);
 plot (x, y1, "-r", x, y2, "-g", x, y3, "-y", x, y4, "-m", ...
       x, y5, "-k", x, y6, "-b", x, y7, "-c")
 grid on
 xlim ([0, 3])
 ylim ([0, 2])
 legend ({"μ = 0.5, ω = 1", "μ = 1, ω = 1", "μ = 1, ω = 2", ...
          "μ = 1, ω = 3", "μ = 2, ω = 1", "μ = 2, ω = 2", ...
          "μ = 5, ω = 1"}, "location", "northeast")
 title ("Nakagami PDF")
 xlabel ("values in x")
 ylabel ("density")
***** shared x, y
 x = [-1, 0, 1, 2, Inf];
 y = [0, 0, 0.73575888234288467, 0.073262555554936715, 0];
***** assert (nakapdf (x, ones (1,5), ones (1,5)), y, eps)
***** assert (nakapdf (x, 1, 1), y, eps)
***** assert (nakapdf (x, [1, 1, NaN, 1, 1], 1), [y(1:2), NaN, y(4:5)], eps)
***** assert (nakapdf (x, 1, [1, 1, NaN, 1, 1]), [y(1:2), NaN, y(4:5)], eps)
***** assert (nakapdf ([x, NaN], 1, 1), [y, NaN], eps)
***** assert (nakapdf (single ([x, NaN]), 1, 1), single ([y, NaN]))
***** assert (nakapdf ([x, NaN], single (1), 1), single ([y, NaN]))
***** assert (nakapdf ([x, NaN], 1, single (1)), single ([y, NaN]))
***** error<nakapdf: function called with too few input arguments.> nakapdf ()
***** error<nakapdf: function called with too few input arguments.> nakapdf (1)
***** error<nakapdf: function called with too few input arguments.> nakapdf (1, 2)
***** error<nakapdf: X, MU, and OMEGA must be of common size or scalars.> ...
 nakapdf (ones (3), ones (2), ones(2))
***** error<nakapdf: X, MU, and OMEGA must be of common size or scalars.> ...
 nakapdf (ones (2), ones (3), ones(2))
***** error<nakapdf: X, MU, and OMEGA must be of common size or scalars.> ...
 nakapdf (ones (2), ones (2), ones(3))
***** error<nakapdf: X, MU, and OMEGA must not be complex.> nakapdf (i, 4, 3)
***** error<nakapdf: X, MU, and OMEGA must not be complex.> nakapdf (1, i, 3)
***** error<nakapdf: X, MU, and OMEGA must not be complex.> nakapdf (1, 4, i)
17 tests, 17 passed, 0 known failure, 0 skipped
[inst/dist_fun/nakarnd.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/nakarnd.m
***** assert (size (nakarnd (1, 1)), [1 1])
***** assert (size (nakarnd (1, ones (2,1))), [2, 1])
***** assert (size (nakarnd (1, ones (2,2))), [2, 2])
***** assert (size (nakarnd (ones (2,1), 1)), [2, 1])
***** assert (size (nakarnd (ones (2,2), 1)), [2, 2])
***** assert (size (nakarnd (1, 1, 3)), [3, 3])
***** assert (size (nakarnd (1, 1, [4, 1])), [4, 1])
***** assert (size (nakarnd (1, 1, 4, 1)), [4, 1])
***** assert (size (nakarnd (1, 1, 4, 1, 5)), [4, 1, 5])
***** assert (size (nakarnd (1, 1, 0, 1)), [0, 1])
***** assert (size (nakarnd (1, 1, 1, 0)), [1, 0])
***** assert (size (nakarnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
***** assert (class (nakarnd (1, 1)), "double")
***** assert (class (nakarnd (1, single (1))), "single")
***** assert (class (nakarnd (1, single ([1, 1]))), "single")
***** assert (class (nakarnd (single (1), 1)), "single")
***** assert (class (nakarnd (single ([1, 1]), 1)), "single")
***** error<nakarnd: function called with too few input arguments.> nakarnd ()
***** error<nakarnd: function called with too few input arguments.> nakarnd (1)
***** error<nakarnd: MU and OMEGA must be of common size or scalars.> ...
 nakarnd (ones (3), ones (2))
***** error<nakarnd: MU and OMEGA must be of common size or scalars.> ...
 nakarnd (ones (2), ones (3))
***** error<nakarnd: MU and OMEGA must not be complex.> nakarnd (i, 2, 3)
***** error<nakarnd: MU and OMEGA must not be complex.> nakarnd (1, i, 3)
***** error<nakarnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 nakarnd (1, 2, -1)
***** error<nakarnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 nakarnd (1, 2, 1.2)
***** error<nakarnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 nakarnd (1, 2, ones (2))
***** error<nakarnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 nakarnd (1, 2, [2 -1 2])
***** error<nakarnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 nakarnd (1, 2, [2 0 2.5])
***** error<nakarnd: dimensions must be non-negative integers.> ...
 nakarnd (1, 2, 2, -1, 5)
***** error<nakarnd: dimensions must be non-negative integers.> ...
 nakarnd (1, 2, 2, 1.5, 5)
***** error<nakarnd: MU and OMEGA must be scalars or of size SZ.> ...
 nakarnd (2, ones (2), 3)
***** error<nakarnd: MU and OMEGA must be scalars or of size SZ.> ...
 nakarnd (2, ones (2), [3, 2])
***** error<nakarnd: MU and OMEGA must be scalars or of size SZ.> ...
 nakarnd (2, ones (2), 3, 2)
33 tests, 33 passed, 0 known failure, 0 skipped
[inst/dist_fun/burrcdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/burrcdf.m
***** demo
 ## Plot various CDFs from the Burr type XII distribution
 x = 0.001:0.001:5;
 p1 = burrcdf (x, 1, 1, 1);
 p2 = burrcdf (x, 1, 1, 2);
 p3 = burrcdf (x, 1, 1, 3);
 p4 = burrcdf (x, 1, 2, 1);
 p5 = burrcdf (x, 1, 3, 1);
 p6 = burrcdf (x, 1, 0.5, 2);
 plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", ...
       x, p4, "-c", x, p5, "-m", x, p6, "-k")
 grid on
 legend ({"λ = 1, c = 1, k = 1", "λ = 1, c = 1, k = 2", ...
          "λ = 1, c = 1, k = 3", "λ = 1, c = 2, k = 1", ...
          "λ = 1, c = 3, k = 1", "λ = 1, c = 0.5, k = 2"}, ...
         "location", "southeast")
 title ("Burr type XII CDF")
 xlabel ("values in x")
 ylabel ("probability")
***** shared x, y
 x = [-1, 0, 1, 2, Inf];
 y = [0, 0, 1/2, 2/3, 1];
***** assert (burrcdf (x, ones(1,5), ones (1,5), ones (1,5)), y, eps)
***** assert (burrcdf (x, 1, 1, 1), y, eps)
***** assert (burrcdf (x, [1, 1, NaN, 1, 1], 1, 1), [y(1:2), NaN, y(4:5)], eps)
***** assert (burrcdf (x, 1, [1, 1, NaN, 1, 1], 1), [y(1:2), NaN, y(4:5)], eps)
***** assert (burrcdf (x, 1, 1, [1, 1, NaN, 1, 1]), [y(1:2), NaN, y(4:5)], eps)
***** assert (burrcdf ([x, NaN], 1, 1, 1), [y, NaN], eps)
***** assert (burrcdf (single ([x, NaN]), 1, 1, 1), single ([y, NaN]), eps("single"))
***** assert (burrcdf ([x, NaN], single (1), 1, 1), single ([y, NaN]), eps("single"))
***** assert (burrcdf ([x, NaN], 1, single (1), 1), single ([y, NaN]), eps("single"))
***** assert (burrcdf ([x, NaN], 1, 1, single (1)), single ([y, NaN]), eps("single"))
***** error<burrcdf: function called with too few input arguments.> burrcdf ()
***** error<burrcdf: function called with too few input arguments.> burrcdf (1)
***** error<burrcdf: function called with too few input arguments.> burrcdf (1, 2)
***** error<burrcdf: function called with too few input arguments.> burrcdf (1, 2, 3)
***** error<burrcdf: function called with too many inputs> ...
 burrcdf (1, 2, 3, 4, 5, 6)
***** error<burrcdf: invalid argument for upper tail.> burrcdf (1, 2, 3, 4, "tail")
***** error<burrcdf: invalid argument for upper tail.> burrcdf (1, 2, 3, 4, 5)
***** error<burrcdf: X, LAMBDA, C, and K must be of common size or scalars.> ...
 burrcdf (ones (3), ones (2), ones(2), ones(2))
***** error<burrcdf: X, LAMBDA, C, and K must be of common size or scalars.> ...
 burrcdf (ones (2), ones (3), ones(2), ones(2))
***** error<burrcdf: X, LAMBDA, C, and K must be of common size or scalars.> ...
 burrcdf (ones (2), ones (2), ones(3), ones(2))
***** error<burrcdf: X, LAMBDA, C, and K must be of common size or scalars.> ...
 burrcdf (ones (2), ones (2), ones(2), ones(3))
***** error<burrcdf: X, LAMBDA, C, and K must not be complex.> burrcdf (i, 2, 3, 4)
***** error<burrcdf: X, LAMBDA, C, and K must not be complex.> burrcdf (1, i, 3, 4)
***** error<burrcdf: X, LAMBDA, C, and K must not be complex.> burrcdf (1, 2, i, 4)
***** error<burrcdf: X, LAMBDA, C, and K must not be complex.> burrcdf (1, 2, 3, i)
25 tests, 25 passed, 0 known failure, 0 skipped
[inst/dist_fun/bvncdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/bvncdf.m
***** demo
 mu = [1, -1];
 sigma = [0.9, 0.4; 0.4, 0.3];
 [X1, X2] = meshgrid (linspace (-1, 3, 25)', linspace (-3, 1, 25)');
 x = [X1(:), X2(:)];
 p = bvncdf (x, mu, sigma);
 Z = reshape (p, 25, 25);
 surf (X1, X2, Z);
 title ("Bivariate Normal Distribution");
 ylabel "X1"
 xlabel "X2"
***** test
 mu = [1, -1];
 sigma = [0.9, 0.4; 0.4, 0.3];
 [X1,X2] = meshgrid (linspace (-1, 3, 25)', linspace (-3, 1, 25)');
 x = [X1(:), X2(:)];
 p = bvncdf (x, mu, sigma);
 p_out = [0.00011878988774500, 0.00034404112322371, ...
          0.00087682502191813, 0.00195221905058185, ...
          0.00378235566873474, 0.00638175749734415, ...
          0.00943764224329656, 0.01239164888125426, ...
          0.01472750274376648, 0.01623228313374828]';
 assert (p([1:10]), p_out, 1e-16);
***** test
 mu = [1, -1];
 sigma = [0.9, 0.4; 0.4, 0.3];
 [X1,X2] = meshgrid (linspace (-1, 3, 25)', linspace (-3, 1, 25)');
 x = [X1(:), X2(:)];
 p = bvncdf (x, mu, sigma);
 p_out = [0.8180695783608276, 0.8854485749482751, ...
          0.9308108777385832, 0.9579855743025508, ...
          0.9722897881414742, 0.9788150170059926, ...
          0.9813597788804785, 0.9821977956568989, ...
          0.9824283794464095, 0.9824809345614861]';
 assert (p([616:625]), p_out, 3e-16);
***** error bvncdf (randn (25,3), [], [1, 1; 1, 1]);
***** error bvncdf (randn (25,2), [], [1, 1; 1, 1]);
***** error bvncdf (randn (25,2), [], ones (3, 2));
5 tests, 5 passed, 0 known failure, 0 skipped
[inst/dist_fun/nctinv.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/nctinv.m
***** demo
 ## Plot various iCDFs from the noncentral T distribution
 p = 0.001:0.001:0.999;
 x1 = nctinv (p, 1, 0);
 x2 = nctinv (p, 4, 0);
 x3 = nctinv (p, 1, 2);
 x4 = nctinv (p, 4, 2);
 plot (p, x1, "-r", p, x2, "-g", p, x3, "-k", p, x4, "-m")
 grid on
 ylim ([-5, 5])
 legend ({"df = 1, μ = 0", "df = 4, μ = 0", ...
          "df = 1, μ = 2", "df = 4, μ = 2"}, "location", "northwest")
 title ("Noncentral T iCDF")
 xlabel ("probability")
 ylabel ("values in x")
***** demo
 ## Compare the noncentral T iCDF with MU = 1 to the T iCDF
 ## with the same number of degrees of freedom (10).

 p = 0.001:0.001:0.999;
 x1 = nctinv (p, 10, 1);
 x2 = tinv (p, 10);
 plot (p, x1, "-", p, x2, "-");
 grid on
 ylim ([-5, 5])
 legend ({"Noncentral T(10,1)", "T(10)"}, "location", "northwest")
 title ("Noncentral T vs T quantile functions")
 xlabel ("probability")
 ylabel ("values in x")
***** test
 x = [-Inf,-0.3347,0.1756,0.5209,0.8279,1.1424,1.5021,1.9633,2.6571,4.0845,Inf];
 assert (nctinv ([0:0.1:1], 2, 1), x, 1e-4);
***** test
 x = [-Inf,1.5756,2.0827,2.5343,3.0043,3.5406,4.2050,5.1128,6.5510,9.6442,Inf];
 assert (nctinv ([0:0.1:1], 2, 3), x, 1e-4);
***** test
 x = [-Inf,2.2167,2.9567,3.7276,4.6464,5.8455,7.5619,10.3327,15.7569,31.8159,Inf];
 assert (nctinv ([0:0.1:1], 1, 4), x, 1e-4);
***** test
 x = [1.7791   1.9368   2.0239   2.0801   2.1195   2.1489];
 assert (nctinv (0.05, [1, 2, 3, 4, 5, 6], 4), x, 1e-4);
***** test
 x = [-0.7755, 0.3670, 1.2554, 2.0239, 2.7348, 3.4154];
 assert (nctinv (0.05, 3, [1, 2, 3, 4, 5, 6]), x, 1e-4);
***** test
 x = [-0.7183, 0.3624, 1.2878, 2.1195, -3.5413, 3.6430];
 assert (nctinv (0.05, 5, [1, 2, 3, 4, -1, 6]), x, 1e-4);
***** test
 assert (nctinv (0.996, 5, 8), 30.02610554063658, 2e-11);
***** error<nctinv: function called with too few input arguments.> nctinv ()
***** error<nctinv: function called with too few input arguments.> nctinv (1)
***** error<nctinv: function called with too few input arguments.> nctinv (1, 2)
***** error<nctinv: P, DF, and MU must be of common size or scalars.> ...
 nctinv (ones (3), ones (2), ones (2))
***** error<nctinv: P, DF, and MU must be of common size or scalars.> ...
 nctinv (ones (2), ones (3), ones (2))
***** error<nctinv: P, DF, and MU must be of common size or scalars.> ...
 nctinv (ones (2), ones (2), ones (3))
***** error<nctinv: P, DF, and MU must not be complex.> nctinv (i, 2, 2)
***** error<nctinv: P, DF, and MU must not be complex.> nctinv (2, i, 2)
***** error<nctinv: P, DF, and MU must not be complex.> nctinv (2, 2, i)
16 tests, 16 passed, 0 known failure, 0 skipped
[inst/dist_fun/mvtcdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/mvtcdf.m
***** demo
 ## Compute the cdf of a multivariate Student's t distribution with
 ## correlation parameters rho = [1, 0.4; 0.4, 1] and 2 degrees of freedom.

 rho = [1, 0.4; 0.4, 1];
 df = 2;
 [X1, X2] = meshgrid (linspace (-2, 2, 25)', linspace (-2, 2, 25)');
 X = [X1(:), X2(:)];
 p = mvtcdf (X, rho, df);
 surf (X1, X2, reshape (p, 25, 25));
 title ("Bivariate Student's t cummulative distribution function");
***** test
 x = [1, 2];
 rho = [1, 0.5; 0.5, 1];
 df = 4;
 a = [-1, 0];
 assert (mvtcdf(a, x, rho, df), 0.294196905339283, 1e-14);
***** test
 x = [1, 2;2, 4;1, 5];
 rho = [1, 0.5; 0.5, 1];
 df = 4;
 p =[0.790285178602166; 0.938703291727784; 0.81222737321336];
 assert (mvtcdf(x, rho, df), p, 1e-14);
***** test
 x = [1, 2, 2, 4, 1, 5];
 rho = eye (6);
 rho(rho == 0) = 0.5;
 df = 4;
 assert (mvtcdf(x, rho, df), 0.6874, 1e-4);
***** error mvtcdf (1)
***** error mvtcdf (1, 2)
***** error<mvtcdf: correlation matrix RHO does not match dimensions in data.> ...
 mvtcdf (1, [2, 3; 3, 2], 1)
***** error<mvtcdf: correlation matrix RHO does not match dimensions in data.> ...
 mvtcdf ([2, 3, 4], ones (2), 1)
***** error<mvtcdf: X_LO and X_UP must be of the same size.> ...
 mvtcdf ([1, 2, 3], [2, 3], ones (2), 1)
***** error<mvtcdf: correlation matrix RHO must be positive semi-definite.> ...
 mvtcdf ([2, 3], ones (2), [1, 2, 3])
***** error<mvtcdf: DF must be a scalar or a vector with the same samples as in> ...
 mvtcdf ([2, 3], [1, 0.5; 0.5, 1], [1, 2, 3])
10 tests, 10 passed, 0 known failure, 0 skipped
[inst/dist_fun/nbinrnd.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/nbinrnd.m
***** assert (size (nbinrnd (1, 0.5)), [1 1])
***** assert (size (nbinrnd (1, 0.5 * ones (2,1))), [2, 1])
***** assert (size (nbinrnd (1, 0.5 * ones (2,2))), [2, 2])
***** assert (size (nbinrnd (ones (2,1), 0.5)), [2, 1])
***** assert (size (nbinrnd (ones (2,2), 0.5)), [2, 2])
***** assert (size (nbinrnd (1, 0.5, 3)), [3, 3])
***** assert (size (nbinrnd (1, 0.5, [4, 1])), [4, 1])
***** assert (size (nbinrnd (1, 0.5, 4, 1)), [4, 1])
***** assert (size (nbinrnd (1, 0.5, 4, 1, 5)), [4, 1, 5])
***** assert (size (nbinrnd (1, 0.5, 0, 1)), [0, 1])
***** assert (size (nbinrnd (1, 0.5, 1, 0)), [1, 0])
***** assert (size (nbinrnd (1, 0.5, 1, 2, 0, 5)), [1, 2, 0, 5])
***** assert (class (nbinrnd (1, 0.5)), "double")
***** assert (class (nbinrnd (1, single (0.5))), "single")
***** assert (class (nbinrnd (1, single ([0.5, 0.5]))), "single")
***** assert (class (nbinrnd (single (1), 0.5)), "single")
***** assert (class (nbinrnd (single ([1, 1]), 0.5)), "single")
***** error<nbinrnd: function called with too few input arguments.> nbinrnd ()
***** error<nbinrnd: function called with too few input arguments.> nbinrnd (1)
***** error<nbinrnd: R and PS must be of common size or scalars.> ...
 nbinrnd (ones (3), ones (2))
***** error<nbinrnd: R and PS must be of common size or scalars.> ...
 nbinrnd (ones (2), ones (3))
***** error<nbinrnd: R and PS must not be complex.> nbinrnd (i, 2, 3)
***** error<nbinrnd: R and PS must not be complex.> nbinrnd (1, i, 3)
***** error<nbinrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 nbinrnd (1, 2, -1)
***** error<nbinrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 nbinrnd (1, 2, 1.2)
***** error<nbinrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 nbinrnd (1, 2, ones (2))
***** error<nbinrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 nbinrnd (1, 2, [2 -1 2])
***** error<nbinrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 nbinrnd (1, 2, [2 0 2.5])
***** error<nbinrnd: dimensions must be non-negative integers.> ...
 nbinrnd (1, 2, 2, -1, 5)
***** error<nbinrnd: dimensions must be non-negative integers.> ...
 nbinrnd (1, 2, 2, 1.5, 5)
***** error<nbinrnd: R and PS must be scalars or of size SZ.> ...
 nbinrnd (2, ones (2), 3)
***** error<nbinrnd: R and PS must be scalars or of size SZ.> ...
 nbinrnd (2, ones (2), [3, 2])
***** error<nbinrnd: R and PS must be scalars or of size SZ.> ...
 nbinrnd (2, ones (2), 3, 2)
33 tests, 33 passed, 0 known failure, 0 skipped
[inst/dist_fun/gamrnd.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/gamrnd.m
***** assert (size (gamrnd (1, 1)), [1 1])
***** assert (size (gamrnd (1, ones (2,1))), [2, 1])
***** assert (size (gamrnd (1, ones (2,2))), [2, 2])
***** assert (size (gamrnd (ones (2,1), 1)), [2, 1])
***** assert (size (gamrnd (ones (2,2), 1)), [2, 2])
***** assert (size (gamrnd (1, 1, 3)), [3, 3])
***** assert (size (gamrnd (1, 1, [4, 1])), [4, 1])
***** assert (size (gamrnd (1, 1, 4, 1)), [4, 1])
***** assert (size (gamrnd (1, 1, 4, 1, 5)), [4, 1, 5])
***** assert (size (gamrnd (1, 1, 0, 1)), [0, 1])
***** assert (size (gamrnd (1, 1, 1, 0)), [1, 0])
***** assert (size (gamrnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
***** assert (class (gamrnd (1, 1)), "double")
***** assert (class (gamrnd (1, single (1))), "single")
***** assert (class (gamrnd (1, single ([1, 1]))), "single")
***** assert (class (gamrnd (single (1), 1)), "single")
***** assert (class (gamrnd (single ([1, 1]), 1)), "single")
***** error<gamrnd: function called with too few input arguments.> gamrnd ()
***** error<gamrnd: function called with too few input arguments.> gamrnd (1)
***** error<gamrnd: A and B must be of common size or scalars.> ...
 gamrnd (ones (3), ones (2))
***** error<gamrnd: A and B must be of common size or scalars.> ...
 gamrnd (ones (2), ones (3))
***** error<gamrnd: A and B must not be complex.> gamrnd (i, 2, 3)
***** error<gamrnd: A and B must not be complex.> gamrnd (1, i, 3)
***** error<gamrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 gamrnd (1, 2, -1)
***** error<gamrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 gamrnd (1, 2, 1.2)
***** error<gamrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 gamrnd (1, 2, ones (2))
***** error<gamrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 gamrnd (1, 2, [2 -1 2])
***** error<gamrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 gamrnd (1, 2, [2 0 2.5])
***** error<gamrnd: dimensions must be non-negative integers.> ...
 gamrnd (1, 2, 2, -1, 5)
***** error<gamrnd: dimensions must be non-negative integers.> ...
 gamrnd (1, 2, 2, 1.5, 5)
***** error<gamrnd: A and B must be scalars or of size SZ.> ...
 gamrnd (2, ones (2), 3)
***** error<gamrnd: A and B must be scalars or of size SZ.> ...
 gamrnd (2, ones (2), [3, 2])
***** error<gamrnd: A and B must be scalars or of size SZ.> ...
 gamrnd (2, ones (2), 3, 2)
33 tests, 33 passed, 0 known failure, 0 skipped
[inst/dist_fun/copularnd.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/copularnd.m
***** test
 theta = 0.5;
 r = copularnd ("Gaussian", theta);
 assert (size (r), [1, 2]);
 assert (all ((r >= 0) & (r <= 1)));
***** test
 theta = 0.5;
 df = 2;
 r = copularnd ("t", theta, df);
 assert (size (r), [1, 2]);
 assert (all ((r >= 0) & (r <= 1)));
***** test
 theta = 0.5;
 r = copularnd ("Clayton", theta);
 assert (size (r), [1, 2]);
 assert (all ((r >= 0) & (r <= 1)));
***** test
 theta = 0.5;
 n = 2;
 r = copularnd ("Clayton", theta, n);
 assert (size (r), [n, 2]);
 assert (all ((r >= 0) & (r <= 1)));
***** test
 theta = [1; 2];
 n = 2;
 d = 3;
 r = copularnd ("Clayton", theta, n, d);
 assert (size (r), [n, d]);
 assert (all ((r >= 0) & (r <= 1)));
5 tests, 5 passed, 0 known failure, 0 skipped
[inst/dist_fun/mvnpdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/mvnpdf.m
***** demo
 mu = [1, -1];
 sigma = [0.9, 0.4; 0.4, 0.3];
 [X1, X2] = meshgrid (linspace (-1, 3, 25)', linspace (-3, 1, 25)');
 x = [X1(:), X2(:)];
 p = mvnpdf (x, mu, sigma);
 surf (X1, X2, reshape (p, 25, 25));
***** error<mvnpdf: too few input arguments.> y = mvnpdf ();
***** error<mvnpdf: too few dimensions in X.> y = mvnpdf ([]);
***** error<mvnpdf: wrong dimensions in X.> y = mvnpdf (ones (3,3,3));
***** error<mvnpdf: columns in X and MU mismatch.> ...
 y = mvnpdf (ones (10, 2), [4, 2, 3]);
***** error<mvnpdf: rows in X and MU mismatch.> ...
 y = mvnpdf (ones (10, 2), [4, 2; 3, 2]);
***** error<mvnpdf: wrong size of MU.> ...
 y = mvnpdf (ones (10, 2), ones (3, 3, 3));
***** shared x, mu, sigma
 x = [1, 2, 5, 4, 6];
 mu = [2, 0, -1, 1, 4];
 sigma = [2, 2, 2, 2, 2];
***** assert (mvnpdf (x), 1.579343404440977e-20, 1e-30);
***** assert (mvnpdf (x, mu), 1.899325144348102e-14, 1e-25);
***** assert (mvnpdf (x, mu, sigma), 2.449062307156273e-09, 1e-20);
9 tests, 9 passed, 0 known failure, 0 skipped
[inst/dist_fun/gppdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/dist_fun/gppdf.m
***** demo
 ## Plot various PDFs from the generalized Pareto distribution
 x = 0:0.001:5;
 y1 = gppdf (x, 1, 1, 0);
 y2 = gppdf (x, 5, 1, 0);
 y3 = gppdf (x, 20, 1, 0);
 y4 = gppdf (x, 1, 2, 0);
 y5 = gppdf (x, 5, 2, 0);
 y6 = gppdf (x, 20, 2, 0);
 plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", ...
       x, y4, "-c", x, y5, "-m", x, y6, "-k")
 grid on
 xlim ([0, 5])
 ylim ([0, 1])
 legend ({"k = 1, σ = 1, θ = 0", "k = 5, σ = 1, θ = 0", ...
          "k = 20, σ = 1, θ = 0", "k = 1, σ = 2, θ = 0", ...
          "k = 5, σ = 2, θ = 0", "k = 20, σ = 2, θ = 0"}, ...
         "location", "northeast")
 title ("Generalized Pareto PDF")
 xlabel ("values in x")
 ylabel ("density")
***** shared x, y1, y2, y3
 x = [-Inf, -1, 0, 1/2, 1, Inf];
 y1 = [0, 0, 1, 0.6065306597126334, 0.36787944117144233, 0];
 y2 = [0, 0, 1, 4/9, 1/4, 0];
 y3 = [0, 0, 1, 1, 1, 0];
***** assert (gppdf (x, zeros (1,6), ones (1,6), zeros (1,6)), y1, eps)
***** assert (gppdf (x, 0, 1, zeros (1,6)), y1, eps)
***** assert (gppdf (x, 0, ones (1,6), 0), y1, eps)
***** assert (gppdf (x, zeros (1,6), 1, 0), y1, eps)
***** assert (gppdf (x, 0, 1, 0), y1, eps)
***** assert (gppdf (x, 0, 1, [0, 0, 0, NaN, 0, 0]), [y1(1:3), NaN, y1(5:6)])
***** assert (gppdf (x, 0, [1, 1, 1, NaN, 1, 1], 0), [y1(1:3), NaN, y1(5:6)])
***** assert (gppdf (x, [0, 0, 0, NaN, 0, 0], 1, 0), [y1(1:3), NaN, y1(5:6)])
***** assert (gppdf ([x(1:3), NaN, x(5:6)], 0, 1, 0), [y1(1:3), NaN, y1(5:6)])
***** assert (gppdf (x, ones (1,6), ones (1,6), zeros (1,6)), y2, eps)
***** assert (gppdf (x, 1, 1, zeros (1,6)), y2, eps)
***** assert (gppdf (x, 1, ones (1,6), 0), y2, eps)
***** assert (gppdf (x, ones (1,6), 1, 0), y2, eps)
***** assert (gppdf (x, 1, 1, 0), y2, eps)
***** assert (gppdf (x, 1, 1, [0, 0, 0, NaN, 0, 0]), [y2(1:3), NaN, y2(5:6)])
***** assert (gppdf (x, 1, [1, 1, 1, NaN, 1, 1], 0), [y2(1:3), NaN, y2(5:6)])
***** assert (gppdf (x, [1, 1, 1, NaN, 1, 1], 1, 0), [y2(1:3), NaN, y2(5:6)])
***** assert (gppdf ([x(1:3), NaN, x(5:6)], 1, 1, 0), [y2(1:3), NaN, y2(5:6)])
***** assert (gppdf (x, -ones (1,6), ones (1,6), zeros (1,6)), y3, eps)
***** assert (gppdf (x, -1, 1, zeros (1,6)), y3, eps)
***** assert (gppdf (x, -1, ones (1,6), 0), y3, eps)
***** assert (gppdf (x, -ones (1,6), 1, 0), y3, eps)
***** assert (gppdf (x, -1, 1, 0), y3, eps)
***** assert (gppdf (x, -1, 1, [0, 0, 0, NaN, 0, 0]), [y3(1:3), NaN, y3(5:6)])
***** assert (gppdf (x, -1, [1, 1, 1, NaN, 1, 1], 0), [y3(1:3), NaN, y3(5:6)])
***** assert (gppdf (x, [-1, -1, -1, NaN, -1, -1], 1, 0), [y3(1:3), NaN, y3(5:6)])
***** assert (gppdf ([x(1:3), NaN, x(5:6)], -1, 1, 0), [y3(1:3), NaN, y3(5:6)])
***** assert (gppdf (single ([x, NaN]), 0, 1, 0), single ([y1, NaN]))
***** assert (gppdf ([x, NaN], 0, 1, single (0)), single ([y1, NaN]))
***** assert (gppdf ([x, NaN], 0, single (1), 0), single ([y1, NaN]))
***** assert (gppdf ([x, NaN], single (0), 1, 0), single ([y1, NaN]))
***** assert (gppdf (single ([x, NaN]), 1, 1, 0), single ([y2, NaN]))
***** assert (gppdf ([x, NaN], 1, 1, single (0)), single ([y2, NaN]))
***** assert (gppdf ([x, NaN], 1, single (1), 0), single ([y2, NaN]))
***** assert (gppdf ([x, NaN], single (1), 1, 0), single ([y2, NaN]))
***** assert (gppdf (single ([x, NaN]), -1, 1, 0), single ([y3, NaN]))
***** assert (gppdf ([x, NaN], -1, 1, single (0)), single ([y3, NaN]))
***** assert (gppdf ([x, NaN], -1, single (1), 0), single ([y3, NaN]))
***** assert (gppdf ([x, NaN], single (-1), 1, 0), single ([y3, NaN]))
***** error<gpcdf: function called with too few input arguments.> gpcdf ()
***** error<gpcdf: function called with too few input arguments.> gpcdf (1)
***** error<gpcdf: function called with too few input arguments.> gpcdf (1, 2)
***** error<gpcdf: function called with too few input arguments.> gpcdf (1, 2, 3)
***** error<gpcdf: X, K, SIGMA, and THETA must be of common size or scalars.> ...
 gpcdf (ones (3), ones (2), ones(2), ones(2))
***** error<gpcdf: X, K, SIGMA, and THETA must be of common size or scalars.> ...
 gpcdf (ones (2), ones (3), ones(2), ones(2))
***** error<gpcdf: X, K, SIGMA, and THETA must be of common size or scalars.> ...
 gpcdf (ones (2), ones (2), ones(3), ones(2))
***** error<gpcdf: X, K, SIGMA, and THETA must be of common size or scalars.> ...
 gpcdf (ones (2), ones (2), ones(2), ones(3))
***** error<gpcdf: X, K, SIGMA, and THETA must not be complex.> gpcdf (i, 2, 3, 4)
***** error<gpcdf: X, K, SIGMA, and THETA must not be complex.> gpcdf (1, i, 3, 4)
***** error<gpcdf: X, K, SIGMA, and THETA must not be complex.> gpcdf (1, 2, i, 4)
***** error<gpcdf: X, K, SIGMA, and THETA must not be complex.> gpcdf (1, 2, 3, i)
51 tests, 51 passed, 0 known failure, 0 skipped
[inst/glmfit.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/glmfit.m
***** demo
 rand ("seed", 1);
 X = rand (100, 1);
 b_true = [0.5; -1.2];
 mu = exp (b_true(1) + b_true(2) * X);
 randp ("seed", 1);
 y = poissrnd (mu);
 ## Fit a GLM model using the poisson distribution
 [b,dev] = glmfit (X, y, 'poisson');
***** demo
 x = [2100 2300 2500 2700 2900 3100 3300 3500 3700 3900 4100 4300]';
 n = [48 42 31 34 31 21 23 23 21 16 17 21]';
 y = [1 2 0 3 8 8 14 17 19 15 17 21]';
 [b,dev] = glmfit (x,[y n],'binomial','Link','probit');
***** test
 rand ("seed", 1);
 X = rand (50, 1);
 b_true = [0.4; 1.5];
 mu_true = exp (b_true(1) + b_true(2) * X);
 randp ("seed", 1);
 y = poissrnd (mu_true);
 b = glmfit (X, y, "poisson", "link", "log");
 assert (b(1), b_true(1), 0.5);
 assert (b(2), b_true(2), 0.5);
***** test
 rand ("seed", 1);
 X1 = rand (50, 1);
 X2 = rand (50, 1) * 0.5;
 b_true = [0.4; 1.5; -0.7];
 mu_true = exp (b_true(1) + b_true(2) * X1 + b_true(3) * X2);
 randp ("seed", 1);
 y = poissrnd(mu_true);
 [b, dev] = glmfit ([X1, X2], y, "poisson", "link", "log");
 assert (b(1), b_true(1), 1);
 assert (b(2), b_true(2), 1);
 assert (b(3), b_true(3), 1);
 assert (dev < 60, true);
***** error <glmfit: too few input arguments.> glmfit ()
***** error <glmfit: too few input arguments.> glmfit (1)
***** error <glmfit: too few input arguments.> glmfit (1, 2)
***** error <glmfit: Name-Value arguments must be in pairs.> ...
 glmfit (rand (6, 1), rand (6, 1), 'poisson', 'link')
***** error <glmfit: X must be a numeric.> ...
 glmfit ('abc', rand (6, 1), 'poisson')
***** error <glmfit: Y must be a numeric.> ...
 glmfit (rand (5, 2), 'abc', 'poisson')
***** error <glmfit: X and Y must have the same number of observations.> ...
 glmfit (rand (5, 2), rand (6, 1), 'poisson')
***** error <glmfit: DISTRIBUTION must be a character vector.> ...
 glmfit (rand (6, 2), rand (6, 1), 3)
***** error <glmfit: DISTRIBUTION must be a character vector.> ...
 glmfit (rand (6, 2), rand (6, 1), {'poisson'})
***** error <glmfit: for a binomial distribution, Y must be an n-by-1 or n-by-2 matrix.> ...
 glmfit (rand (5, 2), rand (5, 3), 'binomial')
***** error <glmfit: for distributions other than the binomial, Y must be an n-by-1 column vector> ...
 glmfit (rand (5, 2), rand (5, 2), 'normal')
***** error <glmfit: 'gamma' distribution is not supported yet.> ...
 glmfit (rand (5, 2), rand (5, 1), 'gamma')
***** error <glmfit: 'inverse gaussian' distribution is not supported yet.> ...
 glmfit (rand (5, 2), rand (5, 1), 'inverse gaussian')
***** error <glmfit: unknown distribution.> ...
 glmfit (rand (5, 2), rand (5, 1), 'loguniform')
***** error <glmfit: custom link functions must be in a three-element cell array.> ...
 glmfit (rand(5,2), rand(5,1), 'poisson', 'link', {'log'})
***** error <glmfit: custom link functions must be in a three-element cell array.> ...
 glmfit (rand(5,2), rand(5,1), 'poisson', 'link', {'log', 'hijy'})
***** error <glmfit: custom link functions must be function handles.> ...
 glmfit (rand(5,2), rand(5,1), 'poisson', 'link', {'log','dfv','dfgvd'})
***** error <glmfit: custom link functions must be function handles.> ...
 glmfit (rand(5,2), rand(5,1), 'poisson', 'link', {@log, 'derivative', @exp})
***** error <glmfit: custom inverse link function must return output of the same size as input.> ...
 glmfit (rand(5,2), rand(5,1), 'poisson', 'link', {@exp, @log, @(x) eye(e)})
***** error <glmfit: unsupported link function.> ...
 glmfit (rand(5,2), rand(5,1), 'poisson', 'link', 'somelinkfunction')
***** error <glmfit: invalid value for link function.> ...
 glmfit (rand(5,2), rand(5,1), 'poisson', 'link', 2)
***** error <glmfit: constant should be either 'on' or 'off'.> ...
 glmfit (rand(5,2), rand(5,1), 'poisson', 'link', 'log', 'constant', 0)
***** error <glmfit: constant should be either 'on' or 'off'.> ...
 glmfit (rand(5,2), rand(5,1), 'poisson', 'link', 'log', 'constant', 'asda')
***** error <glmfit: unknown parameter name.> ...
 glmfit (rand(5,2), rand(5,1), 'poisson', 'param', 'log', 'constant', 'on')
26 tests, 26 passed, 0 known failure, 0 skipped
[inst/barttest.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/barttest.m
***** error<barttest: invalid number of input arguments.> barttest ()
***** error<barttest: NaN values in input are not allowed.> barttest ([2,NaN;3,4])
***** error<barttest: wrong value for alpha.> barttest (ones (30, 4), "alpha")
***** error<barttest: wrong value for alpha.> barttest (ones (30, 4), 0)
***** error<barttest: wrong value for alpha.> barttest (ones (30, 4), 1.2)
***** error<barttest: wrong value for alpha.> barttest (ones (30, 4), [0.2, 0.05])
***** error<barttest: not enough data in X.> barttest (ones (30, 1))
***** error<barttest: not enough data in X.> barttest (ones (30, 1), 0.05)
***** test
 x = [2, 3, 4, 5, 6, 7, 8, 9; 1, 2, 3, 4, 5, 6, 7, 8]';
 [ndim, pval, chisq] = barttest (x);
 assert (ndim, 2);
 assert (pval, 0);
 ## assert (chisq, 512.0558, 1e-4); Result differs between octave 6 and 7 ?
***** test
 x = [0.53767,  0.62702,   -0.10224,   -0.25485,   1.4193,   1.5237  ; ...
      1.8339,   1.6452,    -0.24145,   -0.23444,   0.29158,  0.1634  ; ...
     -2.2588,  -2.1351,     0.31286,    0.39396,   0.19781,  0.20995 ; ...
      0.86217,  1.0835,     0.31286,    0.46499,   1.5877,   1.495   ; ...
      0.31877,  0.38454,   -0.86488,   -0.63839,  -0.80447, -0.7536  ; ...
     -1.3077,  -1.1487,    -0.030051,  -0.017629,  0.69662,  0.60497 ; ...
     -0.43359, -0.32672,   -0.16488,   -0.37364,   0.83509,  0.89586 ; ...
      0.34262,  0.29639,    0.62771,    0.51672,  -0.24372, -0.13698 ; ...
      3.5784,   3.5841,     1.0933,     0.93258,   0.21567,  0.455   ; ...
      2.7694,   2.6307,     1.1093,     1.4298,   -1.1658,  -1.1816  ; ...
     -1.3499,  -1.2111,    -0.86365,   -0.94186,  -1.148,   -1.4381  ; ...
      3.0349,   2.8428,     0.077359,   0.18211,   0.10487, -0.014613; ...
      0.7254,   0.56737,   -1.2141,    -1.2291,    0.72225,  0.90612 ; ...
     -0.063055,-0.17662,   -1.1135,    -0.97701,   2.5855,   2.4084  ; ...
      0.71474,  0.29225,   -0.0068493, -0.11468,  -0.66689, -0.52466 ; ...
     -0.20497, -7.8874e-06, 1.5326,     1.3195,    0.18733,  0.20296 ; ...
     -0.12414, -0.077029,  -0.76967,   -0.96262,  -0.082494, 0.121   ; ...
      1.4897,   1.3683,     0.37138,    0.43653,  -1.933,   -2.1903  ; ...
      1.409,    1.5882,    -0.22558,   -0.24835,  -0.43897, -0.46247 ; ...
      1.4172,   1.1616,     1.1174,     1.0785,   -1.7947,  -1.9471  ];
 [ndim, pval, chisq] = barttest (x);
 assert (ndim, 3);
 assert (pval, [0; 0; 0; 0.52063; 0.34314], 1e-5);
 chisq_out = [251.6802; 210.2670; 153.1773; 4.2026; 2.1392];
 assert (chisq, chisq_out, 1e-4);
10 tests, 10 passed, 0 known failure, 0 skipped
[inst/cluster.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/cluster.m
***** error cluster ()
***** error <Z must be .* generated by the linkage .*> cluster ([1 1], "Cutoff", 1)
***** error <unknown option .*> cluster ([1 2 1], "Bogus", 1)
***** error <C must be a positive scalar .*> cluster ([1 2 1], "Cutoff", -1)
***** error <unknown property .*> cluster ([1 2 1], "Cutoff", 1, "Bogus", 1)
***** test
6 tests, 6 passed, 0 known failure, 0 skipped
[inst/tabulate.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/tabulate.m
***** demo
 ## Generate a frequency table for a vector of data in a cell array
 load patients

 ## Display the first seven entries of the Gender variable
 gender = Gender(1:7)

 ## Compute the equency table that shows the number and
 ## percentage of Male and Female patients
 tabulate (Gender)
***** demo
 ## Create a frequency table for a vector of positive integers
 load patients

 ## Display the first seven entries of the Gender variable
 height = Height(1:7)

 ## Create a frequency table that shows, in its second and third columns,
 ## the number and percentage of patients with a particular height.
 table = tabulate (Height);

 ## Display the first and last seven entries of the frequency table
 first = table(1:7,:)

 last = table(end-6:end,:)
***** demo
 ## Create a frequency table from a character array
 load carsmall

 ## Tabulate the data in the Origin variable, which shows the
 ## country of origin of each car in the data set
 tabulate (Origin)
***** demo
 ## Create a frequency table from a numeric vector with NaN values
 load carsmall

 ## The carsmall dataset contains measurements of 100 cars
 total_cars = length (MPG)
 ## For six cars, the MPG value is missing
 missingMPG = length (MPG(isnan (MPG)))

 ## Create a frequency table using MPG
 tabulate (MPG)
 table = tabulate (MPG);

 ## Only 94 cars were used
 valid_cars = sum (table(:,2))
***** test
 load patients
 table = tabulate (Gender);
 assert (table{1,1}, "Male");
 assert (table{2,1}, "Female");
 assert (table{1,2}, 47);
 assert (table{2,2}, 53);
***** test
 load patients
 table = tabulate (Height);
 assert (table(end-4,:), [68, 15, 15]);
 assert (table(end-3,:), [69, 8, 8]);
 assert (table(end-2,:), [70, 11, 11]);
 assert (table(end-1,:), [71, 10, 10]);
 assert (table(end,:), [72, 4, 4]);
***** error<tabulate: X must be either a numeric vector> tabulate (ones (3))
***** error<tabulate: X must be either a numeric vector> tabulate ({1, 2, 3, 4})
***** error<tabulate: X must be either a numeric vector> ...
 tabulate ({"a", "b"; "a", "c"})
5 tests, 5 passed, 0 known failure, 0 skipped
[inst/boxplot.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/boxplot.m
***** demo
 axis ([0, 3]);
 randn ("seed", 1);    # for reproducibility
 girls = randn (10, 1) * 5 + 140;
 randn ("seed", 2);    # for reproducibility
 boys = randn (13, 1) * 8 + 135;
 boxplot ({girls, boys});
 set (gca (), "xtick", [1 2], "xticklabel", {"girls", "boys"})
 title ("Grade 3 heights");
***** demo
 randn ("seed", 7);    # for reproducibility
 A = randn (10, 1) * 5 + 140;
 randn ("seed", 8);    # for reproducibility
 B = randn (25, 1) * 8 + 135;
 randn ("seed", 9);    # for reproducibility
 C = randn (20, 1) * 6 + 165;
 data = [A; B; C];
 groups = [(ones (10, 1)); (ones (25, 1) * 2); (ones (20, 1) * 3)];
 labels = {"Team A", "Team B", "Team C"};
 pos = [2, 1, 3];
 boxplot (data, groups, "Notch", "on", "Labels", labels, "Positions", pos, ...
          "OutlierTags", "on", "BoxStyle", "filled");
 title ("Example of Group splitting with paired vectors");
***** demo
 randn ("seed", 1);    # for reproducibility
 data = randn (100, 9);
 boxplot (data, "notch", "on", "boxstyle", "filled", ...
          "colors", "ygcwkmb", "whisker", 1.2);
 title ("Example of different colors specified with characters");
***** demo
 randn ("seed", 5);    # for reproducibility
 data = randn (100, 13);
 colors = [0.7 0.7 0.7; ...
           0.0 0.4 0.9; ...
           0.7 0.4 0.3; ...
           0.7 0.1 0.7; ...
           0.8 0.7 0.4; ...
           0.1 0.8 0.5; ...
           0.9 0.9 0.2];
 boxplot (data, "notch", "on", "boxstyle", "filled", ...
          "colors", colors, "whisker", 1.3, "boxwidth", "proportional");
 title ("Example of different colors specified as RGB values");
***** error <numerical array or cell array containing> boxplot ("a")
***** error <data cells must contain> boxplot ({[1 2 3], "a"})
***** error <grouping vector may only be passed> boxplot ([1 2 3], 1, {2, 3})
***** error <grouping vector must be numerical> boxplot ([1 2 3], {"a", "b"})
***** error <'Notch' input argument accepts> boxplot ([1:10], "notch", "any")
***** error <illegal Notch value> boxplot ([1:10], "notch", i)
***** error <illegal Notch value> boxplot ([1:10], "notch", {})
***** error <must be character> boxplot (1, "symbol", 1)
***** error <'Orientation' input argument accepts only> boxplot (1, "orientation", "diagonal")
***** error <illegal Orientation value> boxplot (1, "orientation", {})
***** error <'Whisker' input argument accepts only> boxplot (1, "whisker", "a")
***** error <'Whisker' input argument accepts only> boxplot (1, "whisker", [1 3])
***** error <'OutlierTags' input argument accepts only> boxplot (3, "OutlierTags", "maybe")
***** error <illegal OutlierTags value> boxplot (3, "OutlierTags", {})
***** error <'Sample_IDs' input argument accepts only> boxplot (1, "sample_IDs", 1)
***** error <'BoxWidth' input argument accepts only> boxplot (1, "boxwidth", 2)
***** error <'BoxWidth' input argument accepts only> boxplot (1, "boxwidth", "anything")
***** error <'Widths' input argument accepts only> boxplot (5, "widths", "a")
***** error <'Widths' input argument accepts only> boxplot (5, "widths", [1:4])
***** error <'Widths' input argument accepts only> boxplot (5, "widths", [])
***** error <'CapWidths' input argument accepts only> boxplot (5, "capwidths", "a")
***** error <'CapWidths' input argument accepts only> boxplot (5, "capwidths", [1:4])
***** error <'CapWidths' input argument accepts only> boxplot (5, "capwidths", [])
***** error <'BoxStyle' input argument accepts only> boxplot (1, "Boxstyle", 1)
***** error <'BoxStyle' input argument accepts only> boxplot (1, "Boxstyle", "garbage")
***** error <'Positions' input argument accepts only> boxplot (1, "positions", "aa")
***** error <'Labels' input argument accepts only> boxplot (3, "labels", [1 5])
***** error <'Colors' input argument accepts only> boxplot (1, "colors", {})
***** error <'Colors' input argument accepts only> boxplot (2, "colors", [1 2 3 4])
***** error <Sample_IDs must match the data> boxplot (randn (10, 3), 'Sample_IDs', {"a", "b"})
***** error <with the formalism> boxplot (rand (3, 3), [1 2])
***** test
 hf = figure ("visible", "off");
 unwind_protect
   [a, b] = boxplot (rand (10, 3));
   assert (size (a), [7, 3]);
   assert (numel (b.box), 3);
   assert (numel (b.whisker), 12);
   assert (numel (b.median), 3);
 unwind_protect_cleanup
   close (hf);
 end_unwind_protect
***** test
 hf = figure ("visible", "off");
 unwind_protect
   [~, b] = boxplot (rand (10, 3), "BoxStyle", "filled", "colors", "ybc");
   assert (numel (b.box_fill), 3);
 unwind_protect_cleanup
   close (hf);
 end_unwind_protect
***** test
 hf = figure ("visible", "off");
 unwind_protect
   hold on
   [a, b] = boxplot (rand (10, 3));
   assert (ishold, true);
 unwind_protect_cleanup
   close (hf);
 end_unwind_protect
34 tests, 34 passed, 0 known failure, 0 skipped
[inst/sampsizepwr.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/sampsizepwr.m
***** demo
 ## Compute the mean closest to 100 that can be determined to be
 ## significantly different from 100 using a t-test with a sample size
 ## of 60 and a power of 0.8.
 mu1 = sampsizepwr ("t", [100, 10], [], 0.8, 60);
 disp (mu1);
***** demo
 ## Compute the sample sizes required to distinguish mu0 = 100 from
 ## mu1 = 110 by a two-sample t-test with a ratio of the larger and the
 ## smaller sample sizes of 1.5 and a power of 0.6.
 [N1,N2] = sampsizepwr ("t2", [100, 10], 110, 0.6, [], "ratio", 1.5)
***** demo
 ## Compute the sample size N required to distinguish p=.26 from p=.2
 ## with a binomial test.  The result is approximate, so make a plot to
 ## see if any smaller N values also have the required power of 0.6.
 Napprox = sampsizepwr ("p", 0.2, 0.26, 0.6);
 nn = 1:250;
 pwr = sampsizepwr ("p", 0.2, 0.26, [], nn);
 Nexact = min (nn(pwr >= 0.6));
 plot(nn,pwr,'b-', [Napprox Nexact],pwr([Napprox Nexact]),'ro');
 grid on
***** demo
 ## The company must test 52 bottles to detect the difference between a mean
 ## volume of 100 mL and 102 mL with a power of 0.80.  Generate a power curve
 ## to visualize how the sample size affects the power of the test.

 nout = sampsizepwr('t',[100 5],102,0.80);
 nn = 1:100;
 pwrout = sampsizepwr('t',[100 5],102,[],nn);

 figure;
 plot (nn, pwrout, "b-", nout, 0.8, "ro")
 title ("Power versus Sample Size")
 xlabel ("Sample Size")
 ylabel ("Power")
***** error<sampsizepwr: test type must be a non-empty string.> ...
 out = sampsizepwr ([], [100, 10], [], 0.8, 60);
***** error<sampsizepwr: test type must be a non-empty string.> ...
 out = sampsizepwr (3, [100, 10], [], 0.8, 60);
***** error<sampsizepwr: test type must be a non-empty string.> ...
 out = sampsizepwr ({"t", "t2"}, [100, 10], [], 0.8, 60);
***** error<sampsizepwr: invalid test type.> ...
 out = sampsizepwr ("reg", [100, 10], [], 0.8, 60);
***** error<sampsizepwr: parameters must be numeric.> ...
 out = sampsizepwr ("t", ["a", "e"], [], 0.8, 60);
***** error<sampsizepwr: invalid size of parameters for this test type.> ...
 out = sampsizepwr ("z", 100, [], 0.8, 60);
***** error<sampsizepwr: invalid size of parameters for this test type.> ...
 out = sampsizepwr ("t", 100, [], 0.8, 60);
***** error<sampsizepwr: invalid size of parameters for this test type.> ...
 out = sampsizepwr ("t2", 60, [], 0.8, 60);
***** error<sampsizepwr: invalid size of parameters for this test type.> ...
 out = sampsizepwr ("var", [100, 10], [], 0.8, 60);
***** error<sampsizepwr: invalid size of parameters for this test type.> ...
 out = sampsizepwr ("p", [100, 10], [], 0.8, 60);
***** error<sampsizepwr: invalid size of parameters for this test type.> ...
 out = sampsizepwr ("r", [100, 10], [], 0.8, 60);
***** error<sampsizepwr: wrong number of output arguments for this test type.> ...
 [out, N1] = sampsizepwr ("z", [100, 10], [], 0.8, 60);
***** error<sampsizepwr: wrong number of output arguments for this test type.> ...
 [out, N1] = sampsizepwr ("t", [100, 10], [], 0.8, 60);
***** error<sampsizepwr: wrong number of output arguments for this test type.> ...
 [out, N1] = sampsizepwr ("var", 2, [], 0.8, 60);
***** error<sampsizepwr: wrong number of output arguments for this test type.> ...
 [out, N1] = sampsizepwr ("p", 0.1, [], 0.8, 60);
***** error<sampsizepwr: wrong number of output arguments for this test type.> ...
 [out, N1] = sampsizepwr ("r", 0.5, [], 0.8, 60);
***** error<sampsizepwr: negative or zero variance.> ...
 out = sampsizepwr ("z", [100, 0], [], 0.8, 60);
***** error<sampsizepwr: negative or zero variance.> ...
 out = sampsizepwr ("z", [100, -5], [], 0.8, 60);
***** error<sampsizepwr: negative or zero variance.> ...
 out = sampsizepwr ("t", [100, 0], [], 0.8, 60);
***** error<sampsizepwr: negative or zero variance.> ...
 out = sampsizepwr ("t", [100, -5], [], 0.8, 60);
***** error<sampsizepwr: negative or zero variance.> ...
 [out, N1] = sampsizepwr ("t2", [100, 0], [], 0.8, 60);
***** error<sampsizepwr: negative or zero variance.> ...
 [out, N1] = sampsizepwr ("t2", [100, -5], [], 0.8, 60);
***** error<sampsizepwr: negative or zero variance.> ...
 out = sampsizepwr ("var", 0, [], 0.8, 60);
***** error<sampsizepwr: negative or zero variance.> ...
 out = sampsizepwr ("var", -5, [], 0.8, 60);
***** error<sampsizepwr: out of range probability.> ...
 out = sampsizepwr ("p", 0, [], 0.8, 60);
***** error<sampsizepwr: out of range probability.> ...
 out = sampsizepwr ("p", 1.2, [], 0.8, 60);
***** error<sampsizepwr: out of range regression coefficient.> ...
 out = sampsizepwr ("r", -1.5, [], 0.8, 60);
***** error<sampsizepwr: out of range regression coefficient.> ...
 out = sampsizepwr ("r", -1, [], 0.8, 60);
***** error<sampsizepwr: out of range regression coefficient.> ...
 out = sampsizepwr ("r", 1.2, [], 0.8, 60);
***** error<sampsizepwr: regression coefficient must not be 0.> ...
 out = sampsizepwr ("r", 0, [], 0.8, 60);
***** error<sampsizepwr: invalid value for 'alpha' parameter.> ...
 out = sampsizepwr ("r", 0.2, [], 0.8, 60, "alpha", -0.2);
***** error<sampsizepwr: invalid value for 'alpha' parameter.> ...
 out = sampsizepwr ("r", 0.2, [], 0.8, 60, "alpha", 0);
***** error<sampsizepwr: invalid value for 'alpha' parameter.> ...
 out = sampsizepwr ("r", 0.2, [], 0.8, 60, "alpha", 1.5);
***** error<sampsizepwr: invalid value for 'alpha' parameter.> ...
 out = sampsizepwr ("r", 0.2, [], 0.8, 60, "alpha", "zero");
***** error<sampsizepwr: 'tail' parameter must be a non-empty string.> ...
 out = sampsizepwr ("r", 0.2, [], 0.8, 60, "tail", 1.5);
***** error<sampsizepwr: 'tail' parameter must be a non-empty string.> ...
 out = sampsizepwr ("r", 0.2, [], 0.8, 60, "tail", {"both", "left"});
***** error<sampsizepwr: invalid value for 'tail' parameter.> ...
 out = sampsizepwr ("r", 0.2, [], 0.8, 60, "tail", "other");
***** error<sampsizepwr: invalid value for 'ratio' parameter.> ...
 out = sampsizepwr ("r", 0.2, [], 0.8, 60, "ratio", "some");
***** error<sampsizepwr: invalid value for 'ratio' parameter.> ...
 out = sampsizepwr ("r", 0.2, [], 0.8, 60, "ratio", 0.5);
***** error<sampsizepwr: invalid value for 'ratio' parameter.> ...
 out = sampsizepwr ("r", 0.2, [], 0.8, 60, "ratio", [2, 1.3, 0.3]);
***** error<sampsizepwr: only one of either p1, power, or n must be missing.> ...
 out = sampsizepwr ("z", [100, 5], [], [], 60);
***** error<sampsizepwr: only one of either p1, power, or n must be missing.> ...
 out = sampsizepwr ("z", [100, 5], 110, [], []);
***** error<sampsizepwr: only one of either p1, power, or n must be missing.> ...
 out = sampsizepwr ("z", [100, 5], [], 0.8, []);
***** error<sampsizepwr: only one of either p1, power, or n must be missing.> ...
 out = sampsizepwr ("z", [100, 5], 110, 0.8, 60);
***** error<sampsizepwr: alternative hypothesis parameter must be numeric.> ...
 out = sampsizepwr ("z", [100, 5], "mu", [], 60);
***** error<sampsizepwr: alternative hypothesis parameter out of range.> ...
 out = sampsizepwr ("var", 5, -1, [], 60);
***** error<sampsizepwr: alternative hypothesis parameter out of range.> ...
 out = sampsizepwr ("p", 0.8, 1.2, [], 60, "tail", "right");
***** error<sampsizepwr: alternative hypothesis parameter out of range.> ...
 out = sampsizepwr ("r", 0.8, 1.2, [], 60);
***** error<sampsizepwr: alternative hypothesis parameter out of range.> ...
 out = sampsizepwr ("r", 0.8, -1.2, [], 60);
***** error<sampsizepwr: invalid value for POWER.> ...
 out = sampsizepwr ("z", [100, 5], 110, 1.2);
***** error<sampsizepwr: invalid value for POWER.> ...
 out = sampsizepwr ("z", [100, 5], 110, 0);
***** error<sampsizepwr: Cannot compute N or P1 unless POWER> ...
 out = sampsizepwr ("z", [100, 5], 110, 0.05, [], "alpha", 0.1);
***** error<sampsizepwr: input arguments size mismatch.> ...
 out = sampsizepwr ("z", [100, 5], [], [0.8, 0.7], [60, 80, 100]);
***** error<sampsizepwr: Same value for null and alternative hypothesis.> ...
 out = sampsizepwr ("t", [100, 5], 100, 0.8, []);
***** error<sampsizepwr: Invalid P1 for testing left tail.> ...
 out = sampsizepwr ("t", [100, 5], 110, 0.8, [], "tail", "left");
***** error<sampsizepwr: Invalid P1 for testing right tail.> ...
 out = sampsizepwr ("t", [100, 5], 90, 0.8, [], "tail", "right");
***** warning<sampsizepwr: approximate N.> ...
 Napprox = sampsizepwr ("p", 0.2, 0.26, 0.6);
***** warning<sampsizepwr: approximate N.> ...
 Napprox = sampsizepwr ("p", 0.30, 0.36, 0.8);
***** test
 mu1 = sampsizepwr ("t", [100, 10], [], 0.8, 60);
 assert (mu1, 103.67704316, 1e-8);
***** test
 [N1,N2] = sampsizepwr ("t2", [100, 10], 110, 0.6, [], "ratio", 1.5);
 assert (N1, 9);
 assert (N2, 14);
***** test
 nn = 1:250;
 pwr = sampsizepwr ("p", 0.2, 0.26, [], nn);
 pwr_out = [0, 0.0676, 0.0176, 0.0566, 0.0181, 0.0431, 0.0802, 0.0322];
 assert (pwr([1:8]), pwr_out, 1e-4 * ones (1,8));
 pwr_out = [0.59275, 0.6073, 0.62166, 0.6358, 0.6497, 0.6087, 0.6229, 0.6369];
 assert (pwr([243:end]), pwr_out, 1e-4 * ones (1,8));
***** test
 nout = sampsizepwr ("t", [100, 5], 102, 0.80);
 assert (nout, 52);
***** test
 power = sampsizepwr ("t", [20, 5], 25, [], 5, "Tail", "right");
 assert (power, 0.5797373588621888, 1e-14);
***** test
 nout = sampsizepwr ("t", [20, 5], 25, 0.99, [], "Tail", "right");
 assert (nout, 18);
***** test
 p1out = sampsizepwr ("t", [20, 5], [], 0.95, 10, "Tail", "right");
 assert (p1out, 25.65317979360237, 1e-14);
***** test
 pwr = sampsizepwr ("t2", [1.4, 0.2], 1.7, [], 5, "Ratio", 2);
 assert (pwr, 0.716504004686586, 1e-14);
***** test
 n = sampsizepwr ("t2", [1.4, 0.2], 1.7, 0.9, []);
 assert (n, 11);
***** test
 [n1, n2] = sampsizepwr ("t2", [1.4, 0.2], 1.7, 0.9, [], "Ratio", 2);
 assert ([n1, n2], [8, 16]);
68 tests, 68 passed, 0 known failure, 0 skipped
[inst/manova1.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/manova1.m
***** demo
 load carbig
 [d,p] = manova1([MPG, Acceleration, Weight, Displacement], Origin)
***** test
 load carbig
 [d,p] = manova1([MPG, Acceleration, Weight, Displacement], Origin);
 assert (d, 3);
 assert (p, [0, 3.140583347827075e-07, 0.007510999577743149, ...
             0.1934100745898493]', [1e-12, 1e-12, 1e-12, 1e-12]');
***** test
 load carbig
 [d,p] = manova1([MPG, Acceleration, Weight], Origin);
 assert (d, 2);
 assert (p, [0, 0.00516082975137544, 0.1206528056514453]', ...
            [1e-12, 1e-12, 1e-12]');
2 tests, 2 passed, 0 known failure, 0 skipped
[inst/regress_gp.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/regress_gp.m
***** demo
 ## Linear fitting of 1D Data
 rand ("seed", 125);
 X = 2 * rand (5, 1) - 1;
 randn ("seed", 25);
 Y = 2 * X - 1 + 0.3 * randn (5, 1);

 ## Points for interpolation/extrapolation
 Xfit = linspace (-2, 2, 10)';

 ## Fit regression model
 [Yfit, Yint, m] = regress_gp (X, Y, Xfit);

 ## Plot fitted data
 plot (X, Y, "xk", Xfit, Yfit, "r-", Xfit, Yint, "b-");
 title ("Gaussian process regression with linear kernel");
***** demo
 ## Linear fitting of 2D Data
 rand ("seed", 135);
 X = 2 * rand (4, 2) - 1;
 randn ("seed", 35);
 Y = 2 * X(:,1) - 3 * X(:,2) - 1 + 1 * randn (4, 1);

 ## Mesh for interpolation/extrapolation
 [x1, x2] = meshgrid (linspace (-1, 1, 10));
 Xfit = [x1(:), x2(:)];

 ## Fit regression model
 [Ypred, Yint, Ysd] = regress_gp (X, Y, Xfit);
 Ypred = reshape (Ypred, 10, 10);
 YintU = reshape (Yint(:,1), 10, 10);
 YintL = reshape (Yint(:,2), 10, 10);

 ## Plot fitted data
 plot3 (X(:,1), X(:,2), Y, ".k", "markersize", 16);
 hold on;
 h = mesh (x1, x2, Ypred, zeros (10, 10));
 set (h, "facecolor", "none", "edgecolor", "yellow");
 h = mesh (x1, x2, YintU, ones (10, 10));
 set (h, "facecolor", "none", "edgecolor", "cyan");
 h = mesh (x1, x2, YintL, ones (10, 10));
 set (h, "facecolor", "none", "edgecolor", "cyan");
 hold off
 axis tight
 view (75, 25)
 title ("Gaussian process regression with linear kernel");
***** demo
 ## Projection over basis function with linear kernel
 pp = [2, 2, 0.3, 1];
 n = 10;
 rand ("seed", 145);
 X = 2 * rand (n, 1) - 1;
 randn ("seed", 45);
 Y = polyval (pp, X) + 0.3 * randn (n, 1);

 ## Powers
 px = [sqrt(abs(X)), X, X.^2, X.^3];

 ## Points for interpolation/extrapolation
 Xfit = linspace (-1, 1, 100)';
 pxi = [sqrt(abs(Xfit)), Xfit, Xfit.^2, Xfit.^3];

 ## Define a prior covariance assuming that the sqrt component is not present
 Sp = 100 * eye (size (px, 2) + 1);
 Sp(2,2) = 1; # We don't believe the sqrt(abs(X)) is present

 ## Fit regression model
 [Yfit, Yint, Ysd] = regress_gp (px, Y, pxi, Sp);

 ## Plot fitted data
 plot (X, Y, "xk;Data;", Xfit, Yfit, "r-;Estimation;", ...
                         Xfit, polyval (pp, Xfit), "g-;True;");
 axis tight
 axis manual
 hold on
 plot (Xfit, Yint(:,1), "m-;Upper bound;", Xfit, Yint(:,2), "b-;Lower bound;");
 hold off
 title ("Linear kernel over basis function with prior covariance");
***** demo
 ## Projection over basis function with linear kernel
 pp = [2, 2, 0.3, 1];
 n = 10;
 rand ("seed", 145);
 X = 2 * rand (n, 1) - 1;
 randn ("seed", 45);
 Y = polyval (pp, X) + 0.3 * randn (n, 1);

 ## Powers
 px = [sqrt(abs(X)), X, X.^2, X.^3];

 ## Points for interpolation/extrapolation
 Xfit = linspace (-1, 1, 100)';
 pxi = [sqrt(abs(Xfit)), Xfit, Xfit.^2, Xfit.^3];

 ## Fit regression model without any assumption on prior covariance
 [Yfit, Yint, Ysd] = regress_gp (px, Y, pxi);

 ## Plot fitted data
 plot (X, Y, "xk;Data;", Xfit, Yfit, "r-;Estimation;", ...
                         Xfit, polyval (pp, Xfit), "g-;True;");
 axis tight
 axis manual
 hold on
 plot (Xfit, Yint(:,1), "m-;Upper bound;", Xfit, Yint(:,2), "b-;Lower bound;");
 hold off
 title ("Linear kernel over basis function without prior covariance");
***** demo
 ## Projection over basis function with rbf kernel
 pp = [2, 2, 0.3, 1];
 n = 10;
 rand ("seed", 145);
 X = 2 * rand (n, 1) - 1;
 randn ("seed", 45);
 Y = polyval (pp, X) + 0.3 * randn (n, 1);

 ## Powers
 px = [sqrt(abs(X)), X, X.^2, X.^3];

 ## Points for interpolation/extrapolation
 Xfit = linspace (-1, 1, 100)';
 pxi = [sqrt(abs(Xfit)), Xfit, Xfit.^2, Xfit.^3];

 ## Fit regression model with RBF kernel (standard parameters)
 [Yfit, Yint, Ysd] = regress_gp (px, Y, pxi, "rbf");

 ## Plot fitted data
 plot (X, Y, "xk;Data;", Xfit, Yfit, "r-;Estimation;", ...
                         Xfit, polyval (pp, Xfit), "g-;True;");
 axis tight
 axis manual
 hold on
 plot (Xfit, Yint(:,1), "m-;Upper bound;", Xfit, Yint(:,2), "b-;Lower bound;");
 hold off
 title ("RBF kernel over basis function with standard parameters");
 text (-0.5, 4, "theta = 5\n g = 0.01");
***** demo
 ## Projection over basis function with rbf kernel
 pp = [2, 2, 0.3, 1];
 n = 10;
 rand ("seed", 145);
 X = 2 * rand (n, 1) - 1;
 randn ("seed", 45);
 Y = polyval (pp, X) + 0.3 * randn (n, 1);

 ## Powers
 px = [sqrt(abs(X)), X, X.^2, X.^3];

 ## Points for interpolation/extrapolation
 Xfit = linspace (-1, 1, 100)';
 pxi = [sqrt(abs(Xfit)), Xfit, Xfit.^2, Xfit.^3];

 ## Fit regression model with RBF kernel with different parameters
 [Yfit, Yint, Ysd] = regress_gp (px, Y, pxi, "rbf", 10, 0.01);

 ## Plot fitted data
 plot (X, Y, "xk;Data;", Xfit, Yfit, "r-;Estimation;", ...
                         Xfit, polyval (pp, Xfit), "g-;True;");
 axis tight
 axis manual
 hold on
 plot (Xfit, Yint(:,1), "m-;Upper bound;", Xfit, Yint(:,2), "b-;Lower bound;");
 hold off
 title ("GP regression with RBF kernel and non default parameters");
 text (-0.5, 4, "theta = 10\n g = 0.01");

 ## Fit regression model with RBF kernel with different parameters
 [Yfit, Yint, Ysd] = regress_gp (px, Y, pxi, "rbf", 50, 0.01);

 ## Plot fitted data
 figure
 plot (X, Y, "xk;Data;", Xfit, Yfit, "r-;Estimation;", ...
                         Xfit, polyval (pp, Xfit), "g-;True;");
 axis tight
 axis manual
 hold on
 plot (Xfit, Yint(:,1), "m-;Upper bound;", Xfit, Yint(:,2), "b-;Lower bound;");
 hold off
 title ("GP regression with RBF kernel and non default parameters");
 text (-0.5, 4, "theta = 50\n g = 0.01");

 ## Fit regression model with RBF kernel with different parameters
 [Yfit, Yint, Ysd] = regress_gp (px, Y, pxi, "rbf", 50, 0.001);

 ## Plot fitted data
 figure
 plot (X, Y, "xk;Data;", Xfit, Yfit, "r-;Estimation;", ...
                         Xfit, polyval (pp, Xfit), "g-;True;");
 axis tight
 axis manual
 hold on
 plot (Xfit, Yint(:,1), "m-;Upper bound;", Xfit, Yint(:,2), "b-;Lower bound;");
 hold off
 title ("GP regression with RBF kernel and non default parameters");
 text (-0.5, 4, "theta = 50\n g = 0.001");

 ## Fit regression model with RBF kernel with different parameters
 [Yfit, Yint, Ysd] = regress_gp (px, Y, pxi, "rbf", 50, 0.05);

 ## Plot fitted data
 figure
 plot (X, Y, "xk;Data;", Xfit, Yfit, "r-;Estimation;", ...
                         Xfit, polyval (pp, Xfit), "g-;True;");
 axis tight
 axis manual
 hold on
 plot (Xfit, Yint(:,1), "m-;Upper bound;", Xfit, Yint(:,2), "b-;Lower bound;");
 hold off
 title ("GP regression with RBF kernel and non default parameters");
 text (-0.5, 4, "theta = 50\n g = 0.05");
***** demo
 ## RBF fitting on noiseless 1D Data
 x = [0:2*pi/7:2*pi]';
 y = 5 * sin (x);

 ## Predictive grid of 500 equally spaced locations
 xi = [-0.5:(2*pi+1)/499:2*pi+0.5]';

 ## Fit regression model with RBF kernel
 [Yfit, Yint, Ysd] = regress_gp (x, y, xi, "rbf");

 ## Plot fitted data
 r = mvnrnd (Yfit, diag (Ysd)', 50);
 plot (xi, r', "c-");
 hold on
 plot (xi, Yfit, "r-;Estimation;", xi, Yint, "b-;Confidence interval;");
 plot (x, y, ".k;Predictor points;", "markersize", 20)
 plot (xi, 5 * sin (xi), "-y;True Function;");
 xlim ([-0.5,2*pi+0.5]);
 ylim ([-10,10]);
 hold off
 title ("GP regression with RBF kernel on noiseless 1D data");
 text (0, -7, "theta = 5\n g = 0.01");
***** demo
 ## RBF fitting on noisy 1D Data
 x = [0:2*pi/7:2*pi]';
 x = [x; x];
 y = 5 * sin (x) + randn (size (x));

 ## Predictive grid of 500 equally spaced locations
 xi = [-0.5:(2*pi+1)/499:2*pi+0.5]';

 ## Fit regression model with RBF kernel
 [Yfit, Yint, Ysd] = regress_gp (x, y, xi, "rbf");

 ## Plot fitted data
 r = mvnrnd (Yfit, diag (Ysd)', 50);
 plot (xi, r', "c-");
 hold on
 plot (xi, Yfit, "r-;Estimation;", xi, Yint, "b-;Confidence interval;");
 plot (x, y, ".k;Predictor points;", "markersize", 20)
 plot (xi, 5 * sin (xi), "-y;True Function;");
 xlim ([-0.5,2*pi+0.5]);
 ylim ([-10,10]);
 hold off
 title ("GP regression with RBF kernel on noisy 1D data");
 text (0, -7, "theta = 5\n g = 0.01");
***** error<Invalid call to regress_gp.> regress_gp (ones (20, 2))
***** error<Invalid call to regress_gp.> regress_gp (ones (20, 2), ones (20, 1))
***** error<regress_gp: X must be a 2-D matrix.> ...
 regress_gp (ones (20, 2, 3), ones (20, 1), ones (20, 2))
***** error<regress_gp: Y must be a column vector.> ...
 regress_gp (ones (20, 2), ones (20, 2), ones (20, 2))
***** error<regress_gp: rows in X must equal the length of Y.> ...
 regress_gp (ones (20, 2), ones (15, 1), ones (20, 2))
***** error<regress_gp: X and XI must have the same number of columns.> ...
 regress_gp (ones (20, 2), ones (20, 1), ones (20, 3))
***** error<regress_gp: invalid 4th argument.> ...
 regress_gp (ones (20, 2), ones (20, 1), ones (10, 2), {[3]})
***** error<regress_gp: invalid 4th argument.> ...
 regress_gp (ones (20, 2), ones (20, 1), ones (10, 2), "kernel")
***** error<regress_gp: theta must be a scalar when using RBF kernel.> ...
 regress_gp (ones (20, 2), ones (20, 1), ones (10, 2), "rbf", ones (4))
***** error<regress_gp: wrong size for prior covariance matrix Sp.> ...
 regress_gp (ones (20, 2), ones (20, 1), ones (10, 2), "linear", 1)
***** error<regress_gp: invalid 5th argument.> ...
 regress_gp (ones (20, 2), ones (20, 1), ones (10, 2), "rbf", "value")
***** error<regress_gp: invalid 5th argument.> ...
 regress_gp (ones (20, 2), ones (20, 1), ones (10, 2), "rbf", {5})
***** error<regress_gp: invalid 5th argument.> ...
 regress_gp (ones (20, 2), ones (20, 1), ones (10, 2), ones (3), 5)
***** error<regress_gp: wrong size for prior covariance matrix Sp.> ...
 regress_gp (ones (20, 2), ones (20, 1), ones (10, 2), "linear", 5)
***** error<regress_gp: invalid 6th argument.> ...
 regress_gp (ones (20, 2), ones (20, 1), ones (10, 2), "rbf", 5, {5})
***** error<regress_gp: invalid 6th argument.> ...
 regress_gp (ones (20, 2), ones (20, 1), ones (10, 2), "rbf", 5, ones (2))
***** error<regress_gp: invalid 6th argument.> ...
 regress_gp (ones (20, 2), ones (20, 1), ones (10, 2), 5, 0.01, [1, 1])
***** error<regress_gp: invalid 6th argument.> ...
 regress_gp (ones (20, 2), ones (20, 1), ones (10, 2), 5, 0.01, "f")
***** error<regress_gp: invalid 6th argument.> ...
 regress_gp (ones (20, 2), ones (20, 1), ones (10, 2), 5, 0.01, "f")
***** error<regress_gp: invalid 7th argument.> ...
 regress_gp (ones (20, 2), ones (20, 1), ones (10, 2), "rbf", 5, 0.01, "f")
***** error<regress_gp: invalid 7th argument.> ...
 regress_gp (ones (20, 2), ones (20, 1), ones (10, 2), "rbf", 5, 0.01, [1, 1])
***** error<regress_gp: wrong size for prior covariance matrix Sp.> ...
 regress_gp (ones (20, 2), ones (20, 1), ones (10, 2), "linear", 1)
22 tests, 22 passed, 0 known failure, 0 skipped
[inst/fitcnet.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/fitcnet.m
***** demo
 ## Train a Neural Network on the Fisher's Iris data set and display
 ## a confusion chart with the classification results.

 load fisheriris
 Mdl = fitcnet (meas, species);
 pred_species = resubPredict (Mdl);
 confusionchart (species, pred_species);
***** test
 load fisheriris
 x = meas;
 y = grp2idx (species);
 Mdl = fitcnet (x, y, "IterationLimit", 50);
 assert (class (Mdl), "ClassificationNeuralNetwork");
 assert (numel (Mdl.ModelParameters.LayerWeights), 2);
 assert (size (Mdl.ModelParameters.LayerWeights{1}), [10, 5]);
 assert (size (Mdl.ModelParameters.LayerWeights{2}), [3, 11]);
***** error<fitcnet: too few arguments.> fitcnet ()
***** error<fitcnet: too few arguments.> fitcnet (ones (4,1))
***** error<fitcnet: Name-Value arguments must be in pairs.>
 fitcnet (ones (4,2), ones (4, 1), 'LayerSizes')
***** error<fitcnet: number of rows in X and Y must be equal.>
 fitcnet (ones (4,2), ones (3, 1))
***** error<fitcnet: number of rows in X and Y must be equal.>
 fitcnet (ones (4,2), ones (3, 1), 'LayerSizes', 2)
6 tests, 6 passed, 0 known failure, 0 skipped
[inst/probit.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/probit.m
***** assert (probit ([-1, 0, 0.5, 1, 2]), [NaN, -Inf, 0, Inf, NaN])
***** assert (probit ([0.2, 0.99]), norminv ([0.2, 0.99]))
***** error probit ()
***** error probit (1, 2)
4 tests, 4 passed, 0 known failure, 0 skipped
[inst/correlation_test.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/correlation_test.m
***** error<Invalid call to correlation_test.  Correct usage> correlation_test ();
***** error<Invalid call to correlation_test.  Correct usage> correlation_test (1);
***** error<correlation_test: X must contain finite real numbers.> ...
 correlation_test ([1 2 NaN]', [2 3 4]');
***** error<correlation_test: X must contain finite real numbers.> ...
 correlation_test ([1 2 Inf]', [2 3 4]');
***** error<correlation_test: X must contain finite real numbers.> ...
 correlation_test ([1 2 3+i]', [2 3 4]');
***** error<correlation_test: Y must contain finite real numbers.> ...
 correlation_test ([1 2 3]', [2 3 NaN]');
***** error<correlation_test: Y must contain finite real numbers.> ...
 correlation_test ([1 2 3]', [2 3 Inf]');
***** error<correlation_test: Y must contain finite real numbers.> ...
 correlation_test ([1 2 3]', [3 4 3+i]');
***** error<correlation_test: X and Y must be vectors of equal length.> ...
 correlation_test ([1 2 3]', [3 4 4 5]');
***** error<correlation_test: invalid value for alpha.> ...
 correlation_test ([1 2 3]', [2 3 4]', "alpha", 0);
***** error<correlation_test: invalid value for alpha.> ...
 correlation_test ([1 2 3]', [2 3 4]', "alpha", 1.2);
***** error<correlation_test: invalid value for alpha.> ...
 correlation_test ([1 2 3]', [2 3 4]', "alpha", [.02 .1]);
***** error<correlation_test: invalid value for alpha.> ...
 correlation_test ([1 2 3]', [2 3 4]', "alpha", "a");
***** error<correlation_test: invalid Name argument.> ...
 correlation_test ([1 2 3]', [2 3 4]', "some", 0.05);
***** error<correlation_test: invalid value for tail.>  ...
 correlation_test ([1 2 3]', [2 3 4]', "tail", "val");
***** error<correlation_test: invalid value for tail.>  ...
 correlation_test ([1 2 3]', [2 3 4]', "alpha", 0.01, "tail", "val");
***** error<correlation_test: invalid value for method.>  ...
 correlation_test ([1 2 3]', [2 3 4]', "method", 0.01);
***** error<correlation_test: invalid value for method.>  ...
 correlation_test ([1 2 3]', [2 3 4]', "method", "some");
***** test
 x = [6 7 7 9 10 12 13 14 15 17];
 y = [19 22 27 25 30 28 30 29 25 32];
 [h, pval, stats] = correlation_test (x, y);
 assert (stats.corrcoef, corr (x', y'), 1e-14);
 assert (pval, 0.0223, 1e-4);
***** test
 x = [6 7 7 9 10 12 13 14 15 17]';
 y = [19 22 27 25 30 28 30 29 25 32]';
 [h, pval, stats] = correlation_test (x, y);
 assert (stats.corrcoef, corr (x, y), 1e-14);
 assert (pval, 0.0223, 1e-4);
20 tests, 20 passed, 0 known failure, 0 skipped
[inst/fullfact.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/fullfact.m
***** demo
 ## Full factorial design with 3 binary variables
 fullfact (3)
***** demo
 ## Full factorial design with 3 ordinal variables
 fullfact ([2, 3, 4])
***** error fullfact ();
***** error fullfact (2, 5);
***** error fullfact (2.5);
***** error fullfact (0);
***** error fullfact (-3);
***** error fullfact (3+2i);
***** error fullfact (Inf);
***** error fullfact (NaN);
***** error fullfact ([1, 2, -3]);
***** error fullfact ([0, 1, 2]);
***** error fullfact ([1, 2, NaN]);
***** error fullfact ([1, 2, Inf]);
***** test
 A = fullfact (2);
 assert (A, [0, 0; 0, 1; 1, 0; 1, 1]);
***** test
 A = fullfact ([1, 2]);
 assert (A, [1, 1; 1, 2]);
***** test
 A = fullfact ([1, 2, 4]);
 A_out = [1, 1, 1; 1, 1, 2; 1, 1, 3; 1, 1, 4; ...
          1, 2, 1; 1, 2, 2; 1, 2, 3; 1, 2, 4];
 assert (A, A_out);
15 tests, 15 passed, 0 known failure, 0 skipped
[inst/jackknife.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/jackknife.m
***** demo
 for k = 1:1000
   rand ("seed", k);  # for reproducibility
   x = rand (10, 1);
   s(k) = std (x);
   jackstat = jackknife (@std, x);
   j(k) = 10 * std (x) - 9 * mean (jackstat);
 endfor
 figure();
 hist ([s', j'], 0:sqrt(1/12)/10:2*sqrt(1/12))
***** demo
 for k = 1:1000
   randn ("seed", k); # for reproducibility
   x = randn (1, 50);
   rand ("seed", k);  # for reproducibility
   y = rand (1, 50);
   jackstat = jackknife (@(x) std(x{1})/std(x{2}), y, x);
   j(k) = 50 * std (y) / std (x) - 49 * mean (jackstat);
   v(k) = sumsq ((50 * std (y) / std (x) - 49 * jackstat) - j(k)) / (50 * 49);
 endfor
 t = (j - sqrt (1 / 12)) ./ sqrt (v);
 figure();
 plot (sort (tcdf (t, 49)), ...
       "-;Almost linear mapping indicates good fit with t-distribution.;")
***** test
 ##Example from Quenouille, Table 1
 d=[0.18 4.00 1.04 0.85 2.14 1.01 3.01 2.33 1.57 2.19];
 jackstat = jackknife ( @(x) 1/mean(x), d );
 assert ( 10 / mean(d) - 9 * mean(jackstat), 0.5240, 1e-5 );
1 test, 1 passed, 0 known failure, 0 skipped
[inst/stepwisefit.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/stepwisefit.m
***** test
 % Sample data from Draper and Smith (n = 13, k = 4)
 X = [7 1 11 11 7 11 3 1 2 21 1 11 10; ...
     26 29 56 31 52 55 71 31 54 47 40 66 68; ...
     6 15 8 8 6 9 17 22 18 4 23 9 8; ...
     60 52 20 47 33 22 6 44 22 26 34 12 12]';
 y = [78.5 74.3 104.3 87.6 95.9 109.2 102.7 72.5 93.1 115.9 83.8 113.3 109.4]';
 [X_use, b, bint, r, rint, stats] = stepwisefit(y, X);
 assert(X_use, [4 1])
 assert(b, regress(y, [ones(size(y)) X(:, X_use)], 0.05))
 [X_use, b, bint, r, rint, stats] = stepwisefit(y, X, 0.05, 0.1, "corr");
 assert(X_use, [4 1])
 assert(b, regress(y, [ones(size(y)) X(:, X_use)], 0.05))
 [X_use, b, bint, r, rint, stats] = stepwisefit(y, X, [], [], "p");
 assert(X_use, [4 1])
 assert(b, regress(y, [ones(size(y)) X(:, X_use)], 0.05))
1 test, 1 passed, 0 known failure, 0 skipped
[inst/manovacluster.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/manovacluster.m
***** demo
 load carbig
 X = [MPG Acceleration Weight Displacement];
 [d, p, stats] = manova1 (X, Origin);
 manovacluster (stats)
***** test
 hf = figure ("visible", "off");
 unwind_protect
   load carbig
   X = [MPG Acceleration Weight Displacement];
   [d, p, stats] = manova1 (X, Origin);
   manovacluster (stats);
 unwind_protect_cleanup
   close (hf);
 end_unwind_protect
***** error manovacluster (stats, "some");
2 tests, 2 passed, 0 known failure, 0 skipped
[inst/histfit.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/histfit.m
***** demo
 histfit (randn (100, 1))
***** demo
 histfit (poissrnd (2, 1000, 1), 10, "Poisson")
***** demo
 histfit (betarnd (3, 10, 1000, 1), 10, "beta")
***** test
 hf = figure ("visible", "off");
 unwind_protect
   x = [2, 4, 3, 2, 4, 3, 2, 5, 6, 4, 7, 5, 9, 8, 10, 4, 11];
   histfit (x);
 unwind_protect_cleanup
   close (hf);
 end_unwind_protect
***** test
 hf = figure ("visible", "off");
 unwind_protect
   x = [2, 4, 3, 2, NaN, 3, 2, 5, 6, 4, 7, 5, 9, 8, 10, 4, 11];
   histfit (x);
 unwind_protect_cleanup
   close (hf);
 end_unwind_protect
***** test
 hf = figure ("visible", "off");
 unwind_protect
   x = [2, 4, 3, 2, NaN, 3, 2, 5, 6, 4, 7, 5, 9, 8, 10, 4, 11];
   histfit (x, 3);
 unwind_protect_cleanup
   close (hf);
 end_unwind_protect
***** test
 hf = figure ("visible", "off");
 unwind_protect
   histfit (randn (100, 1));
 unwind_protect_cleanup
   close (hf);
 end_unwind_protect
***** test
 hf = figure ("visible", "off");
 unwind_protect
   histfit (poissrnd (2, 1000, 1), 10, "Poisson");
 unwind_protect_cleanup
   close (hf);
 end_unwind_protect
***** test
 hf = figure ("visible", "off");
 unwind_protect
   histfit (betarnd (3, 10, 1000, 1), 10, "beta");
 unwind_protect_cleanup
   close (hf);
 end_unwind_protect
***** test
 hf = figure ("visible", "off");
 unwind_protect
   ax = gca ();
   histfit (ax, randn (100, 1));
 unwind_protect_cleanup
   close (hf);
 end_unwind_protect
***** test
 hf = figure ("visible", "off");
 unwind_protect
   ax = gca ();
   histfit (ax, poissrnd (2, 1000, 1), 10, "Poisson");
 unwind_protect_cleanup
   close (hf);
 end_unwind_protect
***** test
 hf = figure ("visible", "off");
 unwind_protect
   ax = gca ();
   histfit (ax, betarnd (3, 10, 1000, 1), 10, "beta");
 unwind_protect_cleanup
   close (hf);
 end_unwind_protect
***** test
 hf = figure ("visible", "off");
 unwind_protect
   ax = axes ("parent", hf);
   fail ("histfit (ax)", "histfit: too few input arguments.");
 unwind_protect_cleanup
   close (hf);
 end_unwind_protect
***** error<histfit: X must be a numeric vector of real numbers.> ...
 histfit ('wer')
***** error<histfit: no data in X.> histfit ([NaN, NaN, NaN]);
***** error<histfit: NBINS must be a real scalar integer value.> ...
 histfit (randn (100, 1), 5.6)
***** error<histfit: DISTNAME must be a character vector.> ...
 histfit (randn (100, 1), 8, 5)
***** error<histfit: DISTNAME must be a character vector.> ...
 histfit (randn (100, 1), 8, {'normal'})
***** error<histfit: 'Kernel' distribution is not supported yet.> ...
 histfit (randn (100, 1), 8, 'Kernel')
***** error<histfit: unrecognized distribution name.> ...
 histfit (randn (100, 1), 8, 'ASDASDASD')
17 tests, 17 passed, 0 known failure, 0 skipped
[inst/nanmin.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/nanmin.m
***** demo
 ## Find the column minimum values and their indices
 ## for matrix data with missing values.

 x = magic (3);
 x([1, 6:9]) = NaN
 [y, ind] = nanmin (x)
***** demo
 ## Find the minimum of all the values in an array, ignoring missing values.
 ## Create a 2-by-5-by-3 array x with some missing values.

 x = reshape (1:30, [2, 5, 3]);
 x([10:12, 25]) = NaN

 ## Find the minimum of the elements of x.

 y = nanmin (x, [], 'all')
***** assert (nanmin ([2, 4, NaN, 7]), 2)
***** assert (nanmin ([2, 4, NaN, -Inf]), -Inf)
***** assert (nanmin ([1, NaN, 3; NaN, 5, 6; 7, 8, NaN]), [1, 5, 3])
***** assert (nanmin ([1, NaN, 3; NaN, 5, 6; 7, 8, NaN]'), [1, 5, 7])
***** assert (nanmin (single ([1, NaN, 3; NaN, 5, 6; 7, 8, NaN])), single ([1, 5, 3]))
***** shared x, y
 x(:,:,1) = [1.77, -0.005, NaN, -2.95; NaN, 0.34, NaN, 0.19];
 x(:,:,2) = [1.77, -0.005, NaN, -2.95; NaN, 0.34, NaN, 0.19] + 5;
 y = x;
 y(2,3,1) = 0.51;
***** assert (nanmin (x, [], [1, 2])(:), [-2.95; 2.05])
***** assert (nanmin (x, [], [1, 3])(:), [1.77; -0.005; NaN; -2.95])
***** assert (nanmin (x, [], [2, 3])(:), [-2.95; 0.19])
***** assert (nanmin (x, [], [1, 2, 3]), -2.95)
***** assert (nanmin (x, [], 'all'), -2.95)
***** assert (nanmin (y, [], [1, 3])(:), [1.77; -0.005; 0.51; -2.95])
***** assert (nanmin (x(1,:,1), x(2,:,1)), [1.77, -0.005, NaN, -2.95])
***** assert (nanmin (x(1,:,2), x(2,:,2)), [6.77, 4.995, NaN, 2.05])
***** assert (nanmin (y(1,:,1), y(2,:,1)), [1.77, -0.005, 0.51, -2.95])
***** assert (nanmin (y(1,:,2), y(2,:,2)), [6.77, 4.995, NaN, 2.05])
***** test
 xx = repmat ([1:20;6:25], [5 2 6 3]);
 assert (size (nanmin (xx, [], [3, 2])), [10, 1, 1, 3]);
 assert (size (nanmin (xx, [], [1, 2])), [1, 1, 6, 3]);
 assert (size (nanmin (xx, [], [1, 2, 4])), [1, 1, 6]);
 assert (size (nanmin (xx, [], [1, 4, 3])), [1, 40]);
 assert (size (nanmin (xx, [], [1, 2, 3, 4])), [1, 1]);
***** assert (nanmin (ones (2), [], 3), ones (2, 2))
***** assert (nanmin (ones (2, 2, 2), [], 99), ones (2, 2, 2))
***** assert (nanmin (magic (3), [], 3), magic (3))
***** assert (nanmin (magic (3), [], [1, 3]), [3, 1, 2])
***** assert (nanmin (magic (3), [], [1, 99]), [3, 1, 2])
***** assert (nanmin (ones (2), 3), ones (2,2))
***** error <nanmin: VECDIM must contain non-repeating positive integers.> ...
 nanmin (y, [], [1, 1, 2])
***** error <nanmin: a second output is not supported with this syntax.> ...
 [v, idx] = nanmin(x, y, [1 2])
24 tests, 24 passed, 0 known failure, 0 skipped
[inst/cdfplot.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/cdfplot.m
***** demo
 x = randn(100,1);
 cdfplot (x);
***** test
 hf = figure ("visible", "off");
 unwind_protect
   x = [2, 4, 3, 2, 4, 3, 2, 5, 6, 4];
   [hCDF, stats] = cdfplot (x);
   assert (stats.min, 2);
   assert (stats.max, 6);
   assert (stats.median, 3.5);
   assert (stats.std, 1.35400640077266, 1e-14);
 unwind_protect_cleanup
   close (hf);
 end_unwind_protect
***** test
 hf = figure ("visible", "off");
 unwind_protect
   x = randn(100,1);
   cdfplot (x);
 unwind_protect_cleanup
   close (hf);
 end_unwind_protect
***** error cdfplot ();
***** error cdfplot ([x',x']);
***** error cdfplot ([NaN, NaN, NaN, NaN]);
5 tests, 5 passed, 0 known failure, 0 skipped
[inst/anova2.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/anova2.m
***** demo

 # Factorial (Crossed) Two-way ANOVA with Interaction

 popcorn = [5.5, 4.5, 3.5; 5.5, 4.5, 4.0; 6.0, 4.0, 3.0; ...
            6.5, 5.0, 4.0; 7.0, 5.5, 5.0; 7.0, 5.0, 4.5];

 [p, atab, stats] = anova2(popcorn, 3, "on");
***** demo

 # One-way Repeated Measures ANOVA (Rows are a crossed random factor)

 data = [54, 43, 78, 111;
         23, 34, 37, 41;
         45, 65, 99, 78;
         31, 33, 36, 35;
         15, 25, 30, 26];

 [p, atab, stats] = anova2 (data, 1, "on", "linear");
***** demo

 # Balanced Nested One-way ANOVA (Rows are a nested random factor)

 data = [4.5924 7.3809 21.322; -0.5488 9.2085 25.0426; ...
         6.1605 13.1147 22.66; 2.3374 15.2654 24.1283; ...
         5.1873 12.4188 16.5927; 3.3579 14.3951 10.2129; ...
         6.3092 8.5986 9.8934; 3.2831 3.4945 10.0203];

 [p, atab, stats] = anova2 (data, 4, "on", "nested");
***** test
 ## Test for anova2 ("interaction")
 ## comparison with results from Matlab for column effect
 popcorn = [5.5, 4.5, 3.5; 5.5, 4.5, 4.0; 6.0, 4.0, 3.0; ...
            6.5, 5.0, 4.0; 7.0, 5.5, 5.0; 7.0, 5.0, 4.5];
 [p, atab, stats] = anova2 (popcorn, 3, "off");
 assert (p(1), 7.678957383294716e-07, 1e-14);
 assert (p(2), 0.0001003738963050171, 1e-14);
 assert (p(3), 0.7462153966366274, 1e-14);
 assert (atab{2,5}, 56.700, 1e-14);
 assert (atab{2,3}, 2, 0);
 assert (atab{4,2}, 0.08333333333333348, 1e-14);
 assert (atab{5,4}, 0.1388888888888889, 1e-14);
 assert (atab{5,2}, 1.666666666666667, 1e-14);
 assert (atab{6,2}, 22);
 assert (stats.source, "anova2");
 assert (stats.colmeans, [6.25, 4.75, 4]);
 assert (stats.inter, 1, 0);
 assert (stats.pval, 0.7462153966366274, 1e-14);
 assert (stats.df, 12);
***** test
 ## Test for anova2 ("linear") - comparison with results from GraphPad Prism 8
 data = [54, 43, 78, 111;
         23, 34, 37, 41;
         45, 65, 99, 78;
         31, 33, 36, 35;
         15, 25, 30, 26];
 [p, atab, stats] = anova2 (data, 1, "off", "linear");
 assert (atab{2,2}, 2174.95, 1e-10);
 assert (atab{3,2}, 8371.7, 1e-10);
 assert (atab{4,2}, 2404.3, 1e-10);
 assert (atab{5,2}, 12950.95, 1e-10);
 assert (atab{2,4}, 724.983333333333, 1e-10);
 assert (atab{3,4}, 2092.925, 1e-10);
 assert (atab{4,4}, 200.358333333333, 1e-10);
 assert (atab{2,5}, 3.61843363972882, 1e-10);
 assert (atab{3,5}, 10.445909412303, 1e-10);
 assert (atab{2,6}, 0.087266112738617, 1e-10);
 assert (atab{3,6}, 0.000698397753556, 1e-10);
***** test
 ## Test for anova2 ("nested") - comparison with results from GraphPad Prism 8
 data = [4.5924 7.3809 21.322; -0.5488 9.2085 25.0426; ...
         6.1605 13.1147 22.66; 2.3374 15.2654 24.1283; ...
         5.1873 12.4188 16.5927; 3.3579 14.3951 10.2129; ...
         6.3092 8.5986 9.8934; 3.2831 3.4945 10.0203];
 [p, atab, stats] = anova2 (data, 4, "off", "nested");
 assert (atab{2,2}, 745.360306290833, 1e-10);
 assert (atab{3,2}, 278.01854140125, 1e-10);
 assert (atab{4,2}, 180.180377467501, 1e-10);
 assert (atab{5,2}, 1203.55922515958, 1e-10);
 assert (atab{2,4}, 372.680153145417, 1e-10);
 assert (atab{3,4}, 92.67284713375, 1e-10);
 assert (atab{4,4}, 10.0100209704167, 1e-10);
 assert (atab{2,5}, 4.02146005730833, 1e-10);
 assert (atab{3,5}, 9.25800729165627, 1e-10);
 assert (atab{2,6}, 0.141597630656771, 1e-10);
 assert (atab{3,6}, 0.000636643812875719, 1e-10);
3 tests, 3 passed, 0 known failure, 0 skipped
[inst/multcompare.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/multcompare.m
***** demo

 ## Demonstration using balanced one-way ANOVA from anova1

 x = ones (50, 4) .* [-2, 0, 1, 5];
 randn ("seed", 1);    # for reproducibility
 x = x + normrnd (0, 2, 50, 4);
 groups = {"A", "B", "C", "D"};
 [p, tbl, stats] = anova1 (x, groups, "off");
 multcompare (stats);
***** demo

 ## Demonstration using unbalanced one-way ANOVA example from anovan

 dv =  [ 8.706 10.362 11.552  6.941 10.983 10.092  6.421 14.943 15.931 ...
        22.968 18.590 16.567 15.944 21.637 14.492 17.965 18.851 22.891 ...
        22.028 16.884 17.252 18.325 25.435 19.141 21.238 22.196 18.038 ...
        22.628 31.163 26.053 24.419 32.145 28.966 30.207 29.142 33.212 ...
        25.694 ]';
 g = [1 1 1 1 1 1 1 1 2 2 2 2 2 3 3 3 3 3 3 3 3 ...
      4 4 4 4 4 4 4 5 5 5 5 5 5 5 5 5]';

 [P,ATAB, STATS] = anovan (dv, g, "varnames", "score", "display", "off");

 [C, M, H, GNAMES] = multcompare (STATS, "dim", 1, "ctype", "holm", ...
                                  "ControlGroup", 1, "display", "on")

***** demo

 ## Demonstration using factorial ANCOVA example from anovan

 score = [95.6 82.2 97.2 96.4 81.4 83.6 89.4 83.8 83.3 85.7 ...
 97.2 78.2 78.9 91.8 86.9 84.1 88.6 89.8 87.3 85.4 ...
 81.8 65.8 68.1 70.0 69.9 75.1 72.3 70.9 71.5 72.5 ...
 84.9 96.1 94.6 82.5 90.7 87.0 86.8 93.3 87.6 92.4 ...
 100. 80.5 92.9 84.0 88.4 91.1 85.7 91.3 92.3 87.9 ...
 91.7 88.6 75.8 75.7 75.3 82.4 80.1 86.0 81.8 82.5]';
 treatment = {"yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" ...
              "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" ...
              "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" ...
              "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  ...
              "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  ...
              "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"}';
 exercise = {"lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  ...
             "mid" "mid" "mid" "mid" "mid" "mid" "mid" "mid" "mid" "mid" ...
             "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  ...
             "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  ...
             "mid" "mid" "mid" "mid" "mid" "mid" "mid" "mid" "mid" "mid" ...
             "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"}';
 age = [59 65 70 66 61 65 57 61 58 55 62 61 60 59 55 57 60 63 62 57 ...
 58 56 57 59 59 60 55 53 55 58 68 62 61 54 59 63 60 67 60 67 ...
 75 54 57 62 65 60 58 61 65 57 56 58 58 58 52 53 60 62 61 61]';

 [P, ATAB, STATS] = anovan (score, {treatment, exercise, age}, "model", ...
                            [1 0 0; 0 1 0; 0 0 1; 1 1 0], "continuous", 3, ...
                            "sstype", "h", "display", "off", "contrasts", ...
                            {"simple","poly",""});

 [C, M, H, GNAMES] = multcompare (STATS, "dim", [1 2], "ctype", "holm", ...
                                  "display", "on")

***** demo

 ## Demonstration using one-way ANOVA from anovan, with fit by weighted least
 ## squares to account for heteroskedasticity.

 g = [1, 1, 1, 1, 1, 1, 1, 1, ...
      2, 2, 2, 2, 2, 2, 2, 2, ...
      3, 3, 3, 3, 3, 3, 3, 3]';

 y = [13, 16, 16,  7, 11,  5,  1,  9, ...
      10, 25, 66, 43, 47, 56,  6, 39, ...
      11, 39, 26, 35, 25, 14, 24, 17]';

 [P,ATAB,STATS] = anovan(y, g, "display", "off");
 fitted = STATS.X * STATS.coeffs(:,1); # fitted values
 b = polyfit (fitted, abs (STATS.resid), 1);
 v = polyval (b, fitted);  # Variance as a function of the fitted values
 [P,ATAB,STATS] = anovan (y, g, "weights", v.^-1, "display", "off");
 [C, M] =  multcompare (STATS, "display", "on", "ctype", "mvt")
***** demo

 ## Demonstration of p-value adjustments to control the false discovery rate
 ## Data from Westfall (1997) JASA. 92(437):299-306

 p = [.005708; .023544; .024193; .044895; ...
       .048805; .221227; .395867; .693051; .775755];

 padj = multcompare(p,'ctype','fdr')
***** test

 ## Tests using unbalanced one-way ANOVA example from anovan and anova1

 ## Test for anovan - compare pairwise comparisons with matlab for CTYPE "lsd"

 dv =  [ 8.706 10.362 11.552  6.941 10.983 10.092  6.421 14.943 15.931 ...
        22.968 18.590 16.567 15.944 21.637 14.492 17.965 18.851 22.891 ...
        22.028 16.884 17.252 18.325 25.435 19.141 21.238 22.196 18.038 ...
        22.628 31.163 26.053 24.419 32.145 28.966 30.207 29.142 33.212 ...
        25.694 ]';
 g = [1 1 1 1 1 1 1 1 2 2 2 2 2 3 3 3 3 3 3 3 3 ...
      4 4 4 4 4 4 4 5 5 5 5 5 5 5 5 5]';

 [P, ATAB, STATS] = anovan (dv, g, "varnames", "score", "display", "off");
 [C, M, H, GNAMES] = multcompare (STATS, "dim", 1, "ctype", "lsd", ...
                                  "display", "off");
 assert (C(1,6), 2.85812420217898e-05, 1e-09);
 assert (C(2,6), 5.22936741204085e-07, 1e-09);
 assert (C(3,6), 2.12794763209146e-08, 1e-09);
 assert (C(4,6), 7.82091664406946e-15, 1e-09);
 assert (C(5,6), 0.546591417210693, 1e-09);
 assert (C(6,6), 0.0845897945254446, 1e-09);
 assert (C(7,6), 9.47436557975328e-08, 1e-09);
 assert (C(8,6), 0.188873478781067, 1e-09);
 assert (C(9,6), 4.08974010364197e-08, 1e-09);
 assert (C(10,6), 4.44427348175241e-06, 1e-09);
 assert (M(1,1), 10, 1e-09);
 assert (M(2,1), 18, 1e-09);
 assert (M(3,1), 19, 1e-09);
 assert (M(4,1), 21.0001428571429, 1e-09);
 assert (M(5,1), 29.0001111111111, 1e-09);
 assert (M(1,2), 1.0177537954095, 1e-09);
 assert (M(2,2), 1.28736803631001, 1e-09);
 assert (M(3,2), 1.0177537954095, 1e-09);
 assert (M(4,2), 1.0880245732889, 1e-09);
 assert (M(5,2), 0.959547480416536, 1e-09);

 ## Compare "fdr" adjusted p-values to those obtained using p.adjust in R

 [C, M, H, GNAMES] = multcompare (STATS, "dim", 1, "ctype", "fdr", ...
                                  "display", "off");
 assert (C(1,6), 4.08303457454140e-05, 1e-09);
 assert (C(2,6), 1.04587348240817e-06, 1e-09);
 assert (C(3,6), 1.06397381604573e-07, 1e-09);
 assert (C(4,6), 7.82091664406946e-14, 1e-09);
 assert (C(5,6), 5.46591417210693e-01, 1e-09);
 assert (C(6,6), 1.05737243156806e-01, 1e-09);
 assert (C(7,6), 2.36859139493832e-07, 1e-09);
 assert (C(8,6), 2.09859420867852e-01, 1e-09);
 assert (C(9,6), 1.36324670121399e-07, 1e-09);
 assert (C(10,6), 7.40712246958735e-06, 1e-09);

 ## Compare "hochberg" adjusted p-values to those obtained using p.adjust in R

 [C, M, H, GNAMES] = multcompare (STATS, "dim", 1, "ctype", "hochberg", ...
                                  "display", "off");
 assert (C(1,6), 1.14324968087159e-04, 1e-09);
 assert (C(2,6), 3.13762044722451e-06, 1e-09);
 assert (C(3,6), 1.91515286888231e-07, 1e-09);
 assert (C(4,6), 7.82091664406946e-14, 1e-09);
 assert (C(5,6), 5.46591417210693e-01, 1e-09);
 assert (C(6,6), 2.53769383576334e-01, 1e-09);
 assert (C(7,6), 6.63205590582730e-07, 1e-09);
 assert (C(8,6), 3.77746957562134e-01, 1e-09);
 assert (C(9,6), 3.27179208291358e-07, 1e-09);
 assert (C(10,6), 2.22213674087620e-05, 1e-09);

 ## Compare "holm" adjusted p-values to those obtained using p.adjust in R

 [C, M, H, GNAMES] = multcompare (STATS, "dim", 1, "ctype", "holm", ...
                                  "display", "off");
 assert (C(1,6), 1.14324968087159e-04, 1e-09);
 assert (C(2,6), 3.13762044722451e-06, 1e-09);
 assert (C(3,6), 1.91515286888231e-07, 1e-09);
 assert (C(4,6), 7.82091664406946e-14, 1e-09);
 assert (C(5,6), 5.46591417210693e-01, 1e-09);
 assert (C(6,6), 2.53769383576334e-01, 1e-09);
 assert (C(7,6), 6.63205590582730e-07, 1e-09);
 assert (C(8,6), 3.77746957562134e-01, 1e-09);
 assert (C(9,6), 3.27179208291358e-07, 1e-09);
 assert (C(10,6), 2.22213674087620e-05, 1e-09);

 ## Compare "scheffe" adjusted p-values to those obtained using 'scheffe' in Matlab

 [C, M, H, GNAMES] = multcompare (STATS, "dim", 1, "ctype", "scheffe", ...
                                  "display", "off");
 assert (C(1,6), 0.00108105386141085, 1e-09);
 assert (C(2,6), 2.7779386789517e-05, 1e-09);
 assert (C(3,6), 1.3599854038198e-06, 1e-09);
 assert (C(4,6), 7.58830197867751e-13, 1e-09);
 assert (C(5,6), 0.984039948220281, 1e-09);
 assert (C(6,6), 0.539077018557706, 1e-09);
 assert (C(7,6), 5.59475764460574e-06, 1e-09);
 assert (C(8,6), 0.771173490574105, 1e-09);
 assert (C(9,6), 2.52838425729905e-06, 1e-09);
 assert (C(10,6), 0.000200719143889168, 1e-09);

 ## Compare "bonferroni" adjusted p-values to those obtained using p.adjust in R

 [C, M, H, GNAMES] = multcompare (STATS, "dim", 1, "ctype", "bonferroni", ...
                                  "display", "off");
 assert (C(1,6), 2.85812420217898e-04, 1e-09);
 assert (C(2,6), 5.22936741204085e-06, 1e-09);
 assert (C(3,6), 2.12794763209146e-07, 1e-09);
 assert (C(4,6), 7.82091664406946e-14, 1e-09);
 assert (C(5,6), 1.00000000000000e+00, 1e-09);
 assert (C(6,6), 8.45897945254446e-01, 1e-09);
 assert (C(7,6), 9.47436557975328e-07, 1e-09);
 assert (C(8,6), 1.00000000000000e+00, 1e-09);
 assert (C(9,6), 4.08974010364197e-07, 1e-09);
 assert (C(10,6), 4.44427348175241e-05, 1e-09);

 ## Test for anova1 ("equal")- comparison of results from Matlab

 [P, ATAB, STATS] = anova1 (dv, g, "off", "equal");
 [C, M, H, GNAMES] = multcompare (STATS, "ctype", "lsd", "display", "off");
 assert (C(1,6), 2.85812420217898e-05, 1e-09);
 assert (C(2,6), 5.22936741204085e-07, 1e-09);
 assert (C(3,6), 2.12794763209146e-08, 1e-09);
 assert (C(4,6), 7.82091664406946e-15, 1e-09);
 assert (C(5,6), 0.546591417210693, 1e-09);
 assert (C(6,6), 0.0845897945254446, 1e-09);
 assert (C(7,6), 9.47436557975328e-08, 1e-09);
 assert (C(8,6), 0.188873478781067, 1e-09);
 assert (C(9,6), 4.08974010364197e-08, 1e-09);
 assert (C(10,6), 4.44427348175241e-06, 1e-09);
 assert (M(1,1), 10, 1e-09);
 assert (M(2,1), 18, 1e-09);
 assert (M(3,1), 19, 1e-09);
 assert (M(4,1), 21.0001428571429, 1e-09);
 assert (M(5,1), 29.0001111111111, 1e-09);
 assert (M(1,2), 1.0177537954095, 1e-09);
 assert (M(2,2), 1.28736803631001, 1e-09);
 assert (M(3,2), 1.0177537954095, 1e-09);
 assert (M(4,2), 1.0880245732889, 1e-09);
 assert (M(5,2), 0.959547480416536, 1e-09);

 ## Test for anova1 ("unequal") - comparison with results from GraphPad Prism 8
 [P, ATAB, STATS] = anova1 (dv, g, "off", "unequal");
 [C, M, H, GNAMES] = multcompare (STATS, "ctype", "lsd", "display", "off");
 assert (C(1,6), 0.001247025266382, 1e-09);
 assert (C(2,6), 0.000018037115146, 1e-09);
 assert (C(3,6), 0.000002974595187, 1e-09);
 assert (C(4,6), 0.000000000786046, 1e-09);
 assert (C(5,6), 0.5693192886650109, 1e-09);
 assert (C(6,6), 0.110501699029776, 1e-09);
 assert (C(7,6), 0.000131226488700, 1e-09);
 assert (C(8,6), 0.1912101409715992, 1e-09);
 assert (C(9,6), 0.000005385256394, 1e-09);
 assert (C(10,6), 0.000074089106171, 1e-09);
***** test

 ## Test for anova2 ("interaction") - comparison with results from Matlab for column effect
 popcorn = [5.5, 4.5, 3.5; 5.5, 4.5, 4.0; 6.0, 4.0, 3.0; ...
            6.5, 5.0, 4.0; 7.0, 5.5, 5.0; 7.0, 5.0, 4.5];
 [P, ATAB, STATS] = anova2 (popcorn, 3, "off");
 [C, M, H, GNAMES] = multcompare (STATS, "estimate", "column",...
                                  "ctype", "lsd", "display", "off");
 assert (C(1,6), 1.49311100811177e-05, 1e-09);
 assert (C(2,6), 2.20506904243535e-07, 1e-09);
 assert (C(3,6), 0.00449897860490058, 1e-09);
 assert (M(1,1), 6.25, 1e-09);
 assert (M(2,1), 4.75, 1e-09);
 assert (M(3,1), 4, 1e-09);
 assert (M(1,2), 0.152145154862547, 1e-09);
 assert (M(2,2), 0.152145154862547, 1e-09);
 assert (M(3,2), 0.152145154862547, 1e-09);
***** test

 ## Test for anova2 ("linear") - comparison with results from GraphPad Prism 8
 words = [10 13 13; 6 8 8; 11 14 14; 22 23 25; 16 18 20; ...
          15 17 17; 1 1 4; 12 15 17;  9 12 12;  8 9 12];
 [P, ATAB, STATS] = anova2 (words, 1, "off", "linear");
 [C, M, H, GNAMES] = multcompare (STATS, "estimate", "column",...
                                  "ctype", "lsd", "display", "off");
 assert (C(1,6), 0.000020799832702, 1e-09);
 assert (C(2,6), 0.000000035812410, 1e-09);
 assert (C(3,6), 0.003038942449215, 1e-09);
***** test

 ## Test for anova2 ("nested") - comparison with results from GraphPad Prism 8
 data = [4.5924 7.3809 21.322; -0.5488 9.2085 25.0426; ...
         6.1605 13.1147 22.66; 2.3374 15.2654 24.1283; ...
         5.1873 12.4188 16.5927; 3.3579 14.3951 10.2129; ...
         6.3092 8.5986 9.8934; 3.2831 3.4945 10.0203];
 [P, ATAB, STATS] = anova2 (data, 4, "off", "nested");
 [C, M, H, GNAMES] = multcompare (STATS, "estimate", "column",...
                                  "ctype", "lsd", "display", "off");
 assert (C(1,6), 0.261031111511073, 1e-09);
 assert (C(2,6), 0.065879755907745, 1e-09);
 assert (C(3,6), 0.241874613529270, 1e-09);
***** shared visibility_setting
 visibility_setting = get (0, "DefaultFigureVisible");
***** test
 set (0, "DefaultFigureVisible", "off");

 ## Test for kruskalwallis - comparison with results from MATLAB
 data = [3,2,4; 5,4,4; 4,2,4; 4,2,4; 4,1,5; ...
         4,2,3; 4,3,5; 4,2,4; 5,2,4; 5,3,3];
 group = [1:3] .* ones (10,3);
 [P, ATAB, STATS] = kruskalwallis (data(:), group(:), "off");
 C = multcompare (STATS, "ctype", "lsd", "display", "off");
 assert (C(1,6), 0.000163089828959986, 1e-09);
 assert (C(2,6), 0.630298044801257, 1e-09);
 assert (C(3,6), 0.00100567660695682, 1e-09);
 C = multcompare (STATS, "ctype", "bonferroni", "display", "off");
 assert (C(1,6), 0.000489269486879958, 1e-09);
 assert (C(2,6), 1, 1e-09);
 assert (C(3,6), 0.00301702982087047, 1e-09);
 C = multcompare(STATS, "ctype", "scheffe", "display", "off");
 assert (C(1,6), 0.000819054880289573, 1e-09);
 assert (C(2,6), 0.890628039849261, 1e-09);
 assert (C(3,6), 0.00447816059021654, 1e-09);
 set (0, "DefaultFigureVisible", visibility_setting);
***** test
 set (0, "DefaultFigureVisible", "off");
 ## Test for friedman - comparison with results from MATLAB
 popcorn = [5.5, 4.5, 3.5; 5.5, 4.5, 4.0; 6.0, 4.0, 3.0; ...
            6.5, 5.0, 4.0; 7.0, 5.5, 5.0; 7.0, 5.0, 4.5];
 [P, ATAB, STATS] = friedman (popcorn, 3, "off");
 C = multcompare(STATS, "ctype", "lsd", "display", "off");
 assert (C(1,6), 0.227424558028569, 1e-09);
 assert (C(2,6), 0.0327204848315735, 1e-09);
 assert (C(3,6), 0.353160353315988, 1e-09);
 C = multcompare(STATS, "ctype", "bonferroni", "display", "off");
 assert (C(1,6), 0.682273674085708, 1e-09);
 assert (C(2,6), 0.0981614544947206, 1e-09);
 assert (C(3,6), 1, 1e-09);
 C = multcompare(STATS, "ctype", "scheffe", "display", "off");
 assert (C(1,6), 0.482657360384373, 1e-09);
 assert (C(2,6), 0.102266573027672, 1e-09);
 assert (C(3,6), 0.649836502233148, 1e-09);
 set (0, "DefaultFigureVisible", visibility_setting);
***** test
 set (0, "DefaultFigureVisible", "off");
 ## Test for fitlm - same comparisons as for first anovan example
 y =  [ 8.706 10.362 11.552  6.941 10.983 10.092  6.421 14.943 15.931 ...
        22.968 18.590 16.567 15.944 21.637 14.492 17.965 18.851 22.891 ...
        22.028 16.884 17.252 18.325 25.435 19.141 21.238 22.196 18.038 ...
        22.628 31.163 26.053 24.419 32.145 28.966 30.207 29.142 33.212 ...
        25.694 ]';
 X = [1 1 1 1 1 1 1 1 2 2 2 2 2 3 3 3 3 3 3 3 3 4 4 4 4 4 4 4 5 5 5 5 5 5 5 5 5]';
 [TAB,STATS] = fitlm (X,y,"linear","categorical",1,"display","off",...
                      "contrasts","simple");
 [C, M] = multcompare(STATS, "ctype", "lsd", "display", "off");
 assert (C(1,6), 2.85812420217898e-05, 1e-09);
 assert (C(2,6), 5.22936741204085e-07, 1e-09);
 assert (C(3,6), 2.12794763209146e-08, 1e-09);
 assert (C(4,6), 7.82091664406946e-15, 1e-09);
 assert (C(5,6), 0.546591417210693, 1e-09);
 assert (C(6,6), 0.0845897945254446, 1e-09);
 assert (C(7,6), 9.47436557975328e-08, 1e-09);
 assert (C(8,6), 0.188873478781067, 1e-09);
 assert (C(9,6), 4.08974010364197e-08, 1e-09);
 assert (C(10,6), 4.44427348175241e-06, 1e-09);
 assert (M(1,1), 10, 1e-09);
 assert (M(2,1), 18, 1e-09);
 assert (M(3,1), 19, 1e-09);
 assert (M(4,1), 21.0001428571429, 1e-09);
 assert (M(5,1), 29.0001111111111, 1e-09);
 assert (M(1,2), 1.0177537954095, 1e-09);
 assert (M(2,2), 1.28736803631001, 1e-09);
 assert (M(3,2), 1.0177537954095, 1e-09);
 assert (M(4,2), 1.0880245732889, 1e-09);
 assert (M(5,2), 0.959547480416536, 1e-09);
 set (0, "DefaultFigureVisible", visibility_setting);
***** test
 ## Test p-value adjustments compared to R stats package function p.adjust
 ## Data from Westfall (1997) JASA. 92(437):299-306
 p = [.005708; .023544; .024193; .044895; ...
       .048805; .221227; .395867; .693051; .775755];
 padj = multcompare (p);
 assert (padj(1), 0.051372, 1e-06);
 assert (padj(2), 0.188352, 1e-06);
 assert (padj(3), 0.188352, 1e-06);
 assert (padj(4), 0.269370, 1e-06);
 assert (padj(5), 0.269370, 1e-06);
 assert (padj(6), 0.884908, 1e-06);
 assert (padj(7), 1.000000, 1e-06);
 assert (padj(8), 1.000000, 1e-06);
 assert (padj(9), 1.000000, 1e-06);
 padj = multcompare(p,'ctype','holm');
 assert (padj(1), 0.051372, 1e-06);
 assert (padj(2), 0.188352, 1e-06);
 assert (padj(3), 0.188352, 1e-06);
 assert (padj(4), 0.269370, 1e-06);
 assert (padj(5), 0.269370, 1e-06);
 assert (padj(6), 0.884908, 1e-06);
 assert (padj(7), 1.000000, 1e-06);
 assert (padj(8), 1.000000, 1e-06);
 assert (padj(9), 1.000000, 1e-06);
 padj = multcompare(p,'ctype','hochberg');
 assert (padj(1), 0.051372, 1e-06);
 assert (padj(2), 0.169351, 1e-06);
 assert (padj(3), 0.169351, 1e-06);
 assert (padj(4), 0.244025, 1e-06);
 assert (padj(5), 0.244025, 1e-06);
 assert (padj(6), 0.775755, 1e-06);
 assert (padj(7), 0.775755, 1e-06);
 assert (padj(8), 0.775755, 1e-06);
 assert (padj(9), 0.775755, 1e-06);
 padj = multcompare(p,'ctype','fdr');
 assert (padj(1), 0.0513720, 1e-07);
 assert (padj(2), 0.0725790, 1e-07);
 assert (padj(3), 0.0725790, 1e-07);
 assert (padj(4), 0.0878490, 1e-07);
 assert (padj(5), 0.0878490, 1e-07);
 assert (padj(6), 0.3318405, 1e-07);
 assert (padj(7), 0.5089719, 1e-07);
 assert (padj(8), 0.7757550, 1e-07);
 assert (padj(9), 0.7757550, 1e-07);
8 tests, 8 passed, 0 known failure, 0 skipped
[inst/levene_test.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/levene_test.m
***** error<levene_test: invalid number of input arguments.> levene_test ()
***** error<levene_test: invalid number of input arguments.> ...
 levene_test (1, 2, 3, 4, 5);
***** error<levene_test: wrong value for alpha.> levene_test (randn (50, 2), 0);
***** error<levene_test: GROUP and X mismatch.> ...
 levene_test (randn (50, 2), [1, 2, 3]);
***** error<levene_test: GROUP and X mismatch.> ...
 levene_test (randn (50, 1), ones (55, 1));
***** error<levene_test: invalid second input argument.> ...
 levene_test (randn (50, 1), ones (50, 2));
***** error<levene_test: wrong value for alpha.> ...
 levene_test (randn (50, 2), [], 1.2);
***** error<levene_test: GROUP and X mismatch.> ...
 levene_test (randn (50, 2), "some_string");
***** error<levene_test: invalid third input argument.> ...
 levene_test (randn (50, 2), [], "alpha");
***** error<levene_test: wrong value for alpha.> ...
 levene_test (randn (50, 1), [ones(25, 1); 2*ones(25, 1)], 1.2);
***** error<levene_test: invalid third input argument.> ...
 levene_test (randn (50, 1), [ones(25, 1); 2*ones(25, 1)], "err");
***** error<levene_test: invalid option for TESTTYPE as 4th argument.> ...
 levene_test (randn (50, 1), [ones(25, 1); 2*ones(25, 1)], 0.05, "type");
***** warning<levene_test: GROUP> ...
 levene_test (randn (50, 1), [ones(24, 1); 2*ones(25, 1); 3]);
***** test
 load examgrades
 [h, pval, W, df] = levene_test (grades);
 assert (h, 1);
 assert (pval, 9.523239714592791e-07, 1e-14);
 assert (W, 8.59529, 1e-5);
 assert (df, [4, 595]);
***** test
 load examgrades
 [h, pval, W, df] = levene_test (grades, [], "quadratic");
 assert (h, 1);
 assert (pval, 9.523239714592791e-07, 1e-14);
 assert (W, 8.59529, 1e-5);
 assert (df, [4, 595]);
***** test
 load examgrades
 [h, pval, W, df] = levene_test (grades, [], "median");
 assert (h, 1);
 assert (pval, 1.312093241723211e-06, 1e-14);
 assert (W, 8.415969, 1e-6);
 assert (df, [4, 595]);
***** test
 load examgrades
 [h, pval, W, df] = levene_test (grades(:,[1:3]));
 assert (h, 1);
 assert (pval, 0.004349390980463497, 1e-14);
 assert (W, 5.52139, 1e-5);
 assert (df, [2, 357]);
***** test
 load examgrades
 [h, pval, W, df] = levene_test (grades(:,[1:3]), "median");
 assert (h, 1);
 assert (pval, 0.004355216763951453, 1e-14);
 assert (W, 5.52001, 1e-5);
 assert (df, [2, 357]);
***** test
 load examgrades
 [h, pval, W, df] = levene_test (grades(:,[3,4]), "quadratic");
 assert (h, 0);
 assert (pval, 0.1807494957440653, 2e-14);
 assert (W, 1.80200, 1e-5);
 assert (df, [1, 238]);
***** test
 load examgrades
 [h, pval, W, df] = levene_test (grades(:,[3,4]), "median");
 assert (h, 0);
 assert (pval, 0.1978225622063785, 2e-14);
 assert (W, 1.66768, 1e-5);
 assert (df, [1, 238]);
20 tests, 20 passed, 0 known failure, 0 skipped
[inst/kruskalwallis.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/kruskalwallis.m
***** demo
 x = meshgrid (1:6);
 x = x + normrnd (0, 1, 6, 6);
 kruskalwallis (x, [], 'off');
***** demo
 x = meshgrid (1:6);
 x = x + normrnd (0, 1, 6, 6);
 [p, atab] = kruskalwallis(x);
***** demo
 x = ones (30, 4) .* [-2, 0, 1, 5];
 x = x + normrnd (0, 2, 30, 4);
 group = {"A", "B", "C", "D"};
 kruskalwallis (x, group);
***** test
 data = [1.006, 0.996, 0.998, 1.000, 0.992, 0.993, 1.002, 0.999, 0.994, 1.000, ...
         0.998, 1.006, 1.000, 1.002, 0.997, 0.998, 0.996, 1.000, 1.006, 0.988, ...
         0.991, 0.987, 0.997, 0.999, 0.995, 0.994, 1.000, 0.999, 0.996, 0.996, ...
         1.005, 1.002, 0.994, 1.000, 0.995, 0.994, 0.998, 0.996, 1.002, 0.996, ...
         0.998, 0.998, 0.982, 0.990, 1.002, 0.984, 0.996, 0.993, 0.980, 0.996, ...
         1.009, 1.013, 1.009, 0.997, 0.988, 1.002, 0.995, 0.998, 0.981, 0.996, ...
         0.990, 1.004, 0.996, 1.001, 0.998, 1.000, 1.018, 1.010, 0.996, 1.002, ...
         0.998, 1.000, 1.006, 1.000, 1.002, 0.996, 0.998, 0.996, 1.002, 1.006, ...
         1.002, 0.998, 0.996, 0.995, 0.996, 1.004, 1.004, 0.998, 0.999, 0.991, ...
         0.991, 0.995, 0.984, 0.994, 0.997, 0.997, 0.991, 0.998, 1.004, 0.997];
 group = [1:10] .* ones (10,10);
 group = group(:);
 [p, tbl] = kruskalwallis (data, group, "off");
 assert (p, 0.048229, 1e-6);
 assert (tbl{2,5}, 17.03124, 1e-5);
 assert (tbl{2,3}, 9, 0);
 assert (tbl{4,2}, 82655.5, 1e-16);
 data = reshape (data, 10, 10);
 [p, tbl, stats] = kruskalwallis (data, [], "off");
 assert (p, 0.048229, 1e-6);
 assert (tbl{2,5}, 17.03124, 1e-5);
 assert (tbl{2,3}, 9, 0);
 assert (tbl{4,2}, 82655.5, 1e-16);
 means = [51.85, 60.45, 37.6, 51.1, 29.5, 54.25, 64.55, 66.7, 53.65, 35.35];
 N = 10 * ones (1, 10);
 assert (stats.meanranks, means, 1e-6);
 assert (length (stats.gnames), 10, 0);
 assert (stats.n, N, 0);
1 test, 1 passed, 0 known failure, 0 skipped
[inst/confusionchart.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/confusionchart.m
***** demo
 ## Setting the chart properties
 Yt = [8 5 6 8 5 3 1 6 4 2 5 3 1 4]';
 Yp = [8 5 6 8 5 2 3 4 4 5 5 7 2 6]';
 confusionchart (Yt, Yp, "Title", ...
   "Demonstration with summaries","Normalization",...
   "absolute","ColumnSummary", "column-normalized","RowSummary",...
   "row-normalized")
***** demo
 ## Cellstr as inputs
 Yt = {"Positive", "Positive", "Positive", "Negative", "Negative"};
 Yp = {"Positive", "Positive", "Negative", "Negative", "Negative"};
 m = confusionmat (Yt, Yp);
 confusionchart (m, {"Positive", "Negative"});
***** demo
 ## Editing the object properties
 Yt = {"Positive", "Positive", "Positive", "Negative", "Negative"};
 Yp = {"Positive", "Positive", "Negative", "Negative", "Negative"};
 cm = confusionchart (Yt, Yp);
 cm.Title = "This is an example with a green diagonal";
 cm.DiagonalColor = [0.4660, 0.6740, 0.1880];
***** demo
 ## Confusion chart in a uipanel
 h = uipanel ();
 Yt = {"Positive", "Positive", "Positive", "Negative", "Negative"};
 Yp = {"Positive", "Positive", "Negative", "Negative", "Negative"};
 cm = confusionchart (h, Yt, Yp);
***** demo
 ## Sorting classes
 Yt = [8 5 6 8 5 3 1 6 4 2 5 3 1 4]';
 Yp = [8 5 6 8 5 2 3 4 4 5 5 7 2 6]';
 cm = confusionchart (Yt, Yp, "Title", ...
   "Classes are sorted in ascending order");
 cm = confusionchart (Yt, Yp, "Title", ...
   "Classes are sorted according to clusters");
 sortClasses (cm, "cluster");
***** shared visibility_setting
 visibility_setting = get (0, "DefaultFigureVisible");
***** test
 set (0, "DefaultFigureVisible", "off");
 fail ("confusionchart ()", "Invalid call");
 set (0, "DefaultFigureVisible", visibility_setting);
***** test
 set (0, "DefaultFigureVisible", "off");
 fail ("confusionchart ([1 1; 2 2; 3 3])", "invalid argument");
 set (0, "DefaultFigureVisible", visibility_setting);
***** test
 set (0, "DefaultFigureVisible", "off");
 fail ("confusionchart ([1 2], [0 1], 'xxx', 1)", "invalid property");
 set (0, "DefaultFigureVisible", visibility_setting);
***** test
 set (0, "DefaultFigureVisible", "off");
 fail ("confusionchart ([1 2], [0 1], 'XLabel', 1)", "XLabel .* string");
 set (0, "DefaultFigureVisible", visibility_setting);
***** test
 set (0, "DefaultFigureVisible", "off");
 fail ("confusionchart ([1 2], [0 1], 'YLabel', [1 0])", ...
       ".* YLabel .* string");
 set (0, "DefaultFigureVisible", visibility_setting);
***** test
 set (0, "DefaultFigureVisible", "off");
 fail ("confusionchart ([1 2], [0 1], 'Title', .5)", ".* Title .* string");
 set (0, "DefaultFigureVisible", visibility_setting);
***** test
 set (0, "DefaultFigureVisible", "off");
 fail ("confusionchart ([1 2], [0 1], 'FontName', [])", ...
       ".* FontName .* string");
 set (0, "DefaultFigureVisible", visibility_setting);
***** test
 set (0, "DefaultFigureVisible", "off");
 fail ("confusionchart ([1 2], [0 1], 'FontSize', 'b')", ...
       ".* FontSize .* numeric");
 set (0, "DefaultFigureVisible", visibility_setting);
***** test
 set (0, "DefaultFigureVisible", "off");
 fail ("confusionchart ([1 2], [0 1], 'DiagonalColor', 'h')", ...
       ".* DiagonalColor .* color");
 set (0, "DefaultFigureVisible", visibility_setting);
***** test
 set (0, "DefaultFigureVisible", "off");
 fail ("confusionchart ([1 2], [0 1], 'OffDiagonalColor', [])", ...
       ".* OffDiagonalColor .* color");
 set (0, "DefaultFigureVisible", visibility_setting);
***** test
 set (0, "DefaultFigureVisible", "off");
 fail ("confusionchart ([1 2], [0 1], 'Normalization', '')", ...
       ".* invalid .* Normalization");
 set (0, "DefaultFigureVisible", visibility_setting);
***** test
 set (0, "DefaultFigureVisible", "off");
 fail ("confusionchart ([1 2], [0 1], 'ColumnSummary', [])", ...
       ".* invalid .* ColumnSummary");
 set (0, "DefaultFigureVisible", visibility_setting);
***** test
 set (0, "DefaultFigureVisible", "off");
 fail ("confusionchart ([1 2], [0 1], 'RowSummary', 1)", ...
       ".* invalid .* RowSummary");
 set (0, "DefaultFigureVisible", visibility_setting);
***** test
 set (0, "DefaultFigureVisible", "off");
 fail ("confusionchart ([1 2], [0 1], 'GridVisible', .1)", ...
       ".* invalid .* GridVisible");
 set (0, "DefaultFigureVisible", visibility_setting);
***** test
 set (0, "DefaultFigureVisible", "off");
 fail ("confusionchart ([1 2], [0 1], 'HandleVisibility', .1)", ...
       ".* invalid .* HandleVisibility");
 set (0, "DefaultFigureVisible", visibility_setting);
***** test
 set (0, "DefaultFigureVisible", "off");
 fail ("confusionchart ([1 2], [0 1], 'OuterPosition', .1)", ...
       ".* invalid .* OuterPosition");
 set (0, "DefaultFigureVisible", visibility_setting);
***** test
 set (0, "DefaultFigureVisible", "off");
 fail ("confusionchart ([1 2], [0 1], 'Position', .1)", ...
       ".* invalid .* Position");
 set (0, "DefaultFigureVisible", visibility_setting);
***** test
 set (0, "DefaultFigureVisible", "off");
 fail ("confusionchart ([1 2], [0 1], 'Units', .1)", ".* invalid .* Units");
 set (0, "DefaultFigureVisible", visibility_setting);
18 tests, 18 passed, 0 known failure, 0 skipped
[inst/hmmviterbi.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/hmmviterbi.m
***** test
 sequence = [1, 2, 1, 1, 1, 2, 2, 1, 2, 3, 3, 3, ...
             3, 2, 3, 1, 1, 1, 1, 3, 3, 2, 3, 1, 3];
 transprob = [0.8, 0.2; 0.4, 0.6];
 outprob = [0.2, 0.4, 0.4; 0.7, 0.2, 0.1];
 vpath = hmmviterbi (sequence, transprob, outprob);
 expected = [1, 1, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, ...
             1, 1, 1, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1];
 assert (vpath, expected);
***** test
 sequence = {"A", "B", "A", "A", "A", "B", "B", "A", "B", "C", "C", "C", ...
             "C", "B", "C", "A", "A", "A", "A", "C", "C", "B", "C", "A", "C"};
 transprob = [0.8, 0.2; 0.4, 0.6];
 outprob = [0.2, 0.4, 0.4; 0.7, 0.2, 0.1];
 symbols = {"A", "B", "C"};
 statenames = {"One", "Two"};
 vpath = hmmviterbi (sequence, transprob, outprob, "symbols", symbols, ...
                                                   "statenames", statenames);
 expected = {"One", "One", "Two", "Two", "Two", "One", "One", "One", ...
             "One", "One", "One", "One", "One", "One", "One", "Two", ...
             "Two", "Two", "Two", "One", "One", "One", "One", "One", "One"};
 assert (vpath, expected);
2 tests, 2 passed, 0 known failure, 0 skipped
[inst/bartlett_test.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/bartlett_test.m
***** error<bartlett_test: invalid number of input arguments.> bartlett_test ()
***** error<bartlett_test: invalid number of input arguments.> ...
 bartlett_test (1, 2, 3, 4);
***** error<bartlett_test: wrong value for alpha.> bartlett_test (randn (50, 2), 0);
***** error<bartlett_test: GROUP and X mismatch.> ...
 bartlett_test (randn (50, 2), [1, 2, 3]);
***** error<bartlett_test: GROUP and X mismatch.> ...
 bartlett_test (randn (50, 1), ones (55, 1));
***** error<bartlett_test: invalid second input argument.> ...
 bartlett_test (randn (50, 1), ones (50, 2));
***** error<bartlett_test: wrong value for alpha.> ...
 bartlett_test (randn (50, 2), [], 1.2);
***** error<bartlett_test: wrong value for alpha.> ...
 bartlett_test (randn (50, 2), [], "alpha");
***** error<bartlett_test: wrong value for alpha.> ...
 bartlett_test (randn (50, 1), [ones(25, 1); 2*ones(25, 1)], 1.2);
***** error<bartlett_test: wrong value for alpha.> ...
 bartlett_test (randn (50, 1), [ones(25, 1); 2*ones(25, 1)], "err");
***** warning<bartlett_test: GROUP> ...
 bartlett_test (randn (50, 1), [ones(24, 1); 2*ones(25, 1); 3]);
***** test
 load examgrades
 [h, pval, chisq, df] = bartlett_test (grades);
 assert (h, 1);
 assert (pval, 7.908647337018238e-08, 1e-14);
 assert (chisq, 38.73324, 1e-5);
 assert (df, 4);
***** test
 load examgrades
 [h, pval, chisq, df] = bartlett_test (grades(:,[2:4]));
 assert (h, 1);
 assert (pval, 0.01172, 1e-5);
 assert (chisq, 8.89274, 1e-5);
 assert (df, 2);
***** test
 load examgrades
 [h, pval, chisq, df] = bartlett_test (grades(:,[1,4]));
 assert (h, 0);
 assert (pval, 0.88118, 1e-5);
 assert (chisq, 0.02234, 1e-5);
 assert (df, 1);
***** test
 load examgrades
 grades = [grades; nan(10, 5)];
 [h, pval, chisq, df] = bartlett_test (grades(:,[1,4]));
 assert (h, 0);
 assert (pval, 0.88118, 1e-5);
 assert (chisq, 0.02234, 1e-5);
 assert (df, 1);
***** test
 load examgrades
 [h, pval, chisq, df] = bartlett_test (grades(:,[2,5]), 0.01);
 assert (h, 0);
 assert (pval, 0.01791, 1e-5);
 assert (chisq, 5.60486, 1e-5);
 assert (df, 1);
16 tests, 16 passed, 0 known failure, 0 skipped
[inst/hmmgenerate.m]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/inst/hmmgenerate.m
***** test
 len = 25;
 transprob = [0.8, 0.2; 0.4, 0.6];
 outprob = [0.2, 0.4, 0.4; 0.7, 0.2, 0.1];
 [sequence, states] = hmmgenerate (len, transprob, outprob);
 assert (length (sequence), len);
 assert (length (states), len);
 assert (min (sequence) >= 1);
 assert (max (sequence) <= columns (outprob));
 assert (min (states) >= 1);
 assert (max (states) <= rows (transprob));
***** test
 len = 25;
 transprob = [0.8, 0.2; 0.4, 0.6];
 outprob = [0.2, 0.4, 0.4; 0.7, 0.2, 0.1];
 symbols = {"A", "B", "C"};
 statenames = {"One", "Two"};
 [sequence, states] = hmmgenerate (len, transprob, outprob, ...
                      "symbols", symbols, "statenames", statenames);
 assert (length (sequence), len);
 assert (length (states), len);
 assert (strcmp (sequence, "A") + strcmp (sequence, "B") + ...
                                  strcmp (sequence, "C") == ones (1, len));
 assert (strcmp (states, "One") + strcmp (states, "Two") == ones (1, len));
2 tests, 2 passed, 0 known failure, 0 skipped
Checking C++ files ...
[src/libsvmread.cc]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/src/libsvmread.cc
***** error <libsvmread: filename must be a string.> [L, D] = libsvmread (24);
***** error <libsvmread: wrong number of input or output arguments.> ...
 D = libsvmread ("filename");
***** test
 [L, D] = libsvmread (file_in_loadpath ("heart_scale.dat"));
 assert (size (L), [270, 1]);
 assert (size (D), [270, 13]);
***** test
 [L, D] = libsvmread (file_in_loadpath ("heart_scale.dat"));
 assert (issparse (L), false);
 assert (issparse (D), true);
4 tests, 4 passed, 0 known failure, 0 skipped
[src/fcnntrain.cc]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/src/fcnntrain.cc
***** shared X, Y, MODEL
 load fisheriris
 X = meas;
 Y = grp2idx (species);
***** error <fcnntrain: too few input arguments.> ...
 model = fcnntrain (X, Y);
***** error <fcnntrain: too many output arguments.> ...
 [Q, W] = fcnntrain (X, Y, 10, [1, 1], 0.025, 50, false);
***** error <fcnntrain: X must be a real numeric matrix.> ...
 fcnntrain (complex (X), Y, 10, [1, 1], 0.025, 50, false);
***** error <fcnntrain: X must be a real numeric matrix.> ...
 fcnntrain ({X}, Y, 10, [1, 1], 0.025, 50, false);
***** error <fcnntrain: X cannot be empty.> ...
 fcnntrain ([], Y, 10, [1, 1], 0.025, 50, false);
***** error <fcnntrain: Y must be a real numeric matrix.> ...
 fcnntrain (X, complex (Y), 10, [1, 1], 0.025, 50, false);
***** error <fcnntrain: Y must be a real numeric matrix.> ...
 fcnntrain (X, {Y}, 10, [1, 1], 0.025, 50, false);
***** error <fcnntrain: Y cannot be empty.> ...
 fcnntrain (X, [], 10, [1, 1], 0.025, 50, false);
***** error <fcnntrain: X and Y must have the same number of rows.> ...
 fcnntrain (X, Y([1:50]), 10, [1, 1], 0.025, 50, false);
***** error <fcnntrain: labels in Y must be positive integers.> ...
 fcnntrain (X, Y - 1, 10, [1, 1], 0.025, 50, false);
***** error <fcnntrain: LayerSizes must be a row vector of integer values.> ...
 fcnntrain (X, Y, [10; 5], [1, 1, 1], 0.025, 50, false);
***** error <fcnntrain: LayerSizes must be a row vector of integer values.> ...
 fcnntrain (X, Y, "10", [1, 1], 0.025, 50, false);
***** error <fcnntrain: LayerSizes must be a row vector of integer values.> ...
 fcnntrain (X, Y, {10}, [1, 1], 0.025, 50, false);
***** error <fcnntrain: LayerSizes must be a row vector of integer values.> ...
 fcnntrain (X, Y, complex (10), [1, 1], 0.025, 50, false);
***** error <fcnntrain: Activations must be a row vector of integer values.> ...
 fcnntrain (X, Y, 10, [1; 1], 0.025, 50, false);
***** error <fcnntrain: Activations must be a row vector of integer values.> ...
 fcnntrain (X, Y, 10, {1, 1}, 0.025, 50, false);
***** error <fcnntrain: Activations must be a row vector of integer values.> ...
 fcnntrain (X, Y, 10, "1", 0.025, 50, false);
***** error <fcnntrain: Activations must be a row vector of integer values.> ...
 fcnntrain (X, Y, 10, complex ([1, 1]), 0.025, 50, false);
***** error <fcnntrain: Activations do not match LayerSizes.> ...
 fcnntrain (X, Y, 10, [1, 1, 1], 0.025, 50, false);
***** error <fcnntrain: cannot have a layer of zero size.> ...
 fcnntrain (X, Y, [10, 0, 5], [1, 1, 1, 1], 0.025, 50, false);
***** error <fcnntrain: invalid Activations code.> ...
 fcnntrain (X, Y, 10, [-1, 1], 0.025, 50, false);
***** error <fcnntrain: invalid Activations code.> ...
 fcnntrain (X, Y, 10, [8, 1], 0.025, 50, false);
***** error <fcnntrain: LearningRate must be a positive scalar value.> ...
 fcnntrain (X, Y, 10, [1, 1], -0.025, 50, false);
***** error <fcnntrain: LearningRate must be a positive scalar value.> ...
 fcnntrain (X, Y, 10, [1, 1], 0, 50, false);
***** error <fcnntrain: LearningRate must be a positive scalar value.> ...
 fcnntrain (X, Y, 10, [1, 1], [0.025, 0.001], 50, false);
***** error <fcnntrain: LearningRate must be a positive scalar value.> ...
 fcnntrain (X, Y, 10, [1, 1], {0.025}, 50, false);
***** error <fcnntrain: Epochs must be a positive scalar value.> ...
 fcnntrain (X, Y, 10, [1, 1], 0.025, 0, false);
***** error <fcnntrain: Epochs must be a positive scalar value.> ...
 fcnntrain (X, Y, 10, [1, 1], 0.025, [50, 25], false);
***** error <fcnntrain: DisplayInfo must be a boolean scalar.> ...
 fcnntrain (X, Y, 10, [1, 1], 0.025, 50, 0);
***** error <fcnntrain: DisplayInfo must be a boolean scalar.> ...
 fcnntrain (X, Y, 10, [1, 1], 0.025, 50, 1);
30 tests, 30 passed, 0 known failure, 0 skipped
[src/editDistance.cc]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/src/editDistance.cc
***** error <editDistance: too many input arguments.> d = editDistance (1, 2, 3, 4);
***** error <editDistance: too many output arguments.> ...
 [C, IA, IC, I] = editDistance ({"AS","SD","AD"}, 1);
***** error <editDistance: too many output arguments.> ...
 [C, IA] = editDistance ({"AS","SD","AD"});
***** error <editDistance: minDist must be a scalar value.> ...
 d = editDistance ({"AS","SD","AD"}, [1, 2]);
***** error <editDistance: minDist must be a nonnegative integer.> ...
 d = editDistance ({"AS","SD","AD"}, -2);
***** error <editDistance: minDist must be a nonnegative integer.> ...
 d = editDistance ({"AS","SD","AD"}, 1.25);
***** error <editDistance: minDist must be a scalar value.> ...
 d = editDistance ({"AS","SD","AD"}, {"AS","SD","AD"}, [1, 2]);
***** error <editDistance: minDist must be a nonnegative integer.> ...
 d = editDistance ({"AS","SD","AD"}, {"AS","SD","AD"}, -2);
***** error <editDistance: minDist must be a nonnegative integer.> ...
 d = editDistance ({"AS","SD","AD"}, {"AS","SD","AD"}, 1.25);
***** error <editDistance: minDist must be a scalar value.> ...
 d = editDistance ("string1", "string2", [1, 2]);
***** error <editDistance: minDist must be a nonnegative integer.> ...
 d = editDistance ("string1", "string2", -2);
***** error <editDistance: minDist must be a nonnegative integer.> ...
 d = editDistance ("string1", "string2", 1.25);
***** error <editDistance: tokenizedDocument must contain cellstr arrays.> ...
 d = editDistance ({{"string1", "string2"}, 2});
***** error <editDistance: tokenizedDocument must contain cellstr arrays.> ...
 d = editDistance ({{"string1", "string2"}, 2}, 2);
***** error <editDistance: STR1 must be a cellstr.> ...
 d = editDistance ([1, 2, 3]);
***** error <editDistance: STR1 must be a cellstr.> ...
 d = editDistance (["AS","SD","AD","AS"]);
***** error <editDistance: STR1 must be a cellstr.> ...
 d = editDistance (["AS","SD","AD"], 2);
***** error <editDistance: STR1 and STR2 must be either strings or cellstr.> ...
 d = editDistance (logical ([1,2,3]), {"AS","AS","AD"});
***** error <editDistance: STR1 and STR2 must be either strings or cellstr.> ...
 d = editDistance ({"AS","SD","AD"}, logical ([1,2,3]));
***** error <editDistance: STR1 and STR2 must be either strings or cellstr.> ...
 d = editDistance ([1,2,3], {"AS","AS","AD"});
***** error <editDistance: first tokenizedDocument does not contain cellstr arrays.> ...
 d = editDistance ({1,2,3}, {"AS","SD","AD"});
***** error <editDistance: second tokenizedDocument does not contain cellstr arrays.> ...
 d = editDistance ({"AS","SD","AD"}, {1,2,3});
***** error <editDistance: cellstr input arguments size mismatch.> ...
 d = editDistance ({"AS","SD","AD"}, {"AS", "AS"});
***** test
 d = editDistance ({"AS","SD","AD"});
 assert (d, [2; 1; 1]);
 assert (class (d), "double");
***** test
 C = editDistance ({"AS","SD","AD"}, 1);
 assert (iscellstr (C), true);
 assert (C, {"AS";"SD"});
***** test
 [C, IA] = editDistance ({"AS","SD","AD"}, 1);
 assert (class (IA), "double");
 assert (IA, [1;2]);
***** test
 A = {"ASS"; "SDS"; "FDE"; "EDS"; "OPA"};
 [C, IA] = editDistance (A, 2, "OutputAllIndices", false);
 assert (class (IA), "double");
 assert (A(IA), C);
***** test
 A = {"ASS"; "SDS"; "FDE"; "EDS"; "OPA"};
 [C, IA] = editDistance (A, 2, "OutputAllIndices", true);
 assert (class (IA), "cell");
 assert (C, {"ASS"; "FDE"; "OPA"});
 assert (A(IA{1}), {"ASS"; "SDS"; "EDS"});
 assert (A(IA{2}), {"FDE"; "EDS"});
 assert (A(IA{3}), {"OPA"});
***** test
 A = {"ASS"; "SDS"; "FDE"; "EDS"; "OPA"};
 [C, IA, IC] = editDistance (A, 2);
 assert (class (IA), "double");
 assert (A(IA), C);
 assert (IC, [1; 1; 3; 1; 5]);
***** test
 d = editDistance ({"AS","SD","AD"}, {"AS", "AD", "SE"});
 assert (d, [0; 1; 2]);
 assert (class (d), "double");
***** test
 d = editDistance ({"AS","SD","AD"}, {"AS"});
 assert (d, [0; 2; 1]);
 assert (class (d), "double");
***** test
 d = editDistance ({"AS"}, {"AS","SD","AD"});
 assert (d, [0; 2; 1]);
 assert (class (d), "double");
***** test
 b = editDistance ("Octave", "octave");
 assert (b, 1);
 assert (class (b), "double");
33 tests, 33 passed, 0 known failure, 0 skipped
[src/fcnnpredict.cc]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/src/fcnnpredict.cc
***** shared X, Y, MODEL
 load fisheriris
 X = meas;
 Y = grp2idx (species);
 MODEL = fcnntrain (X, Y, 10, [1, 1], 0.025, 100, false);
***** test
 [Y_pred, Y_scores] = fcnnpredict (MODEL, X);
 assert (numel (Y_pred), numel (Y));
 assert (isequal (size (Y_pred), size (Y)), true);
 assert (columns (Y_scores), numel (unique (Y)));
 assert (rows (Y_scores), numel (Y));
***** error <fcnnpredict: too few input arguments.> ...
 fcnnpredict (MODEL);
***** error <fcnnpredict: too many output arguments.> ...
 [Q, W, E] = fcnnpredict (MODEL, X);
***** error <fcnnpredict: first argument must be a scalar structure.> ...
 fcnnpredict (1, X);
***** error <fcnnpredict: first argument must be a scalar structure.> ...
 fcnnpredict (struct ("L", {1, 2, 3}), X);
***** error <fcnnpredict: model does not have a 'LayerWeights' field.> ...
 fcnnpredict (struct ("L", 1), X);
***** error <fcnnpredict: 'LayerWeights' must be a cell row vector.> ...
 fcnnpredict (struct ("LayerWeights", 1), X);
***** error <fcnnpredict: 'LayerWeights' must be a cell row vector.> ...
 fcnnpredict (struct ("LayerWeights", {1}), X);
***** error <fcnnpredict: 'LayerWeights' must be a cell row vector.> ...
 fcnnpredict (struct ("LayerWeights", {{1; 2; 3}}), X);
***** error <fcnnpredict: model does not have an 'Activations' field.> ...
 fcnnpredict (struct ("LayerWeights", {[{ones(3)},{ones(3)}]}, "R", 2), X);
***** error <fcnnpredict: 'Activations' must be a numeric row vector.> ...
 fcnnpredict (struct ("LayerWeights", {[{ones(3)},{ones(3)}]}, ...
                      "Activations", [2]), X);
***** error <fcnnpredict: 'Activations' must be a numeric row vector.> ...
 fcnnpredict (struct ("LayerWeights", {[{ones(3)},{ones(3)}]}, ...
                      "Activations", [2; 2]), X);
***** error <fcnnpredict: 'Activations' must be a numeric row vector.> ...
 fcnnpredict (struct ("LayerWeights", {[{ones(3)},{ones(3)}]}, ...
                      "Activations", {{2, 2}}), X);
***** error <fcnnpredict: 'Activations' must be a numeric row vector.> ...
 fcnnpredict (struct ("LayerWeights", {[{ones(3)},{ones(3)}]}, ...
                      "Activations", {{"sigmoid", "softmax"}}), X);
***** error <fcnnpredict: 'Activations' must be a numeric row vector.> ...
 fcnnpredict (struct ("LayerWeights", {[{ones(3)},{ones(3)}]}, ...
                      "Activations", "sigmoid"), X);
***** error <fcnnpredict: XC must be a real numeric matrix.> ...
 fcnnpredict (MODEL, complex (X));
***** error <fcnnpredict: XC must be a real numeric matrix.> ...
 fcnnpredict (MODEL, {1, 2, 3, 4});
***** error <fcnnpredict: XC must be a real numeric matrix.> ...
 fcnnpredict (MODEL, "asd");
***** error <fcnnpredict: XC cannot be empty.> ...
 fcnnpredict (MODEL, []);
***** error <fcnnpredict: the features in XC do not match the trained model.> ...
 fcnnpredict (MODEL, X(:,[1:3]));
20 tests, 20 passed, 0 known failure, 0 skipped
[src/svmtrain.cc]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/src/svmtrain.cc
***** test
 [L, D] = libsvmread (file_in_loadpath ("heart_scale.dat"));
 model = svmtrain(L, D, '-c 1 -g 0.07');
 [predict_label, accuracy, dec_values] = svmpredict(L, D, model);
 assert (isstruct (model), true);
 assert (isfield (model, "Parameters"), true);
 assert (model.totalSV, 130);
 assert (model.nr_class, 2);
 assert (size (model.Label), [2, 1]);
***** shared L, D
 [L, D] = libsvmread (file_in_loadpath ("heart_scale.dat"));
***** error <svmtrain: wrong number of output arguments.> [L, D] = svmtrain (L, D);
***** error <svmtrain: label vector and instance matrix must be double.> ...
 model = svmtrain (single (L), D);
***** error <svmtrain: wrong number of input arguments.> ...
 model = svmtrain (L, D, "", "");
4 tests, 4 passed, 0 known failure, 0 skipped
[src/libsvmwrite.cc]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/src/libsvmwrite.cc
***** shared L, D
 [L, D] = libsvmread (file_in_loadpath ("heart_scale.dat"));
***** error <libsvmwrite: error opening file for write.> libsvmwrite ("", L, D);
***** error <libsvmwrite: length of label vector does not match instances.> ...
 libsvmwrite (tempname (), [L;L], D);
***** error <libsvmwrite: wrong number of output arguments.> ...
 OUT = libsvmwrite (tempname (), L, D);
***** error <libsvmwrite: label vector and instance matrix must be double.> ...
 libsvmwrite (tempname (), single (L), D);
***** error <libsvmwrite: filename must be a string.> libsvmwrite (13412, L, D);
***** error <libsvmwrite: instance_matrix must be sparse.> ...
 libsvmwrite (tempname (), L, full (D));
***** error <libsvmwrite: wrong number of input arguments.> ...
 libsvmwrite (tempname (), L, D, D);
7 tests, 7 passed, 0 known failure, 0 skipped
[src/svmpredict.cc]
>>>>> /build/reproducible-path/octave-statistics-1.7.0/src/svmpredict.cc
***** test
 [L, D] = libsvmread (file_in_loadpath ("heart_scale.dat"));
 model = svmtrain (L, D, '-c 1 -g 0.07');
 [predict_label, accuracy, dec_values] = svmpredict (L, D, model);
 assert (size (predict_label), size (dec_values));
 assert (accuracy, [86.666, 0.533, 0.533]', [1e-3, 1e-3, 1e-3]');
 assert (dec_values(1), 1.225836001973273, 1e-14);
 assert (dec_values(2), -0.3212992933043805, 1e-14);
 assert (predict_label(1), 1);
***** shared L, D, model
 [L, D] = libsvmread (file_in_loadpath ("heart_scale.dat"));
 model = svmtrain (L, D, '-c 1 -g 0.07');
***** error <svmpredict: wrong number of output arguments.> ...
 [p, a] = svmpredict (L, D, model);
***** error <svmpredict: wrong number of input arguments.> p = svmpredict (L, D);
***** error <svmpredict: label vector and instance matrix must be double.> ...
 p = svmpredict (single (L), D, model);
***** error <svmpredict: model should be a struct array.> p = svmpredict (L, D, 123);
5 tests, 5 passed, 0 known failure, 0 skipped
Done running the unit tests.
Summary: 11048 tests, 11046 passed, 1 known failures, 1 skipped
   dh_install -O--buildsystem=octave
   dh_installdocs -O--buildsystem=octave
   dh_installchangelogs -O--buildsystem=octave
   dh_octave_changelogs -O--buildsystem=octave
   dh_octave_examples -O--buildsystem=octave
   dh_installsystemduser -O--buildsystem=octave
   dh_perl -O--buildsystem=octave
   dh_link -O--buildsystem=octave
   dh_strip_nondeterminism -O--buildsystem=octave
   dh_compress -O--buildsystem=octave
   dh_fixperms -O--buildsystem=octave
   dh_missing -O--buildsystem=octave
   dh_dwz -a -O--buildsystem=octave
   dh_strip -a -O--buildsystem=octave
   dh_makeshlibs -a -O--buildsystem=octave
   dh_shlibdeps -a -O--buildsystem=octave
   dh_octave_substvar -O--buildsystem=octave
   debian/rules execute_before_dh_installdeb-indep
make[1]: Entering directory '/build/reproducible-path/octave-statistics-1.7.0'
# Remove empty directory
rm -r $(find debian/octave-statistics-common -name doc -type d -empty)
make[1]: Leaving directory '/build/reproducible-path/octave-statistics-1.7.0'
   dh_installdeb -O--buildsystem=octave
   dh_gencontrol -O--buildsystem=octave
dpkg-gencontrol: warning: package octave-statistics: substitution variable ${octave:Upstream-Description} unused, but is defined
dpkg-gencontrol: warning: package octave-statistics-common: substitution variable ${octave:Upstream-Description} unused, but is defined
dpkg-gencontrol: warning: package octave-statistics: substitution variable ${octave:Upstream-Description} unused, but is defined
   dh_md5sums -O--buildsystem=octave
   dh_builddeb -O--buildsystem=octave
dpkg-deb: building package 'octave-statistics' in '../octave-statistics_1.7.0-2_i386.deb'.
dpkg-deb: building package 'octave-statistics-dbgsym' in '../octave-statistics-dbgsym_1.7.0-2_i386.deb'.
dpkg-deb: building package 'octave-statistics-common' in '../octave-statistics-common_1.7.0-2_all.deb'.
 dpkg-genbuildinfo --build=binary -O../octave-statistics_1.7.0-2_i386.buildinfo
 dpkg-genchanges --build=binary -O../octave-statistics_1.7.0-2_i386.changes
dpkg-genchanges: info: binary-only upload (no source code included)
 dpkg-source --after-build .
dpkg-buildpackage: info: binary-only upload (no source included)
dpkg-genchanges: info: not including original source code in upload
I: copying local configuration
I: unmounting dev/ptmx filesystem
I: unmounting dev/pts filesystem
I: unmounting dev/shm filesystem
I: unmounting proc filesystem
I: unmounting sys filesystem
I: cleaning the build env 
I: removing directory /srv/workspace/pbuilder/29294 and its subdirectories
I: Current time: Mon Jan  5 09:00:55 -12 2026
I: pbuilder-time-stamp: 1767646855
Tue Dec  3 14:37:58 UTC 2024  I: 1st build successful. Starting 2nd build on remote node infom07-i386.debian.net.
Tue Dec  3 14:37:58 UTC 2024  I: Preparing to do remote build '2' on infom07-i386.debian.net.
Tue Dec  3 15:13:07 UTC 2024  I: Deleting $TMPDIR on infom07-i386.debian.net.
Tue Dec  3 15:13:08 UTC 2024  I: octave-statistics_1.7.0-2_i386.changes:
Format: 1.8
Date: Mon, 16 Sep 2024 18:55:03 +0200
Source: octave-statistics
Binary: octave-statistics octave-statistics-common octave-statistics-dbgsym
Architecture: all i386
Version: 1.7.0-2
Distribution: unstable
Urgency: medium
Maintainer: Debian Octave Group <team+pkg-octave-team@tracker.debian.org>
Changed-By: Sébastien Villemot <sebastien@debian.org>
Description:
 octave-statistics - additional statistical functions for Octave
 octave-statistics-common - additional statistical functions for Octave (arch-indep files)
Closes: 1081727
Changes:
 octave-statistics (1.7.0-2) unstable; urgency=medium
 .
   * test-ClassificationPartitionedModel.patch: new patch.
     Disables tests that randomly enters an infinite loop. (Closes: #1081727)
Checksums-Sha1:
 67eb603a2797fa364888b68acfb68661223548ed 872160 octave-statistics-common_1.7.0-2_all.deb
 5771714483c8d61f00c79f23c5e2515a516d73f3 3336912 octave-statistics-dbgsym_1.7.0-2_i386.deb
 4c39d0cb050eb8fbcc9dca7e8f52d2737f6b203d 21938 octave-statistics_1.7.0-2_i386.buildinfo
 e3e29c743b6a747be69360f8149f001b5b540d74 146068 octave-statistics_1.7.0-2_i386.deb
Checksums-Sha256:
 ec2f625cb35d17d862147fb3f821206809b4b0e0bc4b0ea35728ab9cbff6d73a 872160 octave-statistics-common_1.7.0-2_all.deb
 09db7302546af8669dc8d9b51e223d36fb51ad498c8bd8889078c96198a7635e 3336912 octave-statistics-dbgsym_1.7.0-2_i386.deb
 938e7f6dc5437a9978e716c2cb86d6ec67714517d02c8d86017d056c9f12203b 21938 octave-statistics_1.7.0-2_i386.buildinfo
 fad4204a5de9aec5a2b8da3666600c61b7590063c27cab3fae53d45f8ffe0267 146068 octave-statistics_1.7.0-2_i386.deb
Files:
 2f03ac4eab8d548fd83a5a72dc6e60c6 872160 math optional octave-statistics-common_1.7.0-2_all.deb
 1c10426583bb09e43e39a625be70c314 3336912 debug optional octave-statistics-dbgsym_1.7.0-2_i386.deb
 ad2c55a3d32d4554171c2d24642e3d0a 21938 math optional octave-statistics_1.7.0-2_i386.buildinfo
 0166d20fe0f5c9fc98aa4376ae4e6c69 146068 math optional octave-statistics_1.7.0-2_i386.deb
Tue Dec  3 15:13:11 UTC 2024  I: diffoscope 283 will be used to compare the two builds:
Running as unit: rb-diffoscope-i386_16-50738.service
# Profiling output for: /usr/bin/diffoscope --timeout 7200 --html /srv/reproducible-results/rbuild-debian/r-b-build.1LVxXKuH/octave-statistics_1.7.0-2.diffoscope.html --text /srv/reproducible-results/rbuild-debian/r-b-build.1LVxXKuH/octave-statistics_1.7.0-2.diffoscope.txt --json /srv/reproducible-results/rbuild-debian/r-b-build.1LVxXKuH/octave-statistics_1.7.0-2.diffoscope.json --profile=- /srv/reproducible-results/rbuild-debian/r-b-build.1LVxXKuH/b1/octave-statistics_1.7.0-2_i386.changes /srv/reproducible-results/rbuild-debian/r-b-build.1LVxXKuH/b2/octave-statistics_1.7.0-2_i386.changes

## command (total time: 0.000s)
       0.000s      1 call     cmp (internal)

## has_same_content_as (total time: 0.000s)
       0.000s      1 call     abc.DotChangesFile

## main (total time: 0.406s)
       0.406s      2 calls    outputs
       0.000s      1 call     cleanup

## recognizes (total time: 0.055s)
       0.055s     12 calls    diffoscope.comparators.binary.FilesystemFile

## specialize (total time: 0.000s)
       0.000s      1 call     specialize
Finished with result: success
Main processes terminated with: code=exited/status=0
Service runtime: 741ms
CPU time consumed: 734ms
Tue Dec  3 15:13:12 UTC 2024  I: diffoscope 283 found no differences in the changes files, and a .buildinfo file also exists.
Tue Dec  3 15:13:12 UTC 2024  I: octave-statistics from trixie built successfully and reproducibly on i386.
Tue Dec  3 15:13:13 UTC 2024  I: Submitting .buildinfo files to external archives:
Tue Dec  3 15:13:13 UTC 2024  I: Submitting 24K	b1/octave-statistics_1.7.0-2_i386.buildinfo.asc
Tue Dec  3 15:13:14 UTC 2024  I: Submitting 24K	b2/octave-statistics_1.7.0-2_i386.buildinfo.asc
Tue Dec  3 15:13:16 UTC 2024  I: Done submitting .buildinfo files to http://buildinfo.debian.net/api/submit.
Tue Dec  3 15:13:16 UTC 2024  I: Done submitting .buildinfo files.
Tue Dec  3 15:13:16 UTC 2024  I: Removing signed octave-statistics_1.7.0-2_i386.buildinfo.asc files:
removed './b1/octave-statistics_1.7.0-2_i386.buildinfo.asc'
removed './b2/octave-statistics_1.7.0-2_i386.buildinfo.asc'