# This file created by conda-build 2.1.17
# meta.yaml template originally from:
# C:\projects\r-learnbayes-feedstock\recipe, last modified Sun Sep 10 11:41:16 2017
# ------------------------------------------------

package:
    name: r-learnbayes
    version: '2.15'
source:
    fn: LearnBayes_2.15.tar.gz
    sha256: 45c91114b4aaa0314feeb4311dbe78f5b75a3b76bb2d1ca0f8adb2e0f1cbe233
    url:
        - https://cran.r-project.org/src/contrib/LearnBayes_2.15.tar.gz
        - https://cran.r-project.org/src/contrib/Archive/LearnBayes/LearnBayes_2.15.tar.gz
build:
    noarch: ''
    noarch_python: false
    number: '0'
    rpaths:
        - lib/R/lib/
        - lib/
    string: r3.3.2_0
requirements:
    build:
        - m2-msys2-runtime 2.5.0.17080.65c939c 3
        - m2w64-libxml2 2.9.3 3
        - m2-libcrypt 1.3 2
        - m2-libexpat 2.1.1 2
        - m2-gcc-libs 5.3.0 4
        - m2-heimdal-libs 1.5.3 10
        - m2-libidn 1.32 2
        - m2-info 6.0 2
        - m2w64-make 4.1.2351.a80a8b8 2
        - m2-bash 4.3.042 5
        - m2-ncurses 6.0.20160220 2
        - m2-libintl 0.19.7 4
        - m2w64-speexdsp 1.2rc3 3
        - m2-icu 56.1 2
        - m2w64-tktable 2.10 5
        - m2w64-headers-git 5.0.0.4636.c0ad18a 2
        - m2-gmp 6.1.0 3
        - m2w64-fftw 3.3.4 6
        - m2w64-gettext 0.19.7 2
        - m2w64-gcc-objc 5.3.0 6
        - m2w64-mpfr 3.1.4 4
        - m2-gzip 1.7 2
        - m2-findutils 4.6.0 2
        - m2-libdb 5.3.28 3
        - m2w64-gcc-ada 5.3.0 6
        - m2-libgdbm 1.11 4
        - m2-texinfo 6.0 2
        - m2-curl 7.47.1 3
        - m2w64-binutils 2.25.1 5
        - r-base 3.3.2 5
        - m2-libpcre 8.38 2
        - m2-libtasn1 4.7 2
        - m2-libp11-kit 0.23.2 2
        - m2-p11-kit 0.23.2 2
        - m2w64-toolchain 5.3.0 7
        - m2w64-gcc-libgfortran 5.3.0 6
        - m2-db 5.3.28 3
        - m2-coreutils 8.25 102
        - m2w64-libwinpthread-git 5.0.0.4634.697f757 2
        - m2w64-wineditline 2.101 5
        - m2w64-libogg 1.3.2 3
        - m2w64-mpc 1.0.3 3
        - m2-libiconv 1.14 3
        - m2-openssl 1.0.2.g 2
        - m2w64-libjpeg-turbo 1.4.2 3
        - m2w64-pcre 8.38 2
        - m2w64-libpng 1.6.21 2
        - m2w64-isl 0.16.1 2
        - m2w64-crt-git 5.0.0.4636.2595836 2
        - m2-texinfo-tex 6.0 2
        - m2-perl 5.22.1 2
        - m2-libmetalink 0.1.2 3
        - m2w64-libtiff 4.0.6 2
        - m2w64-windows-default-manifest 6.4 3
        - m2w64-bzip2 1.0.6 6
        - m2w64-tcl 8.6.5 3
        - m2w64-libmangle-git 5.0.0.4509.2e5a9a2 2
        - m2w64-tk 8.6.5 3
        - m2-libffi 3.2.1 2
        - m2-less 481 2
        - m2w64-pkg-config 0.29.1 2
        - m2w64-bwidget 1.9.10 2
        - m2w64-zlib 1.2.8 10
        - m2w64-gcc-libs 5.3.0 7
        - m2-p7zip 9.38.1 2
        - m2w64-gsl 2.1 2
        - m2w64-winpthreads-git 5.0.0.4634.697f757 2
        - m2-libcurl 7.47.1 3
        - m2-libopenssl 1.0.2.g 2
        - m2-ca-certificates 20150426 103
        - m2-zlib 1.2.8 4
        - m2-sed 4.2.2 3
        - m2w64-tools-git 5.0.0.4592.90b8472 2
        - m2w64-flac 1.3.1 3
        - m2w64-libvorbis 1.3.5 2
        - m2-libreadline 6.3.008 8
        - msys2-conda-epoch 20160418 1
        - m2w64-gcc-fortran 5.3.0 6
        - m2w64-xz 5.2.2 2
        - m2-gdbm 1.11 4
        - m2w64-libiconv 1.14 6
        - m2w64-gcc-libs-core 5.3.0 7
        - m2-libsqlite 3.10.0.0 2
        - m2-libssh2 1.6.0 2
        - m2w64-gcc 5.3.0 6
        - m2w64-expat 2.1.1 2
        - m2-libedit 3.1 20150326
        - m2w64-speex 1.2rc2 3
        - m2w64-gmp 6.1.0 2
        - m2w64-libsndfile 1.0.26 2
    run:
        - r-base
test:
    commands:
        - '"%R%" -e "library(''LearnBayes'')"'
about:
    home: https://CRAN.R-project.org/package=LearnBayes
    license: GPL (>= 2)
    license_family: GPL3
    summary: LearnBayes contains a collection of functions helpful in learning the
        basic tenets of Bayesian statistical inference.  It contains functions for
        summarizing basic one and two parameter posterior distributions and predictive
        distributions.  It contains MCMC algorithms for summarizing posterior distributions
        defined by the user.  It also contains functions for regression models, hierarchical
        models, Bayesian tests, and illustrations of Gibbs sampling.
extra:
    final: true
    recipe-maintainers:
        - johanneskoester
        - bgruening
        - daler
        - jdblischak
