# This file created by conda-build 3.20.2
# meta.yaml template originally from:
# D:\a\1\s\recipe, last modified Tue Sep 15 11:43:40 2020
# ------------------------------------------------

package:
    name: r-bnlearn
    version: '4.6'
source:
    sha256: 7d63290808627f933be37b220fb9bc52decfc9edacab0760904e23979ff93a82
    url:
        - https://cran.r-project.org/src/contrib/Archive/bnlearn/bnlearn_4.6.tar.gz
        - https://cran.r-project.org/src/contrib/bnlearn_4.6.tar.gz
build:
    merge_build_host: true
    number: '0'
    rpaths:
        - lib/
        - lib/R/lib/
    string: r40hda5aaf8_0
requirements:
    build:
        - m2-bash 4.3.042 5
        - m2-coreutils 8.25 102
        - m2-filesystem 2016.04 4
        - m2-gcc-libs 5.3.0 4
        - m2-gmp 6.1.0 3
        - m2-libbz2 1.0.6 3
        - m2-libiconv 1.14 3
        - m2-libintl 0.19.7 4
        - m2-make 4.1 5
        - m2-msys2-runtime 2.5.0.17080.65c939c 3
        - m2-sed 4.2.2 3
        - m2-zip 3.0 2
        - m2w64-binutils 2.25.1 5
        - m2w64-bzip2 1.0.6 6
        - m2w64-crt-git 5.0.0.4636.2595836 2
        - m2w64-gcc 5.3.0 6
        - m2w64-gcc-ada 5.3.0 6
        - m2w64-gcc-fortran 5.3.0 6
        - m2w64-gcc-libgfortran 5.3.0 6
        - m2w64-gcc-libs 5.3.0 7
        - m2w64-gcc-libs-core 5.3.0 7
        - m2w64-gcc-objc 5.3.0 6
        - m2w64-gmp 6.1.0 2
        - m2w64-headers-git 5.0.0.4636.c0ad18a 2
        - m2w64-isl 0.16.1 2
        - m2w64-libiconv 1.14 6
        - m2w64-libmangle-git 5.0.0.4509.2e5a9a2 2
        - m2w64-libwinpthread-git 5.0.0.4634.697f757 2
        - m2w64-make 4.1.2351.a80a8b8 2
        - m2w64-mpc 1.0.3 3
        - m2w64-mpfr 3.1.4 4
        - m2w64-pkg-config 0.29.1 2
        - m2w64-toolchain 5.3.0 7
        - m2w64-toolchain_win-64 2.4.0 0
        - m2w64-tools-git 5.0.0.4592.90b8472 2
        - m2w64-windows-default-manifest 6.4 3
        - m2w64-winpthreads-git 5.0.0.4634.697f757 2
        - m2w64-zlib 1.2.8 10
        - msys2-conda-epoch 20160418 1
    host:
        - _r-mutex 1.0.1 anacondar_1
        - intel-openmp 2020.1 216
        - libblas 3.8.0 16_mkl
        - liblapack 3.8.0 16_mkl
        - m2w64-bwidget 1.9.10 2
        - m2w64-bzip2 1.0.6 6
        - m2w64-expat 2.1.1 2
        - m2w64-fftw 3.3.4 6
        - m2w64-flac 1.3.1 3
        - m2w64-gcc-libgfortran 5.3.0 6
        - m2w64-gcc-libs 5.3.0 7
        - m2w64-gcc-libs-core 5.3.0 7
        - m2w64-gettext 0.19.7 2
        - m2w64-gmp 6.1.0 2
        - m2w64-gsl 2.1 2
        - m2w64-libiconv 1.14 6
        - m2w64-libjpeg-turbo 1.4.2 3
        - m2w64-libogg 1.3.2 3
        - m2w64-libpng 1.6.21 2
        - m2w64-libsndfile 1.0.26 2
        - m2w64-libtiff 4.0.6 2
        - m2w64-libvorbis 1.3.5 2
        - m2w64-libwinpthread-git 5.0.0.4634.697f757 2
        - m2w64-libxml2 2.9.3 4
        - m2w64-mpfr 3.1.4 4
        - m2w64-pcre2 10.34 0
        - m2w64-speex 1.2rc2 3
        - m2w64-speexdsp 1.2rc3 3
        - m2w64-tcl 8.6.5 3
        - m2w64-tk 8.6.5 3
        - m2w64-tktable 2.10 5
        - m2w64-wineditline 2.101 5
        - m2w64-xz 5.2.2 2
        - m2w64-zlib 1.2.8 10
        - mkl 2020.1 216
        - msys2-conda-epoch 20160418 1
        - r-base 4.0.2 h588f722_1
    run:
        - m2w64-gcc-libs
        - r-base >=4.0,<4.1.0a0
test:
    commands:
        - '"%R%" -e "library(''bnlearn'')"'
about:
    home: http://www.bnlearn.com/
    license: GPL-2.0-or-later
    license_family: GPL3
    license_file:
        - D:\bld\r-bnlearn_1600170454416\_h_env/lib/R/share/licenses/GPL-2
    summary: Bayesian network structure learning, parameter learning and inference.
        This package implements constraint-based (PC, GS, IAMB, Inter-IAMB, Fast-IAMB,
        MMPC, Hiton-PC), pairwise (ARACNE and Chow-Liu), score-based (Hill-Climbing
        and Tabu Search) and hybrid (MMHC and RSMAX2) structure learning algorithms
        for discrete, Gaussian and conditional Gaussian networks, along with many
        score functions and conditional independence tests. The Naive Bayes and the
        Tree-Augmented Naive Bayes (TAN) classifiers are also implemented. Some utility
        functions (model comparison and manipulation, random data generation, arc
        orientation testing, simple and advanced plots) are included, as well as support
        for parameter estimation (maximum likelihood and Bayesian) and inference,
        conditional probability queries and cross-validation. Development snapshots
        with the latest bugfixes are available from <http://www.bnlearn.com>.
extra:
    copy_test_source_files: true
    final: true
    recipe-maintainers:
        - ArneKr
        - FrodePedersen
        - MathiasHaudgaard
        - conda-forge/r
