# This file created by conda-build 3.18.8
# meta.yaml template originally from:
# D:\a\1\s\recipe, last modified Fri Jul 19 21:53:45 2019
# ------------------------------------------------

package:
    name: r-bnlearn
    version: 4.4.1
source:
    sha256: 73689fdab937cb5ed5c8ae22607cb3038a161c91030d7e1a1a05145348aa64d9
    url:
        - https://cran.r-project.org/src/contrib/Archive/bnlearn/bnlearn_4.4.1.tar.gz
        - https://cran.r-project.org/src/contrib/bnlearn_4.4.1.tar.gz
build:
    merge_build_host: true
    number: '1'
    rpaths:
        - lib/
        - lib/R/lib/
    string: r35hda5aaf8_1
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
        - 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-openblas 0.2.19 1
        - m2w64-pcre 8.38 2
        - 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
        - msys2-conda-epoch 20160418 1
        - r-base 3.5.1 hdf77c54_1008
    run:
        - m2w64-gcc-libs
        - r-base >=3.5,<3.6.0a0
test:
    commands:
        - '"%R%" -e "library(''bnlearn'')"'
about:
    home: http://www.bnlearn.com/
    license: GPL (>= 2)
    license_family: GPL3
    license_file: D:\bld\r-bnlearn_1563573490558\_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
