# This file created by conda-build 2.1.18
# meta.yaml template originally from:
# C:\projects\r-robustrankaggreg-feedstock\recipe, last modified Sat Feb 03 21:57:29 2018
# ------------------------------------------------

package:
    name: r-robustrankaggreg
    version: '1.1'
source:
    fn: RobustRankAggreg_1.1.tar.gz
    sha256: 23ab88f6f1e56ae85a45bf5107ad0c37202dde275d799ed9fe214a1ab2c754df
    url:
        - https://cran.r-project.org/src/contrib/RobustRankAggreg_1.1.tar.gz
        - https://cran.r-project.org/src/contrib/Archive/RobustRankAggreg/RobustRankAggreg_1.1.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
        - 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
        - m2w64-libxml2 2.9.3 4
        - 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(''RobustRankAggreg'')"'
about:
    home: https://CRAN.R-project.org/package=RobustRankAggreg
    license: GPL-2
    license_family: GPL2
    license_file: C:\bld\r-robustrankaggreg_1517695191098\_b_env\R\share\licenses\GPL-2
    summary: Methods for aggregating ranked lists, especially lists of genes. It implements
        the Robust Rank Aggregation (Kolde et. al in preparation) and some other simple
        algorithms for the task. RRA method uses a probabilistic model for aggregation
        that is robust to noise and also facilitates the calculation of significance
        probabilities for all the elements in the final ranking.
extra:
    final: true
    recipe-maintainers:
        - MathiasHaudgaard
        - FrodePedersen
        - ArneKr
        - johanneskoester
        - bgruening
        - daler
        - jdblischak
