# This file created by conda-build 3.14.1
# meta.yaml template originally from:
# C:\projects\r-huge-feedstock\recipe, last modified Thu Sep  6 23:23:07 2018
# ------------------------------------------------

package:
    name: r-huge
    version: 1.2.7
source:
    fn: huge_1.2.7.tar.gz
    sha256: 042b771c334cf4d1a92c5c546ca025238919f772a50457594b7e0bd243498d8c
    url:
        - https://cran.r-project.org/src/contrib/Archive/huge/huge_1.2.7.tar.gz
        - https://cran.r-project.org/src/contrib/huge_1.2.7.tar.gz
build:
    merge_build_host: true
    number: '2'
    rpaths:
        - lib/
        - lib/R/lib/
    string: r341h6115d3f_2
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-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:
        - 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-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 3.4.1 h8933c1f_8
        - r-igraph 1.2.2 r341h6115d3f_1
        - r-lattice 0.20_35 r341h6115d3f_0
        - r-magrittr 1.5 r341h6115d3f_1
        - r-mass 7.3_50 r341h6115d3f_2
        - r-matrix 1.2_14 r341h6115d3f_2
        - r-pkgconfig 2.0.2 r341h6115d3f_1
    run:
        - m2w64-gcc-libs
        - r-base >=3.4.1,<3.4.2.0a0
        - r-igraph
        - r-lattice
        - r-mass
        - r-matrix
test:
    commands:
        - '"%R%" -e "library(''huge'')"'
about:
    home: https://CRAN.R-project.org/package=huge
    license: GPL-2
    license_family: GPL2
    license_file: C:\bld\r-huge_1536276328578\_h_env\R\share\licenses\GPL-2
    summary: Provides a general framework for high-dimensional undirected graph estimation.
        It integrates data preprocessing, neighborhood screening, graph estimation,
        and model selection techniques into a pipeline. In preprocessing stage, the
        nonparanormal(npn) transformation is applied to help relax the normality assumption.
        In the graph estimation stage, the graph structure is estimated by Meinshausen-Buhlmann
        graph estimation or the graphical lasso, and both methods can be further accelerated
        by the lossy screening rule preselecting the neighborhood of each variable
        by correlation thresholding. We target on high-dimensional data analysis usually
        d >> n, and the computation is memory-optimized using the sparse matrix output.
        We also provide a computationally efficient approach, correlation thresholding
        graph estimation. Three regularization/thresholding parameter selection methods
        are included in this package. (1)stability approach for regularization selection
        (2) rotation information criterion (3) extended Bayesian information criterion
        which is only available for the graphical lasso.
extra:
    copy_test_source_files: true
    final: true
    recipe-maintainers:
        - bgruening
        - cbrueffer
        - daler
        - dbast
        - jdblischak
        - johanneskoester
        - nick-youngblut
