# This file created by conda-build 3.20.4
# meta.yaml template originally from:
# D:\a\1\s\recipe, last modified Tue Oct 20 14:52:13 2020
# ------------------------------------------------

package:
    name: r-softimpute
    version: '1.4'
source:
    fn: softImpute_1.4.tar.gz
    sha256: 98f5e66aefbf50a6a170421316a9f4b96651521a2196424245052340e75f9d1d
    url:
        - https://cran.r-project.org/src/contrib/Archive/softImpute/softImpute_1.4.tar.gz
        - https://cran.r-project.org/src/contrib/softImpute_1.4.tar.gz
build:
    merge_build_host: true
    number: '1005'
    rpaths:
        - lib/
        - lib/R/lib/
    string: r40h87ffe87_1005
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 4.0.3 hc562dc2_2
        - r-lattice 0.20_41 r40hda5aaf8_2
        - r-matrix 1.2_18 r40h0adb578_3
    run:
        - m2w64-gcc-libs
        - r-base >=4.0,<4.1.0a0
        - r-matrix
test:
    commands:
        - '"%R%" -e "library(''softImpute'')"'
about:
    home: https://CRAN.R-project.org/package=softImpute
    license: GPL-2
    license_family: GPL2
    license_file: D:\bld\r-softimpute_1603205819911\_h_env/lib/R/share/licenses/GPL-2
    summary: Iterative methods for matrix completion that use nuclear-norm regularization.
        There are two main approaches.The one approach uses iterative soft-thresholded
        svds to impute the missing values. The second approach uses alternating least
        squares. Both have an "EM" flavor, in that at each iteration the matrix is
        completed with the current estimate. For large matrices there is a special
        sparse-matrix class named "Incomplete" that efficiently handles all computations.
        The package includes procedures for centering and scaling rows, columns or
        both, and for computing low-rank SVDs on large sparse centered matrices (i.e.
        principal components)
extra:
    copy_test_source_files: true
    final: true
    recipe-maintainers:
        - apcamargo
        - conda-forge/r
