# This file created by conda-build 3.19.2
# meta.yaml template originally from:
# D:\a\1\s\recipe, last modified Tue May 12 20:08:57 2020
# ------------------------------------------------

package:
    name: r-rsparse
    version: 0.4.0
source:
    sha256: 29b79607483861a48b2682e1f721a0155ef175bcc11cef8dd500b6a85f2a8fae
    url:
        - https://cran.r-project.org/src/contrib/Archive/rsparse/rsparse_0.4.0.tar.gz
        - https://cran.r-project.org/src/contrib/rsparse_0.4.0.tar.gz
build:
    merge_build_host: true
    number: '0'
    rpaths:
        - lib/
        - lib/R/lib/
    string: r36h796a38f_0
requirements:
    build:
        - m2-autoconf 2.69 4
        - m2-automake-wrapper 10 2
        - m2-automake1.10 1.10.3 4
        - m2-automake1.11 1.11.6 4
        - m2-automake1.12 1.12.6 4
        - m2-automake1.13 1.13.4 5
        - m2-automake1.14 1.14.1 4
        - m2-automake1.15 1.15 3
        - m2-automake1.6 1.6.3 3
        - m2-automake1.7 1.7.9 3
        - m2-automake1.8 1.8.5 4
        - m2-automake1.9 1.9.6 3
        - m2-bash 4.3.042 5
        - m2-coreutils 8.25 102
        - m2-db 5.3.28 3
        - m2-diffutils 3.3 4
        - m2-filesystem 2016.04 4
        - m2-gawk 4.1.3 2
        - m2-gcc-libs 5.3.0 4
        - m2-gdbm 1.11 4
        - m2-gettext 0.19.7 4
        - m2-glib2 2.48.0 2
        - m2-gmp 6.1.0 3
        - m2-grep 2.22 4
        - m2-libasprintf 0.19.7 4
        - m2-libbz2 1.0.6 3
        - m2-libcrypt 1.3 2
        - m2-libdb 5.3.28 3
        - m2-libffi 3.2.1 2
        - m2-libgcrypt 1.6.4 2
        - m2-libgdbm 1.11 4
        - m2-libgettextpo 0.19.7 4
        - m2-libgpg-error 1.21 2
        - m2-libiconv 1.14 3
        - m2-libintl 0.19.7 4
        - m2-liblzma 5.2.2 2
        - m2-libpcre 8.38 2
        - m2-libreadline 6.3.008 8
        - m2-libxml2 2.9.2 3
        - m2-libxslt 1.1.28 8
        - m2-m4 1.4.17 5
        - m2-make 4.1 5
        - m2-mpfr 3.1.4 2
        - m2-msys2-runtime 2.5.0.17080.65c939c 3
        - m2-ncurses 6.0.20160220 2
        - m2-perl 5.22.1 2
        - m2-pkg-config 0.29.1 2
        - m2-sed 4.2.2 3
        - m2-zip 3.0 2
        - m2-zlib 1.2.8 4
        - 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 15_mkl
        - libflang 5.0.0 h6538335_20180525
        - liblapack 3.8.0 15_mkl
        - libopenblas 0.3.9 hcfe7411_0
        - llvm-meta 5.0.0 0
        - 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
        - mkl 2020.0 166
        - msys2-conda-epoch 20160418 1
        - openmp 5.0.0 vc14_1
        - r-base 3.6.3 hf0ea6f9_2
        - r-data.table 1.12.8 r36hda5aaf8_1
        - r-float 0.2_4 r36hc833f60_1
        - r-lattice 0.20_41 r36hda5aaf8_2
        - r-lgr 0.3.4 r36h6115d3f_1
        - r-matrix 1.2_18 r36h0adb578_3
        - r-mlapi 0.1.0 r36h6115d3f_1003
        - r-r6 2.4.1 r36h6115d3f_1
        - r-rcpp 1.0.4.6 r36h796a38f_1
        - r-rcpparmadillo 0.9.870.2.0 r36hb8e368e_1
        - r-rhpcblasctl 0.20_17 r36hda5aaf8_1
        - vc 14.1 h869be7e_1
        - vs2015_runtime 14.16.27012 h30e32a0_2
    run:
        - libopenblas >=0.3.9,<1.0a0
        - m2w64-gcc-libs
        - r-base >=3.6,<3.7.0a0
        - r-data.table >=1.10.0
        - r-float >=0.2_2
        - r-lgr >=0.2
        - r-matrix >=1.2
        - r-mlapi >=0.1.0
        - r-rcpp >=0.11
        - r-rcpparmadillo >=0.9.100.5.0
        - r-rhpcblasctl
test:
    commands:
        - '"%R%" -e "library(''rsparse'')"'
about:
    home: https://github.com/dselivanov/rsparse
    license: GPL-2
    license_family: GPL2
    license_file:
        - D:\bld\r-rsparse_1589314277139\_h_env/lib/R/share/licenses/GPL-2
    summary: 'Implements many algorithms for statistical learning on sparse matrices
        - matrix factorizations, matrix completion, elastic net regressions, factorization
        machines. Also ''rsparse'' enhances ''Matrix'' package by providing methods
        for multithreaded <sparse, dense> matrix products and native slicing of the
        sparse matrices in Compressed Sparse Row (CSR) format. List of the algorithms
        for regression problems: 1) Elastic Net regression via Follow The Proximally-Regularized
        Leader (FTRL) Stochastic Gradient Descent (SGD), as per McMahan et al(, <doi:10.1145/2487575.2488200>)
        2) Factorization Machines via SGD, as per Rendle (2010, <doi:10.1109/ICDM.2010.127>)
        List of algorithms for matrix factorization and matrix completion: 1) Weighted
        Regularized Matrix Factorization (WRMF) via Alternating Least Squares (ALS)
        - paper by Hu, Koren, Volinsky (2008, <doi:10.1109/ICDM.2008.22>) 2) Maximum-Margin
        Matrix Factorization via ALS, paper by Rennie, Srebro (2005, <doi:10.1145/1102351.1102441>)
        3) Fast Truncated Singular Value Decomposition (SVD), Soft-Thresholded SVD,
        Soft-Impute matrix completion via ALS - paper by Hastie, Mazumder et al. (2014,
        <arXiv:1410.2596>) 4) Linear-Flow matrix factorization, from ''Practical linear
        models for large-scale one-class collaborative filtering'' by Sedhain, Bui,
        Kawale et al (2016, ISBN:978-1-57735-770-4) 5) GlobalVectors (GloVe) matrix
        factorization via SGD, paper by Pennington, Socher, Manning (2014, <https://www.aclweb.org/anthology/D14-1162>)
        Package is reasonably fast and memory efficient - it allows to work with large
        datasets - millions of rows and millions of columns. This is particularly
        useful for practitioners working on recommender systems.'
extra:
    copy_test_source_files: true
    final: true
    recipe-maintainers:
        - conda-forge/r
