{% set version = "4.0-2" %}
{% set posix = 'm2-' if win else '' %}
{% set native = 'm2w64-' if win else '' %}

package:
  name: r-glmnet
  version: {{ version|replace("-", "_") }}

source:
  url:
    - {{ cran_mirror }}/src/contrib/glmnet_{{ version }}.tar.gz
    - {{ cran_mirror }}/src/contrib/Archive/glmnet/glmnet_{{ version }}.tar.gz
  sha256: acf7db28f7bf30848daa77472085235851c4bff3e996e017e09649de76e7ba61

build:
  merge_build_host: true  # [win]
  number: 1
  rpaths:
    - lib/R/lib/
    - lib/

requirements:
  build:
    - {{ compiler('c') }}              # [not win]
    - {{ compiler('m2w64_c') }}        # [win]
    - {{ compiler('fortran') }}        # [not win]
    - {{ compiler('m2w64_fortran') }}  # [win]
    - {{ posix }}filesystem        # [win]
    - {{ posix }}make
    - {{ posix }}sed               # [win]
    - {{ posix }}coreutils         # [win]
    - {{ posix }}zip               # [win]
  host:
    - r-base
    - r-matrix >=1.0_6
    - r-foreach
    - r-shape
    - r-survival
  run:
    - r-base
    - {{ native }}gcc-libs         # [win]
    - r-matrix >=1.0_6
    - r-foreach
    - r-shape
    - r-survival

test:
  commands:
    - $R -e "library('glmnet')"           # [not win]
    - "\"%R%\" -e \"library('glmnet')\""  # [win]

about:
  home: https://glmnet.stanford.edu, https://dx.doi.org/10.18637/jss.v033.i01, https://dx.doi.org/10.18637/jss.v039.i05
  license: GPL-2.0-only
  summary: Extremely efficient procedures for fitting the entire lasso or elastic-net regularization path for linear regression, logistic and multinomial regression models, Poisson regression and the Cox model. Two recent additions are the multiple-response Gaussian, and the grouped multinomial regression. The algorithm
    uses cyclical coordinate descent in a path-wise fashion, as described in the papers listed in the URL below.
  license_family: GPL2
  license_file:
    - {{ environ["PREFIX"] }}/lib/R/share/licenses/GPL-2

extra:
  recipe-maintainers:
    - conda-forge/r

# Package: glmnet
# Type: Package
# Title: Lasso and Elastic-Net Regularized Generalized Linear Models
# Version: 3.0-2
# Date: 2019-12-09
# Authors@R: c(person("Jerome", "Friedman", role=c("aut")), person("Trevor", "Hastie", role=c("aut", "cre"), email = "hastie@stanford.edu"), person("Rob", "Tibshirani", role=c("aut")), person("Balasubramanian", "Narasimhan", role=c("aut")), person("Noah", "Simon", role=c("aut")), person("Junyang", "Qian", role=c("ctb")))
# Depends: R (>= 3.6.0), Matrix (>= 1.0-6)
# Imports: methods, utils, foreach, shape
# Suggests: survival, knitr, lars
# Description: Extremely efficient procedures for fitting the entire lasso or elastic-net regularization path for linear regression, logistic and multinomial regression models, Poisson regression and the Cox model. Two recent additions are the multiple-response Gaussian, and the grouped multinomial regression. The algorithm uses cyclical coordinate descent in a path-wise fashion, as described in the papers listed in the URL below.
# License: GPL-2
# VignetteBuilder: knitr
# Encoding: UTF-8
# URL: https://glmnet.stanford.edu, https://dx.doi.org/10.18637/jss.v033.i01, https://dx.doi.org/10.18637/jss.v039.i05
# RoxygenNote: 6.1.1
# NeedsCompilation: yes
# Packaged: 2019-12-11 01:55:13 UTC; hastie
# Author: Jerome Friedman [aut], Trevor Hastie [aut, cre], Rob Tibshirani [aut], Balasubramanian Narasimhan [aut], Noah Simon [aut], Junyang Qian [ctb]
# Maintainer: Trevor Hastie <hastie@stanford.edu>
# Repository: CRAN
# Date/Publication: 2019-12-11 17:00:02 UTC
