package:
  name: r-glmnet
  # Note that conda versions cannot contain -, so any -'s in the version have
  # been replaced with _'s.
  version: "2.0_2"

source:
  fn: glmnet_2.0-2.tar.gz
  url:
    - http://cran.r-project.org/src/contrib/glmnet_2.0-2.tar.gz
    - http://cran.r-project.org/src/contrib/Archive/glmnet/glmnet_2.0-2.tar.gz


  # You can add a hash for the file here, like md5 or sha1
  # md5: 49448ba4863157652311cc5ea4fea3ea
  # sha1: 3bcfbee008276084cbb37a2b453963c61176a322
  # patches:
   # List any patch files here
   # - fix.patch

build:
  # If this is a new build for the same version, increment the build
  # number. If you do not include this key, it defaults to 0.
  # number: 1

  # This is required to make R link correctly on Linux.
  rpaths:
    - lib/R/lib/
    - lib/

# Suggests: survival, knitr, lars
requirements:
  build:
    - r
    - r-matrix >=1.0_6 # [not win]
    - r-foreach
    - gcc # [not win]

  run:
    - r
    - r-matrix >=1.0_6 # [not win]
    - r-foreach
    - libgcc # [not win]

test:
  commands:
    # You can put additional test commands to be run here.
    - $R -e "library('glmnet')" # [not win]
    - "\"%R%\" -e \"library('glmnet')\"" # [win]

  # You can also put a file called run_test.py, run_test.sh, or run_test.bat
  # in the recipe that will be run at test time.

  # requires:
    # Put any additional test requirements here.

about:
  home: http://www.jstatsoft.org/v33/i01/.
  license: GPL-2
  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. The algorithm uses cyclical coordinate descent in a
    path-wise fashion, as described in the paper linked to via the URL below. '
  license_family: GPL2

# The original CRAN metadata for this package was:

# Package: glmnet
# Type: Package
# Title: Lasso and Elastic-Net Regularized Generalized Linear Models
# Version: 2.0-2
# Date: 2015-4-11
# Author: Jerome Friedman, Trevor Hastie, Noah Simon, Rob Tibshirani
# Maintainer: Trevor Hastie <hastie@stanford.edu>
# Depends: Matrix (>= 1.0-6), utils, foreach
# 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. The algorithm uses cyclical coordinate descent in a path-wise fashion, as described in the paper linked to via the URL below.
# License: GPL-2
# VignetteBuilder: knitr
# URL: http://www.jstatsoft.org/v33/i01/.
# Packaged: 2015-04-11 10:00:57 UTC; hastie
# NeedsCompilation: yes
# Repository: CRAN
# Date/Publication: 2015-04-12 00:56:25

# See
# http://docs.continuum.io/conda/build.html for
# more information about meta.yaml
