<commit_before>{% set version = '2.1' %}

{% set posix = 'm2-' if win else '' %}
{% set native = 'm2w64-' if win else '' %}

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

source:
  url:
    - {{ cran_mirror }}/src/contrib/arrApply_{{ version }}.tar.gz
    - {{ cran_mirror }}/src/contrib/Archive/arrApply/arrApply_{{ version }}.tar.gz
  sha256: 671c1e53b418191950cf6ddad56ef28d61dd1f367b69e6f570390354228fc763

build:
  merge_build_host: True  # [win]
  # If this is a new build for the same version, increment the build number.
  number: 0

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

# Suggests: testthat
requirements:
  build:
    - {{ compiler('c') }}              # [not win]
    - {{ compiler('m2w64_c') }}        # [win]
    - {{ compiler('cxx') }}            # [not win]
    - {{ compiler('m2w64_cxx') }}      # [win]
    - {{posix}}filesystem        # [win]
    - {{posix}}make
    - {{posix}}sed               # [win]
    - {{posix}}coreutils         # [win]
    - {{posix}}zip               # [win]

  host:
    - r-base
    - r-rcpparmadillo

  run:
    - r-base
    - {{native}}gcc-libs         # [win]
    - r-rcpparmadillo

test:
  commands:
    # You can put additional test commands to be run here.
    - $R -e "library('arrApply')"           # [not win]
    - "\"%R%\" -e \"library('arrApply')\""  # [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: https://CRAN.R-project.org/package=arrApply
  license: GPL (>= 2)
  summary: 'High performance variant of apply() for a fixed set of functions. Considerable speedup
    is a trade-off for universality, user defined functions cannot be used with this
    package. However, 21 most currently employed functions are available for usage.
    They can be divided in three types: reducing functions (like mean(), sum() etc.,
    giving a scalar when applied to a vector), mapping function (like normalise(), cumsum()
    etc., giving a vector of the same length as the input vector) and finally, vector<commit_msg>removed r-base from deps<commit_after>{% set version = '2.1' %}

{% set posix = 'm2-' if win else '' %}
{% set native = 'm2w64-' if win else '' %}

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

source:
  url:
    - {{ cran_mirror }}/src/contrib/arrApply_{{ version }}.tar.gz
    - {{ cran_mirror }}/src/contrib/Archive/arrApply/arrApply_{{ version }}.tar.gz
  sha256: 671c1e53b418191950cf6ddad56ef28d61dd1f367b69e6f570390354228fc763

build:
  merge_build_host: True  # [win]
  # If this is a new build for the same version, increment the build number.
  number: 0

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

# Suggests: testthat
requirements:
  build:
    - {{ compiler('c') }}              # [not win]
    - {{ compiler('m2w64_c') }}        # [win]
    - {{ compiler('cxx') }}            # [not win]
    - {{ compiler('m2w64_cxx') }}      # [win]
    - {{posix}}filesystem        # [win]
    - {{posix}}make
    - {{posix}}sed               # [win]
    - {{posix}}coreutils         # [win]
    - {{posix}}zip               # [win]

  host:

  run:
    - {{native}}gcc-libs         # [win]
    - r-rcpparmadillo

test:
  commands:
    # You can put additional test commands to be run here.
    - $R -e "library('arrApply')"           # [not win]
    - "\"%R%\" -e \"library('arrApply')\""  # [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: https://CRAN.R-project.org/package=arrApply
  license: GPL (>= 2)
  summary: 'High performance variant of apply() for a fixed set of functions. Considerable speedup
    is a trade-off for universality, user defined functions cannot be used with this
    package. However, 21 most currently employed functions are available for usage.
    They can be divided in three types: reducing functions (like mean(), sum() etc.,
    giving a scalar when applied to a vector), mapping function (like normalise(), cumsum()
    etc., giving a vector of the same length as the input vector) and finally, vector
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