{% set version = '2.0.1' %}
{% set posix = 'm2-' if win else '' %}
{% set native = 'm2w64-' if win else '' %}

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

source:
  url:
    - {{ cran_mirror }}/src/contrib/RBF_{{ version }}.tar.gz
    - {{ cran_mirror }}/src/contrib/Archive/RBF/RBF_{{ version }}.tar.gz
  sha256: f7eb3d9905027a2c04a1037524c6e3c13322cc40bd586f592a10b79a16f472ca

build:
  merge_build_host: True  # [win]
  number: 0

  rpaths:
    - lib/R/lib/
    - lib/

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

  host:
    - r-base

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

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

about:
  home: https://CRAN.R-project.org/package=RBF
  license: GPL-3.0-or-later
  summary: A robust backfitting algorithm for additive models based on (robust) local polynomial
    kernel smoothers. It includes both bounded and re-descending (kernel) M-estimators,
    and it computes predictions for points outside the training set if desired. See
    Boente, Martinez and Salibian-Barrera (2017) <doi:10.1080/10485252.2017.1369077>
    for details.

  license_file: {{ environ["PREFIX"] }}/lib/R/share/licenses/GPL-3

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

# Package: RBF
# Type: Package
# Title: Robust Backfitting
# Version: 2.0.1
# Date: 2020-07-29
# Authors@R: c(person("Matias", "Salibian-Barrera", role = c("aut", "cre"), email = "matias@stat.ubc.ca"), person("Alejandra", "Martinez", role=c("aut"), email="ale_m_martinez@hotmail.com") )
# Description: A robust backfitting algorithm for additive models based on (robust) local polynomial kernel smoothers. It includes both bounded and re-descending (kernel) M-estimators, and it computes predictions for points outside the training set if desired. See Boente, Martinez and Salibian-Barrera (2017) <doi:10.1080/10485252.2017.1369077> for details.
# License: GPL (>= 3.0)
# RoxygenNote: 6.1.1
# Encoding: UTF-8
# Imports: stats, graphics
# NeedsCompilation: yes
# Packaged: 2020-07-29 23:19:53 UTC; Matias
# Author: Matias Salibian-Barrera [aut, cre], Alejandra Martinez [aut]
# Maintainer: Matias Salibian-Barrera <matias@stat.ubc.ca>
# Repository: CRAN
# Date/Publication: 2020-07-30 04:40:02 UTC
