{% set version = "0.10.3" %}
{% set posix = 'm2-' if win else '' %}
{% set native = 'm2w64-' if win else '' %}

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

source:
  url:
    - {{ cran_mirror }}/src/contrib/grf_{{ version }}.tar.gz
    - {{ cran_mirror }}/src/contrib/Archive/grf/grf_{{ version }}.tar.gz
  sha256: 8668345b5c0cda9f072c9eea863ead1ff7803b499025dfb273668d51783ad781

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

requirements:
  build:
    - {{ compiler('c') }}        # [not win]
    - {{ compiler('cxx') }}      # [not win]
    - {{ compiler('m2w64_c') }}        # [win]
    - {{ compiler('m2w64_cxx') }}        # [win]
    - {{posix}}filesystem        # [win]
    - {{posix}}make
    - {{posix}}sed               # [win]
    - {{posix}}coreutils         # [win]
    - {{posix}}zip               # [win]
  host:
    - r-base
    - r-diagrammer
    - r-dicekriging
    - r-matrix
    - r-rcpp >=0.12.15
    - r-rcppeigen
    - r-lmtest
    - r-sandwich >=2.4_0
  run:
    - r-base
    - {{native}}gcc-libs         # [win]
    - r-diagrammer
    - r-dicekriging
    - r-matrix
    - r-rcpp >=0.12.15
    - r-rcppeigen
    - r-lmtest
    - r-sandwich >=2.4_0

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

about:
  home: https://github.com/grf-labs/grf
  license: GPL-3
  summary: A pluggable package for forest-based statistical estimation and inference. GRF currently
    provides methods for non-parametric least-squares regression, quantile regression,
    and treatment effect estimation (optionally using instrumental variables). This
    package is currently in beta, and we expect to make continual improvements to its
    performance and usability.
  license_family: GPL3
  license_file: '{{ environ["PREFIX"] }}/lib/R/share/licenses/GPL-3'

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

# Package: grf
# Title: Generalized Random Forests (Beta)
# Version: 0.10.2
# Author: Julie Tibshirani [aut, cre], Susan Athey [aut], Stefan Wager [aut], Rina Friedberg [ctb], Luke Miner [ctb], Marvin Wright [ctb]
# BugReports: https://github.com/grf-labs/grf/issues
# Maintainer: Julie Tibshirani <jtibs@cs.stanford.edu>
# Description: A pluggable package for forest-based statistical estimation and inference. GRF currently provides methods for non-parametric least-squares regression, quantile regression, and treatment effect estimation (optionally using instrumental variables). This package is currently in beta, and we expect to make continual improvements to its performance and usability.
# Depends: R (>= 3.3.0)
# License: GPL-3
# LinkingTo: Rcpp, RcppEigen
# Imports: DiagrammeR, DiceKriging, lmtest, Matrix, methods, Rcpp (>= 0.12.15), sandwich (>= 2.4-0)
# RoxygenNote: 6.1.1
# Suggests: testthat
# SystemRequirements: GNU make
# URL: https://github.com/grf-labs/grf
# NeedsCompilation: yes
# Packaged: 2018-11-24 03:10:42 UTC; jtibshirani
# Repository: CRAN
# Date/Publication: 2018-11-24 05:20:03 UTC
