{% set version = '2.6-12' %}

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

source:
  url:
    - {{ cran_mirror }}/src/contrib/pcalg_{{ version }}.tar.gz
    - {{ cran_mirror }}/src/contrib/Archive/pcalg/pcalg_{{ version }}.tar.gz
  sha256: 4c18c96dbe38d672666c11bd0dc9d4e93fa29497a1c4b905440f52593c10674a

build:
  number: 6
  rpaths:
    - lib/R/lib/
    - lib/

requirements:
  build:
    - {{ compiler('c') }}
    - {{ compiler('cxx') }}
    - make
  host:
    - r-base
    - r-bh
    - bioconductor-rbgl
    - r-rcpp
    - r-rcpparmadillo
    - r-abind
    - r-bdsmatrix
    - r-clue
    - r-corpcor
    - r-dagitty
    - r-fastica
    - r-ggm
    - bioconductor-graph
    - r-igraph
    - r-robustbase
    - r-sfsmisc >=1.0_26
    - r-vcd
  run:
    - r-base
    - r-bh
    - bioconductor-rbgl
    - r-rcpp
    - r-rcpparmadillo
    - r-abind
    - r-bdsmatrix
    - r-clue
    - r-corpcor
    - r-dagitty
    - r-fastica
    - r-ggm
    - bioconductor-graph
    - r-igraph
    - r-robustbase
    - r-sfsmisc >=1.0_26
    - r-vcd

test:
  commands:
    - $R -e "library('pcalg')"

about:
  home: http://pcalg.r-forge.r-project.org/
  license: GPL-2
  summary: Functions for causal structure learning and causal inference using graphical models.
    The main algorithms for causal structure learning are PC (for observational data
    without hidden variables), FCI and RFCI (for observational data with hidden variables),
    and GIES (for a mix of data from observational studies (i.e. observational data)
    and data from experiments involving interventions (i.e. interventional data) without
    hidden variables). For causal inference the IDA algorithm, the Generalized Backdoor
    Criterion (GBC), the Generalized Adjustment Criterion (GAC) and some related functions
    are implemented. Functions for incorporating background knowledge are provided.
  license_family: GPL2
  license_file:
    - '{{ environ["PREFIX"] }}/lib/R/share/licenses/GPL-2'
