{% set version = '1.5.5' %}
{% set posix = 'm2-' if win else '' %}
{% set native = 'm2w64-' if win else '' %}

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

source:
  url:
    - {{ cran_mirror }}/src/contrib/BAS_{{ version }}.tar.gz
    - {{ cran_mirror }}/src/contrib/Archive/BAS/BAS_{{ version }}.tar.gz
  sha256: 9222694198c9ec62f0bf556881c823ab0e5e247335d5737437d45292cde853c5

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]
    - cross-r-base {{ r_base }}    # [build_platform != target_platform]
  host:
    - r-base
    - libblas
    - liblapack
  run:
    - r-base
    - {{ native }}gcc-libs         # [win]

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

about:
  home: https://www.r-project.org, https://github.com/merliseclyde/BAS
  license: GPL-3.0-or-later
  summary: Package for Bayesian Variable Selection and  Model Averaging in linear models and
    generalized linear models using stochastic or deterministic sampling without replacement
    from posterior distributions.  Prior distributions on coefficients are from Zellner's
    g-prior or mixtures of g-priors corresponding to the Zellner-Siow Cauchy Priors
    or the mixture of g-priors from Liang et al (2008) <DOI:10.1198/016214507000001337>
    for linear models or mixtures of g-priors from  Li and Clyde (2019) <DOI:10.1080/01621459.2018.1469992>
    in generalized linear models. Other model selection criteria include AIC, BIC and
    Empirical Bayes estimates of g. Sampling probabilities may be updated based on the
    sampled models using sampling w/out replacement or an efficient MCMC algorithm which
    samples models using a tree structure of the model space as an efficient hash table.  See  Clyde,
    Ghosh and Littman (2010) <DOI:10.1198/jcgs.2010.09049> for  details on the sampling
    algorithms. Uniform priors over all models or beta-binomial prior distributions
    on model size are allowed, and for large p truncated priors on the model space may
    be used to enforce sampling models that are full rank. The user may force variables
    to always be included in addition to imposing constraints that higher order interactions
    are included only if their parents are included in the model. This material is based
    upon work supported by the National Science Foundation under Division of Mathematical
    Sciences grant 1106891. Any opinions, findings, and conclusions or recommendations
    expressed in this material are those of the author(s) and do not necessarily reflect
    the views of the National Science Foundation.
  license_family: GPL3
  license_file:
    - '{{ environ["PREFIX"] }}/lib/R/share/licenses/GPL-3'

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