# This file created by conda-build 3.21.8
# meta.yaml template originally from:
# D:\a\1\s\recipe, last modified Wed Apr 27 14:10:17 2022
# ------------------------------------------------

package:
  name: r-bas
  version: 1.6.2
source:
  sha256: ecfc7f93d4d92d2c7c4dd72cee9b4843999e3b83ed64d46b207eca57d1931a35
  url:
    - https://cran.r-project.org/src/contrib/Archive/BAS/BAS_1.6.2.tar.gz
    - https://cran.r-project.org/src/contrib/BAS_1.6.2.tar.gz
build:
  merge_build_host: true
  number: '0'
  rpaths:
    - lib/
    - lib/R/lib/
  string: r40hd2e18ed_0
requirements:
  build:
    - m2-bash 4.3.042 5
    - m2-coreutils 8.25 102
    - m2-filesystem 2016.04 4
    - m2-gcc-libs 5.3.0 4
    - m2-gmp 6.1.0 3
    - m2-libbz2 1.0.6 3
    - m2-libiconv 1.14 3
    - m2-libintl 0.19.7 4
    - m2-make 4.1 5
    - m2-msys2-runtime 2.5.0.17080.65c939c 3
    - m2-sed 4.2.2 3
    - m2-zip 3.0 2
    - m2w64-binutils 2.25.1 5
    - m2w64-bzip2 1.0.6 6
    - m2w64-crt-git 5.0.0.4636.2595836 2
    - m2w64-gcc 5.3.0 6
    - m2w64-gcc-ada 5.3.0 6
    - m2w64-gcc-fortran 5.3.0 6
    - m2w64-gcc-libgfortran 5.3.0 6
    - m2w64-gcc-libs 5.3.0 7
    - m2w64-gcc-libs-core 5.3.0 7
    - m2w64-gcc-objc 5.3.0 6
    - m2w64-gmp 6.1.0 2
    - m2w64-headers-git 5.0.0.4636.c0ad18a 2
    - m2w64-isl 0.16.1 2
    - m2w64-libiconv 1.14 6
    - m2w64-libmangle-git 5.0.0.4509.2e5a9a2 2
    - m2w64-libwinpthread-git 5.0.0.4634.697f757 2
    - m2w64-make 4.1.2351.a80a8b8 2
    - m2w64-mpc 1.0.3 3
    - m2w64-mpfr 3.1.4 4
    - m2w64-pkg-config 0.29.1 2
    - m2w64-toolchain 5.3.0 7
    - m2w64-toolchain_win-64 2.4.0 0
    - m2w64-tools-git 5.0.0.4592.90b8472 2
    - m2w64-windows-default-manifest 6.4 3
    - m2w64-winpthreads-git 5.0.0.4634.697f757 2
    - m2w64-zlib 1.2.8 10
    - msys2-conda-epoch 20160418 1
  host:
    - _r-mutex 1.0.1 anacondar_1
    - libblas 3.8.0 11_h8933c1f_netlib
    - liblapack 3.8.0 11_h8933c1f_netlib
    - m2w64-bwidget 1.9.10 2
    - m2w64-bzip2 1.0.6 6
    - m2w64-expat 2.1.1 2
    - m2w64-fftw 3.3.4 6
    - m2w64-flac 1.3.1 3
    - m2w64-gcc-libgfortran 5.3.0 6
    - m2w64-gcc-libs 5.3.0 7
    - m2w64-gcc-libs-core 5.3.0 7
    - m2w64-gettext 0.19.7 2
    - m2w64-gmp 6.1.0 2
    - m2w64-gsl 2.1 2
    - m2w64-libiconv 1.14 6
    - m2w64-libjpeg-turbo 1.4.2 3
    - m2w64-libogg 1.3.2 3
    - m2w64-libpng 1.6.21 2
    - m2w64-libsndfile 1.0.26 2
    - m2w64-libtiff 4.0.6 2
    - m2w64-libvorbis 1.3.5 2
    - m2w64-libwinpthread-git 5.0.0.4634.697f757 2
    - m2w64-libxml2 2.9.3 3
    - m2w64-mpfr 3.1.4 4
    - m2w64-pcre2 10.34 0
    - m2w64-speex 1.2rc2 3
    - m2w64-speexdsp 1.2rc3 3
    - m2w64-tcl 8.6.5 3
    - m2w64-tk 8.6.5 3
    - m2w64-tktable 2.10 5
    - m2w64-wineditline 2.101 5
    - m2w64-xz 5.2.2 2
    - m2w64-zlib 1.2.8 10
    - msys2-conda-epoch 20160418 1
    - r-base 4.0.5 hddad469_6
  run:
    - libblas >=3.8.0,<4.0a0
    - liblapack >=3.8.0,<4.0a0
    - m2w64-gcc-libs
    - r-base >=4.0,<4.1.0a0
test:
  commands:
    - '"%R%" -e "library(''BAS'')"'
about:
  home: https://www.r-project.org, https://github.com/merliseclyde/BAS
  license: GPL-3.0-or-later
  license_family: GPL3
  license_file:
    - D:\bld\r-bas_1651069035073\_h_env/lib/R/share/licenses/GPL-3
  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.
extra:
  copy_test_source_files: true
  final: true
  recipe-maintainers:
    - conda-forge/r
    - izahn
