{% set version = '0.9-7' %}
{% set posix = 'm2-' if win else '' %}
{% set native = 'm2w64-' if win else '' %}

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

source:
  url:
    - {{ cran_mirror }}/src/contrib/mcmc_{{ version }}.tar.gz
    - {{ cran_mirror }}/src/contrib/Archive/mcmc/mcmc_{{ version }}.tar.gz
  sha256: b7c4d3d5f9364c67a4a3cd49296a61c315ad9bd49324a22deccbacb314aa8260

build:
  merge_build_host: True  # [win]
  number: 2
  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
  run:
    - r-base
    - {{ native }}gcc-libs         # [win]

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

about:
  home: http://www.stat.umn.edu/geyer/mcmc/, https://github.com/cjgeyer/mcmc
  license: MIT
  summary: Simulates continuous distributions of random vectors using Markov chain Monte Carlo
    (MCMC).  Users specify the distribution by an R function that evaluates the log
    unnormalized density.  Algorithms are random walk Metropolis algorithm (function
    metrop), simulated tempering (function temper), and morphometric random walk Metropolis
    (Johnson and Geyer, 2012, <doi:10.1214/12-AOS1048>, function morph.metrop), which
    achieves geometric ergodicity by change of variable.
  license_family: MIT
  license_file:
    - '{{ environ["PREFIX"] }}/lib/R/share/licenses/MIT'
    - LICENSE

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

# Package: mcmc
# Version: 0.9-7
# Date: 2020-03-20
# Title: Markov Chain Monte Carlo
# Author: Charles J. Geyer <charlie@stat.umn.edu> and Leif T. Johnson <ltjohnson@google.com>
# Maintainer: Charles J. Geyer <charlie@stat.umn.edu>
# Depends: R (>= 3.0.2)
# Imports: stats
# Suggests: xtable, Iso
# ByteCompile: TRUE
# Description: Simulates continuous distributions of random vectors using Markov chain Monte Carlo (MCMC).  Users specify the distribution by an R function that evaluates the log unnormalized density.  Algorithms are random walk Metropolis algorithm (function metrop), simulated tempering (function temper), and morphometric random walk Metropolis (Johnson and Geyer, 2012, <doi:10.1214/12-AOS1048>, function morph.metrop), which achieves geometric ergodicity by change of variable.
# License: MIT + file LICENSE
# URL: http://www.stat.umn.edu/geyer/mcmc/, https://github.com/cjgeyer/mcmc
# NeedsCompilation: yes
# Packaged: 2020-03-21 00:49:36 UTC; geyer
# Repository: CRAN
# Date/Publication: 2020-03-21 19:00:13 UTC
