# This file created by conda-build 3.21.4
# meta.yaml template originally from:
# D:\a\1\s\recipe, last modified Sun May  9 21:02:54 2021
# ------------------------------------------------

package:
  name: r-rstan
  version: 2.21.2
source:
  sha256: e30e04d38a612e2cb3ac69b53eaa19f7ede8b3548bf82f7892a2e9991d46054a
  url:
    - https://cran.r-project.org/src/contrib/Archive/rstan/rstan_2.21.2.tar.gz
    - https://cran.r-project.org/src/contrib/rstan_2.21.2.tar.gz
build:
  merge_build_host: true
  number: '2'
  rpaths:
    - lib/
    - lib/R/lib/
  string: r36ha856d6a_2
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
    - icu 68.1 h0e60522_0
    - intel-openmp 2021.2.0 h57928b3_616
    - libblas 3.9.0 9_mkl
    - liblapack 3.9.0 9_mkl
    - 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-pcre 8.38 2
    - 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
    - mkl 2021.2.0 hb70f87d_389
    - msys2-conda-epoch 20160418 1
    - r-assertthat 0.2.1 r36h6115d3f_2
    - r-backports 1.2.1 r36h6d2157b_0
    - r-base 3.6.3 hddad469_7
    - r-bh 1.75.0_0 r36hc72bb7e_0
    - r-brio 1.1.2 r36h6d2157b_0
    - r-callr 3.7.0 r36hc72bb7e_0
    - r-checkmate 2.0.0 r36hda5aaf8_1
    - r-cli 2.5.0 r36hc72bb7e_0
    - r-colorspace 2.0_1 r36h6d2157b_0
    - r-crayon 1.4.1 r36hc72bb7e_0
    - r-curl 4.3.1 r36h6d2157b_0
    - r-desc 1.3.0 r36hc72bb7e_0
    - r-diffobj 0.3.3 r36h6d2157b_0
    - r-digest 0.6.27 r36h096a1c2_0
    - r-ellipsis 0.3.2 r36h6d2157b_0
    - r-evaluate 0.14 r36h6115d3f_2
    - r-fansi 0.4.2 r36h6d2157b_0
    - r-farver 2.1.0 r36ha856d6a_0
    - r-ggplot2 3.3.3 r36hc72bb7e_0
    - r-glue 1.4.2 r36h6d2157b_0
    - r-gridextra 2.3 r36h6115d3f_1003
    - r-gtable 0.3.0 r36h6115d3f_3
    - r-inline 0.3.17 r36hc72bb7e_0
    - r-isoband 0.2.4 r36ha856d6a_0
    - r-jsonlite 1.7.2 r36h6d2157b_0
    - r-labeling 0.4.2 r36h142f84f_0
    - r-lattice 0.20_44 r36h6d2157b_0
    - r-lifecycle 1.0.0 r36hc72bb7e_0
    - r-loo 2.4.1 r36hc72bb7e_0
    - r-magrittr 2.0.1 r36h6d2157b_1
    - r-mass 7.3_54 r36h6d2157b_0
    - r-matrix 1.3_2 r36hd2e18ed_0
    - r-matrixstats 0.58.0 r36h6d2157b_0
    - r-mgcv 1.8_34 r36hd2e18ed_0
    - r-munsell 0.5.0 r36h6115d3f_1003
    - r-nlme 3.1_152 r36he816bda_0
    - r-pillar 1.6.0 r36hc72bb7e_0
    - r-pkgbuild 1.2.0 r36hc72bb7e_0
    - r-pkgconfig 2.0.3 r36h6115d3f_1
    - r-pkgload 1.2.1 r36ha856d6a_0
    - r-praise 1.0.0 r36h6115d3f_1004
    - r-prettyunits 1.1.1 r36h6115d3f_1
    - r-processx 3.5.2 r36h6d2157b_0
    - r-ps 1.6.0 r36h6d2157b_0
    - r-r6 2.5.0 r36hc72bb7e_0
    - r-rcolorbrewer 1.1_2 r36h6115d3f_1003
    - r-rcpp 1.0.6 r36ha856d6a_0
    - r-rcppeigen 0.3.3.9.1 r36h399f8d5_0
    - r-rcppparallel 5.1.4 r36ha856d6a_0
    - r-rematch2 2.1.2 r36h6115d3f_1
    - r-rlang 0.4.11 r36h6d2157b_0
    - r-rprojroot 2.0.2 r36hc72bb7e_0
    - r-rstudioapi 0.13 r36hc72bb7e_0
    - r-scales 1.1.1 r36h6115d3f_0
    - r-stanheaders 2.21.0_7 r36h96779af_0
    - r-testthat 3.0.2 r36ha856d6a_0
    - r-tibble 3.1.1 r36h6d2157b_0
    - r-utf8 1.2.1 r36h6d2157b_0
    - r-v8 3.4.2 r36hed87b95_0
    - r-vctrs 0.3.8 r36h6d2157b_0
    - r-viridislite 0.4.0 r36hc72bb7e_0
    - r-waldo 0.2.5 r36hc72bb7e_0
    - r-withr 2.4.2 r36hc72bb7e_0
    - r-zeallot 0.1.0 r36h6115d3f_1002
    - tbb 2021.2.0 h2d74725_0
    - vc 14.2 hb210afc_4
    - vs2015_runtime 14.28.29325 h5e1d092_4
  run:
    - m2w64-gcc-libs
    - r-base >=3.6,<3.7.0a0
    - r-ggplot2 >=3.0.0
    - r-gridextra >=2.0.0
    - r-inline
    - r-loo >=2.3.0
    - r-pkgbuild
    - r-rcpp >=0.12.0
    - r-rcppeigen >=0.3.3.3.0
    - r-rcppparallel >=5.0.1
    - r-stanheaders >=2.21.0
    - r-v8
    - r-withr
test:
  commands:
    - '"%R%" -e "library(''rstan'')"'
about:
  home: http://discourse.mc-stan.org, http://mc-stan.org
  license: GPL-3.0-or-later
  license_family: GPL3
  license_file:
    - D:\bld\r-rstan_1620594420105\_h_env/lib/R/share/licenses/GPL-3
  summary: User-facing R functions are provided to parse, compile, test, estimate,
    and analyze Stan models by accessing the header-only Stan library provided by
    the 'StanHeaders' package. The Stan project develops a probabilistic programming
    language that implements full Bayesian statistical inference via Markov Chain
    Monte Carlo, rough Bayesian inference via 'variational' approximation, and (optionally
    penalized) maximum likelihood estimation via optimization. In all three cases,
    automatic differentiation is used to quickly and accurately evaluate gradients
    without burdening the user with the need to derive the partial derivatives.
extra:
  copy_test_source_files: true
  final: true
  recipe-maintainers:
    - conda-forge/r
    - ndmaxar
    - oblute
    - rluria14
