# This file created by conda-build 3.21.8
# meta.yaml template originally from:
# D:\a\1\s\recipe, last modified Mon Apr 11 21:40:13 2022
# ------------------------------------------------

package:
  name: r-rstan
  version: 2.21.5
source:
  sha256: 86e4fe562d8ddcd0b02336f35a420fa8786dd21de7ca2bebb4ed6e9c252bb9ea
  url:
    - https://cran.r-project.org/src/contrib/Archive/rstan/rstan_2.21.5.tar.gz
    - https://cran.r-project.org/src/contrib/rstan_2.21.5.tar.gz
build:
  merge_build_host: true
  number: '0'
  rpaths:
    - lib/
    - lib/R/lib/
  string: r40ha856d6a_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
    - icu 69.1 h0e60522_0
    - intel-openmp 2022.0.0 h57928b3_3663
    - libblas 3.9.0 14_win64_mkl
    - liblapack 3.9.0 14_win64_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-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
    - mkl 2022.0.0 h0e2418a_796
    - msys2-conda-epoch 20160418 1
    - r-assertthat 0.2.1 r40hc72bb7e_2
    - r-backports 1.4.1 r40h6d2157b_0
    - r-base 4.0.5 hddad469_6
    - r-bh 1.78.0_0 r40hc72bb7e_0
    - r-brio 1.1.3 r40h6d2157b_0
    - r-callr 3.7.0 r40hc72bb7e_0
    - r-checkmate 2.0.0 r40h6d2157b_1
    - r-cli 3.2.0 r40ha856d6a_0
    - r-colorspace 2.0_3 r40h6d2157b_0
    - r-crayon 1.5.1 r40hc72bb7e_0
    - r-curl 4.3.2 r40h6d2157b_0
    - r-desc 1.4.1 r40hc72bb7e_0
    - r-diffobj 0.3.5 r40h6d2157b_0
    - r-digest 0.6.29 r40ha856d6a_0
    - r-ellipsis 0.3.2 r40h6d2157b_0
    - r-evaluate 0.15 r40hc72bb7e_0
    - r-fansi 1.0.3 r40h6d2157b_0
    - r-farver 2.1.0 r40ha856d6a_0
    - r-ggplot2 3.3.5 r40hc72bb7e_0
    - r-glue 1.6.2 r40h6d2157b_0
    - r-gridextra 2.3 r40hc72bb7e_1003
    - r-gtable 0.3.0 r40hc72bb7e_3
    - r-inline 0.3.19 r40hc72bb7e_0
    - r-isoband 0.2.5 r40ha856d6a_0
    - r-jsonlite 1.8.0 r40h6d2157b_0
    - r-labeling 0.4.2 r40hc72bb7e_1
    - r-lattice 0.20_45 r40h6d2157b_0
    - r-lifecycle 1.0.1 r40hc72bb7e_0
    - r-loo 2.5.1 r40hc72bb7e_0
    - r-magrittr 2.0.3 r40h6d2157b_0
    - r-mass 7.3_56 r40h6d2157b_0
    - r-matrix 1.4_1 r40hd2e18ed_0
    - r-matrixstats 0.61.0 r40h6d2157b_0
    - r-mgcv 1.8_40 r40hd2e18ed_0
    - r-munsell 0.5.0 r40hc72bb7e_1004
    - r-nlme 3.1_157 r40he816bda_0
    - r-pillar 1.7.0 r40hc72bb7e_0
    - r-pkgbuild 1.3.1 r40hc72bb7e_0
    - r-pkgconfig 2.0.3 r40hc72bb7e_1
    - r-pkgload 1.2.4 r40ha856d6a_0
    - r-praise 1.0.0 r40hc72bb7e_1005
    - r-prettyunits 1.1.1 r40hc72bb7e_1
    - r-processx 3.5.3 r40h6d2157b_0
    - r-ps 1.6.0 r40h6d2157b_0
    - r-r6 2.5.1 r40hc72bb7e_0
    - r-rcolorbrewer 1.1_3 r40h785f33e_0
    - r-rcpp 1.0.8.3 r40ha856d6a_0
    - r-rcppeigen 0.3.3.9.2 r40h399f8d5_0
    - r-rcppparallel 5.1.5 r40ha856d6a_0
    - r-rematch2 2.1.2 r40hc72bb7e_1
    - r-rlang 1.0.2 r40ha856d6a_0
    - r-rprojroot 2.0.3 r40hc72bb7e_0
    - r-rstudioapi 0.13 r40hc72bb7e_0
    - r-scales 1.1.1 r40hc72bb7e_0
    - r-stanheaders 2.21.0_7 r40h96779af_0
    - r-testthat 3.1.3 r40ha856d6a_0
    - r-tibble 3.1.6 r40h6d2157b_0
    - r-utf8 1.2.2 r40h6d2157b_0
    - r-v8 4.1.0 r40hc2d2cb0_0
    - r-vctrs 0.4.0 r40ha856d6a_0
    - r-viridislite 0.4.0 r40hc72bb7e_0
    - r-waldo 0.4.0 r40hc72bb7e_0
    - r-withr 2.5.0 r40hc72bb7e_0
    - tbb 2021.5.0 h2d74725_1
    - ucrt 10.0.20348.0 h57928b3_0
    - vc 14.2 hb210afc_6
    - vs2015_runtime 14.29.30037 h902a5da_6
  run:
    - m2w64-gcc-libs
    - r-base >=4.0,<4.1.0a0
    - 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_1649713470026\_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
