# This file created by conda-build 3.21.6
# meta.yaml template originally from:
# D:\a\1\s\recipe, last modified Tue Nov 23 04:20:01 2021
# ------------------------------------------------

package:
  name: r-brms
  version: 2.16.3
source:
  sha256: 68302b10b5264f72d163d01c17792c002306cf37f0ee778dcec4c7e118f923e1
  url:
    - https://cran.r-project.org/src/contrib/Archive/brms/brms_2.16.3.tar.gz
    - https://cran.r-project.org/src/contrib/brms_2.16.3.tar.gz
build:
  merge_build_host: true
  number: '0'
  rpaths:
    - lib/
    - lib/R/lib/
  string: r41ha856d6a_0
requirements:
  build:
    - m2-gcc-libs 5.3.0 4
    - m2-libbz2 1.0.6 3
    - m2-msys2-runtime 2.5.0.17080.65c939c 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
    - ca-certificates 2021.10.8 h5b45459_0
    - icu 68.2 h0e60522_0
    - intel-openmp 2021.4.0 h57928b3_3556
    - libblas 3.9.0 12_win64_mkl
    - libflang 5.0.0 h6538335_20180525
    - libiconv 1.16 he774522_0
    - liblapack 3.9.0 12_win64_mkl
    - libopenblas 0.3.18 pthreads_hc469a61_0
    - libxml2 2.9.12 hf5bbc77_1
    - libzlib 1.2.11 h8ffe710_1013
    - llvm-meta 5.0.0 0
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    - m2w64-gcc-libs-core 5.3.0 7
    - m2w64-gettext 0.19.7 2
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    - m2w64-gsl 2.1 2
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    - m2w64-mpfr 3.1.4 4
    - m2w64-nlopt 2.4.2 3
    - 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 2021.4.0 h0e2418a_729
    - msys2-conda-epoch 20160418 1
    - openmp 5.0.0 vc14_1
    - openssl 3.0.0 h8ffe710_2
    - pandoc 2.16.2 h8ffe710_0
    - r-abind 1.4_5 r41hc72bb7e_1003
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    - r-base 4.1.1 hddad469_1
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    - r-bridgesampling 1.1_2 r41hc72bb7e_0
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    - r-haven 2.4.3 r41h09f486c_0
    - r-highr 0.9 r41hc72bb7e_0
    - r-hms 1.1.1 r41hc72bb7e_0
    - r-htmltools 0.5.2 r41ha856d6a_0
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    - r-lme4 1.1_27.1 r41ha856d6a_0
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    - r-magrittr 2.0.1 r41h6d2157b_1
    - r-markdown 1.1 r41h6d2157b_1
    - r-mass 7.3_54 r41h6d2157b_0
    - r-matrix 1.3_4 r41hd2e18ed_0
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    - r-rprojroot 2.0.2 r41hc72bb7e_0
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    - r-rstan 2.21.2 r41ha856d6a_2
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    - r-yaml 2.2.1 r41h6d2157b_1
    - r-zoo 1.8_9 r41h6d2157b_1
    - tbb 2021.4.0 h2d74725_1
    - ucrt 10.0.20348.0 h57928b3_0
    - vc 14.2 hb210afc_5
    - vs2015_runtime 14.29.30037 h902a5da_5
    - zlib 1.2.11 h8ffe710_1013
  run:
    - m2w64-gcc-libs
    - r-abind
    - r-backports
    - r-base >=4.1,<4.2.0a0
    - r-bayesplot >=1.5.0
    - r-bridgesampling >=0.3_0
    - r-coda
    - r-future >=1.19.0
    - r-ggplot2 >=2.0.0
    - r-glue >=1.3.0
    - r-loo >=2.3.1
    - r-matrix >=1.1.1
    - r-matrixstats
    - r-mgcv >=1.8_13
    - r-nleqslv
    - r-nlme
    - r-posterior >=1.0.0
    - r-projpred >=2.0.0
    - r-rcpp >=0.12.0
    - r-rstan >=2.19.2
    - r-rstantools >=2.1.1
    - r-shinystan >=2.4.0
test:
  commands:
    - '"%R%" -e "library(''brms'')"'
about:
  home: https://github.com/paul-buerkner/brms, http://discourse.mc-stan.org
  license: GPL-2.0-only
  license_family: GPL2
  license_file:
    - D:\bld\r-brms_1637641519787\_h_env/lib/R/share/licenses/GPL-2
  summary: "Fit Bayesian generalized (non-)linear multivariate multilevel models using\
    \ 'Stan' for full Bayesian inference. A wide range of distributions  and link\
    \ functions are supported, allowing users to fit -- among others --  linear, robust\
    \ linear, count data, survival, response times, ordinal,  zero-inflated, hurdle,\
    \ and even self-defined mixture models all in a  multilevel context. Further modeling\
    \ options include non-linear and  smooth terms, auto-correlation structures, censored\
    \ data, meta-analytic  standard errors, and quite a few more. In addition, all\
    \ parameters of the  response distribution can be predicted in order to perform\
    \ distributional  regression. Prior specifications are flexible and explicitly\
    \ encourage  users to apply prior distributions that actually reflect their beliefs.\
    \ Model fit can easily be assessed and compared with posterior predictive  checks\
    \ and leave-one-out cross-validation. References: B\xFCrkner (2017) <doi:10.18637/jss.v080.i01>;\
    \ B\xFCrkner (2018) <doi:10.32614/RJ-2018-017>; Carpenter et al. (2017) <doi:10.18637/jss.v076.i01>."
extra:
  copy_test_source_files: true
  final: true
  recipe-maintainers:
    - conda-forge/r
