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author | Glenn Johnson <glenn-johnson@uiowa.edu> | 2021-04-26 03:01:47 -0500 |
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committer | GitHub <noreply@github.com> | 2021-04-26 10:01:47 +0200 |
commit | 7952a802ed357e615f8232fcda4c5086185d36c6 (patch) | |
tree | 05733096e0cf7cfebc15d87faf7863abe27265da /var | |
parent | 4b62344163a083f011f12850add4b17465d569ed (diff) | |
download | spack-7952a802ed357e615f8232fcda4c5086185d36c6.tar.gz spack-7952a802ed357e615f8232fcda4c5086185d36c6.tar.bz2 spack-7952a802ed357e615f8232fcda4c5086185d36c6.tar.xz spack-7952a802ed357e615f8232fcda4c5086185d36c6.zip |
r-brms: new package (#23233)
Diffstat (limited to 'var')
-rw-r--r-- | var/spack/repos/builtin/packages/r-brms/package.py | 52 |
1 files changed, 52 insertions, 0 deletions
diff --git a/var/spack/repos/builtin/packages/r-brms/package.py b/var/spack/repos/builtin/packages/r-brms/package.py new file mode 100644 index 0000000000..7da8d8fa12 --- /dev/null +++ b/var/spack/repos/builtin/packages/r-brms/package.py @@ -0,0 +1,52 @@ +# Copyright 2013-2021 Lawrence Livermore National Security, LLC and other +# Spack Project Developers. See the top-level COPYRIGHT file for details. +# +# SPDX-License-Identifier: (Apache-2.0 OR MIT) + +from spack import * + + +class RBrms(RPackage): + """Bayesian Regression Models using 'Stan': + + 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: Burkner (2017) + <doi:10.18637/jss.v080.i01>; Burkner (2018) <doi:10.32614/RJ-2018-017>; + Carpenter et al. (2017) <doi:10.18637/jss.v076.i01>.""" + + homepage = "https://github.com/paul-buerkner/brms" + cran = "brms" + + version('2.15.0', sha256='c11701d1d8758590b74bb845b568b736e4455a81b114c7dfde0b27b7bd1bcc2f') + + depends_on('r@3.5.0:', type=('build', 'run')) + depends_on('r-rcpp@0.12.0:', type=('build', 'run')) + depends_on('r-rstan@2.19.2:', type=('build', 'run')) + depends_on('r-ggplot2@2.0.0:', type=('build', 'run')) + depends_on('r-loo@2.3.1:', type=('build', 'run')) + depends_on('r-matrix@1.1.1:', type=('build', 'run')) + depends_on('r-mgcv@1.8-13:', type=('build', 'run')) + depends_on('r-rstantools@2.1.1:', type=('build', 'run')) + depends_on('r-bayesplot@1.5.0:', type=('build', 'run')) + depends_on('r-shinystan@2.4.0:', type=('build', 'run')) + depends_on('r-projpred@2.0.0:', type=('build', 'run')) + depends_on('r-bridgesampling@0.3-0:', type=('build', 'run')) + depends_on('r-glue@1.3.0:', type=('build', 'run')) + depends_on('r-future@1.19.0:', type=('build', 'run')) + depends_on('r-matrixstats', type=('build', 'run')) + depends_on('r-nleqslv', type=('build', 'run')) + depends_on('r-nlme', type=('build', 'run')) + depends_on('r-coda', type=('build', 'run')) + depends_on('r-abind', type=('build', 'run')) + depends_on('r-backports', type=('build', 'run')) |