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Diffstat (limited to 'var/spack/repos/builtin/packages/r-rstan/package.py')
| -rw-r--r-- | var/spack/repos/builtin/packages/r-rstan/package.py | 57 |
1 files changed, 57 insertions, 0 deletions
diff --git a/var/spack/repos/builtin/packages/r-rstan/package.py b/var/spack/repos/builtin/packages/r-rstan/package.py new file mode 100644 index 0000000000..00fd5116d9 --- /dev/null +++ b/var/spack/repos/builtin/packages/r-rstan/package.py @@ -0,0 +1,57 @@ +############################################################################## +# Copyright (c) 2013-2016, Lawrence Livermore National Security, LLC. +# Produced at the Lawrence Livermore National Laboratory. +# +# This file is part of Spack. +# Created by Todd Gamblin, tgamblin@llnl.gov, All rights reserved. +# LLNL-CODE-647188 +# +# For details, see https://github.com/llnl/spack +# Please also see the LICENSE file for our notice and the LGPL. +# +# This program is free software; you can redistribute it and/or modify +# it under the terms of the GNU Lesser General Public License (as +# published by the Free Software Foundation) version 2.1, February 1999. +# +# This program is distributed in the hope that it will be useful, but +# WITHOUT ANY WARRANTY; without even the IMPLIED WARRANTY OF +# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the terms and +# conditions of the GNU Lesser General Public License for more details. +# +# You should have received a copy of the GNU Lesser General Public +# License along with this program; if not, write to the Free Software +# Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA +############################################################################## +from spack import * + + +class RRstan(Package): + """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.""" + + homepage = "http://mc-stan.org/" + url = "https://cran.r-project.org/src/contrib/rstan_2.10.1.tar.gz" + list_url = "https://cran.r-project.org/src/contrib/Archive/rstan" + + version('2.10.1', 'f5d212f6f8551bdb91fe713d05d4052a') + + extends('R') + + depends_on('r-ggplot2') + depends_on('r-stanheaders') + depends_on('r-inline') + depends_on('r-gridextra') + depends_on('r-rcpp') + depends_on('r-rcppeigen') + depends_on('r-bh') + + def install(self, spec, prefix): + R('CMD', 'INSTALL', '--library={0}'.format(self.module.r_lib_dir), + self.stage.source_path) |