New package - RStan

This PR creates the RStan package and its dependencies.

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)