r-mice: new package (#8423)

* r-mice: new package

* flake8 fixed

1 files changed, 57 insertions, 0 deletions

diff --git a/var/spack/repos/builtin/packages/r-mice/package.py b/var/spack/repos/builtin/packages/r-mice/package.py
new file mode 100644
index 0000000000..43bf35dbdd
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+++ b/var/spack/repos/builtin/packages/r-mice/package.py
@@ -0,0 +1,57 @@
+##############################################################################
+# Copyright (c) 2013-2018, 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/spack/spack
+# Please also see the NOTICE and LICENSE files 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 RMice(RPackage):
+    """Multiple imputation using Fully Conditional Specification (FCS)
+    implemented by the MICE algorithm as described in Van Buuren and
+    Groothuis-Oudshoorn (2011) <doi:10.18637/jss.v045.i03>.
+
+    Each variable has its own imputation model. Built-in imputation models are
+    provided for continuous data (predictive mean matching, normal), binary
+    data (logistic regression), unordered categorical data (polytomous logistic
+    regression) and ordered categorical data (proportional odds). MICE can
+    also impute continuous two-level data (normal model, pan, second-level
+    variables). Passive imputation can be used to maintain consistency between
+    variables. Various diagnostic plots are available to inspect the quality
+    of the imputations."""
+
+    homepage = "https://cran.r-project.org/package=mice"
+    url      = "https://cran.r-project.org/src/contrib/mice_3.0.0.tar.gz"
+    list_url = "https://cran.r-project.org/src/contrib/Archive/mice"
+
+    version('3.0.0', 'fb54a29679536c474c756cca4538d7e3')
+
+    depends_on('r-broom', type=('build', 'run'))
+    depends_on('r-dplyr', type=('build', 'run'))
+    depends_on('r-mass', type=('build', 'run'))
+    depends_on('r-mitml', type=('build', 'run'))
+    depends_on('r-nnet', type=('build', 'run'))
+    depends_on('r-rcpp', type=('build', 'run'))
+    depends_on('r-rlang', type=('build', 'run'))
+    depends_on('r-rpart', type=('build', 'run'))
+    depends_on('r-survival', type=('build', 'run'))
+    depends_on('r-lattice', type=('build', 'run'))