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| -rw-r--r-- | var/spack/repos/builtin/packages/r-mice/package.py | 57 |
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 --- /dev/null +++ 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')) |