zhangyiss/spack
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

r-mice: new package (#8423)

Browse Source 
* r-mice: new package

* flake8 fixed
This commit is contained in:
Yifan Zhu
2018-06-08 14:48:54 -05:00
committed by Adam J. Stewart
parent 4a53942ee1
commit ceb2790f30
1 changed files with 57 additions and 0 deletions
Download Patch File Download Diff File Expand all files Collapse all files

57
var/spack/repos/builtin/packages/r-mice/package.py Normal file
View File

@@ -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'))