mice: Multivariate Imputation by Chained Equations

Multiple imputation using Fully Conditional Specification (FCS) implemented by the MICE algorithm. 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.

Version: 2.21
Depends: R (≥ 2.10.0), methods, Rcpp (≥ 0.10.6)
Imports: lattice, MASS, nnet, randomForest, rpart
LinkingTo: Rcpp
Suggests: AGD, gamlss, lme4, mitools, nlme, pan, survival, Zelig
Published: 2014-02-05
Author: Stef van Buuren [aut, cre], Karin Groothuis-Oudshoorn [aut], Alexander Robitzsch [ctb], Gerko Vink [ctb], Lisa Doove [ctb], Shahab Jolani [ctb]
Maintainer: Stef van Buuren <stef.vanbuuren at tno.nl>
License: GPL-2 | GPL-3
URL: http://www.stefvanbuuren.nl , http://www.multiple-imputation.com
NeedsCompilation: yes
Citation: mice citation info
Materials: NEWS
In views: Multivariate, OfficialStatistics, SocialSciences
CRAN checks: mice results

Downloads:

Reference manual: mice.pdf
Package source: mice_2.21.tar.gz
OS X binary: mice_2.21.tgz
Windows binary: mice_2.21.zip
Old sources: mice archive

Reverse dependencies:

Reverse depends: BaM, CALIBERrfimpute, HardyWeinberg, logistf, miceadds, miP, SYNCSA
Reverse suggests: Hmisc, HSAUR3, Lambda4, MissingDataGUI, rattle, semTools