Multiple imputation using Fully Conditional Specification (FCS) implemented by the MICE algorithm as described in Van Buuren and GroothuisOudshoorn (2011) <doi:10.18637/jss.v045.i03>. Each variable has its own imputation model. Builtin 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 twolevel data (normal model, pan, secondlevel variables). Passive imputation can be used to maintain consistency between variables. Various diagnostic plots are available to inspect the quality of the imputations.
Package details 


Author  Stef van Buuren [aut, cre], Karin GroothuisOudshoorn [aut], Gerko Vink [ctb], Rianne Schouten [ctb], Alexander Robitzsch [ctb], Patrick Rockenschaub [ctb], Lisa Doove [ctb], Shahab Jolani [ctb], Margarita MorenoBetancur [ctb], Ian White [ctb], Philipp Gaffert [ctb], Florian Meinfelder [ctb], Bernie Gray [ctb], Vincent ArelBundock [ctb] 
Maintainer  Stef van Buuren <stef.vanbuuren@tno.nl> 
License  GPL2  GPL3 
Version  3.12.0 
URL  https://github.com/amices/mice https://amices.org/mice/ https://stefvanbuuren.name/fimd/ 
Package repository  View on CRAN 
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