mice: Multivariate Imputation by Chained Equations

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.

Version: 3.16.0
Depends: R (≥ 2.10.0)
Imports: broom, dplyr, generics, glmnet, graphics, grDevices, lattice, methods, mitml, nnet, Rcpp, rpart, rlang, stats, tidyr, utils
LinkingTo: cpp11, Rcpp
Suggests: broom.mixed, future, furrr, haven, knitr, lme4, MASS, miceadds, pan, parallelly, purrr, ranger, randomForest, rmarkdown, rstan, survival, testthat
Published: 2023-06-05
DOI: 10.32614/CRAN.package.mice
Author: Stef van Buuren [aut, cre], Karin Groothuis-Oudshoorn [aut], Gerko Vink [ctb], Rianne Schouten [ctb], Alexander Robitzsch [ctb], Patrick Rockenschaub [ctb], Lisa Doove [ctb], Shahab Jolani [ctb], Margarita Moreno-Betancur [ctb], Ian White [ctb], Philipp Gaffert [ctb], Florian Meinfelder [ctb], Bernie Gray [ctb], Vincent Arel-Bundock [ctb], Mingyang Cai [ctb], Thom Volker [ctb], Edoardo Costantini [ctb], Caspar van Lissa [ctb], Hanne Oberman [ctb]
Maintainer: Stef van Buuren <stef.vanbuuren at tno.nl>
BugReports: https://github.com/amices/mice/issues
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: https://github.com/amices/mice, https://amices.org/mice/, https://stefvanbuuren.name/fimd/
NeedsCompilation: yes
Citation: mice citation info
Materials: README NEWS
In views: MissingData, MixedModels
CRAN checks: mice results


Reference manual: mice.pdf


Package source: mice_3.16.0.tar.gz
Windows binaries: r-devel: mice_3.16.0.zip, r-release: mice_3.16.0.zip, r-oldrel: mice_3.16.0.zip
macOS binaries: r-release (arm64): mice_3.16.0.tgz, r-oldrel (arm64): mice_3.16.0.tgz, r-release (x86_64): mice_3.16.0.tgz, r-oldrel (x86_64): mice_3.16.0.tgz
Old sources: mice archive

Reverse dependencies:

Reverse depends: accelmissing, CALIBERrfimpute, HardyWeinberg, ImputeRobust, micd, miceadds, micemd, RfEmpImp, TestDataImputation
Reverse imports: autoReg, BaM, basecamb, bootImpute, censcyt, ClustAll, clusterMI, dlookr, dynr, eatRep, finalfit, gFormulaMI, ggmice, hhsmm, hot.deck, howManyImputations, idem, intmed, JWileymisc, logistf, MatchThem, mi4p, miceafter, mifa, MIIPW, missCompare, missMDA, mixgb, MixtureMissing, mlim, MRPC, MSiP, NIMAA, OTrecod, psfmi, RBtest, realTimeloads, rexposome, RNAseqCovarImpute, rqlm, semmcci, seqimpute, smdi, sociome, StackImpute, superMICE, SynDI, synergyfinder, vsmi, weights
Reverse suggests: adjustedCurves, alookr, betaMC, BGGM, bipd, brms, brokenstick, broom.helpers, cati, cobalt, dynamite, FLAME, flevr, gerbil, ggeffects, gtsummary, Hmisc, holodeck, HSAUR3, insight, IPWboxplot, konfound, LMMstar, LSAmitR, manymome, marginaleffects, medflex, metavcov, miceFast, microeco, midastouch, midfieldr, misaem, miselect, missDiag, mitml, miWQS, MKinfer, modelsummary, monoClust, mvnimpute, nncc, ordbetareg, parameters, pema, pre, qgcomp, Qtools, rattle, regmedint, rms, rmsb, semTools, shapeNA, sjmisc, svyweight, tidySEM
Reverse enhances: emmeans, joinet, mdmb


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