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: |
3.17.0 |
| Depends: |
R (≥ 2.10.0) |
| Imports: |
broom, dplyr, glmnet, graphics, grDevices, lattice, mitml, nnet, Rcpp, rpart, stats, tidyr, utils |
| LinkingTo: |
cpp11, Rcpp |
| Suggests: |
broom.mixed, future, furrr, haven, knitr, literanger, lme4, MASS, miceadds, pan, parallelly, purrr, ranger, randomForest, rmarkdown, rstan, survival, testthat |
| Published: |
2024-11-27 |
| 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],
Stephen Wade [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 |
| Reverse depends: |
accelmissing, CALIBERrfimpute, HardyWeinberg, ImputeRobust, micd, miceadds, micemd, RfEmpImp, TestDataImputation |
| Reverse imports: |
autoReg, BaM, basecamb, bootImpute, censcyt, CIMPLE, ClustAll, clusterMI, cmahalanobis, dlookr, dynr, eatRep, finalfit, gFormulaMI, ggmice, hhsmm, hot.deck, howManyImputations, idem, intmed, JWileymisc, logistf, MatchThem, mi4p, miceafter, midoc, mifa, MIIPW, missCompare, missMDA, mixgb, MixtureMissing, mlim, MRPC, MSiP, NIMAA, OTrecod, psfmi, RBtest, realTimeloads, RefBasedMI, rexposome, RNAseqCovarImpute, rqlm, RSquaredMI, 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, joinet, konfound, LMMstar, LSAmitR, manymome, marginaleffects, medflex, metavcov, miceFast, microeco, midastouch, midfieldr, misaem, miselect, missDiag, misty, mitml, miWQS, MKinfer, modelsummary, monoClust, mvnimpute, mvs, nncc, ordbetareg, parameters, pema, pre, qgcomp, Qtools, rattle, regmedint, rms, rmsb, semTools, shapeNA, sjmisc, svyweight, tidySEM |
| Reverse enhances: |
emmeans, mdmb |