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:
3.18.0
Depends:
R (≥ 2.10.0)
Imports:
broom,
dplyr,
glmnet, graphics, grDevices,
lattice,
mitml,
nnet,
Rcpp,
rpart, stats,
tidyr, utils
Suggests:
broom.mixed,
future,
furrr,
haven,
knitr,
literanger,
lme4,
MASS,
miceadds,
pan,
parallelly,
purrr,
ranger,
randomForest,
rmarkdown,
rstan,
survival,
testthat
Published:
2025年05月27日
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>
NeedsCompilation:
yes
CRAN checks:
mice results [issues need fixing before 2025年12月22日]
Documentation:
Downloads:
Reverse dependencies:
Reverse imports:
autoReg,
BaM,
basecamb,
BMIselect,
bootImpute,
censcyt,
CIMPLE,
ClustAll,
clusterMI,
DataFusionGDM,
dlookr,
dsBase,
dynr,
eatRep,
fastml,
finalfit,
FuzzyImputationTest,
geospatialsuite,
gFormulaMI,
ggmice,
GPAbin,
hbsaems,
hhsmm,
hot.deck,
howManyImputations,
intmed,
JWileymisc,
logistf,
MatchThem,
mi4p,
miceafter,
midoc,
mifa,
MIGEE,
MIIPW,
missCompare,
missMDA,
mixgb,
MixtureMissing,
mlim,
MRPC,
MSiP,
MVN,
NIMAA,
opImputation,
OTrecod,
psfmi,
PVBcorrect,
RBtest,
realTimeloads,
RefBasedMI,
rexposome,
RNAseqCovarImpute,
rqlm,
RSquaredMI,
RulesTools,
scHiCcompare,
semmcci,
seqimpute,
smdi,
sociome,
StackImpute,
SynDI,
synergyfinder,
vsmi,
waou,
weights
Reverse suggests:
adjustedCurves,
alookr,
betaMC,
BGGM,
bipd,
booami,
brms,
brokenstick,
broom.helpers,
cati,
cobalt,
dynamite,
FLAME,
flassomsm,
flevr,
gerbil,
ggeffects,
gtsummary,
Hmisc,
holodeck,
HSAUR3,
insight,
IPWboxplot,
joinet,
konfound,
lavaan.mi,
LMMstar,
LSAmitR,
mantar,
manymome,
marginaleffects,
medflex,
metafor,
metavcov,
miceFast,
microeco,
midastouch,
midfieldr,
misaem,
miselect,
missDiag,
misty,
mitml,
MKinfer,
modelbased,
modelsummary,
ModStatR,
monoClust,
multilevelPSA,
mvnimpute,
mvs,
nncc,
ordbetareg,
parameters,
pema,
pminternal,
pre,
qgcomp,
Qtools,
rattle,
regmedint,
rms,
rmsb,
semTools,
shapeNA,
sjmisc,
SSVS,
svyweight,
tidySEM
Linking:
Please use the canonical form
https://CRAN.R-project.org/package=mice
to link to this page.