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) . 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.


The mice package implements a method to deal with missing data. The package creates multiple imputations (replacement values) for multivariate missing data. The method is based on Fully Conditional Specification, where each incomplete variable is imputed by a separate model. The MICE algorithm can impute mixes of continuous, binary, unordered categorical and ordered categorical data. In addition, MICE can impute continuous two-level data, and maintain consistency between imputations by means of passive imputation. Many diagnostic plots are implemented to inspect the quality of the imputations.

Installation

The mice package can be installed from CRAN as follows:

install.packages("mice")

The latest version is can be installed from GitHub as follows:

install.packages("devtools")
devtools::install_github(repo = "stefvanbuuren/mice")

See MICE: Multivariate Imputation by Chained Equations for more details.

News


title: "News" output: github_document

mice 3.3.6

  • Add a hex sticker to the mice package. Designed by Jaden M. Walters.
  • Specify the R3.5.0 random generator in order to pass CRAN tests

mice 3.3.5

  • Remove test-fix.coef.R from tests
  • Adds a rotate.names argument to md.pattern() (#154, #160)

mice 3.3.4

  • Fix to solve the name-matching problem (#156, #149, #147)

mice 3.3.3

  • Fix that removes the pre-check for existence of mice.impute.xxx() so that mice::mice() works as expected (#55)

mice 3.3.2

  • Solves a bug that crashed mids2spss(), thanks Edgar Schoreit (#149)

mice 3.3.1

  • Solves a problem in the routing logic (#149) causing that passive imputation was not done when no predictors were specified. No passive imputation correctly will ignore any the specification of predictorMatrix.
  • Implements an alternative solution for #93 and #96. Instead of skipping imputation of variables without predictors, mice 3.3.1 will impute those variables using the intercept only
  • Adds a routine contributed by Simon Grund that checks for deprecated arguments #137
  • Improves the nelsonaalen() function for data where variables time or status have already been defined (#140), thanks matthieu-faron

mice 3.3.0

  • Solves bug in passive imputation (#130). Warning: This bug may have caused invalid imputations in mice 3.0.0 - mice 3.2.0 under passive imputation.
  • Updates code to broom 0.5.0 (#128)
  • Solves problem with mice.impute.2l.norm() (#129)
  • Use explicit foreign function calls in tests

mice 3.2.0

  • Skip tests for mice.impute.2l.norm() (#129)
  • Skip tests for D1() (#128)
  • Solve problem with md.pattern (#126)
  • Evades warning in rbind and cbind (#114)
  • Solves rbind problem when method is a list (#113)
  • More efficient use of parlmice (#109)
  • Add dfcom argument to pool() (#105, #110)
  • Updates to parlmice + bugfix (#107)

mice 3.1.0

  • New parallel functionality: parlmice (#104)
  • Incorporate suggestion of @JoergMBeyer to flux (#102)
  • Replace duplicate code by estimice (#101)
  • Better checking for empty methods (#99)
  • Remove problem with parent.frame (#98)
  • Set empty method for complete data (#93)
  • Add NEWS.md, index.Rmd and online package documentation
  • Track .R instead of .r
  • Patch issue with updateLog (#8, @alexanderrobitzsch)
  • Extend README
  • Repair issue md.pattern (#90)
  • Repair check on m (#89)

mice 3.0.0

Version 3.0 represents a major update that implements the following features:

  1. blocks: The main algorithm iterates over blocks. A block is simply a collection of variables. In the common MICE algorithm each block was equivalent to one variable, which - of course - is the default; The blocks argument allows mixing univariate imputation method multivariate imputation methods. The blocks feature bridges two seemingly disparate approaches, joint modeling and fully conditional specification, into one framework;

  2. where: The where argument is a logical matrix of the same size of data that specifies which cells should be imputed. This opens up some new analytic possibilities;

  3. Multivariate tests: There are new functions D1(), D2(), D3() and anova() that perform multivariate parameter tests on the repeated analysis from on multiply-imputed data;

  4. formulas: The old form argument has been redesign and is now renamed to formulas. This provides an alternative way to specify imputation models that exploits the full power of R's native formula's.

  5. Better integration with the tidyverse framework, especially for packages dplyr, tibble and broom;

  6. Improved numerical algorithms for low-level imputation function. Better handling of duplicate variables.

  7. Last but not least: A brand new edition AND online version of Flexible Imputation of Missing Data. Second Edition.

mice 2.46.9 (2017-12-08)

  • simplify code for mids object in mice (thanks stephematician) (#61)
  • simplify code in rbind.mids (thanks stephematician) (#59)
  • repair bug in pool.compare() in handling factors (#60)
  • fixed bug in rbind.mids in handling where (#59)
  • add new arguments to as.mids(), add as()
  • update contact info
  • resolved problem cart not accepting a matrix (thanks Joerg Drechsler)
  • Adds generalized pool() to list of models
  • Switch to 3-digit versioning

mice 2.46 (2017-10-22)

  • Allow for capitals in imputation methods

mice 2.45 (2017-10-21)

  • Reorganized vignettes to land on GitHUB pages

mice 2.44 (2017-10-18)

  • Code changes for robustness, style and efficiency (Bernie Gray)

mice 2.43 (2017-07-20)

  • Updates the ampute function and vignettes (Rianne Schouten)

mice 2.42 (2017-07-11)

  • Rename mice.impute.2l.sys to mice.impute.2l.lmer

mice 2.41 (2017-07-10)

  • Add new feature: whereargument to mice
  • Add new wy argument to imputation functions
  • Add mice.impute.2l.sys(), author Shahab Jolani
  • Update with many simplifications and code enhancements
  • Fixed broken cbind() function
  • Fixed Bug that made the pad element disappear from mids object

mice 2.40 (2017-07-07)

  • Fixed integration with lattice package
  • Updates colors in xyplot.mads
  • Add support for factors in mice.impute.2lonly.pmm()
  • Create more robust version of as.mids()
  • Update of ampute() by Rianne Schouten
  • Fix timestamp problem by rebuilding vignette using R 3.4.0.

mice 2.34 (2017-04-24)

  • Update to roxygen 6.0.1
  • Stylistic changes to mice function (thanks Ben Ogorek)
  • Calls to cbind.mids() replaced by calls to cbind()

mice 2.31 (2017-02-23)

  • Add link to miceVignettes on github (thanks Gerko Vink)
  • Add package documentation
  • Add README for GitHub
  • Add new ampute functions and vignette (thanks Rianne Schouten)
  • Rename ccn --> ncc, icn --> nic
  • Change helpers cc(), ncc(), cci(), ic(), nic() and ici() use S3 dispatch
  • Change issues tracker on Github - add BugReports URL #21
  • Fixed multinom MaxNWts type fix in polyreg and polr #9
  • Fix checking of nested models in pool.compare #12
  • Fix as.mids if names not same as all columns #11
  • Fix extension for glmer models #5

mice 2.29 (2016-10-05)

  • Add midastouch: predictive mean matching for small samples (thanks Philip Gaffert, Florian Meinfelder)

mice 2.28 (2016-10-05)

  • Repaired dots problem in rpart call

mice 2.27 (2016-07-27)

  • Add ridge to 2l.norm()
  • Remove .o files

mice 2.25 (2015-11-09)

  • Fix as.mids() bug that crashed miceadds::mice.1chain()

mice 2.23 (2015-11-04)

  • Update of example code on /doc

  • Remove lots of dependencies, general cleanup

  • Fix impute.polyreg() bug that bombed if there were no predictors (thanks Jan Graffelman)

  • Fix as.mids() bug that gave incorrect $m$ (several users)

  • Fix pool.compare() error for lmer object (thanks Claudio Bustos)

  • Fix error in mice.impute.2l.norm() if just one NA (thanks Jeroen Hoogland)

mice 2.22 (2014-06-11)

  • Add about six times faster predictive mean matching
  • pool.scalar() now can do Barnard-Rubin adjustment
  • pool() now handles class lmerMod from the lme4 package
  • Added automatic bounds on donors in .pmm.match() for safety
  • Added donors argument to mice.impute.pmm() for increased visibility
  • Changes default number of trees in mice.impute.rf() from 100 to 10 (thanks Anoop Shah)
  • long2mids() deprecated. Use as.mids() instead
  • Put lattice back into DEPENDS to find generic xyplot() and friends
  • Fix error in 2lonly.pmm (thanks Alexander Robitzsch, Gerko Vink, Judith Godin)
  • Fix number of imputations in as.mids() (thanks Tommy Nyberg, Gerko Vink)
  • Fix colors to mdc() in example mice.impute.quadratic()
  • Fix error in mice.impute.rf() if just one NA (thanks Anoop Shah)
  • Fix error in summary.mipo() when names(x$qbar) equals NULL (thanks Aiko Kuhn)
  • Fix improper testing in ncol() in mice.impute.2lonly.mean()

mice 2.21 02-05-2014 SvB

  • FIXED: compilation problem in match.cpp on solaris CC

mice 2.20 02-02-2014 SvB

  • ADDED: experimental fastpmm() function using Rcpp
  • FIXED: fixes to mice.impute.cart() and mice.impute.rf() (thanks Anoop Shah)

mice 2.19 21-01-2014 SvB

  • ADDED: mice.impute.rf() for random forest imputation (thanks Lisa Doove)
  • CHANGED: default number of donors in mice.impute.pmm() changed from 3 to 5. Use mice(..., donors = 3) to get the old behavior.
  • CHANGED: speedup in .norm.draw() by using crossprod() (thanks Alexander Robitzsch)
  • CHANGED: speedup in .imputation.level2() (thanks Alexander Robitzsch)
  • FIXED: define MASS, nnet, lattice as imports instead of depends
  • FIXED: proper handling of rare case in remove.lindep() that removed all predictors (thanks Jaap Brand)

mice 2.18 31-07-2013 SvB

  • ADDED: as.mids() for converting long format in a mids object (thanks Gerko Vink)
  • FIXED: mice.impute.logreg.boot() now properly exported (thanks Suresh Pujar)
  • FIXED: two bugs in rbind.mids() (thanks Gerko Vink)

mice 2.17 10-05-2013 SvB

  • ADDED: new form argument to mice() to specify imputation models using forms (contributed Ross Boylan)
  • FIXED: with.mids(), is.mids(), is.mira() and is.mipo() exported
  • FIXED: eliminated errors in the documentation of pool.scalar()
  • FIXED: error in mice.impute.ri() (thanks Shahab Jolani)

mice 2.16 27-04-2013 SvB

  • ADDED: random indicator imputation by mice.impute.ri() for nonignorable models (thanks Shahab Jolani)
  • ADDED: workhorse functions .norm.draw() and .pmm.match() are exported
  • FIXED: bug in 2.14 and 2.15 in mice.impute.pmm() that produced an error on factors
  • FIXED: bug that crashed R when the class variable was incomplete (thanks Robert Long)
  • FIXED: bug in 2l.pan and 2l.norm by convert a class factor to integer (thanks Robert Long)
  • FIXED: warning eliminated caused by character variables (thanks Robert Long)

mice 2.15 - 02-04-2013 SvB

  • CHANGED: complete reorganization of documentation and source files
  • ADDED: source published on GitHub.com
  • ADDED: new imputation method mice.impute.cart() (thanks Lisa Doove)
  • FIXED: calculation of degrees of freedom in pool.compare() (thanks Lorenz Uhlmann)
  • FIXED: error in DESCRIPTION file (thanks Kurt Hornik)

mice 2.14 - 11-03-2013 / SvB

  • ADDED: mice.impute.2l.mean() for imputing class means at level 2
  • ADDED: sampler(): new checks of degrees of freedom per variable at iteration 1
  • ADDED: function check.df() to throw a warning about low degrees of freedom
  • FIXED: tolower() added in "2l" test in sampler()
  • FIXED: conversion of factors that have other roles (multilevel) in padModel()
  • FIXED: family argument in call to glm() in glm.mids() (thanks Nicholas Horton)
  • FIXED: .norm.draw(): evading NaN imputed values by setting df in rchisq() to a minimum of 1
  • FIXED: bug in mice.df() that prevented the classic Rubin df calculation (thanks Jean-Batiste Pingaul)
  • FIXED: bug fixed in mice.impute.2l.norm() (thanks Robert Long)
  • CHANGED: faster .pmm.match2() from version 2.12 renamed to default .pmm.match()

mice 2.13 - 03-07-2012 / SvB

  • ADDED: new multilevel functions 2l.pan(), 2lonly.norm(), 2lonly.pmm() (contributed by Alexander Robitzsch)
  • ADDED: new quadratic imputation function: quadratic() (contributed by Gerko Vink)
  • ADDED: pmm2(), five times faster than pmm()
  • ADDED: new argument data.init in mice() for initialization (suggested by Alexander Robitzsch)
  • ADDED: mice() now accepts pmm as method for (ordered) factors
  • ADDED: warning and a note to 2l.norm() that advises to use type=2 for the predictors
  • FIXED: bug that chrashed plot.mids() if there was only one incomplete variable (thanks Dennis Prangle)
  • FIXED: bug in sample() in .pmm.match() when donor=1 (thanks Alexander Robitzsch)
  • FIXED: bug in sample() in mice.impute.sample()
  • FIXED: fixed '?data' bug in check.method()
  • REMOVED: wp.twin(). Now available from the AGD package

mice 2.12 - 25-03-2012 / SvB

  • UPDATE: version for launch of Flexible Imputation of Missing Data (FIMD)
  • ADDED: code fimd1.r-fim9.r to inst/doc for calculating solutions in FIMD
  • FIXED: more robust version of supports.transparent() (thanks Brian Ripley)
  • ADDED: auxiliary functions ifdo(), long2mids(), appendbreak(), extractBS(), wp.twin()
  • ADDED: getfit() function
  • ADDED: datasets: tbc, potthoffroy, selfreport, walking, fdd, fdgs, pattern1-pattern4, mammalsleep
  • FIXED: as.mira() added to namespace
  • ADDED: functions flux(), fluxplot() and fico() for missing data patterns
  • ADDED: function nelsonaalen() for imputing survival data
  • CHANGED: rm.whitespace() shortened
  • FIXED: bug in pool() that crashed on nonstandard behavior of survreg() (thanks Erich Studerus)
  • CHANGED: pool() streamlined, warnings about incompatibility in lengths of coef() and vcov()
  • FIXED: mdc() bug that ignored transparent=FALSE argument, now made visible
  • FIXED: bug in md.pattern() for >32 variables (thanks Sascha Vieweg, Joshua Wiley)

mice 2.11 - 21-11-2011 / SvB

  • UPDATE: definite reference to JSS paper
  • ADDED: rm.whitespace() to do string manipulation (thanks Gerko Vink)
  • ADDED: function mids2mplus() to export data to Mplus (thanks Gerko Vink)
  • CHANGED: plot.mids() changed into trellis version
  • ADDED: code used in JSS-paper
  • FIXED: bug in check.method() (thanks Gerko Vink)

mice 2.10 - 14-09-2011 / SvB

  • FIXED: arguments dec and sep in mids2spss (thanks Nicole Haag)
  • FIXED: bug in keyword "monotone" in mice() (thanks Alain D)

mice 2.9 - 31-08-2011 / SvB

  • FIXED: appropriate trimming of ynames and xnames in Trellis plots
  • FIXED: exported: spss2mids(), mice.impute.2L.norm()
  • ADDED: mice.impute.norm.predict(), mice.impute.norm.boot(), mice.impute.logreg.boot()
  • ADDED: supports.transparent() to detect whether .Device can do semi-transparent colors
  • FIXED: stringr package is now properly loaded
  • ADDED: trellis version of plot.mids()
  • ADDED: automatic semi-transparancy detection in mdc()
  • FIXED: documentation of mira class (thanks Sandro Tsang)

mice 2.8 - 24-03-2011 / SvB

  • FIXED: bug fixed in find.collinear() that bombed when only one variable was left

mice 2.7 - 16-03-2011 / SvB

  • CHANGED: check.data(), remove.lindep(): fully missing variables are imputed if allow.na=TRUE (Alexander Robitzsch)
  • FIXED: bug in check.data(). Now checks collinearity in predictors only (Alexander Robitzsch)
  • CHANGED: abbreviations of arguments eliminated to evade linux warnings

mice 2.6 - 03-03-2011 / SvB

  • ADDED: bwplot(), stripplot(), densityplot() and xyplot() for creating Trellis graphs
  • ADDED: function mdc() and mice.theme() for graphical parameters
  • ADDED: argument passing from mice() to lower-level functions (requested by Juned Siddique)
  • FIXED: erroneous rgamma() replaced by rchisq() in .norm.draw, lowers variance a bit for small n
  • ADDED: with.mids() extended to handle expression objects
  • FIXED: reporting bug in summary.mipo()
  • CHANGED: df calculation in pool(), intervals may become slightly wider
  • ADDED: internal functions mice.df() and df.residual()
  • FIXED: error in rm calculation for "likelihood" in pool.compare()
  • CHANGED: default ridge parameter changed

mice 2.5 - 06-01-2011 / SvB

  • ADDED: various stability enhancements and code clean-up
  • ADDED: find.collinear() function
  • CHANGED: automatic removal of constant and collinear variables
  • ADDED: ridge parameter in .norm.draw() and .norm.fix()
  • ADDED: mice.impute.polr() for ordered factors
  • FIXED: chainMean and chainVar in mice.mids()
  • FIXED: iteration counter for mice.mids and sampler()
  • ADDED: component 'loggedEvents' to mids-object for logging actions
  • REMOVED: annoying warnings about removed predictors
  • ADDED: updateLog() function
  • CHANGED: smarter handling of model setup in mice()
  • CHANGED: .pmm.match() now draws from the three closest donors
  • ADDED: mids2spss() for shipping a mids-object to SPSS
  • FIXED: change in summary.mipo() to work with as.mira()
  • ADDED: function mice.impute.2L.norm.noint()
  • ADDED: function as.mira()
  • FIXED: global assign() removed from mice.impute.polyreg()
  • FIXED: improved handling of factors by complete()
  • FIXED: improved labeling of nhanes2 data

mice 2.4 - 17-10-2010 / SvB

  • ADDED: pool() now supports class 'polr' (Jean-Baptiste Pingault)
  • FIXED: solved problem in mice.impute.polyreg when one of the variables was named y or x
  • FIXED: remove.lindep: intercept prediction bug
  • ADDED: version() function
  • ADDED: cc(), cci() and ccn() convenience functions

mice 2.3 - 14-02-2010 / SvB

  • FIXED: check.method: logicals are now treated as binary variables (Emmanuel Charpentier)
  • FIXED: complete: the NULL imputation case is now properly handled
  • FIXED: mice.impute.pmm: now creates between imputation variability for univariate predictor
  • FIXED: remove.lindep: returns 'keep' vector instead of data

mice 2.2 - 13-01-2010 / SvB

  • ADDED: pool() now supports class 'multinom' (Jean-Baptiste Pingault)
  • FIXED: bug fixed in check.data for data consisting of two columns (Rogier Donders, Thomas Koepsell)
  • ADDED: new function remove.lindep() that removes predictors that are (almost) linearly dependent
  • FIXED: bug fixed in pool() that produced an (innocent) warning message (Qi Zheng)

mice 2.1 - 14-09-2009 / SvB

  • ADDED: pool() now also supports class 'mer'
  • CHANGED: nlme and lme4 are now only loaded if needed (by pool())
  • FIXED: bug fixed in mice.impute.polyreg() when there was one missing entry (Emmanuel Charpentier)
  • FIXED: bug fixed in plot.mids() when there was one missing entry (Emmanuel Charpentier)
  • CHANGED: NAMESPACE expanded to allow easy access to function code
  • FIXED: mice() can now find mice.impute.xxx() functions in the .GlobalEnv

mice 2.0 - 26-08-2009 / SvB, KO Major upgrade for JSS manuscript

  • ADDED: new functions cbind.mids(), rbind.mids(), ibind()
  • ADDED: new argument in mice(): 'post' in post-processing imputations
  • ADDED: new functions: pool.scaler(), pool.compare(), pool.r.squared()
  • ADDED: new data: boys, popmis, windspeed
  • FIXED: function summary.mipo all(object$df) command fixed
  • REMOVED: data.frame.to.matrix replaced by the internal data.matrix function
  • ADDED: new imputation method mice.impute.2l.norm() for multilevel data
  • CHANGED: pool now works for any class having a vcov() method
  • ADDED: with.mids() provides a general complete-data analysis
  • ADDED: type checking in mice() to ensure appropriate imputation methods
  • ADDED: warning added in mice() for constant predictors
  • ADDED: prevention of perfect prediction in mice.impute.logreg() and mice.impute.polyreg()
  • CHANGED: mice.impute.norm.improper() changed into mice.impute.norm.nob()
  • REMOVED: mice.impute.polyreg2() deleted
  • ADDED: new 'include' argument in complete()
  • ADDED: support for the empty imputation method in mice()
  • ADDED: new function md.pairs()
  • ADDED: support for intercept imputation
  • ADDED: new function quickpred()
  • FIXED: plot.mids() bug fix when number of variables > 5

mice 1.21 - 15/3/2009 SvB Maintainance release

  • FIXED: Stricter type checking on logicals in mice() to evade warnings.
  • CHANGED: Modernization of all help files.
  • FIXED: padModel: treatment changed to contr.treatment
  • CHANGED: Functions check.visitSequence, check.predictorMatrix, check.imputationMethod are now coded as local to mice()
  • FIXED: existsFunction in check.imputationMethod now works both under S-Plus and R

mice 1.16 - 6/25/2007

  • FIXED: The impution function impute.logreg used convergence criteria that were too optimistic when fitting a GLM with glm.fit. Thanks to Ulrike Gromping.

mice 1.15 - 01/09/2006

  • FIXED: In the lm.mids and glm.mids functions, parameters were not passed through to glm and lm.

mice 1.14R - 9/26/2005 11:44AM

  • FIXED: Passive imputation works again. (Roel de Jong)
  • CHANGED: Random seed is now left alone, UNLESS the argument "seed" is specified. This means that unless you specify identical seed values, imputations of the same dataset will be different for multiple calls to mice. (Roel de Jong)
  • FIXED: (docs): Documentation for "impute.mean" (Roel de Jong)
  • FIXED: Function 'summary.mids' now works (Roel de Jong)
  • FIXED: Imputation function 'impute.polyreg' and 'impute.lda' should now work under R

mice 1.13

  • Changed function checkImputationMethod, Feb 6, 2004

mice 1.12

  • Maintainance, S-Plus 6.1 and R 1.8 unicode, January 2004

mice 1.1

  • R version (with help of Peter Malewski and Frank Harrell), Feb 2001

mice 1.0

  • Original S-PLUS release, June 14 2000

Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.

install.packages("mice")

3.6.0 by Stef van Buuren, 11 days ago


http://stefvanbuuren.github.io/mice/ , http://www.stefvanbuuren.name , http://www.stefvanbuuren.name/fimd/


Report a bug at https://github.com/stefvanbuuren/mice/issues


Browse source code at https://github.com/cran/mice


Authors: Stef van Buuren [aut, cre] , Karin Groothuis-Oudshoorn [aut] , Alexander Robitzsch [ctb] , Gerko Vink [ctb] , Lisa Doove [ctb] , Shahab Jolani [ctb] , Rianne Schouten [ctb] , Philipp Gaffert [ctb] , Florian Meinfelder [ctb] , Bernie Gray [ctb]


Documentation:   PDF Manual  


Task views: Multivariate Statistics, Official Statistics & Survey Methodology, Statistics for the Social Sciences, Missing Data


GPL-2 | GPL-3 license


Imports broom, dplyr, grDevices, graphics, MASS, mitml, nnet, parallel, Rcpp, rlang, rpart, splines, stats, survival, utils

Depends on methods, lattice

Suggests AGD, CALIBERrfimpute, gamlss, lme4, mitools, nlme, pan, randomForest, Zelig, BSDA, knitr, rmarkdown, testthat, HSAUR3, micemd, miceadds, tidyr

Linking to Rcpp


Imported by BaM, JWileymisc, MRPC, PathSelectMP, Qtools, RBtest, RegularizedSCA, bootImpute, dlookr, dynr, finalfit, hmi, logistf, mipred, missCompare, missMDA.

Depended on by CALIBERrfimpute, HardyWeinberg, ImputeRobust, MatchIt.mice, Replication, TestDataImputation, accelmissing, genpathmox, hot.deck, miceMNAR, miceadds, micemd, roughrf, smartdata, weightTAPSPACK, weights.

Suggested by BaBooN, HSAUR3, Hmisc, IPWboxplot, LSAmitR, Lambda4, MissingDataGUI, NNLM, brms, bucky, cobalt, holodeck, konfound, lavaan.survey, medflex, miWQS, miceFast, midastouch, missRanger, mitml, modelsummary, rattle, semTools, sjmisc.

Enhanced by emmeans, mdmb.


See at CRAN