CDM (>= 5.8-5), coda, graphics, lme4, MASS, methods, mitools,
mvtnorm, Rcpp, sirt, stats, TAM (>= 2.9-13), utils
Amelia, BIFIEsurvey, blme, car, foreign, grouped, haven,
inline, jomo, MBESS, MCMCglmm, mitml, multiwayvcov, numDeriv,
pan, sjlabelled

Contains functions for multiple imputation which
complements existing functionality in R.
In particular, several imputation methods for the
mice package (van Buuren & Groothuis-Oudshoorn, 2011,
<doi:10.18637/jss.v045.i03>) are included.
Main features of the miceadds package include
plausible value imputation (Mislevy, 1991,
<doi:10.1007/BF02294457>), multilevel imputation for
variables at any level or with any number of hierarchical
and non-hierarchical levels (Grund, Luedtke & Robitzsch,
2018, <doi:10.1177/1094428117703686>; van Buuren, 2018,
Ch.7, <doi:10.1201/9780429492259>), imputation using
partial least squares (PLS) for high dimensional
predictors (Robitzsch, Pham & Yanagida, 2016),
and nested multiple imputation (Rubin, 2003,
<doi:10.1111/1467-9574.00217>).
CDM (>= 5.8-5), coda, graphics, lme4, MASS, methods, mitools,
mvtnorm, Rcpp, sirt, stats, TAM, utils

Contains some auxiliary functions for multiple
imputation which complements existing functionality
in R.
In addition to some utility functions, main features
include plausible value imputation, multilevel
imputation functions, imputation using partial least
squares (PLS) for high dimensional predictors, nested
multiple imputation.
blme, car, CDM, foreign, graphics, grouped, lme4, MASS,
MCMCglmm, methods, mitools, multiwayvcov, mvtnorm, Rcpp, sirt,
sjmisc, stats, TAM, utils

blme, car, CDM, foreign, graphics, grouped, lme4, MASS,
MCMCglmm, methods, mitools, multiwayvcov, mvtnorm, Rcpp, sirt,
sjlabelled, stats, TAM, utils

CDM (>= 5.8-5), graphics, lme4, methods, mitools, mvtnorm,
Rcpp, sirt, stats, TAM (>= 2.5-5), utils
Amelia, BIFIEsurvey, blme, car, foreign, grouped, haven, hmi,
Hmisc, inline, jomo, MBESS, MCMCglmm, micemd, mitml,
multiwayvcov, pan, pls, sjlabelled, Zelig