CDM (>= 5.8-5), graphics, lme4, MASS, methods, mitools,
mvtnorm, Rcpp, sirt, stats, TAM (>= 2.5-5), utils

Amelia, BIFIEsurvey, blme, car, foreign, grouped, haven,
inline, jomo, MBESS, MCMCglmm, mitml, multiwayvcov, numDeriv,
pan, readxl, sjlabelled

Alexander Robitzsch [aut,cre] (<https://orcid.org/0000-0002-8226-3132>),
Simon Grund [aut] (<https://orcid.org/0000-0002-1290-8986>),
Thorsten Henke [ctb]
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),
nested multiple imputation (Rubin, 2003,
<doi:10.1111/1467-9574.00217>), substantive model
compatible imputation (Bartlett et al., 2015,
<doi:10.1177/0962280214521348>), and features
for the generation of synthetic datasets
(Reiter, 2005, <doi:10.1111/j.1467-985X.2004.00343.x>;
Nowok, Raab, & Dibben, 2016, <doi:10.18637/jss.v074.i11>).
graphics, methods, mitools, Rcpp, stats, utils
BIFIEsurvey, blme, car, CDM, coda, foreign, grouped, imputeR,
inline, lme4, MASS, MBESS, MCMCglmm, mdmb, numDeriv, readxl,
sandwich, simputation, sirt, sjlabelled, synthpop, TAM

BIFIEsurvey, blme, car, CDM, coda, foreign, inline, lme4,
MASS, MBESS, MCMCglmm, mdmb, numDeriv, readxl, sandwich, sirt,
sjlabelled, synthpop, TAM
Amelia, imputeR, jomo, micemd, mitml, pan, simputation

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 implemented.
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),
nested multiple imputation (Rubin, 2003,
<doi:10.1111/1467-9574.00217>), substantive model
compatible imputation (Bartlett et al., 2015,
<doi:10.1177/0962280214521348>), and features
for the generation of synthetic datasets
(Reiter, 2005, <doi:10.1111/j.1467-985X.2004.00343.x>;
Nowok, Raab, & Dibben, 2016, <doi:10.18637/jss.v074.i11>).
BIFIEsurvey, blme, car, CDM, coda, foreign, inline, lme4,
MASS, MBESS, MCMCglmm, mdmb, pls, numDeriv, readxl, sandwich,
sirt, sjlabelled, synthpop, TAM