predictors, nested multiple imputation, and two-way imputation.
Suggests: Amelia, Zelig, jomo, haven, BIFIEsurvey
Packaged: 2015-05-20 17:54:56 UTC; robitzsch

Imports: methods, stats, graphics, utils, Rcpp, TAM, lme4, inline, car,
foreign, sjmisc, multiwayvcov, grouped, MBESS, bayesm, sirt,
Suggests: Amelia, Zelig, jomo, haven, BIFIEsurvey, mitml, pan
Packaged: 2015-07-10 09:36:02 UTC; robitzsch

Packaged: 2015-11-04 15:45:44 UTC; robitzsch

Alexander Robitzsch [aut, cre], Simon Grund [ctb],
Contains some auxiliary functions for multiple
imputation which complements existing functionality
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, and two-way imputation.
Imports: bayesm, car, foreign, graphics, grouped, Hmisc, inline, lme4,
MASS, MBESS, MCMCglmm, methods, mitools, multiwayvcov, mvtnorm,
pls, Rcpp, sirt, sjmisc, stats, TAM, utils
Suggests: Amelia, BIFIEsurvey, haven, jomo, mitml, pan, Zelig

Alexander Robitzsch [aut, cre], Simon Grund [aut],
Imports: car, foreign, graphics, grouped, lme4, MASS, MCMCglmm,
methods, mitools, multiwayvcov, mvtnorm, Rcpp, sirt, sjmisc,
Suggests: Amelia, BIFIEsurvey, haven, Hmisc, inline, jomo, MBESS,

Imports: blme, car, foreign, graphics, grouped, lme4, MASS, MCMCglmm,

imputation functions (arbitrary number of levels,
imputation using partial least squares (PLS) for
high dimensional predictors, and nested multiple
Imports: CDM (>= 5.8-5), graphics, lme4, MASS, methods, mitools,
mvtnorm, Rcpp, sirt, stats, TAM (>= 2.9-13), utils
Suggests: Amelia, BIFIEsurvey, blme, car, foreign, grouped, haven,
inline, jomo, MBESS, MCMCglmm, mitml, multiwayvcov, pan,

Imports: CDM (>= 5.8-5), coda, graphics, lme4, MASS, methods, mitools,
inline, jomo, MBESS, MCMCglmm, mitml, multiwayvcov, numDeriv,

Contains functions for multiple imputation which
In particular, several imputation methods for the
mice package (van Buuren & Groothuis-Oudshoorn, 2011,
Main features of the miceadds package include
plausible value imputation (Mislevy, 1991,
variables at any level or with any number of hierarchical
and non-hierarchical levels (Grund, Luedtke & Robitzsch,
partial least squares (PLS) for high dimensional
predictors (Robitzsch, Pham & Yanagida, 2016),
and nested multiple imputation (Rubin, 2003,