miceadds

Some Additional Multiple Imputation Functions, Especially for 'mice'

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 (arbitrary number of levels, hierarchical and non-hierarchical datasets), imputation using partial least squares (PLS) for high dimensional predictors, and nested multiple imputation.

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Description file content

Package
miceadds
Type
Package
Title
Some Additional Multiple Imputation Functions, Especially for 'mice'
Version
2.11-87
Date
2018-05-21 10:30:50
Author
Alexander Robitzsch [aut, cre], Simon Grund [aut], Thorsten Henke [aut]
Maintainer
Alexander Robitzsch
Description
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 (arbitrary number of levels, hierarchical and non-hierarchical datasets), imputation using partial least squares (PLS) for high dimensional predictors, and nested multiple imputation.
Depends
R (>= 3.0), mice
Imports
CDM (>= 5.8-5), graphics, lme4, MASS, methods, mitools, mvtnorm, Rcpp, sirt, stats, TAM (>= 2.9-13), utils
LinkingTo
Rcpp, RcppArmadillo
Suggests
Amelia, BIFIEsurvey, blme, car, foreign, grouped, haven, inline, jomo, MBESS, MCMCglmm, mitml, multiwayvcov, pan, sjlabelled
URL
License
GPL (>= 2)
NeedsCompilation
yes
Packaged
2018-05-21 08:32:04 UTC; sunpn563
Repository
CRAN
Date/Publication
2018-05-21 15:17:52 UTC

install.packages('miceadds')

2.11-87

5 days ago

https://github.com/alexanderrobitzsch/miceadds

Alexander Robitzsch

GPL (>= 2)

Depends on

R (>= 3.0), mice

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, sjlabelled

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