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>).">

miceadds: Some Additional Multiple Imputation Functions, Especially for 'mice' (original) (raw)

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>).

Version: 3.17-44
Depends: R (≥ 3.5-0), mice (≥ 3.0.0)
Imports: graphics, methods, mitools, Rcpp, stats, utils
LinkingTo: Rcpp, RcppArmadillo
Suggests: BIFIEsurvey, blme, car, CDM, coda, foreign, inline, lme4, MASS, Matrix, MBESS, MCMCglmm, mdmb, pls, numDeriv, readxl, sandwich, sirt, sjlabelled, synthpop, TAM
Enhances: Amelia, imputeR, jomo, micemd, mitml, pan, simputation
Published: 2024-01-09
DOI: 10.32614/CRAN.package.miceadds
Author: Alexander Robitzsch [aut,cre] (https://orcid.org/0000-0002-8226-3132), Simon Grund [aut] (https://orcid.org/0000-0002-1290-8986), Thorsten Henke [ctb]
Maintainer: Alexander Robitzsch
BugReports: https://github.com/alexanderrobitzsch/miceadds/issues?state=open
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: https://github.com/alexanderrobitzsch/miceadds,https://sites.google.com/view/alexander-robitzsch/software
NeedsCompilation: yes
Citation: miceadds citation info
Materials: README
In views: MissingData
CRAN checks: miceadds results

Documentation:

Downloads:

Reverse dependencies:

Reverse imports: BIFIEsurvey, Conigrave, eatRep, mdmb, MKinfer
Reverse linking to: mdmb
Reverse suggests: bipd, CDM, hot.deck, LSAmitR, marginaleffects, mice, mitml, sirt, TAM
Reverse enhances: texreg

Linking:

Please use the canonical formhttps://CRAN.R-project.org/package=miceaddsto link to this page.