missMDA: Handling Missing Values with Multivariate Data Analysis

Imputation of incomplete continuous or categorical datasets; Missing values are imputed with a principal component analysis (PCA), a multiple correspondence analysis (MCA) model or a multiple factor analysis (MFA) model; Perform multiple imputation with and in PCA or MCA.

Version:	1.18
Depends:	R (≥ 3.3.0)
Imports:	FactoMineR (≥ 2.3), ggplot2, graphics, grDevices, mice, mvtnorm, stats, utils, doParallel, parallel, foreach
Suggests:	knitr, markdown
Published:	2020-12-11
Author:	Francois Husson, Julie Josse
Maintainer:	Francois Husson <husson at agrocampus-ouest.fr>
License:	GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL:	http://factominer.free.fr/missMDA/index.html
NeedsCompilation:	no
Citation:	missMDA citation info
Materials:	README
In views:	MissingData, Psychometrics
CRAN checks:	missMDA results

Documentation:

Reference manual:	missMDA.pdf
Vignettes:	MulitpleImputation missMDA

Downloads:

Package source:	missMDA_1.18.tar.gz
Windows binaries:	r-devel: missMDA_1.18.zip, r-release: missMDA_1.18.zip, r-oldrel: missMDA_1.18.zip
macOS binaries:	r-release (arm64): missMDA_1.18.tgz, r-oldrel (arm64): missMDA_1.18.tgz, r-release (x86_64): missMDA_1.18.tgz, r-oldrel (x86_64): missMDA_1.18.tgz
Old sources:	missMDA archive

Reverse dependencies:

Reverse depends:	imp4p
Reverse imports:	Factoshiny, geneticae, INSPIRE, missCompare, NADIA, NIMAA, OTrecod
Reverse suggests:	denoiseR, FactoMineR, padma

Linking:

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