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: 1.19
Depends: R (≥ 4.0)
Imports: FactoMineR (≥ 2.3), ggplot2, graphics, grDevices, mice, mvtnorm, stats, utils, doParallel, parallel, foreach
Suggests: knitr, markdown
Published: 2023-11-17
DOI: 10.32614/CRAN.package.missMDA
Author: Francois Husson, Julie Josse
Maintainer: Francois Husson <francois.husson at institut-agro.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


Reference manual: missMDA.pdf
Vignettes: MulitpleImputation


Package source: missMDA_1.19.tar.gz
Windows binaries: r-devel: missMDA_1.19.zip, r-release: missMDA_1.19.zip, r-oldrel: missMDA_1.19.zip
macOS binaries: r-release (arm64): missMDA_1.19.tgz, r-oldrel (arm64): missMDA_1.19.tgz, r-release (x86_64): missMDA_1.19.tgz, r-oldrel (x86_64): missMDA_1.19.tgz
Old sources: missMDA archive

Reverse dependencies:

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


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