FactoMineR: Multivariate Exploratory Data Analysis and Data Mining

Exploratory data analysis methods to summarize, visualize and describe datasets. The main principal component methods are available, those with the largest potential in terms of applications: principal component analysis (PCA) when variables are quantitative, correspondence analysis (CA) and multiple correspondence analysis (MCA) when variables are categorical, Multiple Factor Analysis when variables are structured in groups, etc. and hierarchical cluster analysis.

Version: 1.34
Depends: R (≥ 3.3.0)
Imports: car, cluster, ellipse, flashClust, graphics, grDevices, lattice, leaps, MASS, scatterplot3d, stats, knitr, utils
Suggests: missMDA
Published: 2016-11-17
Author: Francois Husson, Julie Josse, Sebastien Le, Jeremy Mazet
Maintainer: Francois Husson <francois.husson at agrocampus-ouest.fr>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: http://factominer.free.fr
NeedsCompilation: no
Citation: FactoMineR citation info
Materials: README
In views: Multivariate, Psychometrics
CRAN checks: FactoMineR results


Reference manual: FactoMineR.pdf
Vignettes: FactoMineR
Package source: FactoMineR_1.34.tar.gz
Windows binaries: r-devel: FactoMineR_1.34.zip, r-release: FactoMineR_1.34.zip, r-oldrel: FactoMineR_1.33.zip
OS X Mavericks binaries: r-release: FactoMineR_1.34.tgz, r-oldrel: FactoMineR_1.33.tgz
Old sources: FactoMineR archive

Reverse dependencies:

Reverse depends: ClustGeo, EnQuireR, Factoshiny, HDoutliers, qha, RcmdrPlugin.FactoMineR, SensoMineR
Reverse imports: denoiseR, EMA, freqweights, GDAtools, HistDAWass, IntClust, IRTpp, LatentREGpp, missMDA, pcaBootPlot, RSDA, RVAideMemoire, SHLR, uHMM
Reverse suggests: bibliometrix, DiscriMiner, explor, factoextra, MetaQC, plsdepot, plspm


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