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. F. Husson, S. Le and J. Pages (2017) <doi:10.1201/b10345-2>.
| Version: | 1.41 |
| Depends: | R (≥ 3.0.0) |
| Imports: | car, cluster, ellipse, flashClust, graphics, grDevices, lattice, leaps, MASS, scatterplot3d, stats, utils |
| Suggests: | missMDA, knitr |
| Published: | 2018-05-04 |
| 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 clustering |
| Package source: | FactoMineR_1.41.tar.gz |
| Windows binaries: | r-devel: FactoMineR_1.41.zip, r-release: FactoMineR_1.41.zip, r-oldrel: FactoMineR_1.41.zip |
| OS X binaries: | r-release: FactoMineR_1.41.tgz, r-oldrel: FactoMineR_1.41.tgz |
| Old sources: | FactoMineR archive |
| Reverse depends: | Factoshiny, GDAtools, HDoutliers, lori, qha, R.temis, RcmdrPlugin.FactoMineR, SensoMineR, Xplortext |
| Reverse imports: | bibliometrix, CAinterprTools, CINNA, ClustBlock, CONS, CSFA, denoiseR, EMA, factoextra, FactoInvestigate, GraphPCA, HistDAWass, IntClust, mimi, missMDA, MLGL, pcaBootPlot, RSDA, RVAideMemoire, sbpiper, tabula, tetraclasse, uHMM, wilson |
| Reverse suggests: | DiscriMiner, explor, forwards, plsdepot, plspm |
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