FactoMineR (2.1)

6 users

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. F. Husson, S. Le and J. Pages (2017).

Maintainer: Francois Husson
Author(s): Francois Husson, Julie Josse, Sebastien Le, Jeremy Mazet

License: GPL (>= 2)

Uses: car, cluster, ellipse, flashClust, ggplot2, ggrepel, lattice, leaps, MASS, scatterplot3d, missMDA, knitr, Factoshiny
Reverse depends: ClustGeo, ClustOfVar, dynGraph, EMA, EnQuireR, factas, Factoshiny, fdm2id, GDAtools, HDoutliers, lori, missMDA, qha, R.temis, RcmdrPlugin.FactoMineR, RcmdrPlugin.SensoMineR, RSDA, SensoMineR, SesIndexCreatoR, TextoMineR
Reverse suggests: bibliometrix, DiscriMiner, explor, factoextra, forwards, glmtree, MetaQC, parameters, plsdepot, plspm

Released 11 days ago.

33 previous versions



  3.5/5 (4 votes)


  3.0/5 (3 votes)

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Related packages: ade4, ca, cocorresp, e1071, eRm, fastICA, GPArotation, homals, ltm, MCMCpack, nFactors, paran, pcaPP, poLCA, polycor, pls, psy, PTAk, sem, SensoMineR(20 best matches, based on common tags.)

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