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

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Description file content

Package
FactoMineR
Version
1.42
Date
2019-07-03
Title
Multivariate Exploratory Data Analysis and Data Mining
Author
Francois Husson, Julie Josse, Sebastien Le, Jeremy Mazet
Maintainer
Francois Husson
Depends
R (>= 3.0.0)
Imports
car,cluster,ellipse,flashClust,graphics,grDevices,lattice,leaps,MASS,scatterplot3d,stats,utils
Suggests
missMDA,knitr
Description
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).
License
GPL (>= 2)
URL
Encoding
latin1
VignetteBuilder
knitr
NeedsCompilation
no
Packaged
2019-07-03 09:52:25 UTC; husson
Repository
CRAN
Date/Publication
2019-07-03 10:20:03 UTC

install.packages('FactoMineR')

1.42

a month ago

http://factominer.free.fr

Francois Husson

GPL (>= 2)

Depends on

R (>= 3.0.0)

Imports

car,cluster,ellipse,flashClust,graphics,grDevices,lattice,leaps,MASS,scatterplot3d,stats,utils

Suggests

missMDA,knitr

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