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r-factominer

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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>.

Installation

To install this package, run one of the following:

Conda
$conda install f30a78ec8::r-factominer

Usage Tracking

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Downloads (Last 6 months): 0

About

Summary

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>.

Last Updated

Dec 29, 2019 at 22:54

License

GPL-2

Total Downloads

15

Supported Platforms

noarch