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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).

Installation

To install this package, run one of the following:

Conda
$conda install conda-forge::r-factominer

Usage Tracking

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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).

Last Updated

Apr 10, 2026 at 15:38

License

GPL-2.0-or-later

Supported Platforms

macOS-arm64
macOS-64
win-64
linux-ppc64le
linux-aarch64
linux-64

Unsupported Platforms

noarch Last supported version: 2.4