FactoMineR-package

FactoMineR

The method proposed in this package are exploratory mutlivariate methods such as principal component analysis, correspondence analysis or clustering.

factor analysis

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).

Francois Husson

Multivariate Exploratory Data Analysis and Data Mining

FactoMineR-package function

Francois Husson, Julie Josse, Sebastien Le, Jeremy Mazet
Maintainer: <a href="/link/francois.husson%40institut-agro.fr?package=FactoMineR&version=2.9" data-mini-rdoc="FactoMineR::francois.husson@institut-agro.fr">francois.husson@institut-agro.fr</a>

Author

Multivariate Exploratory Data Analysis and Data Mining with R — FactoMineR-package

Francois Husson, Julie Josse, Sebastien Le, Jeremy Mazet
Maintainer: <a href='mailto:francois.husson@institut-agro.fr'>francois.husson@institut-agro.fr</a>

FactoMineR-package: Multivariate Exploratory Data Analysis and Data Mining with R

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References