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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).
Getting started
Browse package contents
Package details |
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| Author | Francois Husson, Julie Josse, Sebastien Le, Jeremy Mazet |
| Maintainer | Francois Husson <francois.husson@institut-agro.fr> |
| License | GPL (>= 2) |
| Version | 2.11 |
| URL | http://factominer.free.fr |
| Package repository | View on CRAN |
| Installation |
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