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.

Home http://factominer.free.fr
Versions 1.35, 1.38
License GPL (>= 2)
Recipe https://github.com/bioconda/bioconda-recipes/tree/master/recipes/r-factominer


With an activated Bioconda channel (see 2. Set up channels), install with:

conda install r-factominer

and update with:

conda update r-factominer


A Docker container is available at https://quay.io/repository/biocontainers/r-factominer.