conda-forge / packages / r-factominer 1.42

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).


Info: This package contains files in non-standard labels.

conda install

  • linux-64  v1.41
  • noarch  v1.42
  • osx-64  v1.41
  • win-64  v1.41
To install this package with conda run one of the following:
conda install -c conda-forge r-factominer
conda install -c conda-forge/label/gcc7 r-factominer
conda install -c conda-forge/label/cf201901 r-factominer


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