libstatistics-pca-perl - 0.0.1-2 main

Statistics::PCA provides functions for principal component analysis (PCA).
PCA transforms higher-dimensional data consisting of a number of possibly
correlated variables into a smaller number of uncorrelated variables termed
principal components (PCs). The higher the ranking of the PCs the greater the
amount of variability that the PC accounts for.
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This PCA procedure involves the calculation of the eigenvalue decomposition
from a data covariance matrix after mean centering the data.
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See https://en.wikipedia.org/wiki/Principal_component_analysis