Package dev.nm.stat.factor.pca
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Interface Summary Interface Description PCA Principal Component Analysis (PCA) is a mathematical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of uncorrelated variables called principal components. -
Class Summary Class Description PCAbyEigen This class performs Principal Component Analysis (PCA) on a data matrix, using eigen decomposition on the correlation or covariance matrix.PCAbySVD This class performs Principal Component Analysis (PCA) on a data matrix, using the preferred Singular Value Decomposition (SVD) method.