Eigenvectors from Eigenvalues Sparse Principal Component Analysis (EESPCA)

Contains logic for computing sparse principal components via the EESPCA method, which is based on an approximation of the eigenvector/eigenvalue identity. Includes logic to support execution of the TPower and rifle sparse PCA methods, as well as logic to estimate the sparsity parameters used by EESPCA, TPower and rifle via cross-validation to minimize the out-of-sample reconstruction error. H. Robert Frost (2021) .


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install.packages("EESPCA")

0.3.0 by H. Robert Frost, 2 months ago


Browse source code at https://github.com/cran/EESPCA


Authors: H. Robert Frost


Documentation:   PDF Manual  


GPL (>= 2) license


Depends on rifle, MASS, PMA


See at CRAN