EESPCA: 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) <arXiv:2006.01924>.

Version: 0.3.0
Depends: R (≥ 3.6.0), rifle (≥ 1.0.0), MASS, PMA
Published: 2021-07-16
Author: H. Robert Frost
Maintainer: H. Robert Frost <rob.frost at dartmouth.edu>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
Copyright: Dartmouth College
NeedsCompilation: no
CRAN checks: EESPCA results

Downloads:

Reference manual: EESPCA.pdf
Vignettes: EESPCA example
Package source: EESPCA_0.3.0.tar.gz
Windows binaries: r-devel: EESPCA_0.3.0.zip, r-release: EESPCA_0.3.0.zip, r-oldrel: EESPCA_0.3.0.zip
macOS binaries: r-release (arm64): EESPCA_0.3.0.tgz, r-release (x86_64): EESPCA_0.3.0.tgz, r-oldrel: EESPCA_0.3.0.tgz

Linking:

Please use the canonical form https://CRAN.R-project.org/package=EESPCA to link to this page.