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) <doi:10.1080/10618600.2021.1987254>.

Version:	0.7.0
Depends:	R (≥ 3.6.0), rifle (≥ 1.0.0), MASS, PMA
Published:	2022-06-15
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

Documentation:

Reference manual:	EESPCA.pdf
Vignettes:	EESPCA example

Downloads:

Package source:	EESPCA_0.7.0.tar.gz
Windows binaries:	r-devel: EESPCA_0.7.0.zip, r-release: EESPCA_0.7.0.zip, r-oldrel: EESPCA_0.7.0.zip
macOS binaries:	r-release (arm64): EESPCA_0.7.0.tgz, r-oldrel (arm64): EESPCA_0.7.0.tgz, r-release (x86_64): EESPCA_0.7.0.tgz, r-oldrel (x86_64): EESPCA_0.7.0.tgz
Old sources:	EESPCA archive

Linking:

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