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EESPCA (version 0.8.0)

Eigenvectors from Eigenvalues Sparse Principal Component Analysis (EESPCA)

Description

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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Version

Install

install.packages('EESPCA')

Monthly Downloads

208

Version

0.8.0

License

GPL (>= 2)

Maintainer

H Robert Frost

Last Published

July 21st, 2025

Functions in EESPCA (0.8.0)

riflePCACV

Sparsity parameter selection via cross-validation for rifle method of Tan et al.
tpower

Implementation of the Yuan and Zhang TPower method.
eespcaForK

Multi-PC version of Eigenvectors from Eigenvalues Sparse Principal Component Analysis (EESPCA)
reconstructionError

Calculates the reduced rank reconstruction error
EESPCA-package

Eigenvectors
computeApproxNormSquaredEigenvector

Approximates the normed squared eigenvector loadings
rifleInit

Computes the initial eigenvector for the rifle method of Tan et al.
computeResidualMatrix

Calculates the residual matrix from the reduced rank reconstruction
eespca

Eigenvectors from Eigenvalues Sparse Principal Component Analysis (EESPCA)
eespcaCV

Cross-validation for Eigenvectors from Eigenvalues Sparse Principal Component Analysis (EESPCA)
reconstruct

Calculates the reduced rank reconstruction
powerIteration

Power iteration method for calculating principal eigenvector and eigenvalue.
tpowerPCACV

Sparsity parameter selection for the Yuan and Zhang TPower method using cross-validation.