R: Eigenvectors from Eigenvalues Sparse Principal Component Analysis (EESPCA)

# Eigenvectors from Eigenvalues Sparse Principal Component Analysis (EESPCA)

## Documentation for package ‘EESPCA’ version 0.8.0

- DESCRIPTION file
- User guides, package vignettes and other documentation
- Package NEWS

## Help Pages

EESPCA-package | Eigenvectors
computeApproxNormSquaredEigenvector | Approximates the normed squared eigenvector loadings
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)
eespcaForK | Multi-PC version of Eigenvectors from Eigenvalues Sparse Principal Component Analysis (EESPCA)
powerIteration | Power iteration method for calculating principal eigenvector and eigenvalue.
reconstruct | Calculates the reduced rank reconstruction
reconstructionError | Calculates the reduced rank reconstruction error
rifleInit | Computes the initial eigenvector for the rifle method of Tan et al.
riflePCACV | Sparsity parameter selection via cross-validation for rifle method of Tan et al.
tpower | Implementation of the Yuan and Zhang TPower method.
tpowerPCACV | Sparsity parameter selection for the Yuan and Zhang TPower method using cross-validation.