Sparse Principal Component Analysis via Random Projections (SPCAvRP)

Documentation for package ‘SPCAvRP’ version 0.3

final_estimator	Computes the leading eigenvector from its support
project_covariance	Projects the sample covariance
select_projection	Selects the best projection
select_projections_subspace	Selects the best projections for the subspace estimation
SPCAvRP	Computes the leading eigenvector using the SPCAvRP algorithm
SPCAvRP_deflation	Computes the leading eigenvectors using the modified deflation scheme
SPCAvRP_parallel	Parallel implementation of the SPCAvRP algorithm
SPCAvRP_ranking	Ranks the variables
SPCAvRP_subspace	Computes the leading eigenspace using the SPCAvRP algorithm for eigenspace estimation