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SPCAvRP (version 0.4)

Sparse Principal Component Analysis via Random Projections (SPCAvRP)

Description

Implements the SPCAvRP algorithm, developed and analysed in "Sparse principal component analysis via random projections" Gataric, M., Wang, T. and Samworth, R. J. (2018) . The algorithm is based on the aggregation of eigenvector information from carefully-selected random projections of the sample covariance matrix.

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Install

install.packages('SPCAvRP')

Monthly Downloads

134

Version

0.4

License

GPL-3

Maintainer

Milana Gataric

Last Published

May 3rd, 2019

Functions in SPCAvRP (0.4)

SPCAvRP

Computes the leading eigenvector using the SPCAvRP algorithm
SPCAvRP_subspace

Computes the leading eigenspace using the SPCAvRP algorithm for the eigenspace estimation
SPCAvRP_deflation

Computes multiple principal components using our modified deflation scheme