SPCAvRP: Sparse Principal Component Analysis via Random Projections (SPCAvRP)

Implements the SPCAvRP algorithm, developed and analysed in "Sparse principal component analysis via random projections" Gataric, M., Wang, T. and Samworth, R. J. (2018) <doi:10.48550/arXiv.1712.05630>. The algorithm is based on the aggregation of eigenvector information from carefully-selected random projections of the sample covariance matrix.

Version: 0.4
Depends: R (≥ 3.0.0), parallel, MASS
Published: 2019-05-03
DOI: 10.32614/CRAN.package.SPCAvRP
Author: Milana Gataric, Tengyao Wang and Richard J. Samworth
Maintainer: Milana Gataric <m.gataric at statslab.cam.ac.uk>
License: GPL-3
URL: https://arxiv.org/abs/1712.05630
NeedsCompilation: no
CRAN checks: SPCAvRP results


Reference manual: SPCAvRP.pdf


Package source: SPCAvRP_0.4.tar.gz
Windows binaries: r-devel: SPCAvRP_0.4.zip, r-release: SPCAvRP_0.4.zip, r-oldrel: SPCAvRP_0.4.zip
macOS binaries: r-release (arm64): SPCAvRP_0.4.tgz, r-oldrel (arm64): SPCAvRP_0.4.tgz, r-release (x86_64): SPCAvRP_0.4.tgz, r-oldrel (x86_64): SPCAvRP_0.4.tgz
Old sources: SPCAvRP archive


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