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. (2017) <arXiv:1712.05630>. The algorithm is based on the aggregation of eigenvector information from carefully-selected random projections of the sample covariance matrix.

Version: 0.2
Depends: R (≥ 3.0.0), parallel, MASS
Published: 2018-01-16
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

Downloads:

Reference manual: SPCAvRP.pdf
Package source: SPCAvRP_0.2.tar.gz
Windows binaries: r-devel: SPCAvRP_0.2.zip, r-release: SPCAvRP_0.2.zip, r-oldrel: SPCAvRP_0.2.zip
OS X El Capitan binaries: r-release: SPCAvRP_0.2.tgz
OS X Mavericks binaries: r-oldrel: SPCAvRP_0.1.tgz
Old sources: SPCAvRP archive

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