psvd: Eigendecomposition, Singular-Values and the Power Method

For a data matrix with m rows and n columns (m>=n), the power method is used to compute, simultaneously, the eigendecomposition of a square symmetric matrix. This result is used to obtain the singular value decomposition (SVD) and the principal component analysis (PCA) results. Compared to the classical SVD method, the first r singular values can be computed.

Version:	0.1-0
Depends:	R (≥ 4.0)
Published:	2024-10-25
DOI:	10.32614/CRAN.package.psvd
Author:	Doulaye Dembele [aut, cre]
Maintainer:	Doulaye Dembele <doulaye at igbmc.fr>
License:	GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation:	yes
CRAN checks:	psvd results

Documentation:

Reference manual:

psvd.pdf

Downloads:

Package source:	psvd_0.1-0.tar.gz
Windows binaries:	r-devel: psvd_0.1-0.zip, r-release: psvd_0.1-0.zip, r-oldrel: psvd_0.1-0.zip
macOS binaries:	r-release (arm64): psvd_0.1-0.tgz, r-oldrel (arm64): psvd_0.1-0.tgz, r-release (x86_64): psvd_0.1-0.tgz, r-oldrel (x86_64): psvd_0.1-0.tgz

Linking:

Please use the canonical form https://CRAN.R-project.org/package=psvd to link to this page.