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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 ORCID iD [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:

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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.