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: |
1.1-0 |
| Depends: |
R (≥ 4.0) |
| Published: |
2025-11-23 |
| 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 |
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