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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.
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Package details |
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| Author | Doulaye Dembele [aut, cre] (<https://orcid.org/0000-0003-3879-6940>) |
| Maintainer | Doulaye Dembele <doulaye@igbmc.fr> |
| License | GPL (>= 2) |
| Version | 0.1-0 |
| Package repository | View on CRAN |
| Installation |
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