rsvddpd: Robust Singular Value Decomposition using Density Power
Divergence
Computing singular value decomposition with robustness is a challenging task.
This package provides an implementation of computing robust SVD using density power
divergence (<doi:10.48550/arXiv.2109.10680>). It combines the idea of robustness and efficiency in estimation
based on a tuning parameter. It also provides utility functions to simulate various
scenarios to compare performances of different algorithms.
| Version: |
1.0.0 |
| Imports: |
Rcpp (≥ 1.0.5), MASS, stats, utils, matrixStats |
| LinkingTo: |
Rcpp, RcppArmadillo |
| Suggests: |
knitr, rmarkdown, microbenchmark, pcaMethods |
| Published: |
2021-10-27 |
| DOI: |
10.32614/CRAN.package.rsvddpd |
| Author: |
Subhrajyoty Roy [aut, cre] |
| Maintainer: |
Subhrajyoty Roy <subhrajyotyroy at gmail.com> |
| BugReports: |
https://github.com/subroy13/rsvddpd/issues |
| License: |
MIT + file LICENSE |
| URL: |
https://github.com/subroy13/rsvddpd |
| NeedsCompilation: |
yes |
| Materials: |
README, NEWS |
| CRAN checks: |
rsvddpd results |
Documentation:
Downloads:
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