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.">

rsvddpd: Robust Singular Value Decomposition using Density Power Divergence (original) (raw)

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: 1.0.1
Imports: Rcpp (≥ 1.0.5), MASS, stats, utils, matrixStats
LinkingTo: Rcpp, RcppArmadillo
Suggests: knitr, rmarkdown, microbenchmark, pcaMethods, V8
Published: 2025-09-20
DOI: 10.32614/CRAN.package.rsvddpd
Author: Subhrajyoty Roy [aut, cre]
Maintainer: Subhrajyoty Roy
BugReports: https://github.com/subroy13/rsvddpd/issues
License: MIT + file
URL: https://github.com/subroy13/rsvddpd
NeedsCompilation: yes
Materials: README, NEWS
CRAN checks: rsvddpd results

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