Robust Singular Value Decomposition using Density Power Divergence
Overview
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.
- Added a new function
rank.rSVDdpdwhich is used to determine the rank for matrix factorization. This is internally applied to the existing functionrSVDdpdfor automatic matrix rank determination.
From NEWS.md in the release
Install
Health
- OK13 OK2026-08-04
- NOTE12 OK, 1 NOTE2026-08-01
- OK14 OK2026-04-22
- ERROR13 OK, 1 ERROR2026-04-18
- OK14 OK2026-03-10
Documentation
- Help pages
- 5
- Examples that run
- 100%3 run in a plain check
- Exported functions with examples
- 60%
- Documented parameters
- not tracked
- Return-value docs
- not tracked
- Help pages citing references
- 40%
Downloads
Repository
5 development-tooling and community-health practices detected across 4 families in the upstream repository
Checks run against github.com/subroy13/rsvddpd on 2026-10-22.
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Dependencies
Installing rsvddpd adds 3 packages (3 with compiled code, 3.0 MB of source tarballs). Heaviest: Rcpp 1.8 MB, RcppArmadillo 1.0 MB, matrixStats 207 KB.
Code & Tests
People & History
Pick two releases to compare their code metrics.
- RR 4.6.0 released · 2026-04-24
- 1.0.1Latest
- RR 4.5.0 released · 2025-04-11
- RR 4.4.0 released · 2024-04-24
- RR 4.3.0 released · 2023-04-21
- RR 4.2.0 released · 2022-04-22
- 1.0.02021-10-27
- RR 4.1.0 released · 2021-05-18
Package metadata
- First published
- 2021-10-27
- Total releases
- 2 / 5 yrs
- License
- MIT + file LICENSE OSI
- Download size
- 32 KB
Cite
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