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RobustPCA: Decompose a Matrix into Low-Rank and Sparse Components

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3.3Kdownloads / year
–test coverage
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Overview

About

Suppose we have a data matrix, which is the superposition of a low-rank component and a sparse component. Candes, E. J., Li, X., Ma, Y., & Wright, J. (2011). Robust principal component analysis?. Journal of the ACM (JACM), 58(3), 11. prove that we can recover each component individually under some suitable assumptions. It is possible to recover both the low-rank and the sparse components exactly by solving a very convenient convex program called Principal Component Pursuit; among all feasible decompositions, simply minimize a weighted combination of the nuclear norm and of the L1 norm. This package implements this decomposition algorithm resulting with Robust PCA approach.

Install

Health

CRAN checks
13OK
Slowest check: 48 s · r-devel-windows-x86_64
Status changes
  • OK13 OK2026-08-04
  • NOTE12 OK, 1 NOTE2026-08-01
  • OK14 OK2026-03-10
Advisories

none known

Documentation

In release 0.2.3
READMENoVignettesNoNEWSNoCitation fileNoWebsitenot declared in DESCRIPTION
Help pages
5includes a package overview page
Examples that run
100%4 run in a plain check
Exported functions with examples
100%
Documented parameters
88%
Return-value docs
100%
Help pages citing references
100%

Downloads

3.3K
CRAN downloads in the past year
Rank #20,034 · ~9/day · ~275/mo
Daily download trend is not available in this view yet.
22030 days
66590 days
3.3K1 year
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Dependencies

Declared dependencies
0 external dependencies (excludes base and recommended)

Installing rpca adds no packages beyond those that come with R.

Depends (0)
none
Imports (1)
compiler
LinkingTo (0)
none
Suggests (0)
none
Enhances (0)
none
Reverse dependencies
0direct
0indirect

Nothing depends on this yet.

People & History

People (1)
Author, Maintainer since 0.2.3 (2015)
Releases (1)
  • R
    R 4.6.0 released · 2026-04-24
  • R
    R 4.5.0 released · 2025-04-11
  • R
    R 4.4.0 released · 2024-04-24
  • R
    R 4.3.0 released · 2023-04-21
  • R
    R 4.2.0 released · 2022-04-22
  • R
    R 4.1.0 released · 2021-05-18
  • R
    R 4.0.0 released · 2020-04-24
  • R
    R 3.6.0 released · 2019-04-26
  • R
    R 3.5.0 released · 2018-04-23
  • R
    R 3.4.0 released · 2017-04-21
  • R
    R 3.3.0 released · 2016-05-03
  • 0.2.3Latest
    2015-07-31 · current release
  • R
    R 3.2.0 released · 2015-04-16

Package metadata

First published
2015-07-31
Total releases
1 / 11 yrs
License
GPL-2 | GPL-3 OSI
Download size
18 KB
Appears in task views

Cite

Cite this package

Run in R for the authors' preferred citation:

citation("rpca")
Sykulski, M. (2015). rpca: RobustPCA: Decompose a Matrix into Low-Rank and Sparse Components (Version 0.2.3) [Computer software]. https://doi.org/10.32614/CRAN.package.rpca

This package has no citation file, so citation() returns this entry built from DESCRIPTION.

Cite the R Observatory

For a number measured here: a download total, a coverage figure, an archival date.

APA

Balamuta, J. J. (2026). R Observatory: Metrics for rpca version 0.2.3 [Data set]. HJJB, LLC. Data release v2026-10-24. https://doi.org/10.5281/zenodo.21843040

From data release v2026-10-24, which the citation names so these numbers can be found later. More on citing and the projects behind them.

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