Robust Principal Component Analysis Using the Cauchy Distribution
Overview
A new robust principal component analysis algorithm is implemented that relies upon the Cauchy Distribution. The algorithm is suitable for high dimensional data even if the sample size is less than the number of variables. The methodology is described in this paper: Fayomi A., Pantazis Y., Tsagris M. and Wood A.T.A. (2024). "Cauchy robust principal component analysis with applications to high-dimensional data sets". Statistics and Computing, 34: 26. doi:10.1007/s11222-023-10328-x.
Install
Health
- OK13 OK2026-08-05
- NOTE12 OK, 1 NOTE2026-08-01
- OK14 OK2026-03-10
Documentation
- Help pages
- 3includes a package overview page
- Examples that run
- 100%2 run in a plain check
- Exported functions with examples
- 100%
- Documented parameters
- 100%
- Return-value docs
- 100%
- Help pages citing references
- 100%
Downloads
Dependencies
Installing cauchypca adds 8 packages (5 with compiled code, 4.4 MB of source tarballs). Heaviest: Rcpp 1.8 MB, RcppArmadillo 1.0 MB, RcppParallel 744 KB.
Code & Tests
People & History
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- 1.4Latest
- RR 4.6.0 released · 2026-04-24
- RR 4.5.0 released · 2025-04-11
- RR 4.4.0 released · 2024-04-24
- 1.3
- 1.2
- 1.12023-09-15 · diff ↗
- RR 4.3.0 released · 2023-04-21
- 1.02022-11-09
- RR 4.2.0 released · 2022-04-22
Package metadata
- First published
- 2022-11-09
- Total releases
- 5 / 4 yrs
- License
- GPL (>= 2) OSI
- Minimum R
- ≥ 4.0
- Download size
- 4.4 KB
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