cauchypca: Robust Principal Component Analysis Using the Cauchy Distribution

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>.

Version:	1.3
Depends:	R (≥ 4.0)
Imports:	doParallel, foreach, parallel, Rfast, Rfast2, stats
Published:	2024-01-24
DOI:	10.32614/CRAN.package.cauchypca
Author:	Michail Tsagris [aut, cre], Aisha Fayomi [ctb], Yannis Pantazis [ctb], Andrew T.A. Wood [ctb]
Maintainer:	Michail Tsagris <mtsagris at uoc.gr>
License:	GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation:	no
CRAN checks:	cauchypca results

Documentation:

Reference manual:

cauchypca.pdf

Downloads:

Package source:	cauchypca_1.3.tar.gz
Windows binaries:	r-devel: cauchypca_1.3.zip, r-release: cauchypca_1.3.zip, r-oldrel: cauchypca_1.3.zip
macOS binaries:	r-release (arm64): cauchypca_1.3.tgz, r-oldrel (arm64): cauchypca_1.3.tgz, r-release (x86_64): cauchypca_1.3.tgz, r-oldrel (x86_64): cauchypca_1.3.tgz
Old sources:	cauchypca archive

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