The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.

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

Linking:

Please use the canonical form https://CRAN.R-project.org/package=cauchypca to link to this page.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.