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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 |
| Reference manual: | cauchypca.pdf |
| 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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