ktaucenters: Robust Clustering Procedures

A clustering algorithm similar to K-Means is implemented, it has two main advantages, namely (a) The estimator is resistant to outliers, that means that results of estimator are still correct when there are atypical values in the sample and (b) The estimator is efficient, roughly speaking, if there are no outliers in the sample, results will be similar to those obtained by a classic algorithm (K-Means). Clustering procedure is carried out by minimizing the overall robust scale so-called tau scale. (see Gonzalez, Yohai and Zamar (2019) <doi:10.48550/arXiv.1906.08198>).

Version: 1.0.0
Depends: R (≥ 2.10), MASS, stats, GSE
Imports: Rcpp (≥ 1.0.9)
LinkingTo: Rcpp
Suggests: jpeg, tclust, knitr, rmarkdown, testthat (≥ 3.1.0)
Published: 2024-01-16
Author: Juan Domingo Gonzalez [cre, aut], Victor J. Yohai [aut], Ruben H. Zamar [aut], Douglas Alberto Carmona Guanipa [aut]
Maintainer: Juan Domingo Gonzalez <juanrst at hotmail.com>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: yes
Language: en-US
Materials: README NEWS
CRAN checks: ktaucenters results

Documentation:

Reference manual: ktaucenters.pdf

Downloads:

Package source: ktaucenters_1.0.0.tar.gz
Windows binaries: r-devel: ktaucenters_1.0.0.zip, r-release: ktaucenters_1.0.0.zip, r-oldrel: ktaucenters_1.0.0.zip
macOS binaries: r-release (arm64): ktaucenters_1.0.0.tgz, r-oldrel (arm64): ktaucenters_1.0.0.tgz, r-release (x86_64): ktaucenters_1.0.0.tgz
Old sources: ktaucenters archive

Reverse dependencies:

Reverse imports: RMBC

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