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 than 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) <arXiv:1906.08198>).

Version: 0.1.0
Depends: R (≥ 2.10), MASS, methods, dplyr, dbscan, stats, GSE
Suggests: jpeg, tclust, knitr
Published: 2019-08-03
Author: Juan Domingo Gonzalez [cre, aut], Victor J. Yohai [aut], Ruben H. Zamar [aut]
Maintainer: Juan Domingo Gonzalez <juanrst at hotmail.com>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
Materials: README
CRAN checks: ktaucenters results


Reference manual: ktaucenters.pdf


Package source: ktaucenters_0.1.0.tar.gz
Windows binaries: r-devel: ktaucenters_0.1.0.zip, r-release: ktaucenters_0.1.0.zip, r-oldrel: ktaucenters_0.1.0.zip
macOS binaries: r-release (arm64): ktaucenters_0.1.0.tgz, r-oldrel (arm64): ktaucenters_0.1.0.tgz, r-release (x86_64): ktaucenters_0.1.0.tgz, r-oldrel (x86_64): ktaucenters_0.1.0.tgz

Reverse dependencies:

Reverse imports: RMBC


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