Package: ktaucenters
Type: Package
Title: Robust Clustering Procedures
Version: 1.0.0
Authors@R: c(
  person("Juan Domingo", "Gonzalez", email = "juanrst@hotmail.com", role = c("cre", "aut")),
  person("Victor J.", "Yohai", email = "victoryohai@gmail.com", role = "aut"),
  person("Ruben H.", "Zamar", email = "ruben@stat.ubc.ca", role = "aut"),
  person("Douglas Alberto", "Carmona Guanipa", email = "douglas.albertocg@gmail.com", role = "aut")
  )
Description: 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) <arxiv:1906.08198>).
License: GPL (>= 2)
Encoding: UTF-8
Depends: R (>= 2.10), MASS, stats, GSE
Language: en-US
LazyData: true
RoxygenNote: 7.2.3
Suggests: jpeg, tclust, knitr, rmarkdown, testthat (>= 3.1.0)
Imports: Rcpp (>= 1.0.9)
LinkingTo: Rcpp
Config/testthat/edition: 3
NeedsCompilation: yes
Packaged: 2024-01-16 12:58:52 UTC; douglasc
Author: Juan Domingo Gonzalez [cre, aut],
  Victor J. Yohai [aut],
  Ruben H. Zamar [aut],
  Douglas Alberto Carmona Guanipa [aut]
Maintainer: Juan Domingo Gonzalez <juanrst@hotmail.com>
Repository: CRAN
Date/Publication: 2024-01-16 14:00:02 UTC
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