CRAN/E | ktaucenters

ktaucenters

Robust Clustering Procedures

Installation

About

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) ).

Key Metrics

Version 1.0.0
R ≥ 2.10
Published 2024-01-16 111 days ago
Needs compilation? yes
License GPL-2
License GPL-3
CRAN checks ktaucenters results
Language en-US

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Maintainer

Maintainer

Juan Domingo Gonzalez

juanrst@hotmail.com

Authors

Juan Domingo Gonzalez

cre / aut

Victor J. Yohai

aut

Ruben H. Zamar

aut

Douglas Alberto Carmona Guanipa

aut

Material

README
NEWS
Reference manual
Package source

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

Windows

r-devel

x86_64

r-release

x86_64

r-oldrel

x86_64

Old Sources

ktaucenters archive

Depends

R ≥ 2.10
MASS
stats
GSE

Imports

Rcpp ≥ 1.0.9

Suggests

jpeg
tclust
knitr
rmarkdown
testthat ≥ 3.1.0

LinkingTo

Rcpp

Reverse Imports

RMBC