RMBC: Robust Model Based Clustering

A robust clustering algorithm (Model-Based) similar to Expectation Maximization for finite mixture normal distributions is implemented, its main advantage is that the estimator is resistant to outliers, that means that results of parameter estimation are still correct when there are atypical values in the sample (see Gonzalez, Maronna, Yohai and Zamar (2021) <doi:10.48550/arXiv.2102.06851>).

Version: 0.1.0
Depends: R (≥ 3.5.0), stats
Imports: ktaucenters, mvtnorm, MASS
Suggests: tclust, knitr, testthat (≥ 2.1.0), rmarkdown
Published: 2021-07-22
DOI: 10.32614/CRAN.package.RMBC
Author: Juan Domingo Gonzalez [cre, aut], Victor J. Yohai [aut], Ruben H. Zamar [aut], Ricardo Maronna [aut]
Maintainer: Juan Domingo Gonzalez <juanrst at hotmail.com>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
CRAN checks: RMBC results


Reference manual: RMBC.pdf


Package source: RMBC_0.1.0.tar.gz
Windows binaries: r-devel: RMBC_0.1.0.zip, r-release: RMBC_0.1.0.zip, r-oldrel: RMBC_0.1.0.zip
macOS binaries: r-release (arm64): RMBC_0.1.0.tgz, r-oldrel (arm64): RMBC_0.1.0.tgz, r-release (x86_64): RMBC_0.1.0.tgz, r-oldrel (x86_64): RMBC_0.1.0.tgz


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