pivmet: Pivotal Methods for Bayesian Relabelling and k-Means Clustering

Collection of pivotal algorithms for: relabelling the MCMC chains in order to undo the label switching problem in Bayesian mixture models, as proposed in Egidi, Pappadà, Pauli and Torelli (2018a)<doi:10.1007/s11222-017-9774-2>; initializing the centers of the classical k-means algorithm in order to obtain a better clustering solution. For further details see Egidi, Pappadà, Pauli and Torelli (2018b)<ISBN:9788891910233>.

Version: 0.3.0
Depends: R (≥ 3.1.0)
Imports: cluster, mclust, MASS, corpcor, runjags, rstan, bayesmix, rjags, mvtnorm, RcmdrMisc, bayesplot
Suggests: knitr, rmarkdown
Published: 2020-06-03
Author: Leonardo Egidi[aut, cre], Roberta Pappadà[aut], Francesco Pauli[aut], Nicola Torelli[aut]
Maintainer: Leonardo Egidi <legidi at units.it>
License: GPL-2
URL: https://github.com/leoegidi/pivmet
NeedsCompilation: no
SystemRequirements: pandoc (>= 1.12.3), pandoc-citeproc
Materials: README NEWS
CRAN checks: pivmet results


Reference manual: pivmet.pdf
Vignettes: K-means clustering using MUS and other pivotal algorithms
Dealing with label switching: relabelling in Bayesian mixture models by pivotal units
Package source: pivmet_0.3.0.tar.gz
Windows binaries: r-devel: pivmet_0.3.0.zip, r-release: pivmet_0.3.0.zip, r-oldrel: pivmet_0.3.0.zip
macOS binaries: r-release: pivmet_0.3.0.tgz, r-oldrel: pivmet_0.3.0.tgz
Old sources: pivmet archive


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