[R-pkgs] pivmet 0.2.0 - Pivotal Methods for Bayesian Relabelling and k-Means Clustering

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[R-pkgs] pivmet 0.2.0 - Pivotal Methods for Bayesian Relabelling and k-Means Clustering

legidi

The new version (0.2.0) of rhe 'pivmet' package is released on CRAN:
https://CRAN.R-project.org/package=pivmet.

The package offers a 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 et al. (2018a);
initializing the centers of the classical k-means algorithm
in order to obtain a better clustering solution (Egidi et al., 2018b).

The new version incorporates the Stan language for fitting
efficient mixture models.

github package page at:

https://github.com/LeoEgidi/pivmet

Here are the two referred articles:

Egidi et al. (2018a)
https://link.springer.com/article/10.1007/s11222-017-9774-2

Egidi et al. (2018b)
https://www.researchgate.net/profile/Leonardo_Egidi/publication/326225330_K-means_seeding_via_MUS_algorithm_-_Inizializzazione_del_K-means_tramite_l%27algoritmo_MUS/links/5b3f2c2caca27207851c7865/K-means-seeding-via-MUS-algorithm-Inizializzazione-del-K-means-tramite-lalgoritmo-MUS.pdf


All the best

Leonardo Egidi
Postdoctoral researcher
University of Trieste

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