Dear R users,
I am glad to announce the release (version 0.1) of the pivmet package,
which proposes some pivotal methods in order to:
* undo the label switching problem which naturally arises during the
MCMC sampling in Bayesian mixture models [pivotal relabelling] (Egidi
et al. 2018a)
* initialize the K-means algorithm aimed at obtaining a good
clustering solution [pivotal seeding] (Egidi et al. 2018b)
The package includes two vignettes for easing its use and is here available:
https://cran.r-project.org/web/packages/pivmet/index.html
and developed in github 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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