LeoEgidi/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; fitting sparse finite mixtures; 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>.
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Package details |
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| Author | Leonardo Egidi[aut, cre], Roberta Pappadà[aut], Francesco Pauli[aut], Nicola Torelli[aut] |
| Maintainer | Leonardo Egidi <legidi@units.it> |
| License | GPL-2 |
| Version | 0.6.0 |
| URL | https://github.com/leoegidi/pivmet |
| Package repository | View on GitHub |
| Installation |
Install the latest version of this package by entering the following in R:
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