PReMiuM : an R package for profile regression mixture models using Dirichlet processes
Abstract
PReMiuM is a recently developed R package for Bayesian clustering using a Dirichlet process mixture model. This model is an alternative to regression models, non-parametrically linking a response vector to covariate data through cluster membership (Molitor, Papathomas, Jerrett, and Richardson 2010). The package allows binary, categorical, count and continuous response, as well as continuous and discrete covariates. Additionally, predictions may be made for the response, and missing values for the covariates are handled. Several samplers and label switching moves are implemented along with diagnostic tools to assess convergence. A number of R functions for post-processing of the output are also provided. In addition to fitting mixtures, it may additionally be of interest to determine which covariates actively drive the mixture components. This is implemented in the package as variable selection.
Citation
Liverani , S , Hastie , D , Azizi , L , Papathomas , M & Richardson , S 2015 , ' PReMiuM : an R package for profile regression mixture models using Dirichlet processes ' , Journal of Statistical Software , vol. 64 , no. 7 . https://doi.org/10.18637/jss.v064.i07
Publication
Journal of Statistical Software
Status
Peer reviewed
ISSN
1548-7660Type
Journal article
Rights
This work is licensed under a Creative Commons Attribution 3.0 Unported License which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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