PReMiuM: an R package for profile regression mixture models using Dirichlet processes

Silvia Liverani, David Hastie, Lamiae Azizi, Michail Papathomas, Sylvia Richardson

Research output: Contribution to journalArticlepeer-review

52 Citations (Scopus)


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.
Original languageEnglish
Number of pages30
JournalJournal of Statistical Software
Issue number7
Publication statusPublished - 20 Mar 2015


  • Profile regression
  • Clustering
  • Dirichlet process mixture model


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