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dc.contributor.authorLiverani, Silvia
dc.contributor.authorHastie, David
dc.contributor.authorAzizi, Lamiae
dc.contributor.authorPapathomas, Michail
dc.contributor.authorRichardson, Sylvia
dc.identifier.citationLiverani , 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 .
dc.identifier.otherPURE: 240101541
dc.identifier.otherPURE UUID: 83b2786b-4d80-4bcb-83e2-af4371f279c1
dc.identifier.otherScopus: 84924938753
dc.identifier.otherORCID: /0000-0002-5897-695X/work/58755497
dc.identifier.otherWOS: 000352914600001
dc.description.abstractPReMiuM 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.
dc.relation.ispartofJournal of Statistical Softwareen
dc.rightsThis 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.en
dc.subjectProfile regressionen
dc.subjectDirichlet process mixture modelen
dc.subjectQA75 Electronic computers. Computer scienceen
dc.titlePReMiuM : an R package for profile regression mixture models using Dirichlet processesen
dc.typeJournal articleen
dc.description.versionPublisher PDFen
dc.contributor.institutionUniversity of St Andrews.Statisticsen
dc.contributor.institutionUniversity of St Andrews.Centre for Research into Ecological & Environmental Modellingen
dc.description.statusPeer revieweden

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