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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.date.accessioned2016-06-03T14:30:02Z
dc.date.available2016-06-03T14:30:02Z
dc.date.issued2015-03-20
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 . DOI: 10.18637/jss.v064.i07en
dc.identifier.issn1548-7660
dc.identifier.otherPURE: 240101541
dc.identifier.otherPURE UUID: 83b2786b-4d80-4bcb-83e2-af4371f279c1
dc.identifier.otherScopus: 84924938753
dc.identifier.urihttp://hdl.handle.net/10023/8931
dc.identifier.urihttps://www.jstatsoft.org/article/view/v064i07en
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 tting 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.en
dc.format.extent30en
dc.language.isoeng
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.subjectClusteringen
dc.subjectDirichlet process mixture modelen
dc.subjectQA Mathematicsen
dc.subjectQA75 Electronic computers. Computer scienceen
dc.subjectDASen
dc.subject.lccQAen
dc.subject.lccQA75en
dc.titlePReMiuM : an R package for profile regression mixture models using Dirichlet processesen
dc.typeJournal articleen
dc.contributor.institutionUniversity of St Andrews. Statisticsen
dc.contributor.institutionUniversity of St Andrews. Centre for Research into Ecological & Environmental Modellingen
dc.identifier.doihttps://doi.org/10.18637/jss.v064.i07
dc.description.statusPeer revieweden


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