PReMiuM
Dirichlet Process Bayesian Clustering, Profile Regression
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. The package allows Bernoulli, Binomial, Poisson, Normal, survival and categorical response, as well as Normal and discrete covariates. It also allows for fixed effects in the response model, where a spatial CAR (conditional autoregressive) term can be also included. 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. The main reference for the package is Liverani, Hastie, Azizi, Papathomas and Richardson (2015) doi:10.18637/jss.v064.i07.
- Version3.2.13
- R version≥ 3.5.1
- LicenseGPL-2
- Needs compilation?Yes
- PReMiuM citation info
- Last release01/09/2024
Documentation
Team
Silvia Liverani
Xi Liu
Show author detailsRolesContributorSylvia Richardson
Show author detailsRolesAuthorDavid I. Hastie
Show author detailsRolesAuthorAurore J. Lavigne
Show author detailsRolesContributorLucy Leigh
Show author detailsRolesContributorLamiae Azizi
Show author detailsRolesContributorRuizhu Huang
Show author detailsRolesContributorAustin Gratton
Show author detailsRolesContributorWei Jing
Show author detailsRolesContributor
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- Imports7 packages
- Suggests1 package
- Linking To3 packages