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PReMiuM
View on CRAN: Click
here
Download and install PReMiuM package within the R console
Install from CRAN:
install.packages("PReMiuM")
Install from Github:
library("remotes")
install_github("cran/PReMiuM") Install by package version:
library("remotes")
install_version("PReMiuM", "3.2.13") Attach the package and use:
library("PReMiuM")
Maintained by
Silvia Liverani
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2013-01-29
Latest Update: 2024-01-09
Description:
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) .
How to cite:
Silvia Liverani (2013). PReMiuM: Dirichlet Process Bayesian Clustering, Profile Regression. R package version 3.2.13, https://cran.r-project.org/web/packages/PReMiuM. Accessed 04 Jun. 2026.
Previous versions and publish date:
3.0.11 (2013-01-29 18:33), 3.0.13 (2013-01-30 13:56), 3.0.15 (2013-02-07 22:53), 3.0.16 (2013-03-15 17:56), 3.0.17 (2013-04-22 22:21), 3.0.18 (2013-04-24 07:46), 3.0.20 (2013-05-04 15:08), 3.0.21 (2013-10-01 07:41), 3.0.23 (2014-04-03 14:41), 3.0.24 (2014-04-07 15:08), 3.0.26 (2014-05-06 07:51), 3.0.28 (2014-06-30 17:45), 3.0.29 (2014-09-09 15:43), 3.0.30 (2014-09-12 16:32), 3.0.32 (2014-12-29 13:40), 3.1.0 (2015-03-13 17:18), 3.1.1 (2015-06-12 17:07), 3.1.2 (2015-08-26 15:14), 3.1.3 (2016-02-23 23:02), 3.1.4 (2016-12-28 18:03), 3.1.5 (2017-10-27 17:39), 3.1.6 (2017-11-02 19:22), 3.1.7 (2018-04-01 22:51), 3.1.8 (2018-06-14 23:27), 3.2.0 (2018-06-15 15:52), 3.2.1 (2018-09-26 16:20), 3.2.2 (2019-06-20 15:20), 3.2.3 (2019-11-02 16:10), 3.2.5 (2021-04-08 23:50), 3.2.6 (2021-04-26 22:40), 3.2.7 (2021-10-08 23:00), 3.2.8 (2022-10-13 20:12), 3.2.9 (2023-06-06 13:20), 3.2.10 (2023-08-26 16:50), 3.2.11 (2023-11-13 18:53)
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Other R packages that PReMiuM depends,
imports, suggests or enhances
Complete documentation for PReMiuM
Functions, R codes and Examples using
the PReMiuM R package
Some associated functions: PReMiuM-package . calcAvgRiskAndProfile . calcDissimilarityMatrix . calcOptimalClustering . calcPredictions . clusSummaryBernoulliDiscrete . computeRatioOfVariance . generateSampleDataFile . globalParsTrace . heatDissMat . is.wholenumber . mapforGeneratedData . margModelPosterior . plotPredictions . plotRiskProfile . profRegr . rALD . setHyperparams . simBenchmark . summariseVarSelectRho . vec2mat .
Some associated R codes: generateData.R . postProcess.R . Full PReMiuM package functions and examples
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