Package: PReMiuM 3.2.13

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>.

Authors:David I. Hastie, Silvia Liverani <[email protected]> and Sylvia Richardson with contributions from Aurore J. Lavigne, Lucy Leigh, Lamiae Azizi, Xi Liu, Ruizhu Huang, Austin Gratton, Wei Jing

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PReMiuM.pdf |PReMiuM.html
PReMiuM/json (API)

# Install 'PReMiuM' in R:
install.packages('PReMiuM', repos = c('https://silvialiverani.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Uses libs:
  • c++– GNU Standard C++ Library v3

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

3.40 score 126 scripts 696 downloads 10 mentions 36 exports 50 dependencies

Last updated 10 months agofrom:c0dbdc2f1e. Checks:OK: 9. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 05 2024
R-4.5-win-x86_64OKNov 05 2024
R-4.5-linux-x86_64OKNov 05 2024
R-4.4-win-x86_64OKNov 05 2024
R-4.4-mac-x86_64OKNov 05 2024
R-4.4-mac-aarch64OKNov 05 2024
R-4.3-win-x86_64OKNov 05 2024
R-4.3-mac-x86_64OKNov 05 2024
R-4.3-mac-aarch64OKNov 05 2024

Exports:calcAvgRiskAndProfilecalcDissimilarityMatrixcalcOptimalClusteringcalcPredictionsclusSummaryBernoulliDiscreteclusSummaryBernoulliDiscreteSmallclusSummaryBernoulliMixedclusSummaryBernoulliNormalclusSummaryBinomialNormalclusSummaryCategoricalDiscreteclusSummaryGammaNormalclusSummaryNormalDiscreteclusSummaryNormalNormalclusSummaryNormalNormalSpatialclusSummaryPoissonDiscreteclusSummaryPoissonNormalclusSummaryPoissonNormalSpatialclusSummaryQuantileNormalclusSummaryVarSelectBernoulliDiscreteclusSummaryWeibullDiscretecomputeRatioOfVariancegenerateSampleDataFileglobalParsTraceheatDissMatis.wholenumbermapforGeneratedDatamargModelPosteriorplotPredictionsplotRiskProfileprofRegrqALDrALDsetHyperparamssimBenchmarksummariseVarSelectRhovec2mat

Dependencies:BHbootclassclassIntcliclustercolorspacedata.tableDBIdeldire1071fansifarvergamlss.distggplot2gluegtableisobandKernSmoothlabelinglatticelifecyclemagrittrMASSMatrixmgcvmunsellnlmepillarpkgconfigplotrixproxyR6RColorBrewerRcppRcppEigenrlangs2scalessfspspDataspdeptibbleunitsutf8vctrsviridisLitewithrwk

Readme and manuals

Help Manual

Help pageTopics
Dirichlet Process Bayesian ClusteringPReMiuM-package PReMiuM PReMiuMpackage
Calculation of the average risks and profilescalcAvgRiskAndProfile
Calculates the dissimilarity matrixcalcDissimilarityMatrix
Calculation of the optimal clusteringcalcOptimalClustering
Calculates the predictionscalcPredictions
Sample datasets for profile regressionclusSummaryBernoulliDiscrete clusSummaryBernoulliDiscreteSmall clusSummaryBernoulliMixed clusSummaryBernoulliNormal clusSummaryBinomialNormal clusSummaryCategoricalDiscrete clusSummaryGammaNormal clusSummaryNormalDiscrete clusSummaryNormalNormal clusSummaryNormalNormalSpatial clusSummaryPoissonDiscrete clusSummaryPoissonNormal clusSummaryPoissonNormalSpatial clusSummaryQuantileNormal clusSummaryVarSelectBernoulliDiscrete clusSummaryWeibullDiscrete
computeRatioOfVariancecomputeRatioOfVariance
Generate sample data files for profile regressiongenerateSampleDataFile
Plot of the trace of some of the global parametersglobalParsTrace
Plot the heatmap of the dissimilarity matrixheatDissMat
Function to check if a number is a whole numberis.wholenumber
Map generated datamapforGeneratedData
Marginal Model PosteriormargModelPosterior
Plot the conditional density using the predicted scenariosplotPredictions
Plot the Risk ProfilesplotRiskProfile
Profile RegressionprofRegr
Asymmetric Laplace DistributionqALD rALD
Definition of characteristics of sample datasets for profile regressionsetHyperparams
Benchmark for simulated examplessimBenchmark
summariseVarSelectRhosummariseVarSelectRho
Vector to upper triangular matrixvec2mat