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:
PReMiuM_3.2.13.tar.gz
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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')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 10 months agofrom:c0dbdc2f1e. Checks:OK: 9. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Nov 05 2024 |
R-4.5-win-x86_64 | OK | Nov 05 2024 |
R-4.5-linux-x86_64 | OK | Nov 05 2024 |
R-4.4-win-x86_64 | OK | Nov 05 2024 |
R-4.4-mac-x86_64 | OK | Nov 05 2024 |
R-4.4-mac-aarch64 | OK | Nov 05 2024 |
R-4.3-win-x86_64 | OK | Nov 05 2024 |
R-4.3-mac-x86_64 | OK | Nov 05 2024 |
R-4.3-mac-aarch64 | OK | Nov 05 2024 |
Exports:calcAvgRiskAndProfilecalcDissimilarityMatrixcalcOptimalClusteringcalcPredictionsclusSummaryBernoulliDiscreteclusSummaryBernoulliDiscreteSmallclusSummaryBernoulliMixedclusSummaryBernoulliNormalclusSummaryBinomialNormalclusSummaryCategoricalDiscreteclusSummaryGammaNormalclusSummaryNormalDiscreteclusSummaryNormalNormalclusSummaryNormalNormalSpatialclusSummaryPoissonDiscreteclusSummaryPoissonNormalclusSummaryPoissonNormalSpatialclusSummaryQuantileNormalclusSummaryVarSelectBernoulliDiscreteclusSummaryWeibullDiscretecomputeRatioOfVariancegenerateSampleDataFileglobalParsTraceheatDissMatis.wholenumbermapforGeneratedDatamargModelPosteriorplotPredictionsplotRiskProfileprofRegrqALDrALDsetHyperparamssimBenchmarksummariseVarSelectRhovec2mat
Dependencies:BHbootclassclassIntcliclustercolorspacedata.tableDBIdeldire1071fansifarvergamlss.distggplot2gluegtableisobandKernSmoothlabelinglatticelifecyclemagrittrMASSMatrixmgcvmunsellnlmepillarpkgconfigplotrixproxyR6RColorBrewerRcppRcppEigenrlangs2scalessfspspDataspdeptibbleunitsutf8vctrsviridisLitewithrwk
Readme and manuals
Help Manual
Help page | Topics |
---|---|
Dirichlet Process Bayesian Clustering | PReMiuM-package PReMiuM PReMiuMpackage |
Calculation of the average risks and profiles | calcAvgRiskAndProfile |
Calculates the dissimilarity matrix | calcDissimilarityMatrix |
Calculation of the optimal clustering | calcOptimalClustering |
Calculates the predictions | calcPredictions |
Sample datasets for profile regression | clusSummaryBernoulliDiscrete clusSummaryBernoulliDiscreteSmall clusSummaryBernoulliMixed clusSummaryBernoulliNormal clusSummaryBinomialNormal clusSummaryCategoricalDiscrete clusSummaryGammaNormal clusSummaryNormalDiscrete clusSummaryNormalNormal clusSummaryNormalNormalSpatial clusSummaryPoissonDiscrete clusSummaryPoissonNormal clusSummaryPoissonNormalSpatial clusSummaryQuantileNormal clusSummaryVarSelectBernoulliDiscrete clusSummaryWeibullDiscrete |
computeRatioOfVariance | computeRatioOfVariance |
Generate sample data files for profile regression | generateSampleDataFile |
Plot of the trace of some of the global parameters | globalParsTrace |
Plot the heatmap of the dissimilarity matrix | heatDissMat |
Function to check if a number is a whole number | is.wholenumber |
Map generated data | mapforGeneratedData |
Marginal Model Posterior | margModelPosterior |
Plot the conditional density using the predicted scenarios | plotPredictions |
Plot the Risk Profiles | plotRiskProfile |
Profile Regression | profRegr |
Asymmetric Laplace Distribution | qALD rALD |
Definition of characteristics of sample datasets for profile regression | setHyperparams |
Benchmark for simulated examples | simBenchmark |
summariseVarSelectRho | summariseVarSelectRho |
Vector to upper triangular matrix | vec2mat |