Package: dirichletprocess 0.4.2

dirichletprocess: Build Dirichlet Process Objects for Bayesian Modelling

Perform nonparametric Bayesian analysis using Dirichlet processes without the need to program the inference algorithms. Utilise included pre-built models or specify custom models and allow the 'dirichletprocess' package to handle the Markov chain Monte Carlo sampling. Our Dirichlet process objects can act as building blocks for a variety of statistical models including and not limited to: density estimation, clustering and prior distributions in hierarchical models. See Teh, Y. W. (2011) <https://www.stats.ox.ac.uk/~teh/research/npbayes/Teh2010a.pdf>, among many other sources.

Authors:Gordon J. Ross [aut], Dean Markwick [aut, cre], Kees Mulder [ctb], Giovanni Sighinolfi [ctb], Filippo Fiocchi [ctb]

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dirichletprocess.pdf |dirichletprocess.html✨
dirichletprocess/json (API)
NEWS

# Install 'dirichletprocess' in R:
install.packages('dirichletprocess', repos = c('https://dm13450.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/dm13450/dirichletprocess/issues

Datasets:
  • rats - Tumour incidences in rats

On CRAN:

bayesianbayesian-inferencebayesian-statisticsdirichlet-processmcmc

58 exports 57 stars 3.44 score 30 dependencies 2 dependents 70 scripts 363 downloads

Last updated 1 years agofrom:e161437e3d. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKSep 18 2024
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R-4.5-linuxOKSep 18 2024
R-4.4-winOKSep 18 2024
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Exports:AlphaPriorPosteriorPlotAlphaTraceplotBetaMixture2CreateBetaMixtureCreateBurnChangeObservationsClusterComponentUpdateClusterLabelPredictClusterParameterUpdateClusterTraceplotDiagnosticPlotsDirichletHMMCreateDirichletProcessBetaDirichletProcessBeta2DirichletProcessCreateDirichletProcessExponentialDirichletProcessGaussianDirichletProcessGaussianFixedVarianceDirichletProcessHierarchicalBetaDirichletProcessHierarchicalMvnormal2DirichletProcessMvnormalDirichletProcessMvnormal2DirichletProcessWeibullExponentialMixtureCreateFitGaussianFixedVarianceMixtureCreateGaussianMixtureCreateGlobalParameterUpdateHierarchicalBetaCreateHierarchicalMvnormal2CreateInitialiseLikelihoodLikelihoodDPLikelihoodFunctionLikelihoodTraceplotMixingDistributionMvnormal2CreateMvnormalCreatePenalisedLikelihoodpiDirichletplot_dirichletprocess_multivariateplot_dirichletprocess_univariatePosteriorClustersPosteriorDrawPosteriorFramePosteriorFunctionPosteriorParametersPredictivePriorClustersPriorDensityPriorDrawPriorParametersUpdateStickBreakingtrue_cluster_labelsUpdateAlphaUpdateAlphaBetaWeibullMixtureCreateweighted_function_generator

Dependencies:clicolorspacefansifarverggplot2gluegtablegtoolsisobandlabelinglatticelifecyclemagrittrMASSMatrixmgcvmunsellmvtnormnlmepillarpkgconfigR6RColorBrewerrlangscalestibbleutf8vctrsviridisLitewithr

dirichletprocess: An R Package for Fitting Complex Bayesian Nonparametric Models

Rendered fromdirichletprocess.Rnwusingutils::Sweaveon Sep 18 2024.

Last update: 2020-10-14
Started: 2017-12-08

Readme and manuals

Help Manual

Help pageTopics
Create a Beta mixture with zeros at the boundaries.BetaMixture2Create
Create a Beta mixing distribution.BetaMixtureCreate
Add burn-in to a dirichletprocess objectBurn
Change the observations of fitted Dirichlet Process.ChangeObservations
Update the component of the Dirichlet processClusterComponentUpdate ClusterComponentUpdate.conjugate ClusterComponentUpdate.hierarchical
Predict the cluster labels of some new data.ClusterLabelPredict
Update the cluster parameters of the Dirichlet process.ClusterParameterUpdate
Diagnostic plots for dirichletprocess objectsAlphaPriorPosteriorPlot AlphaTraceplot ClusterTraceplot DiagnosticPlots LikelihoodTraceplot
Create a generic Dirichlet process hidden Markov ModelDirichletHMMCreate
A flexible package for fitting Bayesian non-parametric models.dirichletprocess
Dirichlet process mixture of the Beta distribution.DirichletProcessBeta
Dirichlet process mixture of Beta distributions with a Uniform Pareto base measure.DirichletProcessBeta2
Create a Dirichlet Process objectDirichletProcessCreate
Create a Dirichlet Mixture of ExponentialsDirichletProcessExponential
Create a Dirichlet Mixture of GaussiansDirichletProcessGaussian
Create a Dirichlet Mixture of the Gaussian Distribution with fixed variance.DirichletProcessGaussianFixedVariance
Create a Hierarchical Dirichlet Mixture of Beta DistributionsDirichletProcessHierarchicalBeta
Create a Hierarchical Dirichlet Mixture of semi-conjugate Multivariate Normal DistributionsDirichletProcessHierarchicalMvnormal2
Create a Dirichlet mixture of multivariate normal distributions.DirichletProcessMvnormal
Create a Dirichlet mixture of multivariate normal distributions with semi-conjugate prior.DirichletProcessMvnormal2
Create a Dirichlet Mixture of the Weibull distributionDirichletProcessWeibull
Create a Exponential mixing distributionExponentialMixtureCreate
Fit the Dirichlet process objectFit
Fit a Hidden Markov Dirichlet Process ModelFit.markov
Create a Gaussian Mixing Distribution with fixed variance.GaussianFixedVarianceMixtureCreate
Create a Normal mixing distributionGaussianMixtureCreate
Update the parameters of the hierarchical Dirichlet process object.GlobalParameterUpdate
Create a Mixing Object for a hierarchical Beta Dirichlet process object.HierarchicalBetaCreate
Create a Mixing Object for a hierarchical semi-conjugate Multivariate Normal Dirichlet process object.HierarchicalMvnormal2Create
Initialise a Dirichlet process objectInitialise
Mixing Distribution LikelihoodLikelihood Likelihood.beta Likelihood.beta2 Likelihood.exponential Likelihood.mvnormal Likelihood.mvnormal2 Likelihood.normal Likelihood.normalFixedVariance
The likelihood of the Dirichlet process objectLikelihoodDP
The Likelihood function of a Dirichlet process object.LikelihoodFunction
Create a mixing distribution objectMixingDistribution
Create a multivariate normal mixing distribution with semi conjugate priorMvnormal2Create
Create a multivariate normal mixing distributionMvnormalCreate
Calculate the parameters that maximise the penalised likelihood.PenalisedLikelihood PenalisedLikelihood.beta PenalisedLikelihood.default
Plot the Dirichlet process objectplot.dirichletprocess plot_dirichletprocess_multivariate plot_dirichletprocess_univariate
Generate the posterior clusters of a Dirichlet ProcessPosteriorClusters
Draw from the posterior distributionPosteriorDraw PosteriorDraw.exponential PosteriorDraw.mvnormal PosteriorDraw.mvnormal2 PosteriorDraw.normal PosteriorDraw.normalFixedVariance PosteriorDraw.weibull
Calculate the posterior mean and quantiles from a Dirichlet process object.PosteriorFrame
Generate the posterior function of the Dirichlet functionPosteriorFunction
Calculate the posterior parameters for a conjugate prior.PosteriorParameters PosteriorParameters.mvnormal PosteriorParameters.normal PosteriorParameters.normalFixedVariance
Calculate how well the prior predicts the data.Predictive Predictive.exponential Predictive.mvnormal Predictive.normal Predictive.normalFixedVariance
Print the Dirichlet process objectprint.dirichletprocess
Draw prior clusters and weights from the Dirichlet processPriorClusters
Calculate the prior density of a mixing distributionPriorDensity PriorDensity.beta PriorDensity.beta2 PriorDensity.weibull
Draw from the prior distributionPriorDraw PriorDraw.beta PriorDraw.beta2 PriorDraw.exponential PriorDraw.mvnormal PriorDraw.mvnormal2 PriorDraw.normal PriorDraw.normalFixedVariance PriorDraw.weibull
Generate the prior function of the Dirichlet processPriorFunction
Update the prior parameters of a mixing distributionPriorParametersUpdate PriorParametersUpdate.beta PriorParametersUpdate.weibull
Tumour incidences in ratsrats
The Stick Breaking representation of the Dirichlet process.piDirichlet StickBreaking
Identifies the correct clusters labels, in any dimension, when cluster parameters and global parameters are matched.true_cluster_labels
Update the Dirichlet process concentration parameter.UpdateAlpha UpdateAlpha.default UpdateAlpha.hierarchical
Update the alpha and beta parameter of a hidden Markov Dirichlet process model.UpdateAlphaBeta
Create a Weibull mixing distribution.WeibullMixtureCreate
Generate a weighted function.weighted_function_generator