Package: dirichletprocess 0.4.2.9000

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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manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
dirichletprocess/json (API)

# Install 'dirichletprocess' in R:

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

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

Pkgdown/docs site:https://dm13450.github.io

Datasets:

rats - Tumour incidences in rats

On CRAN:

bayesian bayesian-inference bayesian-statistics dirichlet-process mcmc

7.76 score 62 stars 2 packages 99 scripts 1.6k downloads 58 exports 19 dependencies

Last updated from:6b0b15bb7a. Checks:9 OK. Indexed: yes.

Target	Result	Time
linux-devel	OK	148
source / vignettes	OK	203
linux-release	OK	147
macos-release	OK	150
macos-oldrel	OK	237
windows-devel	OK	121
windows-release	OK	103
windows-oldrel	OK	159
wasm-release	OK	92

Exports:AlphaPriorPosteriorPlot AlphaTraceplot BetaMixture2Create BetaMixtureCreate Burn ChangeObservations ClusterComponentUpdate ClusterLabelPredict ClusterParameterUpdate ClusterTraceplot DiagnosticPlots DirichletHMMCreate DirichletProcessBeta DirichletProcessBeta2 DirichletProcessCreate DirichletProcessExponential DirichletProcessGaussian DirichletProcessGaussianFixedVariance DirichletProcessHierarchicalBeta DirichletProcessHierarchicalMvnormal2 DirichletProcessMvnormal DirichletProcessMvnormal2 DirichletProcessWeibull ExponentialMixtureCreate Fit GaussianFixedVarianceMixtureCreate GaussianMixtureCreate GlobalParameterUpdate HierarchicalBetaCreate HierarchicalMvnormal2Create Initialise Likelihood LikelihoodDP LikelihoodFunction LikelihoodTraceplot MixingDistribution Mvnormal2Create MvnormalCreate PenalisedLikelihood piDirichlet plot_dirichletprocess_multivariate plot_dirichletprocess_univariate PosteriorClusters PosteriorDraw PosteriorFrame PosteriorFunction PosteriorParameters Predictive PriorClusters PriorDensity PriorDraw PriorParametersUpdate StickBreaking true_cluster_labels UpdateAlpha UpdateAlphaBeta WeibullMixtureCreate weighted_function_generator

Dependencies:cli cpp11 farver ggplot2 glue gtable gtools isoband labeling lifecycle mvtnorm R6 RColorBrewer rlang S7 scales vctrs viridisLite withr

dirichletprocess: An R Package for Fitting Complex Bayesian Nonparametric Models

Introduction | Background information | Package Overview | Advanced Features

Rendered fromdirichletprocess.Rnwusingutils::Sweave

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

Help page	Topics
Create a Beta mixture with zeros at the boundaries.	BetaMixture2Create
Create a Beta mixing distribution.	BetaMixtureCreate
Add burn-in to a dirichletprocess object	Burn
Change the observations of fitted Dirichlet Process.	ChangeObservations
Update the component of the Dirichlet process	ClusterComponentUpdate 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 objects	AlphaPriorPosteriorPlot AlphaTraceplot ClusterTraceplot DiagnosticPlots LikelihoodTraceplot
Create a generic Dirichlet process hidden Markov Model	DirichletHMMCreate
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 object	DirichletProcessCreate
Create a Dirichlet Mixture of Exponentials	DirichletProcessExponential
Create a Dirichlet Mixture of Gaussians	DirichletProcessGaussian
Create a Dirichlet Mixture of the Gaussian Distribution with fixed variance.	DirichletProcessGaussianFixedVariance
Create a Hierarchical Dirichlet Mixture of Beta Distributions	DirichletProcessHierarchicalBeta
Create a Hierarchical Dirichlet Mixture of semi-conjugate Multivariate Normal Distributions	DirichletProcessHierarchicalMvnormal2
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 distribution	DirichletProcessWeibull
Create a Exponential mixing distribution	ExponentialMixtureCreate
Fit the Dirichlet process object	Fit
Fit a Hidden Markov Dirichlet Process Model	Fit.markov
Create a Gaussian Mixing Distribution with fixed variance.	GaussianFixedVarianceMixtureCreate
Create a Normal mixing distribution	GaussianMixtureCreate
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 object	Initialise
Mixing Distribution Likelihood	Likelihood Likelihood.beta Likelihood.beta2 Likelihood.exponential Likelihood.mvnormal Likelihood.mvnormal2 Likelihood.normal Likelihood.normalFixedVariance
The likelihood of the Dirichlet process object	LikelihoodDP
The Likelihood function of a Dirichlet process object.	LikelihoodFunction
Create a mixing distribution object	MixingDistribution
Create a multivariate normal mixing distribution with semi conjugate prior	Mvnormal2Create
Create a multivariate normal mixing distribution	MvnormalCreate
Calculate the parameters that maximise the penalised likelihood.	PenalisedLikelihood PenalisedLikelihood.beta PenalisedLikelihood.default
Plot the Dirichlet process object	plot.dirichletprocess plot_dirichletprocess_multivariate plot_dirichletprocess_univariate
Generate the posterior clusters of a Dirichlet Process	PosteriorClusters
Draw from the posterior distribution	PosteriorDraw 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 function	PosteriorFunction
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 object	print.dirichletprocess
Draw prior clusters and weights from the Dirichlet process	PriorClusters
Calculate the prior density of a mixing distribution	PriorDensity PriorDensity.beta PriorDensity.beta2 PriorDensity.weibull
Draw from the prior distribution	PriorDraw PriorDraw.beta PriorDraw.beta2 PriorDraw.exponential PriorDraw.mvnormal PriorDraw.mvnormal2 PriorDraw.normal PriorDraw.normalFixedVariance PriorDraw.weibull
Generate the prior function of the Dirichlet process	PriorFunction
Update the prior parameters of a mixing distribution	PriorParametersUpdate PriorParametersUpdate.beta PriorParametersUpdate.weibull
Tumour incidences in rats	rats
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

Package: dirichletprocess 0.4.2.9000

dirichletprocess: Build Dirichlet Process Objects for Bayesian Modelling

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