All functions

BetaMixture2Create()

Create a Beta mixture with zeros at the boundaries.

BetaMixtureCreate()

Create a Beta mixing distribution.

Burn()

Add burn-in to a dirichletprocess object

ChangeObservations()

Change the observations of fitted Dirichlet Process.

ClusterComponentUpdate()

Update the component of the Dirichlet process

ClusterLabelPredict()

Predict the cluster labels of some new data.

ClusterParameterUpdate()

Update the cluster parameters of the Dirichlet process.

DiagnosticPlots() AlphaTraceplot() AlphaPriorPosteriorPlot() ClusterTraceplot() LikelihoodTraceplot()

Diagnostic plots for dirichletprocess objects

DirichletHMMCreate()

Create a generic Dirichlet process hidden Markov Model

DirichletProcessBeta()

Dirichlet process mixture of the Beta distribution.

DirichletProcessBeta2()

Dirichlet process mixture of Beta distributions with a Uniform Pareto base measure.

DirichletProcessCreate()

Create a Dirichlet Process object

DirichletProcessExponential()

Create a Dirichlet Mixture of Exponentials

DirichletProcessGaussian()

Create a Dirichlet Mixture of Gaussians

DirichletProcessGaussianFixedVariance()

Create a Dirichlet Mixture of the Gaussian Distribution with fixed variance.

DirichletProcessHierarchicalBeta()

Create a Hierarchical Dirichlet Mixture of Beta Distributions

DirichletProcessHierarchicalMvnormal2()

Create a Hierarchical Dirichlet Mixture of semi-conjugate Multivariate Normal Distributions

DirichletProcessMvnormal()

Create a Dirichlet mixture of multivariate normal distributions.

DirichletProcessMvnormal2()

Create a Dirichlet mixture of multivariate normal distributions with semi-conjugate prior.

DirichletProcessWeibull()

Create a Dirichlet Mixture of the Weibull distribution

ExponentialMixtureCreate()

Create a Exponential mixing distribution

Fit()

Fit the Dirichlet process object

Fit(<markov>)

Fit a Hidden Markov Dirichlet Process Model

GaussianFixedVarianceMixtureCreate()

Create a Gaussian Mixing Distribution with fixed variance.

GaussianMixtureCreate()

Create a Normal mixing distribution

GlobalParameterUpdate()

Update the parameters of the hierarchical Dirichlet process object.

HierarchicalBetaCreate()

Create a Mixing Object for a hierarchical Beta Dirichlet process object.

HierarchicalMvnormal2Create()

Create a Mixing Object for a hierarchical semi-conjugate Multivariate Normal Dirichlet process object.

Initialise()

Initialise a Dirichlet process object

Likelihood()

Mixing Distribution Likelihood

LikelihoodDP()

The likelihood of the Dirichlet process object

LikelihoodFunction()

The Likelihood function of a Dirichlet process object.

MixingDistribution()

Create a mixing distribution object

Mvnormal2Create()

Create a multivariate normal mixing distribution with semi conjugate prior

MvnormalCreate()

Create a multivariate normal mixing distribution

PenalisedLikelihood()

Calculate the parameters that maximise the penalised likelihood.

PosteriorClusters()

Generate the posterior clusters of a Dirichlet Process

PosteriorDraw()

Draw from the posterior distribution

PosteriorFrame()

Calculate the posterior mean and quantiles from a Dirichlet process object.

PosteriorFunction()

Generate the posterior function of the Dirichlet function

PosteriorParameters()

Calculate the posterior parameters for a conjugate prior.

Predictive()

Calculate how well the prior predicts the data.

PriorClusters()

Draw prior clusters and weights from the Dirichlet process

PriorDensity()

Calculate the prior density of a mixing distribution

PriorDraw()

Draw from the prior distribution

PriorFunction()

Generate the prior function of the Dirichlet process

PriorParametersUpdate()

Update the prior parameters of a mixing distribution

StickBreaking() piDirichlet()

The Stick Breaking representation of the Dirichlet process.

UpdateAlpha()

Update the Dirichlet process concentration parameter.

UpdateAlphaBeta()

Update the \(\alpha\) and \(\beta\) parameter of a hidden Markov Dirichlet process model.

WeibullMixtureCreate()

Create a Weibull mixing distribution.

dirichletprocess

A flexible package for fitting Bayesian non-parametric models.

plot(<dirichletprocess>) plot_dirichletprocess_univariate() plot_dirichletprocess_multivariate()

Plot the Dirichlet process object

print(<dirichletprocess>)

Print the Dirichlet process object

rats

Tumour incidences in rats

true_cluster_labels()

Identifies the correct clusters labels, in any dimension, when cluster parameters and global parameters are matched.

weighted_function_generator()

Generate a weighted function.