Function reference • dirichletprocess

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.

Reference

All functions