R: Build Dirichlet Process Objects for Bayesian Modelling

# Build Dirichlet Process Objects for Bayesian Modelling

## Documentation for package ‘dirichletprocess’ version 0.4.2

- DESCRIPTION file
- User guides, package vignettes and other documentation
- Package NEWS
- Package README

## Help Pages

A B C D E F G H I L M P R S T U W

## -- A --

AlphaPriorPosteriorPlot | Diagnostic plots for dirichletprocess objects
AlphaTraceplot | Diagnostic plots for dirichletprocess objects

## -- B --

BetaMixture2Create | Create a Beta mixture with zeros at the boundaries.
BetaMixtureCreate | Create a Beta mixing distribution.
Burn | Add burn-in to a dirichletprocess object

## -- C --

ChangeObservations | Change the observations of fitted Dirichlet Process.
ClusterComponentUpdate | Update the component of the Dirichlet process
ClusterComponentUpdate.conjugate | Update the component of the Dirichlet process
ClusterComponentUpdate.hierarchical | 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.
ClusterTraceplot | Diagnostic plots for dirichletprocess objects

## -- D --

DiagnosticPlots | Diagnostic plots for dirichletprocess objects
DirichletHMMCreate | Create a generic Dirichlet process hidden Markov Model
dirichletprocess | A flexible package for fitting Bayesian non-parametric models.
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

## -- E --

ExponentialMixtureCreate | Create a Exponential mixing distribution

## -- F --

Fit | Fit the Dirichlet process object
Fit.markov | Fit a Hidden Markov Dirichlet Process Model

## -- G --

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.

## -- H --

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.

## -- I --

Initialise | Initialise a Dirichlet process object

## -- L --

Likelihood | Mixing Distribution Likelihood
Likelihood.beta | Mixing Distribution Likelihood
Likelihood.beta2 | Mixing Distribution Likelihood
Likelihood.exponential | Mixing Distribution Likelihood
Likelihood.mvnormal | Mixing Distribution Likelihood
Likelihood.mvnormal2 | Mixing Distribution Likelihood
Likelihood.normal | Mixing Distribution Likelihood
Likelihood.normalFixedVariance | Mixing Distribution Likelihood
LikelihoodDP | The likelihood of the Dirichlet process object
LikelihoodFunction | The Likelihood function of a Dirichlet process object.
LikelihoodTraceplot | Diagnostic plots for dirichletprocess objects

## -- M --

MixingDistribution | Create a mixing distribution object
Mvnormal2Create | Create a multivariate normal mixing distribution with semi conjugate prior
MvnormalCreate | Create a multivariate normal mixing distribution

## -- P --

PenalisedLikelihood | Calculate the parameters that maximise the penalised likelihood.
PenalisedLikelihood.beta | Calculate the parameters that maximise the penalised likelihood.
PenalisedLikelihood.default | Calculate the parameters that maximise the penalised likelihood.
piDirichlet | The Stick Breaking representation of the Dirichlet process.
plot.dirichletprocess | Plot the Dirichlet process object
plot_dirichletprocess_multivariate | Plot the Dirichlet process object
plot_dirichletprocess_univariate | Plot the Dirichlet process object
PosteriorClusters | Generate the posterior clusters of a Dirichlet Process
PosteriorDraw | Draw from the posterior distribution
PosteriorDraw.exponential | Draw from the posterior distribution
PosteriorDraw.mvnormal | Draw from the posterior distribution
PosteriorDraw.mvnormal2 | Draw from the posterior distribution
PosteriorDraw.normal | Draw from the posterior distribution
PosteriorDraw.normalFixedVariance | Draw from the posterior distribution
PosteriorDraw.weibull | 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.
PosteriorParameters.mvnormal | Calculate the posterior parameters for a conjugate prior.
PosteriorParameters.normal | Calculate the posterior parameters for a conjugate prior.
PosteriorParameters.normalFixedVariance | Calculate the posterior parameters for a conjugate prior.
Predictive | Calculate how well the prior predicts the data.
Predictive.exponential | Calculate how well the prior predicts the data.
Predictive.mvnormal | Calculate how well the prior predicts the data.
Predictive.normal | Calculate how well the prior predicts the data.
Predictive.normalFixedVariance | Calculate how well the prior predicts the data.
print.dirichletprocess | Print the Dirichlet process object
PriorClusters | Draw prior clusters and weights from the Dirichlet process
PriorDensity | Calculate the prior density of a mixing distribution
PriorDensity.beta | Calculate the prior density of a mixing distribution
PriorDensity.beta2 | Calculate the prior density of a mixing distribution
PriorDensity.weibull | Calculate the prior density of a mixing distribution
PriorDraw | Draw from the prior distribution
PriorDraw.beta | Draw from the prior distribution
PriorDraw.beta2 | Draw from the prior distribution
PriorDraw.exponential | Draw from the prior distribution
PriorDraw.mvnormal | Draw from the prior distribution
PriorDraw.mvnormal2 | Draw from the prior distribution
PriorDraw.normal | Draw from the prior distribution
PriorDraw.normalFixedVariance | Draw from the prior distribution
PriorDraw.weibull | Draw from the prior distribution
PriorFunction | Generate the prior function of the Dirichlet process
PriorParametersUpdate | Update the prior parameters of a mixing distribution
PriorParametersUpdate.beta | Update the prior parameters of a mixing distribution
PriorParametersUpdate.weibull | Update the prior parameters of a mixing distribution

## -- R --

rats | Tumour incidences in rats

## -- S --

StickBreaking | The Stick Breaking representation of the Dirichlet process.

## -- T --

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

## -- U --

UpdateAlpha | Update the Dirichlet process concentration parameter.
UpdateAlpha.default | Update the Dirichlet process concentration parameter.
UpdateAlpha.hierarchical | Update the Dirichlet process concentration parameter.
UpdateAlphaBeta | Update the alpha and beta parameter of a hidden Markov Dirichlet process model.

## -- W --

WeibullMixtureCreate | Create a Weibull mixing distribution.
weighted_function_generator | Generate a weighted function.