Build Dirichlet Process Objects for Bayesian Modelling

Documentation for package ‘dirichletprocess’ version 0.4.2

Help Pages

-- 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.