A B C D E F G H I L M P R S T U W
| dirichletprocess-package | A flexible package for fitting Bayesian non-parametric models. |
| AlphaPriorPosteriorPlot | Diagnostic plots for dirichletprocess objects |
| AlphaTraceplot | Diagnostic plots for dirichletprocess objects |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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. |
| 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 |
| rats | Tumour incidences in rats |
| StickBreaking | The Stick Breaking representation of the Dirichlet process. |
| true_cluster_labels | Identifies the correct clusters labels, in any dimension, when cluster parameters and global parameters are matched. |
| 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. |
| WeibullMixtureCreate | Create a Weibull mixing distribution. |
| weighted_function_generator | Generate a weighted function. |