Dirichlet Process Clustering with Dissimilarities
Calculate the Bayesian Silhouette Score
Extract clusters from MCMC samples
Create a diagonal covariance matrix
Create a spherical covariance matrix
Plot the Object Configuration
Posterior Predictive Check
Prior Predictive Check
Procrustes Transformation
Run Dirichlet Process Clustering with Dissimilarities
A Bayesian hierarchical model for clustering dissimilarity data using the Dirichlet process. The latent configuration of objects and the number of clusters are automatically inferred during the fitting process. The package supports multiple models which are available to detect clusters of various shapes and sizes using different covariance structures. Additional functions are included to ensure adequate model fits through prior and posterior predictive checks.