dirichletprocess: An R Package for Fitting Complex Bayesian Nonparametric Models

TL;DR

The dirichletprocess R package enables flexible nonparametric Bayesian analysis with pre-built models and MCMC sampling, simplifying complex Dirichlet process applications.

Contribution

It introduces an R package that facilitates nonparametric Bayesian modeling with minimal programming, supporting various statistical applications.

Findings

Supports density estimation, clustering, and hierarchical priors.

Allows users to specify custom models easily.

Handles MCMC sampling internally.

Abstract

The dirichletprocess package provides software for creating flexible Dirichlet process objects. Users can perform nonparametric Bayesian analysis using Dirichlet processes without the need to program their own inference algorithms. Instead, the user can utilise our pre-built models or specify their own models whilst allowing the dirichletprocess package to handle the Markov chain Monte Carlo sampling. Our Dirichlet process objects can act as building blocks for a variety of statistical models including: density estimation, clustering and prior distributions in hierarchical models.