dirichletprocess: An R Package for Fitting Complex Bayesian Nonparametric Models

2026-05-02

AI Summary

This work addresses the high implementation complexity and accessibility barriers of inference algorithms in Bayesian nonparametric modeling by proposing a flexible Dirichlet process (DP) framework implemented in R. The framework encapsulates the DP as a reusable object that supports density estimation, clustering, and hierarchical model prior construction, while automatically performing Markov chain Monte Carlo (MCMC) posterior inference. Users can either directly apply pre-specified models or customize base distributions and mixture structures without manually implementing sampling algorithms. By abstracting away computational intricacies while preserving substantial modeling flexibility, this approach significantly lowers the practical barrier to applying Bayesian nonparametric methods across a wide range of statistical analysis tasks.

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.

Problem

• Dirichlet process
• Bayesian nonparametric models
• density estimation
• clustering
• hierarchical models

Innovation

• Dirichlet process
• Bayesian nonparametrics
• R package
• MCMC sampling
• density estimation