sanba: An R Package for Bayesian Clustering of Distributions via Shared Atoms Nested Models

TL;DR

Sanba is an R package that enables flexible Bayesian clustering of hierarchical grouped data using nested mixture models with shared atoms, combining advanced inference methods for efficiency and scalability.

Contribution

The paper introduces sanba, an R package implementing Bayesian nested mixture models with shared atoms, incorporating efficient MCMC and variational inference algorithms for hierarchical data analysis.

Findings

Provides a fast, user-friendly R package for Bayesian nested clustering.

Supports scalable inference with MCMC and variational algorithms.

Facilitates hierarchical density estimation in grouped data.

Abstract

Nested data structures arise when observations are grouped into distinct units, such as patients within hospitals or students within schools. Accounting for this hierarchical organization is essential for valid inference, as ignoring it can lead to biased estimates and poor generalization. This article addresses the challenge of clustering both individual observations and their corresponding groups while flexibly estimating group-specific densities. Bayesian nested mixture models offer a principled and robust framework for this task. However, their practical use has often been limited by computational complexity. To overcome this barrier, we present sanba, an R package for Bayesian analysis of grouped data using nested mixture models with a shared set of atoms, a structure recently introduced in the statistical literature. The package provides multiple inference strategies, including state-of-the-art Markov Chain Monte Carlo routines and variational inference algorithms tailored for large-scale datasets. All core functions are implemented in C++ and seamlessly integrated into R, making sanba a fast and user-friendly tool for fitting nested mixture models with modern Bayesian algorithms.