Hobbs: A General-Purpose Probabilistic Programming System for High Dimensional Bayesian Data Analysis in R

Abstract

Abstract: General-purpose probabilistic programming makes Bayesian data analysis broadly accessible, but its computational cost can become prohibitive as model dimension grows. The R package hobbs (High dimensiOnal Bayesian omniBus Sampler) is a probabilistic programming language and system designed to make high-dimensional Bayesian analysis practical by trading modest syntactic complexity for substantially greater computational efficiency. It allows reusable code for model-specific computational strategies to be expressed directly within the model program. hobbs implements adaptive scalar Metropolis-within-Gibbs sampling using parameter-local blocks that evaluate only the posterior terms affected by each proposal. Deterministic caches update linear predictors and other derived quantities incrementally, while optional distribution caches reduce repeated numerical calculations. The R interface translates model programs into compiled C code, while a reusable Rust engine performs sampling and writes posterior draws and diagnostics. Two high-dimensional case studies demonstrate how these features support models with tens of thousands to more than one hundred thousand parameters within a single general modeling framework. Together, these results show that hobbs can retain the flexibility of general-purpose probabilistic programming while making large, computationally intensive Bayesian models practical.