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

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.