hdbayes: An R Package for Bayesian Analysis of Generalized Linear Models Using Historical Data

2025/06/24 by Ethan M. Alt, Alt, Ethan M., Xinxin Chen +5 · 1 citation
Computer Science · #Computation (stat.CO) #Data Analysis with R #FOS: Computer and information sciences #Methodology (stat.ME)

paper · pdf · doi:10.48550/arxiv.2506.20060

openalex publication_date 2025/06/24 · openalex created_date 2025/10/09 · openalex updated_date 2026/07/28

Abstract

There has been increased interest in the use of historical data to formulate informative priors in regression models. While many such priors for incorporating historical data have been proposed, adoption is limited due to access to software. Where software does exist, the implementations between different methods could be vastly different, making comparisons between methods difficult. In this paper, we introduce the R package hdbayes, an implementation of the power prior, normalized power prior, Bayesian hierarchical model, robust meta-analytic prior, commensurate prior, and latent exchangeability prior for generalized linear models. The bulk of the package is written in the Stan programming language, with user-friendly R wrapper functions to call samplers.

Citations

• LEAP: The latent exchangeability prior for borrowing information from historical data
• bridgesampling: An R Package for Estimating Normalizing Constants
• The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo
• Power prior distributions for regression models

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• Bayesian weighted discrete-time dynamic models for association football prediction

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