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

TL;DR

The paper introduces hdbayes, an R package that implements various Bayesian methods for incorporating historical data into generalized linear models, facilitating easier comparison and application of these techniques.

Contribution

It provides the first comprehensive R package implementing multiple Bayesian priors for historical data in GLMs, with user-friendly interfaces and Stan-based computation.

Findings

Enables practical application of Bayesian priors with historical data

Facilitates comparison of different Bayesian methods in GLMs

Improves accessibility of advanced Bayesian techniques for practitioners

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.