Additive Bayesian Network Modelling with the R Package abn
This work provides a tool for researchers in fields like veterinary science to model complex observational data with mixed variable types, but it is incremental as it builds on existing Bayesian network methodologies.
The authors introduced the R package abn for fitting additive Bayesian models to observational datasets, enabling the selection of optimal Bayesian networks through exact and greedy search algorithms, and demonstrated its application on a veterinary dataset concerning respiratory diseases in swine.
The R package abn is designed to fit additive Bayesian models to observational datasets. It contains routines to score Bayesian networks based on Bayesian or information theoretic formulations of generalized linear models. It is equipped with exact search and greedy search algorithms to select the best network. It supports a possible blend of continuous, discrete and count data and input of prior knowledge at a structural level. The Bayesian implementation supports random effects to control for one-layer clustering. In this paper, we give an overview of the methodology and illustrate the package's functionalities using a veterinary dataset about respiratory diseases in commercial swine production.