The R package abn is designed to fit additive Bayesian network models to observa-tional datasets and 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, and supports continuous, discrete and count data in the same model and input of prior knowledge at a structural level. The Bayesian implementation supports random effects to control for one-layer clus-tering. In this paper, we give an overview of the methodology and illustrate the package’s functionality using a veterinary dataset concerned with respiratory diseases in commercial swine production.

Additive Bayesian Network Modeling with the R Package abn / Kratzer G.; Lewis F.; Comin A.; Pittavino M.; Furrer R.. - In: JOURNAL OF STATISTICAL SOFTWARE. - ISSN 1548-7660. - ELETTRONICO. - 105:(2023), pp. 1-41. [10.18637/jss.v105.i08]

Additive Bayesian Network Modeling with the R Package abn

Pittavino M.;
2023

Abstract

The R package abn is designed to fit additive Bayesian network models to observa-tional datasets and 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, and supports continuous, discrete and count data in the same model and input of prior knowledge at a structural level. The Bayesian implementation supports random effects to control for one-layer clus-tering. In this paper, we give an overview of the methodology and illustrate the package’s functionality using a veterinary dataset concerned with respiratory diseases in commercial swine production.
2023
105
1
41
Kratzer G.; Lewis F.; Comin A.; Pittavino M.; Furrer R.
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Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/1322111
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