Learning Bayesian Networks with the<b>bnlearn</b><i>R</i>Package

TLDR

bnlearn is an R package that includes algorithms for learning Bayesian network structures with discrete or continuous variables. It implements constraint‑based and score‑based algorithms, supports parallel computing via the snow package, offers various network scores and conditional independence tests, and provides advanced plotting through Rgraphviz. © 2010.

Abstract

<b>bnlearn</b> is an <b>R</b> package (R Development Core Team 2010) which includes several algorithms for learning the structure of Bayesian networks with either discrete or continuous variables. Both constraint-based and score-based algorithms are implemented, and can use the functionality provided by the <b>snow</b> package (Tierney et al. 2008) to improve their performance via parallel computing. Several network scores and conditional independence algorithms are available for both the learning algorithms and independent use. Advanced plotting options are provided by the <b>Rgraphviz</b> package (Gentry et al. 2010).