Learning Bayesian Networks with the bnlearn R Package

• Main Author: Marco Scutari
• Format: Article
• Language: English
• Published: Foundation for Open Access Statistics 2010-10-01
• Series: Journal of Statistical Software
• Subjects: bayesian networks; R; structure learning algorithms; constraint-based algorithms; score-based algorithms; conditional independence tests
• Online Access: http://www.jstatsoft.org/v35/i03/paper

<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 snow 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).