Published by the Foundation for Open Access Statistics
Editors-in-chief: Bettina GrĂ¼n, Edzer Pebesma & Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
Learning Bayesian Networks with the bnlearn R Package | Scutari | Journal of Statistical Software
Authors: Marco Scutari
Title: Learning Bayesian Networks with the bnlearn R Package
Abstract: bnlearn is an R 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 Rgraphviz package (Gentry et al. 2010).

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Paper: Learning Bayesian Networks with the bnlearn R Package     Download PDF (Downloads: 30926)
bnlearn_2.1.1.tar.gz: R source package Download (Downloads: 803; 1MB)
v35i03.R: R example code from the paper Download (Downloads: 919; 5KB)

DOI: 10.18637/jss.v035.i03

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Paper: Creative Commons Attribution 3.0 Unported License
Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.