Learning Bayesian Networks with the bnlearn R Package

In recent years Bayesian networks have been used in many fields, from Online Analytical Processing (OLAP) performance enhancement (Margaritis 2003) to medical service performance analysis (Acid et al. 2004), gene expression analysis (Friedman et al. 2000), breast cancer prognosis and epidemiology (Holmes and Jain 2008). The high dimensionality of the data sets common in these domains have led to the development of several learning algorithms focused on reducing computational complexity while still learning the correct network. Some examples are the Grow-Shrink algorithm in Margaritis (2003), the Incremental Association algorithm and its derivatives in Tsamardinos et al. (2003) and in Yaramakala and Margaritis (2005), the Sparse Candidate algorithm in Friedman et al. (1999), the Optimal Reinsertion in Moore and Wong (2003) and the Greedy Equivalent Search in Chickering (2002). The aim of the bnlearn package is to provide a free implementation of some of these structure learning algorithms along with the conditional independence tests and network scores used 2 Learning Bayesian Networks with the bnlearn R Package to construct the Bayesian network. Both discrete and continuous data are supported. Furthermore, the learning algorithms can be chosen separately from the statistical criterion they are based on (which is usually not possible in the reference implementation provided by the algorithms' authors), so that the best combination for the data at hand can be used.