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Learning graphical models from data is an important problem with wideapplications, ranging from genomics to the social sciences. Nowadays datasetsoften have upwards of thousands---sometimes tens or hundreds of thousands---ofvariables and far fewer samples. To meet this challenge, we have developed anew R package called sparsebn for learning the structure of large, sparsegraphical models with a focus on Bayesian networks. While there are manyexisting software packages for this task, this package focuses on the uniquesetting of learning large networks from high-dimensional data, possibly withinterventions. As such, the methods provided place a premium on scalability andconsistency in a high-dimensional setting. Furthermore, in the presence ofinterventions, the methods implemented here achieve the goal of learning acausal network from data. Additionally, the sparsebn package is fullycompatible with existing software packages for network analysis.

Learning Large-Scale Bayesian Networks with the sparsebn Package