Learning Large-Scale Bayesian Networks with the sparsebn Package

Abstract

Learning graphical models from data is an important problem with wide applications, ranging from genomics to the social sciences. Nowadays datasets often have upwards of thousands---sometimes tens or hundreds of thousands---of variables and far fewer samples. To meet this challenge, we have developed a new R package called sparsebn for learning the structure of large, sparse graphical models with a focus on Bayesian networks. While there are many existing software packages for this task, this package focuses on the unique setting of learning large networks from high-dimensional data, possibly with interventions. As such, the methods provided place a premium on scalability and consistency in a high-dimensional setting. Furthermore, in the presence of interventions, the methods implemented here achieve the goal of learning a causal network from data. Additionally, the sparsebn package is fully compatible with existing software packages for network analysis.

To appear in the Journal of Statistical Software, 39 pages, 7 figures

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Cited by in corpus (8)

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• Distributed Learning of Generalized Linear Causal Networks
• Learning big Gaussian Bayesian networks: partition, estimation, and fusion
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• Learning complex dependency structure of gene regulatory networks from high dimensional micro-array data with Gaussian Bayesian networks
• Causal network learning with non-invertible functional relationships
• Learning Gaussian DAGs from Network Data