Learning Large-Scale Bayesian Networks with the sparsebn Package
This provides a scalable tool for researchers in fields like genomics and social sciences dealing with high-dimensional datasets, though it is incremental as it builds on existing methods for graphical models.
The authors tackled the problem of learning large-scale Bayesian networks from high-dimensional data with few samples, developing the sparsebn R package that focuses on scalability and consistency, and can handle interventions to learn causal networks.
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.