Learning Large-Scale Bayesian Networks with the sparsebn Package

• Main Authors: Bryon Aragam, Jiaying Gu, Qing Zhou
• Format: Article
• Language: English
• Published: Foundation for Open Access Statistics 2019-11-01
• Series: Journal of Statistical Software
• Subjects: bayesian networks; causal networks; graphical models; machine learning; structural equation modeling; multi-logit regression; experimental data
• Online Access: https://www.jstatsoft.org/index.php/jss/article/view/3051

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.