sparsebn: Learning Sparse Bayesian Networks from High-Dimensional Data

Fast methods for learning sparse Bayesian networks from high-dimensional data using sparse regularization, as described in Aragam, Gu, and Zhou (2017) <arXiv:1703.04025>. Designed to handle mixed experimental and observational data with thousands of variables with either continuous or discrete observations.

Package details

AuthorBryon Aragam [aut, cre], Jiaying Gu [aut], Dacheng Zhang [aut], Qing Zhou [aut]
MaintainerBryon Aragam <>
LicenseGPL (>= 2)
Package repositoryView on CRAN
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sparsebn documentation built on Sept. 13, 2020, 5:10 p.m.