sparsebn: Learning Sparse Bayesian Networks from High-Dimensional Data
Version 0.0.4

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

Package details

AuthorBryon Aragam [aut, cre]
Date of publication2017-03-16 01:08:03
MaintainerBryon Aragam <>
LicenseGPL (>= 2)
Package repositoryView on CRAN
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sparsebn documentation built on May 30, 2017, 6:29 a.m.