sparsebn (0.1.0)

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) . Designed to handle mixed experimental and observational data with thousands of variables with either continuous or discrete observations.

Maintainer: Bryon Aragam
Author(s): Bryon Aragam [aut, cre], Jiaying Gu [aut], Dacheng Zhang [aut], Qing Zhou [aut]

License: GPL (>= 2)

Uses: ccdrAlgorithm, discretecdAlgorithm, sparsebnUtils, graph, igraph, mvtnorm, testthat, knitr, rmarkdown

Released about 1 month ago.