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.
|Maintainer||Bryon Aragam <[email protected]>|
|License||GPL (>= 2)|
|Package repository||View on GitHub|
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