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Fast methods for learning sparse Bayesian networks from highdimensional 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 


Author  Bryon Aragam [aut, cre], Jiaying Gu [aut], Dacheng Zhang [aut], Qing Zhou [aut] 
Maintainer  Bryon Aragam <[email protected]> 
License  GPL (>= 2) 
Version  0.0.5 
URL  https://github.com/itsrainingdata/sparsebn 
Package repository  View on CRAN 
Installation 
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