r-sparsebn

condav0.1.2

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.

License GPL-2strong copyleft4 versions1 maintainers0 deps221 weekly dl
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[email protected] is safe to use (health: 52/100)

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10/25
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3/20
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25/25
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12/15
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2/15
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First published · 2020-09-10 14:47:54.395000+00:00

Last updated · 2025-09-24 20:49:16.136000+00:00