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.
copied from cf-staging / r-sparsebnLabel | Latest Version |
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main | 0.1.2 |
gcc7 | 0.0.5 |
cf201901 | 0.0.5 |
cf202003 | 0.1.0 |