r.blip

Allows the user to learn Bayesian networks from datasets containing thousands of variables. It focuses on score-based learning, mainly the 'BIC' and the 'BDeu' score functions. It provides state-of-the-art algorithms for the following tasks: (1) parent set identification - Mauro Scanagatta (2015) http://papers.nips.cc/paper/5803-learning-bayesian-networks-with-thousands-of-variables; (2) general structure optimization - Mauro Scanagatta (2018) doi:10.1007/s10994-018-5701-9, Mauro Scanagatta (2018) http://proceedings.mlr.press/v73/scanagatta17a.html; (3) bounded treewidth structure optimization - Mauro Scanagatta (2016) http://papers.nips.cc/paper/6232-learning-treewidth-bounded-bayesian-networks-with-thousands-of-variables; (4) structure learning on incomplete data sets - Mauro Scanagatta (2018) doi:10.1016/j.ijar.2018.02.004. Distributed under the LGPL-3 by IDSIA.

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