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) ; (2) general structure optimization - Mauro Scanagatta (2018) doi:10.1007/s10994-018-5701-9, Mauro Scanagatta (2018) ; (3) bounded treewidth structure optimization - Mauro Scanagatta (2016) ; (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.