This algorithm reconstructs Bayesian Networks from observational data. Therefor it first builds the skeleton of the DAG (directed acyclic graph) with the max-min parents and children (mmpc) algorithm. Afterwards it directs the edges between the vertices with the Bayesian Dirichlet likelihood-equivalence uniform (BDeu) score. For more information on that read the report appended or "The max-min hill-climbing Bayesian network structure learning algorithm", by Ioannis Tsamardinos, Laura E. Brown & Constantin F. Aliferis.
Package details |
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Author | Michael Bauer |
Maintainer | Michael Bauer <michael1.bauer@ur.de> |
License | No License |
Version | 1.0 |
Package repository | View on GitHub |
Installation |
Install the latest version of this package by entering the following in R:
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