Bayesian network structure learning
Description
Bayesian network structure learning via constraint-based
(also known as 'conditional independence'), score-based and
hybrid algorithms. This package implements the Grow-Shrink (GS)
algorithm, the Incremental Association (IAMB) algorithm, the
Interleaved-IAMB (Inter-IAMB) algorithm, the Fast-IAMB
(Fast-IAMB) algorithm, the Max-Min Parents and Children (MMPC)
algorithm, the Hill-Climbing (HC) greedy search algorithm, the
Max-Min Hill-Climbing (MMHC) algorithm for both discrete and
Gaussian networks, along with many score functions and
conditional independence tests. Some utility functions (model
comparison and manipulation, random data generation, arc
orientation testing, simple and advanced plots) are included,
as well as basic parametric and bootstrap inference functions.