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
Tabu Search (TABU) algorithm, the Max-Min Hill-Climbing (MMHC)
algorithm and the two-stage Restricted Maximization (RSMAX2)
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.