Bayesian network structure learning, parameter learning and
inference
Description
Bayesian network structure learning (via constraint-based,
score-based and hybrid algorithms), parameter learning (via ML
and Bayesian estimators) and inference. 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 ARACNE and Chow-Liu algorithms,
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 support for parameter estimation and
inference, conditional probability queries and
cross-validation.