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.

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 support for basic parametric
and bootstrap inference, conditional probability queries and
cross-validation.

Bayesian network structure learning, parameter learning and
inference
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 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.