Learn the structure of a Bayesian network with the Max-Min
  Hill Climbing (MMHC) and the more general 2-phase Restricted
  Maximization (RSMAX2) hybrid algorithms.

multivariate

models

graphs

hybrid algorithms

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.

hybrid algorithms: Hybrid structure learning algorithms

Description

Usage

Arguments

Value

References

See Also