Description: Bayesian network structure learning (via constraint-based, score-based and
hybrid algorithms), parameter learning (via ML and Bayesian estimators) and
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 Hiton-PC
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.
The Naive Bayes and the Tree-Augmented Naive Bayes (TAN) classifiers are also implemented.
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.

Suggests: parallel, graph, Rgraphviz, lattice, gRain
Description: Bayesian network structure learning, parameter learning and
This package implements constraint-based (GS, IAMB, Inter-IAMB, Fast-IAMB,
MMPC, Hiton-PC), pairwise (ARACNE and Chow-Liu), score-based (Hill-Climbing
and Tabu Search) and hybrid (MMHC and RSMAX2) structure learning algorithms
for both discrete and Gaussian networks, along with many score functions
The Naive Bayes and the Tree-Augmented Naive Bayes (TAN) classifiers are
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 (maximum likelihood
and Bayesian) and inference, conditional probability queries and
cross-validation. Development snapshots with the latest bugfixes are

Title: Bayesian Network Structure Learning, Parameter Learning and
for discrete, Gaussian and conditional Gaussian networks, along with many
score functions and conditional independence tests.