bnlearn (2.7)

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 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.

Maintainer: Marco Scutari
Author(s): Marco Scutari

License: GPL (>= 2)

Uses: graph, lattice, snow
Reverse depends: BNSL, geneNetBP
Reverse suggests: BNDataGenerator, bnpa, BTR, CompareCausalNetworks, mcmcabn, OGI, ParallelPC, rbmn, sparsebnUtils

Released almost 8 years ago.