bnlearn (1.8)

Bayesian network structure learning.

http://www.bnlearn.com/
http://cran.r-project.org/web/packages/bnlearn

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.

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 over 9 years ago.