Bayesian network structure learning via constraint-based (also known as 'conditional independence') and score-based 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 and the Hill-Climbing (HC) greedy search 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) are also included.
| Version: | 1.0 |
| Depends: | R (≥ 2.6.0) |
| Suggests: | snow |
| Date: | 2008-11-01 |
| Author: | Marco Scutari |
| Maintainer: | Marco Scutari <marco.scutari at gmail.com> |
| License: | GPL (≥ 2) |
| In views: | gR |
| CRAN checks: | bnlearn results |
Downloads:
| Package source: | bnlearn_1.0.tar.gz |
| MacOS X binary: | bnlearn_1.0.tgz |
| Windows binary: | bnlearn_1.0.zip |
| Reference manual: | bnlearn.pdf |
| News/ChangeLog: | ChangeLog |
| Old sources: | bnlearn archive |