Package: bnlearn
Type: Package
Title: Bayesian network structure learning
Version: 1.7
Date: 2009-10-25
Depends: R (>= 2.8.0), utils,
Suggests: snow, graph, Rgraphviz (>= 1.20.3), lattice
Author: Marco Scutari
Maintainer: Marco Scutari <marco.scutari@gmail.com>
Description: 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
Max-Min Hill-Climbing (MMHC) 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.
URL: http://www.bnlearn.com/
License: GPL (>= 2)
LazyLoad: yes
LazyData: yes
Packaged: 2009-10-25 05:17:08 UTC; fizban
Repository: CRAN
Date/Publication: 2009-10-26 19:26:46

Package: bnlearn
Type: Package
Title: Bayesian network structure learning
Version: 1.8
Date: 2010-01-05
Depends: R (>= 2.8.0), utils,
Suggests: snow, graph, Rgraphviz (>= 1.20.3), lattice
Author: Marco Scutari
Maintainer: Marco Scutari <marco.scutari@gmail.com>
Description: 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.
URL: http://www.bnlearn.com/
License: GPL (>= 2)
LazyLoad: yes
LazyData: yes
Packaged: 2010-01-07 10:55:20 UTC; fizban
Repository: CRAN
Date/Publication: 2010-01-07 15:41:42