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 discrete, Gaussian and conditional Gaussian networks, along with many
score functions and conditional independence tests.
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
|Author||Marco Scutari [aut, cre], Robert Ness [ctb]|
|Date of publication||2017-07-03 13:35:15 UTC|
|Maintainer||Marco Scutari <firstname.lastname@example.org>|
|License||GPL (>= 2)|
|Package repository||View on CRAN|
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