Bayesian network structure learning, parameter learning and inference. This package implements constraintbased (PC, GS, IAMB, InterIAMB, FastIAMB, MMPC, HitonPC, HPC), pairwise (ARACNE and ChowLiu), scorebased (HillClimbing and Tabu Search) and hybrid (MMHC, RSMAX2, H2PC) structure learning algorithms for discrete, Gaussian and conditional Gaussian networks, along with many score functions and conditional independence tests. The Naive Bayes and the TreeAugmented Naive Bayes (TAN) classifiers are also implemented. 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, crossvalidation, bootstrap and model averaging. Development snapshots with the latest bugfixes are available from <https://www.bnlearn.com/>.
Package details 


Author  Marco Scutari [aut, cre], Tomi Silander [ctb], Robert Ness [ctb] 
Maintainer  Marco Scutari <marco.scutari@gmail.com> 
License  GPL (>= 2) 
Version  4.7 
URL  https://www.bnlearn.com/ 
Package repository  View on CRAN 
Installation 
Install the latest version of this package by entering the following in R:

Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.