ceg: Chain Event Graph

Create and learn Chain Event Graph (CEG) models using a Bayesian framework. It provides us with a Hierarchical Agglomerative algorithm to search the CEG model space. The package also includes several facilities for visualisations of the objects associated with a CEG. The CEG class can represent a range of relational data types, and supports arbitrary vertex, edge and graph attributes. A Chain Event Graph is a tree-based graphical model that provides a powerful graphical interface through which domain experts can easily translate a process into sequences of observed events using plain language. CEGs have been a useful class of graphical model especially to capture context-specific conditional independences. References: Collazo R, Gorgen C, Smith J. Chain Event Graph. CRC Press, ISBN 9781498729604, 2018 (forthcoming); and Barday LM, Collazo RA, Smith JQ, Thwaites PA, Nicholson AE. The Dynamic Chain Event Graph. Electronic Journal of Statistics, 9 (2) 2130-2169 <doi:10.1214/15-EJS1068>.

Package details

AuthorRodrigo Collazo [aut], Pier Taranti [aut, cre]
MaintainerPier Taranti <ptaranti@gmail.com>
LicenseGPL-2 | file LICENSE
URL https://github.com/ptaranti/ceg
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:

Try the ceg package in your browser

Any scripts or data that you put into this service are public.

ceg documentation built on May 2, 2019, 1:46 p.m.