ceg: Chain Event Graph
Version 0.1.0

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 .

Package details

AuthorRodrigo Collazo [aut], Pier Taranti [aut, cre]
Date of publication2017-11-27 12:44:01 UTC
MaintainerPier Taranti <[email protected]>
LicenseGPL-2 | file LICENSE
URL https://github.com/ptaranti/ceg
Package repositoryView on CRAN
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ceg documentation built on Nov. 27, 2017, 5:05 p.m.