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>.

Version: 0.1.0
Depends: R (≥ 3.2.2)
Imports: graph, grDevices, graphics, methods, stats, utils, Rgraphviz
Suggests: testthat
Published: 2017-11-27
Author: Rodrigo Collazo [aut], Pier Taranti [aut, cre]
Maintainer: Pier Taranti <ptaranti at gmail.com>
BugReports: https://github.com/ptaranti/ceg/issues
License: GPL-2 | file LICENSE
URL: https://github.com/ptaranti/ceg
NeedsCompilation: no
CRAN checks: ceg results


Reference manual: ceg.pdf
Package source: ceg_0.1.0.tar.gz
Windows binaries: r-devel: ceg_0.1.0.zip, r-release: ceg_0.1.0.zip, r-oldrel: ceg_0.1.0.zip
OS X binaries: r-release: ceg_0.1.0.tgz, r-oldrel: not available


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