OAHC

numeric - level under optimisation

level

(list of matrices) - see function
<code>prior.distribution</code>

prior.distribution

(list of matrices) - see function ContingencyTable

contingency.table

tree

This function calculates the best stage configuration of a hyperstage
 associated with a specific variable of time-slice t_0 or t_k, k&gt;=1, using
 the oahc algorithm (oahc - Optimised Agglomerative Hierarchical Clustering)

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>.

OAHC: OAHC Constructor

Description

Usage

Arguments

Value

See Also