essentialGraph

Find the essential graph from a given directed acyclic graph.

multivariate

models

graphs

Provides functions for defining
mixed graphs containing three types of edges, directed,
undirected and bi-directed, with possibly multiple edges.
These graphs are useful because they capture fundamental
independence structures in multivariate distributions
and in the induced distributions after marginalization
and conditioning.
The package is especially concerned with Gaussian graphical
models for
(i) ML estimation for directed acyclic graphs, undirected and
bi-directed graphs and ancestral graph models
(ii) testing several conditional independencies
(iii) checking global identification of DAG Gaussian models
with one latent variable
(iv) testing Markov equivalences and generating Markov
equivalent graphs of specific types.

Giovanni M Marchetti

Graphical Markov Models with Mixed Graphs

Giovanni M. Marchetti

Mathias Drton

Kayvan Sadeghi

essentialGraph function

<dl> <dt>dagx</dt>
<dd>a square binary matrix, the adjacency matrix of a directed
 acyclic graph. The names of rows and of the columns
 are the nodes of the DAG.</dd></dl>

Arguments

Giovanni M. Marchetti, from a MATLAB function
 by Tomas Kocka, AAU

Author

Essential graph — essentialGraph

<dl>

 <dt>dagx</dt>
<dd>a square binary matrix, the adjacency matrix of a directed
 acyclic graph. The names of rows and of the columns
 are the nodes of the DAG.</dd>

</dl>

essentialGraph: Essential graph

Description

Usage

Value

Arguments

Author

Details

References

See Also

Examples