cmpGraph

Finds the complementary graph of an undirected 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

cmpGraph function

<dl> <dt>amat</dt>
<dd>the adjacency matrix of an undirected graph</dd></dl>

Arguments

Author

The complementary graph — cmpGraph

<dl>

 <dt>amat</dt>
<dd>the adjacency matrix of an undirected graph</dd>

</dl>

cmpGraph: The complementary graph

Description

Usage

Value

Arguments

Author

Details

References

See Also

Examples