ggm - Graphical Markov Models with Mixed 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.