Graphical Markov Models with Mixed Graphs
Adjacency matrix of a graph
Ancestral graph
All edges of a graph
Basis set of a DAG
Breadth first search
Inverts a marginal log-linear parametrization
Block diagonal matrix
Block diagonal matrix
Identifiability of a model with one latent variable
The complementary graph
Connectivity components
Marginal and partial correlations
Fundamental cycles
Directed acyclic graphs (DAGs)
Directed graphs
Matrix product with a diagonal matrix
Drawing a graph with a simple point and click interface.
d-separation
Edge matrix of a graph
Essential graph
Finding paths
Fitting of Gaussian Ancestral Graph Models
Fitting a Gaussian concentration graph model
Fitting of Gaussian covariance graph models
Fitting of Gaussian DAG models
Fitting Gaussian DAG models with one latent variable
Multivariate logistic models
Fundamental cycles
The package ggm: summary information
Graph to adjacency matrix
Iterative conditional fitting
Indicator matrix
Graphs induced by marginalization or conditioning
Graph queries
Acyclic directed mixed graphs
Ancestral graph
G-identifiability of an UG
Maximal ancestral graph
Mixed Graphs
Link function of marginal log-linear parameterization
Markov equivalence of maximal ancestral graphs
Markov equivalence for regression chain graphs.
Multivariate logistic parametrization
Maximisation for graphs
Maximal ribbonless graph
The m-separation criterion
Maximal summary graph
Null space of a matrix
Partial correlations
Partial correlation
Test for zero partial association
Plot of a mixed graph
Power set
Random correlation matrix
Representational Markov equivalence to bidirected graphs.
Representational Markov equivalence to directed acyclic graphs.
Representational Markov equivalence to undirected graphs.
Ribbonless graph
Random sample from a decomposable Gaussian model
Random vectors on a sphere
summary graph
Test of all independencies implied by a given DAG
Simple graph operations
Sweep operator
Topological sort
Transitive closure of a graph
Triangular decomposition of a covariance matrix
Defining an undirected graph (UG)
Loopless mixed graphs components
Utility functions
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.