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.

Getting started

Package details

AuthorGiovanni M. Marchetti [aut, cre], Mathias Drton [aut], Kayvan Sadeghi [aut]
MaintainerGiovanni M. Marchetti <giovanni.marchetti@unifi.it>
LicenseGPL-2
Version2.5.1
URL https://github.com/StaThin/ggm
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:
install.packages("ggm")

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ggm documentation built on May 29, 2024, 7:27 a.m.