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.

Version: 2.5.1
Depends: R (≥ 3.6.0), methods
Imports: BiocManager, graph, igraph
Published: 2024-01-25
DOI: 10.32614/CRAN.package.ggm
Author: Giovanni M. Marchetti [aut, cre], Mathias Drton [aut], Kayvan Sadeghi [aut]
Maintainer: Giovanni M. Marchetti <giovanni.marchetti at>
License: GPL-2
NeedsCompilation: no
Materials: NEWS
In views: GraphicalModels
CRAN checks: ggm results


Reference manual: ggm.pdf


Package source: ggm_2.5.1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): ggm_2.5.1.tgz, r-oldrel (arm64): ggm_2.5.1.tgz, r-release (x86_64): ggm_2.5.1.tgz, r-oldrel (x86_64): ggm_2.5.1.tgz
Old sources: ggm archive

Reverse dependencies:

Reverse imports: MoTBFs, nethet, pcalg, pcgen, phylopath, SEMgraph, stablespec, TGS


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