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Inference in Graphical Gaussian Models with Edge and Vertex Symmetries with the gRc Package for R

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  • Højsgaard, Søren
  • Lauritzen, Steffen L.

Abstract

In this paper we present the R package gRc for statistical inference in graphical Gaussian models in which symmetry restrictions have been imposed on the concentration or partial correlation matrix. The models are represented by coloured graphs where parameters associated with edges or vertices of same colour are restricted to being identical. We describe algorithms for maximum likelihood estimation and discuss model selection issues. The paper illustrates the practical use of the gRc package.

Suggested Citation

  • Højsgaard, Søren & Lauritzen, Steffen L., 2007. "Inference in Graphical Gaussian Models with Edge and Vertex Symmetries with the gRc Package for R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 23(i06).
    • Handle: RePEc:jss:jstsof:v:023:i06
    DOI: http://hdl.handle.net/10.18637/jss.v023.i06
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    Cited by:

    1. Kalisch, Markus & Mächler, Martin & Colombo, Diego & Maathuis, Marloes H. & Bühlmann, Peter, 2012. "Causal Inference Using Graphical Models with the R Package pcalg," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 47(i11).
    2. repec:jss:jstsof:35:i03 is not listed on IDEAS
    3. Saverio Ranciati & Alberto Roverato & Alessandra Luati, 2021. "Fused graphical lasso for brain networks with symmetries," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(5), pages 1299-1322, November.

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