Inference in graphical Gaussian models with edge and vertex symmetries with the gRc package for R

Soren Hojsgaard; Steffen L. Lauritzen

Inference in graphical Gaussian models with edge and vertex symmetries with the gRc package for R

11 Citations (Scopus)

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.

Original language	English
Journal	Journal of Statistical Software
Volume	23
Issue number	6
Number of pages	36
ISSN	1548-7660
Publication status	Published - 2007
Externally published	Yes

Cite this

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title = "Inference in graphical Gaussian models with edge and vertex symmetries with the gRc package for R",

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.",

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AU - Hojsgaard, Soren

AU - Lauritzen, Steffen L.

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AB - 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.

M3 - Journal article

SN - 1548-7660

VL - 23

JO - Journal of Statistical Software

JF - Journal of Statistical Software

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ER -