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High-Dimensional Graphical Model Search with the gRapHD R Package

Author

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  • Abreu, Gabriel C. G.
  • Labouriau, Rodrigo
  • Edwards, David

Abstract

This paper presents the R package gRapHD for efficient selection of high-dimensional undirected graphical models. The package provides tools for selecting trees, forests, and decomposable models minimizing information criteria such as AIC or BIC, and for displaying the independence graphs of the models. It has also some useful tools for analysing graphical structures. It supports the use of discrete, continuous, or both types of variables.

Suggested Citation

  • Abreu, Gabriel C. G. & Labouriau, Rodrigo & Edwards, David, 2010. "High-Dimensional Graphical Model Search with the gRapHD R Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 37(i01).
    • Handle: RePEc:jss:jstsof:v:037:i01
    DOI: http://hdl.handle.net/10.18637/jss.v037.i01
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    Cited by:

    1. Rogelio Salinas-Gutiérrez & Arturo Hernández-Aguirre & Enrique Villa-Diharce, 2014. "Copula selection for graphical models in continuous Estimation of Distribution Algorithms," Computational Statistics, Springer, vol. 29(3), pages 685-713, June.
    2. Carota, Cinzia & Durio, Alessandra & Guerzoni, Marco, 2014. "An Application of Graphical Models to the Innobarometer Survey: A Map of Firms’ Innovative Behaviour," Department of Economics and Statistics Cognetti de Martiis. Working Papers 201444, University of Turin.
    3. Gonzalo Perez-de-la-Cruz & Guillermina Eslava-Gomez, 2019. "Discriminant analysis for discrete variables derived from a tree-structured graphical model," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 13(4), pages 855-876, December.

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