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gRapHD: Efficient Selection of Undirected Graphical Models for High-Dimensional Datasets
Performs efficient selection of high-dimensional undirected graphical models as described in Abreu, Edwards and Labouriau (2010) <doi:10.18637/jss.v037.i01>. 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.
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Package details |
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| Author | Gabriel Coelho Goncalves de Abreu <abreu_ga@yahoo.com.br>, Rodrigo Labouriau <rodrigo.labouriau@math.au.dk>, David Edwards <david.edwards@agrsci.dk>. |
| Maintainer | Rodrigo Labouriau <rodrigo.labouriau@math.au.dk> |
| License | GPL (>= 3) |
| Version | 0.2.6 |
| Package repository | View on CRAN |
| Installation |
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