gRapHD: Efficient selection of undirected graphical models for high-dimensional datasets. gRapHD is designed 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.
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References in zbMATH (referenced in 2 articles )
Showing results 1 to 2 of 2.
- Pircalabelu, Eugen; Claeskens, Gerda; Waldorp, Lourens: A focused information criterion for graphical models (2015)
- Salinas-Gutiérrez, Rogelio; Hernández-Aguirre, Arturo; Villa-Diharce, Enrique R.: Copula selection for graphical models in continuous estimation of distribution algorithms (2014)