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.

Version: 0.2.5
Depends: R (≥ 2.9.0), methods
Imports: graph
Published: 2018-01-09
Author: Gabriel Coelho Goncalves de Abreu, Rodrigo Labouriau, David Edwards.
Maintainer: Rodrigo Labouriau <rodrigo.labouriau at math.au.dk>
License: GPL (≥ 3)
NeedsCompilation: yes
Citation: gRapHD citation info
CRAN checks: gRapHD results

Downloads:

Reference manual: gRapHD.pdf
Package source: gRapHD_0.2.5.tar.gz
Windows binaries: r-devel: gRapHD_0.2.5.zip, r-release: gRapHD_0.2.5.zip, r-oldrel: gRapHD_0.2.5.zip
OS X El Capitan binaries: r-release: not available
OS X Mavericks binaries: r-oldrel: gRapHD_0.2.5.tgz
Old sources: gRapHD archive

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