Abstract
The gRbase package is intended to set the framework for computer packages for data analysis using graphical models. The gRbase package is developed for the open source language, R, and is available for several platforms. The package is intended to be widely extendible and flexible so that package developers may implement further types of graphical models using the available methods. The gRbase package consists of a set of S version 3 classes and associated methods for representing data and models. The package is linked to the dynamicGraph package (Badsberg 2005), an interactive graphical user interface for manipulating graphs. In this paper, we show how these building blocks can be combined and integrated with inference engines in the special cases of hierarchical loglinear models. We also illustrate how to extend the package to deal with other types of graphical models, in this case the graphical Gaussian models.
Highlights
Graphical models in their modern form have been around for nearly a quarter of a century
The source code of BUGS and MIM were released as open source, making it possible to extend the functionality by integrating these packages into more general tools
In this paper we have described gRbase, a package that provides tools for creating new packages for graphical models in R
Summary
Graphical models in their modern form have been around for nearly a quarter of a century. Some examples of free software programs are BUGS (Thomas 1994), CoCo (Badsberg 2001), Digram (Klein, Keiding, and Kreiner 1995), MIM (Edwards 2000), and Tetrad (Glymour, Scheines, Spirtes, Graphical Models in R and Kelley 1987) Most such packages for graphical models have common characteristics: 1) They are tailor-made to analyse a particular class of models, 2) they have their own command language. The work in the gR initiative is organized in three levels: A core group works with defining data structures and standard methods, in particular developing the packages gRbase, dynamicGraph (see Section 6), and giRaph The latter package, which is not described in this paper provides methods for representing and manipulating graphs efficiently. We will refer to the dataset HairEyeColor (Snee 1974), included in R
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