Graphical models in their modern form have been around since the late 1970s and appear today in
many areas of the sciences. Along with the ongoing developments of graphical models a number
of different graphical modeling software programs have been written over the years. In recent
years many of these software developments have taken place within the R community either in
the form of new packages or by providing an R interface to existing software. This book
attempts to give the reader a gentle introduction to graphical modeling using R and the main
features of some of these packages. In addition the book provides examples of how more
advanced aspects of graphical modeling can be represented and handled within R. Topics covered
in the seven chapters include graphical models for contingency tables Gaussian and mixed
graphical models Bayesian networks and modeling high dimensional data.
