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.