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.

Leaders in the field instruct using graphs and color images Provides valuable information on graphical modelling with R Including instructions to better understand relevant software programs Includes supplementary material: sn.pub/extras

Autorentext

Søren Højsgaard is Associate Professor in Statistics and Head of the Department of Mathematical Sciences at Aalborg University.

David Edwards is Associate Professor at the Department of Molecular Biology and Genetics, Aarhus University.

Steffen Lauritzen is Professor of Statistics and Head of the Department of Statistics at the University of Oxford.

Inhalt
Graphs and Conditional Independence.- Log-Linear Models.- Bayesian Networks.- Gaussian Graphical Models.- Mixed Interaction Models.- Graphical Models for Complex Stochastic Systems.- High dimensional modelling.- References.- Index.