Graphical Models with R [Paperback]

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.

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.

This book is useful for readers who want to analyze graphical models with R and who are searching for an initial aid in programming and a guide through the jungle of different R packages for graphical models. & I recommend the book to readers whose aim is primarily to apply graphical models in R and who are therefore looking for a good introductory book. (Ronja Foraita, Biometrical Journal, Vol. 56 (2), 2014)

The book, written by some of the people who laid the foundations of work in this area, would be ideal for researchers who had read up on the theory of graphicl³«