For practical applications of the methods introduced here, several up-to-date tools for data analysis are freely available in the R system for statistical computing (R Development Core Team 2008). Regarding this choice of software, we believe that the supposed disadvantage of command line data analysis criticized by Berk (2006) is easily outweighed by the advanced functionality of the R language and its add-on packages at the state of the art of statistical research. However, in statistical computing the textbooks also lag behind the latest scientific developments: The standard reference Venables and Ripley (2002) does not (yet) cover random forests either, while the handbook of Everitt and Hothorn (2006) gives a short introduction to the use of both classification trees and random forests. This handbook, together with the instructive examples in the following section and the R-code provided in a supplement to this work, can offer a good starting point for applying random forests to your data. Interactive means of visual data exploration in R, that can support further interpretation, are described in Cook and Swayne (2007).
