Introduction * Data Manipulation * The S Language * Graphics * Univariate Statistics * Linear Statistical Models * Generalized Linear Models * Non-linear and Smooth Regression * Tree-based Methods * Random and Mixed Effects * Exploratory Multivariate Analysis * Classification * Survival Analysis * Time Series Analysis * Spatial Statistics * Optimization

S is a powerful environment for the statistical and graphical analysis
of data. It provides the tools to implement many statistical ideas
that have been made possible by the widespread availability of
workstations having good graphics and computational capabilities. This
book is a guide to using S environments to perform statistical
analyses and provides both an introduction to the use of S and a
course in modern statistical methods. Implementations of S are
available commercially in S-PLUS(R) workstations and as the Open
Source R for a wide range of computer systems.
The aim of this book is to show how to use S as a powerful and
graphical data analysis system. Readers are assumed to have a basic
grounding in statistics, and so the book is intended for would-be
users of S-PLUS or R and both students and researchers using
statistics. Throughout, the emphasis is on presenting practical
problems and full analyses of real data sets. Many of the methods
discussed are state of the art approaches to topics such as linear,
nonlinear and smooth regression models, tree-based methods,
multivariate analysis, pattern recognition, survival analysis, time
series and spatial statistics. Throughout modern techniques such as
robust methods, non-parametric smoothing and bootstrapping are used
where appropriate.
This fourth edition is intended for users of S-PLUS 6.0 or R 1.5.0 or
later. A substantial change from the third edition is updating for the
current versions of S-PLUS and adding coverage of R. The introductory
material has been rewritten to emphasis the import, export and
manipulation of data. Increased computational power allows even more
computer-intensive methods to be used, and methods such as GLMMs,