S is a high-level language for manipulating, analysing and displaying data. It forms the basis of two highly acclaimed and widely used data analysis software systems, the commercial S-PLUS(R) and the Open Source R. This book provides an in-depth guide to writing software in the S language under either or both of those systems. It is intended for readers who have some acquaintance with S language and want to know how to use it more effectively, for example to build re-usable tools for streamlining routine data analysis or to implement new statistical methods. One ofhe most outstanding strengths of the S language is the ease with which it can be extended by users. S is a functional language, and functions written by users are first-class objects treated in the same way as functions provided by the system. S code is eminently readable and so a good way to document precisely what algorithms were used, and as much of the implementations are themselves written in S, they can be studied as models and to understand their subtleties. The current implementations also provide easy ways for S functions to call compiled code written in C, Fortran and similar languages; this is documented here in depth. Increasingly S is being used for statistical or graphical analysis within larger software systems or for whole vertical-market applications. The interface facilities are most developed on Windows(R) and these are covered with worked examples. The authors have written the widely adopted 'Modern Applied Statistics with S-PLUS', now in its third edition, and several software libraries that enhance S-PLUS and R; these and the examples used in both books are available on the Internet. Dr. W.N. Venables is a senior Statistician with the CSIRO/CMIS Environmentrics Project in Autralia, having been at the Department of Statistics, University of Adelaide for many years previously. Professor B.D. Ripley holds the Chair of Applied Statistics at the University of Oxford, and is the author of four other books on spatial statistics, simulation, pattern recognition and neural networks. Both authors are known and respected thorughout the international S and R communities, for their books, workshops, short courses, freely available software and through their extensive contributions to the S-news and R mailing lists.

S-PLUS is a powerful environment for the statistical and graphical analysis of data. It provides the tools to implement many statistical ideas which have been made possible by the widespread availability of workstations having good graphics and computational capabilities. This book is a guide to using S-PLUS to perform statistical analyses and provides both an introduction to the use of S-PLUS and a course in modern statistical methods. S-PLUS is available for both Windows and UNIX workstations, and both versions are covered in depth. The aim of the book is to show how to use S-PLUS as a powerful and graphical data analysis system. Readers are assumed to have a basic grounding in statistics, and so the book in intended for would-be users of S-PLUS 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 and 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 third edition is intended for users of S-PLUS 4.5, 5.0, 2000 or later, although S-PLUS 3.3/4 are also considered. The major change from the second edition is coverage of the current versions of S-PLUS. The material has been extensively rewritten using new examples and the latest computationally intensive methods. The companion volume on S Programming will provide an in-depth guide for those writing software in the S language. The authors have written several software libraries that enhance S-PLUS; these and all the datasets used are available on the Internet in versions for Windows and UNIX. There are extensive on-line complements covering advanced material, user-contributed extensions, further exercises, and new features of S-PLUS as they are introduced. Dr. Venables is now Statistician with CSRIO in Queensland, having been at the Department of Statistics, University of Adelaide, for many years previously. He has given many short courses on S-PLUS in Australia, Europe, and the USA. Professor Ripley holds the Chair of Applied Statistics at the University of Oxford, and is the author of four other books on spatial statistics, simulation, pattern recognition, and neural networks.

S is a high-level language for manipulating, analysing and displaying

data. It forms the basis of two highly acclaimed and widely used data

analysis software systems, the commercial S-PLUS(r) and the Open

Source R. This book provides an in-depth guide to writing software in

the S language under either or both of those systems. It is intended

for readers who have some acquaintance with the S language and want to

know how to use it more effectively, for example to build re-usable

tools for streamlining routine data analysis or to implement new

statistical methods.

One of the outstanding strengths of the S language is the ease with

which it can be extended by users. S is a functional language, and

functions written by users are first-class objects treated in the same

way as functions provided by the system. S code is eminently readable

and so a good way to document precisely what algorithms were used, and

as much of the implementations are themselves written in S, they can be

studied as models and to understand their subtleties. The current

implementations also provide easy ways for S functions to call

compiled code written in C, Fortran and similar languages; this is

documented here in depth.

Increasingly S is being used for statistical or graphical analysis

within larger software systems or for whole vertical-market

applications. The interface facilities are most developed on

Windows(r) and these are covered with worked examples.

The authors have written the widely used Modern Applied Statistics

with S-PLUS, now in its third edition, and several software libraries

that enhance S-PLUS and R; these and the examples used in both books

are available on the Internet.

Dr. W.N. Venables is a senior Statistician with the CSIRO/CMIS

Environmetrics Project in Australia, having been at the Department of

Statistics, University of Adelaide for many years previously.

Professor B.D. Ripley holds the Chair of Applied Statistics at the

University of Oxford, and is the author of four other books on spatial

statistics, simulation, pattern recognition and neural networks. Both

authors are known and respected throughout the international S and R

communities, for their books, workshops, short courses, freely

available software and through their extensive contributions to the

S-news and R mailing lists.

A guide to using S environments to perform statistical analyses providing both an introduction to the use of S and a course in modern statistical methods. The emphasis is on presenting practical problems and full analyses of real data sets.

The Wiley-Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians, and scientists.

"Books such as this that bring together, clarify, and summarize recent research can lead to a great increase of interest in the area. . . . a major achievement in describing many aspects of spatial data and discussing, with examples, different methods of analysis."
-Royal Statistical Society

"Dr. Ripley's book is an excellent survey of the spatial statistical methodology. It is very well illustrated with examples [that] give a clear view of the wide scope of the subject, the way in which techniques often have to be tailored to particular applications, and the different sorts of spatial data that arise."
-The Bulletin of the London Mathematics Society

Spatial Statistics provides a comprehensive guide to the analysis of spatial data. Each chapter covers a particular data format and the associated class of problems, introducing theory, giving computational suggestions, and providing examples. Methods are illustrated by computer-drawn figures. The book serves as an introduction to this rapidly growing research area for mathematicians and statisticians, and as a reference to new computer methods for researchers in ecology, geology, archaeology, and the earth sciences.

The study of spatial processes and their applications is an important topic in statistics and finds wide application particularly in computer vision and image processing. This book is devoted to statistical inference in spatial statistics and is intended for specialists needing an introduction to the subject and to its applications.

One of the themes of the book is to show how these techniques give new insights into classical procedures (including new examples in likelihood theory) and newer statistical paradigms such as Monte-Carlo inference and pseudo-likelihood. Professor Ripley also stresses the importance of edge effects and of the lack of a unique asymptotic setting in spatial problems.

Throughout, the author discusses the foundational issues posed and the difficulties, both computational and philosophical, which arise. The final chapters consider image restoration and segmentation methods and the averaging and summarizing of images. Thus, the book will find wide appeal to researchers in computer vision, image processing, and those applying microscopy in biology, geology and materials science, as well as to statisticians interested in the foundations of their discipline.

WILEY-INTERSCIENCE PAPERBACK SERIES

The Wiley-Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians, and scientists.

." . .this is a very competently written and useful addition to the statistical literature; a book every statistician should look at and that many should study!"
--Short Book Reviews, International Statistical Institute

." . .reading this book was an enjoyable learning experience. The suggestions and recommendations on the methods [make] this book an excellent reference for anyone interested in simulation. With its compact structure and good coverage of material, it [is] an excellent textbook for a simulation course."
--Technometrics

." . .this work is an excellent comprehensive guide to simulation methods, written by a very competent author. It is especially recommended for those users of simulation methods who want more than a 'cook book'. "
--Mathematics Abstracts

This book is a comprehensive guide to simulation methods with explicit recommendations of methods and algorithms. It covers both the technical aspects of the subject, such as the generation of random numbers, non-uniform random variates and stochastic processes, and the use of simulation. Supported by the relevant mathematical theory, the text contains a great deal of unpublished research material, including coverage of the analysis of shift-register generators, sensitivity analysis of normal variategenerators, analysis of simulation output, and more.