B.D. Ripley – författare

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.

S is a high-level language for manipulating, analysing and displayingdata. It forms the basis of two highly acclaimed and widely used dataanalysis software systems, the commercial S-PLUS and the OpenSource R. This book provides an in-depth guide to writing software inthe S language under either or both of those systems. It is intendedfor readers who have some acquaintance with the S language and want toknow how to use it more effectively, for example to build re-usabletools for streamlining routine data analysis or to implement newstatistical methods.One of the outstanding strengths of the S language is the ease withwhich it can be extended by users. S is a functional language, andfunctions written by users are first-class objects treated in the sameway as functions provided by the system. S code is eminently readableand so a good way to document precisely what algorithms were used, andas much of the implementations are themselves written in S, they can bestudied as models and to understand their subtleties. The currentimplementations also provide easy ways for S functions to callcompiled code written in C, Fortran and similar languages; this isdocumented here in depth.Increasingly S is being used for statistical or graphical analysiswithin larger software systems or for whole vertical-marketapplications.The interface facilities are most developed onWindows and these are covered with worked examples.The authors have written the widely used Modern Applied Statisticswith S-PLUS, now in its third edition, and several software librariesthat enhance S-PLUS and R; these and the examples used in both booksare available on the Internet.Dr. W.N. Venables is a senior Statistician with the CSIRO/CMISEnvironmetrics Project in Australia, having been at the Department ofStatistics, University of Adelaide for many years previously.Professor B.D. Ripley holds the Chair of Applied Statistics at theUniversity of Oxford, and is the author of four other books on spatialstatistics, simulation, pattern recognition and neural networks. Bothauthors are known and respected throughout the international S and Rcommunities, for their books, workshops, short courses, freelyavailable software and through their extensive contributions to theS-news and R mailing lists.

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,

S is a high-level language for manipulating, analysing and displayingdata. It forms the basis of two highly acclaimed and widely used dataanalysis software systems, the commercial S-PLUS® and the OpenSource R. This book provides an in-depth guide to writing software inthe S language under either or both of those systems. It is intendedfor readers who have some acquaintance with the S language and want toknow how to use it more effectively, for example to build re-usabletools for streamlining routine data analysis or to implement newstatistical methods.One of the outstanding strengths of the S language is the ease withwhich it can be extended by users. S is a functional language, andfunctions written by users are first-class objects treated in the sameway as functions provided by the system. S code is eminently readableand so a good way to document precisely what algorithms were used, andas much of the implementations are themselves written in S, they can bestudied as models and to understand their subtleties. The currentimplementations also provide easy ways for S functions to callcompiled code written in C, Fortran and similar languages; this isdocumented here in depth.Increasingly S is being used for statistical or graphical analysiswithin larger software systems or for whole vertical-marketapplications. The interface facilities are most developed onWindows® and these are covered with worked examples.The authors have written the widely used Modern Applied Statisticswith S-PLUS, now in its third edition, and several software librariesthat enhance S-PLUS and R; these and the examples used in both booksare available on the Internet.Dr. W.N. Venables is a senior Statistician with theCSIRO/CMISEnvironmetrics Project in Australia, having been at the Department ofStatistics, University of Adelaide for many years previously.Professor B.D. Ripley holds the Chair of Applied Statistics at theUniversity of Oxford, and is the author of four other books on spatialstatistics, simulation, pattern recognition and neural networks. Bothauthors are known and respected throughout the international S and Rcommunities, for their books, workshops, short courses, freelyavailable software and through their extensive contributions to theS-news and R mailing lists.