Using R for Introductory Statistics Review

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Using R for Introductory Statistics ReviewThis book doesn't show up under most listings for books about R, but it should. It's a very solid introduction to using R -- including installation, configuration, and some progrmaming -- for basic statistical work. My only complaint is that it wasn't quite comprehensive enough -- not enough examples were given and not enough discussion on important functions and parameters were present. Also, the index is atrocious.
I would recommend it as a good book to get going, but for in depth work you'll be referring to the HTML help a lot.Using R for Introductory Statistics OverviewThe cost of statistical computing software has precluded many universities from installing these valuable computational and analytical tools. R, a powerful open-source software package, was created in response to this issue. It has enjoyed explosive growth since its introduction, owing to its coherence, flexibility, and free availability. While it is a valuable tool for students who are first learning statistics, proper introductory materials are needed for its adoption.Using R for Introductory Statistics fills this gap in the literature, making the software accessible to the introductory student. The author presents a self-contained treatment of statistical topics and the intricacies of the R software. The pacing is such that students are able to master data manipulation and exploration before diving into more advanced statistical concepts. The book treats exploratory data analysis with more attention than is typical, includes a chapter on simulation, and provides a unified approach to linear models.This text lays the foundation for further study and development in statistics using R. Appendices cover installation, graphical user interfaces, and teaching with R, as well as information on writing functions and producing graphics. This is an ideal text for integrating the study of statistics with a powerful computational tool.

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S Programming Review

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S Programming Review
For those seeking an introduction to programming in S, or seeking information on how to use S for statistical applications, this is NOT a good choice as a "first purchase", nor is it intended to be. Instead, choose these authors' other textbook, Modern Applied Statistics with S-Plus.
This book isn't primarily about using S, using S-PLUS (a commercial version of S), nor using R (an Open Source version of S), but rather, it's about showing how to write programming extensions to the base S functions. In fact, for those seeking such guidance, this is the book's great virtue.
It's written by two world-class authorities on the subject of S programming, persons who are generous in their efforts to help others and who can be contacted through the Internet. However, this particular text assumes the reader is already committed to using S, and hence, contains little 'motivational' material. Yet for the audience to whom it's addressed, it's essential, or at least highly recommended, reading.
The latest commercial version of S (S-PLUS 2000 from Mathsoft, Inc.) has a graphical user interface (GUI). A chapter is included in the latter portion of the book on how to program such interfaces.
Also, a chapter is devoted to extending S with compiled code written in C, Fortran, or C++. Since S is an interpreted language, using compiled code can increase the speed of newly created functions. This is highly technical, however, and not for the neophyte.

Potential software developers of vertical-market applications involving S-PLUS, among others, will benefit from purchasing "S Programming" by Venables & Ripley.S Programming OverviewS 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.

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Modeling with Data: Tools and Techniques for Scientific Computing Review

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Modeling with Data: Tools and Techniques for Scientific Computing ReviewKlemens teaches how to tame and understand a dataset the way it should be done: in C. Invest some time in this excellent book full of gentle humor and respect for the reader's intelligence. The payoffs will be immense. The best resource (a full set of programs used in the book) accompanying this book is available for FREE(!) on Klemens's website!Modeling with Data: Tools and Techniques for Scientific Computing Overview
Modeling with Data fully explains how to execute computationally intensive analyses on very large data sets, showing readers how to determine the best methods for solving a variety of different problems, how to create and debug statistical models, and how to run an analysis and evaluate the results.

Ben Klemens introduces a set of open and unlimited tools, and uses them to demonstrate data management, analysis, and simulation techniques essential for dealing with large data sets and computationally intensive procedures. He then demonstrates how to easily apply these tools to the many threads of statistical technique, including classical, Bayesian, maximum likelihood, and Monte Carlo methods. Klemens's accessible survey describes these models in a unified and nontraditional manner, providing alternative ways of looking at statistical concepts that often befuddle students. The book includes nearly one hundred sample programs of all kinds. Links to these programs will be available on this page at a later date.

Modeling with Data will interest anyone looking for a comprehensive guide to these powerful statistical tools, including researchers and graduate students in the social sciences, biology, engineering, economics, and applied mathematics.


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Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation (Genetic Algorithms and Evolutionary Computation) Review

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Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation (Genetic Algorithms and Evolutionary Computation) ReviewThis is a good book to learn about Estimation of Distribution Algorithms (EDAs or also called DEAs or Iterated DEAs). These algorithms are similar to evolutionary algorithms, but do not use the crossover or mutation operators of evolutionary search. EDAs instead create a probabilistic model of good solutions and use the model to generate new search points. It's a nifty idea and it works.
Most of the chapters of this edited collection were authored or coauthored by the editors. So, algorithms developed by other people do not get a lot of attention. However, the editors (or is it the authors) manage to include chapters on combinatorial, continuous, and discrete optimization.
There is a section on machine learning applications that is OK, but the last chapter on training neural nets with EDAs is very weak (look ma I used this and it worked...). Except for this chapter, the rest of the chapters in this section use careful experiments and statistics to make their points.
Making the source code available would have improved things and would make it easier for people to try these algorithms.Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation (Genetic Algorithms and Evolutionary Computation) OverviewEstimation of Distribution Algorithms: A New Tool forEvolutionary Computation is devoted to a new paradigm forevolutionary computation, named estimation of distribution algorithms(EDAs). This new class of algorithms generalizes genetic algorithms byreplacing the crossover and mutation operators with learning andsampling from the probability distribution of the best individuals ofthe population at each iteration of the algorithm. Working in such away, the relationships between the variables involved in the problemdomain are explicitly and effectively captured and exploited. This text constitutes the first compilation and review of thetechniques and applications of this new tool for performingevolutionary computation. Estimation of Distribution Algorithms: ANew Tool for Evolutionary Computation is clearly divided intothree parts. Part I is dedicated to the foundations of EDAs. In thispart, after introducing some probabilistic graphical models -Bayesian and Gaussian networks - a review of existing EDAapproaches is presented, as well as some new methods based on moreflexible probabilistic graphical models. A mathematical modeling ofdiscrete EDAs is also presented. Part II covers several applicationsof EDAs in some classical optimization problems: the travellingsalesman problem, the job scheduling problem, and the knapsackproblem. EDAs are also applied to the optimization of some well-knowncombinatorial and continuous functions. Part III presents theapplication of EDAs to solve some problems that arise in the machinelearning field: feature subset selection, feature weighting inK-NN classifiers, rule induction, partial abductive inferencein Bayesian networks, partitional clustering, and the search foroptimal weights in artificial neural networks. Estimation of Distribution Algorithms: A New Tool forEvolutionary Computation is a useful and interesting tool forresearchers working in the field of evolutionary computation and forengineers who face real-world optimization problems. This book mayalso be used by graduate students and researchers in computer science.`... I urge those who are interested in EDAs to study thiswell-crafted book today.' David E. Goldberg, University ofIllinois Champaign-Urbana.

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