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It is also noted that: Each cluster at least consists of one data object. Hence, the aim of the partition based clustering algorithm is to determine the K number of cluster centers in a given dataset. Here, the MCSS algorithm is applied for determining the optimal cluster centers in a dataset.

Clustering has proven its importance in many applications successfully.

State-of-the-art in artificial neural network applications: A survey

Some of these are pattern recognition [ 1 , 2 ], image processing [ 3 — 6 ], process monitoring [ 7 ], machine learning [ 8 , 9 ], quantitative structure activity relationship [ 10 ], document retrieval [ 11 ], bioinformatics [ 12 ], image segmentation [ 13 ], construction management [ 14 ], marketing [ 15 , 16 ] and healthcare [ 17 , 18 ]. Broadly, clustering algorithms can be divided into two categories - partition based clustering algorithms and hierarchal clustering algorithms.

In partition based clustering algorithms, partition a dataset into k clusters on the basis of some fitness functions [ 19 ]. While in hierarchical clustering algorithms, clustering of data occurs in the form of tree representation and this representation is known as dendrogram. Hierarchical clustering algorithms do not require any prerequisite knowledge about number of clusters in a dataset but its only drawback is lacking of dynamism as the objects are tightly bound with respective clusters [ 20 — 23 ].

However, our research is focused on partition clustering, which decomposes the data into several disjoint clusters that are optimal in terms of some predefined criteria. From the literature, it is found that K-means algorithm is the oldest, most popular, and extensively used partition based algorithm for data clustering. It is easy, fast, simple structure, and having linear time complexity [ 24 , 25 ]. In K-means algorithm, a dataset is decomposed into a predefined number of clusters and the data into distinct clusters based on the euclidean distance [ 25 ].

Nowadays, heuristic approaches gain wide popularity to solve the clustering problem and become more successful. Numerous researchers have been applying heuristic approaches in the field of clustering. Some of these are summarized as simulated annealing [ 26 ], tabu search [ 27 , 28 ], genetic algorithm [ 29 — 32 ], particle swarm optimization [ 33 , 34 ], ant colony optimization [ 35 ], artificial bee colony algorithm [ 36 , 37 , 56 ], charged system search algorithm [ 38 , 39 ], cat swarm optimization [ 40 — 42 , 57 ], teacher learning based optimization method [ 43 , 44 ], gravitational search algorithm [ 45 , 46 ] and binary search based clustering algorithm [ 47 ].

The magnetic charged system search MCSS algorithm is a recent meta-heuristic algorithm based on electromagnetic theory [ 48 ]. According to electromagnetic theory, moving charged particles produce an electric field as well as a magnetic field. Movement of the charged particles in a magnetic field enforces a magnetic force on the other charged particles and the resultant force is proportional to the charge mass and speed of the charged particles.

The magnitude and direction of the resultant force depend on the two factors: first, velocity of the charged particles, and secondly, magnitude and direction of the magnetic field. Along this, MCSS can be either attractive or repulsive in nature. This nature of MCSS algorithm generates more promising solutions in random space.

On the other hand, CSS algorithm is attractive by nature.

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Thus, the performance of the algorithm can be affected with small number of CPs. So, the addition of the magnetic force to the already existing electric force, results in enhancement of both the exploration and exploitation capabilities of CSS and this makes the algorithm more realistic one. Hence, the inclusion of magnetic force in the charge system search CSS algorithm results in the formation of a new algorithm known as magnetic charge system search MCSS.

The main steps of the MCSS algorithm are as follows. Algorithm starts by identifying the initial positions of charged particles CPs in d-dimensional space in random order and set the initial velocities of CPs is zero. To determine the initial positions of CPs, equation 1 is used. A variable charge memory CM is used to store the best results. The total force is the combination of the electric force and magnetic force, and this force influences the movement of CPs in d-dimensional space. It can be computed as follows: Determine the electric force — when CPs move in d-dimensional space, an electric field is produced surrounding it, and exerted an electric field on the other CPs.

This electric force is directly proportional to the magnitude of its charge and the distance between CPs. The magnitude of an electric force generated by a charge particle is enforced on another charge particle and it can be measured using equation 2. In equation 2 , q i and q k represents the fitness values of i th and k th CP, r ik denotes the separation distance between i th and k th CPs, w 1 and w 2 are the two variables whose values are either 0 or 1, R represents the radius of CPs which is set to unity and it is assumed that each CPs has uniform volume charge density but changes in every iteration, and P ik denotes the moving probability of each CPs.

Determine the magnetic force — The movement of CPs also produce magnetic field along with the electric field. As a result of this, a magnetic force is imposed on the other CPs and equation 3 is utilized to compute the magnitude of magnetic force exerted by a CP on other CPs. It can be either positive or negative depending on the value of average electric current of the previous iteration.

In equation 3 , q k represents the fitness values of the k th CP, I i is the average electric current, r ik denotes the separation distance between i th data instance and k th CPs, w 1 and w 2 are the two variables whose values are either 0 or 1, R represents the radius of CPs which is set to unity, and PM ik denotes the probability of magnetic influence between i th data instance and k th CP.

In other words, it can be summarized that the magnetic force can be either attractive or repulsive in nature. As a result of this, more promising solutions can be generated during the search. Whereas, the electric force is always attractive in nature. Therefore, this nature of electric force may influence the performance of the algorithm. Hence, to overcome the repulsive nature, a probability function is added with the electric force and finally, the total force acting on other CPs can be computed using equation 4.

Where, p r denotes a probability value to determine either the electric force E ik repelling or attracting, E ik and M ik present the electric and magnetic forces exerted by the k th CP to i th data instance. Newton second law of motion is applied to determine the movement of CPs. The magnitude of the total force with Newtonian laws is used to produce the next positions and velocities of CPs. The new positions and velocities of CPs can be computed using equation 5 and 6. If the maximum iterations is reached and condition is satisfied, then stop the algorithm and obtain the optimal cluster centers.

Otherwise repeat steps 2—4. This section summarizes the pseudo code of the MCSS algorithm for clustering tasks. Step 3: Compute the value of objective function using equation 7 and arrange the data instances to the clusters using minimum value of objective function.

Step 7. Step 9: Calculate the new positions and velocities of charged particles using equation 5 and 6. Step Recalculate the value of objective function using new positions of charge particles. Step Compare the newly generated charge particles to the charge particles reside in CM. This section deals with the experimental setup of our study. It includes the performance measures, parameters settings, datasets to be used, experiment results, and statistical analysis. To prove the effectiveness of the MCSS algorithm, 10 datasets are applied in which two datasets are artificial ones and the rest are taken from UCI repository.

The performance of MCSS algorithm is examined over the sum of intra cluster distance and F-measure parameters. The sum of intra cluster distance can be measured in terms of best case, average case, and worst case solutions including standard deviation parameter which shows the dispersion of the data. F-measure parameter is used to measure the accuracy of proposed method. Performance measures are described as follows:.

Intra cluster distance can be used to measure the quality of clustering [ 35 , 36 ]. It indicates the distance between the data objects within a cluster and its cluster center. His future aspirations include developing analytics tools that combine novel machine learning methods with the emerging field of computational rhetoric for monitoring and interpreting online information in various application areas in real-time. Universal Access Toggle Universal Access. Research Publications.

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    Data Manipulation with R. The ready availability of the program, along with a wide variety of packages and the supportive R community make R an excellent choice for almost any kind of computing task related to statistics. However, many users, especially those with experience in other languages, do not take advantage of the full power of R. Because of the nature of R, solutions that make sense in other languages may not be very efficient in R. This book presents a wide array of methods applicable for reading data into R, and efficiently manipulating that data.

    All of the methods presented take advantage of the core features of R: vectorization, efficient use of subscripting, and the proper use of the varied functions in R that are provided for common data management tasks. Most experienced R users discover that, especially when working with large data sets, it may be helpful to use other programs, notably databases, in conjunction with R. Accordingly, the use of databases in R is covered in detail, along with methods for extracting data from spreadsheets and datasets created by other programs.

    Character manipulation, while sometimes overlooked within R, is also covered in detail, allowing problems that are traditionally solved by scripting languages to be carried out entirely within R. For users with experience in other languages, guidelines for the effective use of programming constructs like loops are provided. Since many statistical modeling and graphics functions need their data presented in a data frame, techniques for converting the output of commonly used functions to data frames are provided throughout the book.

    Using a variety of examples based on data sets included with R, along with easily simulated data sets, the book is recommended to anyone using R who wishes to advance from simple examples to practical real-life data manipulation solutions. Springer, New York, 2nd edition, This book not only introduces the reader to this topic but enables him to conduct the various unit root tests and co-integration methods on his own by utilizing the free statistical programming environment R. The book encompasses seasonal unit roots, fractional integration, coping with structural breaks, and multivariate time series models.

    The book is enriched by numerous programming examples to artificial and real data so that it is ideally suited as an accompanying text book to computer lab classes. The second edition adds a discussion of vector auto-regressive, structural vector auto-regressive, and structural vector error-correction models. To analyze the interactions between the investigated variables, further impulse response function and forecast error variance decompositions are introduced as well as forecasting. The author explains how these model types relate to each other.

    He obtained a diploma and a doctorate degree at the economics department of the latter entity where he was employed as a research and teaching assistant. Introductory Statistics with R. The main mode of presentation is via code examples with liberal commenting of the code and the output, from the computational as well as the statistical viewpoint. A supplementary R package can be downloaded and contains the data sets. The statistical methodology includes statistical standard distributions, one- and two-sample tests with continuous data, regression analysis, one- and two-way analysis of variance, regression analysis, analysis of tabular data, and sample size calculations.

    In addition, the last six chapters contain introductions to multiple linear regression analysis, linear models in general, logistic regression, survival analysis, Poisson regression, and nonlinear regression. Statistical Computing with R. Suitable for an introductory course in computational statistics or for self-study, it includes R code for all examples and R notes to help explain the R programming concepts. Semiparametric Regression for the Social Sciences. Semiparametric Regression for the Social Sciences sets out to address this situation by providing an accessible introduction to the subject, filled with examples drawn from the social and political sciences.

    Readers are introduced to the principles of nonparametric smoothing and to a wide variety of smoothing methods. The author also explains how smoothing methods can be incorporated into parametric linear and generalized linear models. The use of smoothers with these standard statistical models allows the estimation of more flexible functional forms whilst retaining the interpretability of parametric models.

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    The full potential of these techniques is highlighted via the use of detailed empirical examples drawn from the social and political sciences. Each chapter features exercises to aid in the understanding of the methods and applications. All examples in the book were estimated in R. The book contains an appendix with R commands to introduce readers to estimating these models in R.

    All the R code for the examples in the book are available from the author's website and the publishers website. Cryer and Kung-Sik Chan. Although the emphasis is on time domain ARIMA models and their analysis, the new edition devotes two chapters to the frequency domain and three to time series regression models, models for heteroscedasticty, and threshold models.

    All of the ideas and methods are illustrated with both real and simulated data sets. A unique feature of this edition is its integration with the R computing environment. The tables and graphical displays are accompanied by the R commands used to produce them. An extensive R package, TSA, which contains many new or revised R functions and all of the data used in the book, accompanies the written text.

    Script files of R commands for each chapter are available for download. There is also an extensive appendix in the book that leads the reader through the use of R commands and the new R package to carry out the analyses. Software for Data Analysis: Programming with R. This book guides the reader in programming with R, from interactive use and writing simple functions to the design of R packages and intersystem interfaces.

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    World Scientific, Hackensack, NJ, It helps readers choose the best method from a wide array of tools and packages available. The data used in the examples along with R program snippets, illustrate the economic theory and sophisticated statistical methods extending the usual regression. The R program snippets are included on a CD accompanying the book.

    These are not merely given as black boxes, but include detailed comments which help the reader better understand the software steps and use them as templates for possible extension and modification. The book has received endorsements from top econometricians. Wavelet Methods in Statistics with R. This book fulfils three purposes. First, it is a gentle introduction to wavelets and their uses in statistics. Second, it acts as a quick and broad reference to many recent developments in the area.

    The book concentrates on describing the essential elements and provides comprehensive source material references. Third, the book intersperses R code that explains and demonstrates both wavelet and statistical methods. The code permits the user to learn the methods, to carry out their own analyses and further develop their own methods. The book is designed to be read in conjunction with WaveThresh4, the freeware R package for wavelets.

    The book introduces the wavelet transform by starting with the simple Haar wavelet transform and then builds to consider more general wavelets such as the Daubechies compactly supported series. The book then describes the evolution of wavelets in the directions of complex-valued wavelets, non-decimated transforms, multiple wavelets and wavelet packets as well as giving consideration to boundary conditions initialization. Later chapters explain the role of wavelets in nonparametric regression problems via a variety of techniques including thresholding, cross-validation, SURE, false-discovery rate and recent Bayesian methods, and also consider how to deal with correlated and non-Gaussian noise structures.

    The book also looks at how nondecimated and packet transforms can improve performance. The penultimate chapter considers the role of wavelets in both stationary and non-stationary time series analysis. The final chapter describes recent work concerning the role of wavelets for variance stabilization for non-Gaussian intensity estimation.

    The book is aimed at final year undergraduate and Masters students in a numerate discipline such as mathematics, statistics, physics, economics and engineering and would also suit as a quick reference for postgraduate or research level activity. The book would be ideal for a researcher to learn about wavelets, to learn how to use wavelet software and then to adapt the ideas for their own purposes.

    This is a book written in colloquial language, avoiding mathematical formulae as much as possible, trying to explain statistical methods using examples and graphics instead. To use the book efficiently, readers should have some computer experience. The book starts with the simplest of statistical concepts and carries readers forward to a deeper and more extensive understanding of the use of statistics in environmental sciences. The book concerns the application of statistical and other computer methods to the management, analysis and display of spatial data.

    These data are characterised by including locations geographic coordinates , which leads to the necessity of using maps to display the data and the results of the statistical methods. Although the book uses examples from applied geochemistry, and a large geochemical survey in particular, the principles and ideas equally well apply to other natural sciences, e.

    The book is unique because it supplies direct access to software solutions based on R, the Open Source version of the S-language for statistics for applied environmental statistics. For all graphics and tables presented in the book, the R-scripts are provided in the form of executable R-scripts. Statistical Data Analysis Explained: Applied Environmental Statistics with R provides, on an accompanying website, the software to undertake all the procedures discussed, and the data employed for their description in the book.

    Morphometrics with R. The R language and environment offers a single platform to perform a multitude of analyses from the acquisition of data to the production of static and interactive graphs. This offers an ideal environment to analyze shape variation and shape change. This open-source language is accessible for novices and for experienced users. Adopting R gives the user and developer several advantages for performing morphometrics: evolvability, adaptability, interactivity, a single and comprehensive platform, possibility of interfacing with other languages and software, custom analyses, and graphs.

    The book explains how to use R for morphometrics and provides a series of examples of codes and displays covering approaches ranging from traditional morphometrics to modern statistical shape analysis such as the analysis of landmark data, Thin Plate Splines, and Fourier analysis of outlines. The book fills two gaps: the gap between theoreticians and students by providing worked examples from the acquisition of data to analyses and hypothesis testing, and the gap between user and developers by providing and explaining codes for performing all the steps necessary for morphometrics rather than providing a manual for a given software or package.

    Students and scientists interested in shape analysis can use the book as a reference for performing applied morphometrics, while prospective researchers will learn how to implement algorithms or interfacing R for new methods. In addition, adopting the R philosophy will enhance exchanges within and outside the morphometrics community.

    Julien Claude is evolutionary biologist and palaeontologist at the University of Montpellier 2 where he got his Ph. He works on biodiversity and phenotypic evolution of a variety of organisms, especially vertebrates. He teaches evolutionary biology and biostatistics to undergraduate and graduate students and has developed several functions in R for the package APE. Applied Econometrics with R. It presents hands-on examples for a wide range of econometric models, from classical linear regression models for cross-section, time series or panel data and the common non-linear models of microeconometrics such as logit, probit and tobit models, to recent semiparametric extensions.

    In addition, it provides a chapter on programming, including simulations, optimization, and an introduction to R tools enabling reproducible econometric research. It contains some data sets taken from a wide variety of sources, the full source code for all examples used in the text plus further worked examples, e. The data sets are suitable for illustrating, among other things, the fitting of wage equations, growth regressions, hedonic regressions, dynamic regressions and time series models as well as models of labor force participation or the demand for health care.

    The goal of this book is to provide a guide to R for users with a background in economics or the social sciences. Readers are assumed to have a background in basic statistics and econometrics at the undergraduate level. A large number of examples should make the book of interest to graduate students, researchers and practitioners alike. Ecological Models and Data in R. Princeton University Press, In step-by-step detail, the book teaches ecology graduate students and researchers everything they need to know in order to use maximum likelihood, information-theoretic, and Bayesian techniques to analyze their own data using the programming language R.

    The book shows how to choose among and construct statistical models for data, estimate their parameters and confidence limits, and interpret the results. The book also covers statistical frameworks, the philosophy of statistical modeling, and critical mathematical functions and probability distributions. It requires no programming background--only basic calculus and statistics.

    Cambridge University Press, Cambridge, Unlike other introductory books on the R system, this book emphasizes programming, including the principles that apply to most computing languages, and techniques used to develop more complex projects. The key feature of this book is that it covers models that are most commonly used in social science research-including the linear regression model, generalized linear models, hierarchical models, and multivariate regression models-and it thoroughly develops each real-data example in painstaking detail.

    Multiple Testing Procedures and Applications to Genomics. Statistical and Probabilistic Methods in Actuarial Science. It presents an accessible, sound foundation in both the theory and applications of actuarial science. It encourages students to use the statistical software package R to check examples and solve problems. Correspondence Analysis in Practice, Second Edition. T his completely revised, up-to-date edition features a didactic approach with self-contained chapters, extensive marginal notes, informative figure and table captions, and end-of-chapter summaries.

    It includes a computational appendix that provides the R commands that correspond to most of the analyses featured in the book. Data Analysis and Graphics Using R. Cambridge University Press, Cambridge, 2nd edition, There is extensive advice on practical data analysis. Topics covered include exploratory data analysis, tests and confidence intervals, regression, genralized linear models, survival analysis, time series, multi-level models, trees and random forests, classification, and ordination. Focusing on standard statistical models and backed up by discussed real datasets available from the book website, it provides an operational methodology for conducting Bayesian inference, rather than focusing on its theoretical justifications.

    Special attention is paid to the derivation of prior distributions in each case and specific reference solutions are given for each of the models. Similarly, computational details are worked out to lead the reader towards an effective programming of the methods given in the book. While R programs are provided on the book website and R hints are given in the computational sections of the book, The Bayesian Core requires no knowledge of the R language and it can be read and used with any other programming language.

    Interactive and Dynamic Graphics for Data Analysis. Chapters include clustering, supervised classification, and working with missing values. A variety of plots and interaction methods are used in each analysis, often starting with brushing linked low-dimensional views and working up to manual manipulation of tours of several variables. The role of graphical methods is shown at each step of the analysis, not only in the early exploratory phase, but in the later stages, too, when comparing and evaluating models.

    All examples are based on freely available software: GGobi for interactive graphics and R for static graphics, modeling, and programming. The printed book is augmented by a wealth of material on the web, encouraging readers follow the examples themselves. The web site has all the data and code necessary to reproduce the analyses in the book, along with movies demonstrating the examples.

    The Statistics of Gene Mapping. It presents elementary principles of probability and statistics, which are implemented by computational tools based on the R programming language to simulate genetic experiments and evaluate statistical analyses. Each chapter contains exercises, both theoretical and computational, some routine and others that are more challenging. The R programming language is developed in the text.

    The author bases his approach on a framework of penalized regression splines, and builds a well- grounded foundation through motivating chapters on linear and generalized linear models. While firmly focused on the practical aspects of GAMs, discussions include fairly full explanations of the theory underlying the methods. The treatment is rich with practical examples, and it includes an entire chapter on the analysis of real data sets using R and the author's add-on package mgcv.

    Each chapter includes exercises, for which complete solutions are provided in an appendix. Numerous examples using non-trivial data illustrate solutions to problems such as evaluating pain perception experiments using magnetic resonance imaging or monitoring a nuclear test ban treaty. The book is designed to be useful as a text for graduate level students in the physical, biological and social sciences and as a graduate level text in statistics. Some parts may also serve as an undergraduate introductory course.

    Theory and methodology are separated to allow presentations on different levels. Material from the earlier Prentice-Hall text Applied Statistical Time Series Analysis has been updated by adding modern developments involving categorical time sries analysis and the spectral envelope, multivariate spectral methods, long memory series, nonlinear models, longitudinal data analysis, resampling techniques, ARCH models, stochastic volatility, wavelets and Monte Carlo Markov chain integration methods. These add to a classical coverage of time series regression, univariate and multivariate ARIMA models, spectral analysis and state-space models.

    The book is complemented by ofering accessibility, via the World Wide Web, to the data and an exploratory time series analysis program ASTSA for Windows that can be downloaded as Freeware. Model-based Geostatistics. The name reflects its origins in mineral exploration, but the methods are now used in a wide range of settings including public health and the physical and environmental sciences.

    Model-based geostatistics refers to the application of general statistical principles of modeling and inference to geostatistical problems. This volume is the first book-length treatment of model-based geostatistics. It covers a spectrum of technical matters from measurement to environmental epidemiology to risk assessment. It showcases non-stationary vector-valued processes, while treating stationarity as a special case.

    In particular, with members of their research group the authors developed within a hierarchical Bayesian framework, the new statistical approaches presented in the book for analyzing, modeling, and monitoring environmental spatio-temporal processes. Furthermore they indicate new directions for development. Angewandte Statistik. Methodensammlung mit R. Springer, Berlin, Heidelberg, 12th completely revised edition, Das Programm R ist dabei ein leicht erlernbares und flexibel einzusetzendes Werkzeug, mit dem der Prozess der Datenanalyse nachvollziehbar verstanden und gestaltet werden kann.

    Diese The author's treatment is thoroughly modern and covers topics that include GLM diagnostics, generalized linear mixed models, trees, and even the use of neural networks in statistics. To demonstrate the interplay of theory and practice, throughout the book the author weaves the use of the R software environment to analyze the data of real examples, providing all of the R commands necessary to reproduce the analyses.

    Robust Statistical Methods with R. The authors work from underlying mathematical tools to implementation, paying special attention to the computational aspects. They cover the whole range of robust methods, including differentiable statistical functions, distance of measures, influence functions, and asymptotic distributions, in a rigorous yet approachable manner.

    Highlighting hands- on problem solving, many examples and computational algorithms using the R software supplement the discussion.

    Data Mining in Biomedicine Springer Optimization and Its Applications

    The book examines the characteristics of robustness, estimators of real parameter, large sample properties, and goodness-of-fit tests. It also includes a brief overview of R in an appendix for those with little experience using the software. Analysis of Phylogenetics and Evolution with R. Adopting R as a main tool for phylogenetic analyses sease the workflow in biologists' data analyses, ensure greater scientific repeatability, and enhance the exchange of ideas and methodological developments.

    The authors provide a concise introduction to R, including a summary of its most important features. They cover a variety of topics, such as simple inference, generalized linear models, multilevel models, longitudinal data, cluster analysis, principal components analysis, and discriminant analysis.

    With numerous figures and exercises, A Handbook of Statistical Analysis using R provides useful information for students as well as statisticians and data analysts. Computational Genome Analysis: An Introduction. It focuses on com putational and statistical principles applied to genomes, and introduces the mat hematics and statistics that are crucial for understanding these applications.

    A ll computations are done with R. R Graphics. The power and flexibility of grid graphics. Building on top of the base or grid graphics: Trellis graphics and developing new graphics functions. Using R for Introductory Statistics. It includes a large collection of exercises and numerous practical examples from a broad range of scientific disciplines.

    It comes complete with an online resource containing datasets, R functions, selected solutions to exercises, and updates to the latest features. It features a practical presentation of the theory with a range of applications from data mining, financial engineering, and the biosciences. The necessary R and S-Plus code is given for each analysis in the book, with any differences between the two highlighted. Statistics for Biology and Health. Mase, T. Kamakura, M.

    Jimbo, and K. Introduction to Data Science for engineers Data analysis using free statistical software R in Japanese. Suuri-Kogaku-sha, Tokyo, April Heiberger and Burt Holland. Springer Texts in Statistics. Many of the displays appear here for the first time. Discusses construction and interpretation of graphs, principles of graphical design, and relation between graphs and traditional tabular results. Can serve as a graduate-level standalone statistics text and as a reference book for researchers.

    In-depth discussions of regression analysis, analysis of variance, and design of experiments are followed by introductions to analysis of discrete bivariate data, nonparametrics, logistic regression, and ARIMA time series modeling. Concepts and techniques are illustrated with a variety of case studies. S functions are provided for each new graphical display format. All code, transcript and figure files are provided for readers to use as templates for their own analyses.

    Linear Models with R. It clearly demonstrates the different methods available and in which situations each one applies. It covers all of the standard topics, from the basics of estimation to missing data, factorial designs, and block designs, but it also includes discussion of topics, such as model uncertainty, rarely addressed in books of this type. The presentation incorporates an abundance of examples that clarify both the use of each technique and the conclusions one can draw from the results.

    Statistik mit R. Statistical Tools for Nonlinear Regression. Laboratorio di statistica con R. McGraw-Hill, Milano, The Analysis of Gene Expression Data. Modern Applied Statistics with S. Fourth Edition. In the first chapters it gives an introduction to the S language. Then it covers a wide range of statistical methodology, including linear and 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, and optimization. It introduces S, and concentrates on how to use linear and generalized-linear models in S while assuming familiarity with the statistical methodology.

    Control de Calidad. Servicio de Publicaciones de la Universidad de La Rioja, It combines the theoretical basis with applied examples coded in R. This argues the need for a better balance in the literature and in statistical teaching between techniques and problem solving strategies. For example, there are missing data in the majority of datasets one is likely to encounter other than those used in textbooks! S Programming. Its goal is to extend the toolkit beyond the basic triad provided by most statistical packages: the Kaplan-Meier estimator, log-rank test, and Cox regression model.

    Programming with Data. Statistical Models in S. It described software for statistical modeling in S and introduced the S3 version of classes and methods. The New S Language. This file was generated by bibtex2html 1. Jean-Francois Mas. Thomas Rahlf. Steven Murray.

    Lawrence Leemis. Vikram Dayal. Matthias Kohl. Marta Blangiardo and Michela Cameletti. Sarah Stowell.

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    Lise Bellanger and Richard Tomassone. Yvonnick Noel. Yihui Xie. Joseph Hilbe. Christopher Gandrud. Dirk Eddelbuettel. Din Chen. Stano Pekar and Marek Brabec. Bernhard Pfaff. David Lunn. Michael Lawrence. Dimitris Rizopoulos. Brian Dennis. Pierre-Andre Cornillon. Yves Aragon. Paul Teetor. Paul Murrell. Laura Chihara and Tim Hesterberg. John Fox and Sanford Weisberg. Hrishi Mittal. Graham Williams. Bruno Falissard. Shravan Vasishth and Michael Broe.

    Rob Kabacoff. Joseph Adler. David Ruppert. Christian Robert and George Casella. Carlo Gaetan and Xavier Guyon. Victor Bloomfield. Since R is increasingly used in bioinformatics applications such as the BioConductor project, it can serve students as their basic quantitative, statistical, and graphics tool as they develop their careers. Uwe Ligges.

    Paul S. Kurt Varmuza and Peter Filzmoser. Kai Velten. Jim Albert. Hadley Wickham. Gael Millot.

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    Detlev Reymann. Simon Sheather. Deepayan Sarkar. Robert Gentleman. Phil Spector. Peter Dalgaard. Luke Keele. Julien Claude. Christian Kleiber and Achim Zeileis.