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Using the R Commander A Point-and-Click Interface for R

Using the R Commander A Point-and-Click Interface for R

This book provides a general introduction to the R Commander graphical user interface (GUI) to R for readers who are unfamiliar with R. It is suitable for use as a supplementary text in a basic or intermediate-level statistics course. It is not intended to replace a basic or other statistics text but rather to complement it although it does promote sound statistical practice in the examples. The book should also be useful to individual casual or occasional users of R for whom the standard command-line interface is an obstacle. tinyurl. com/RcmdrBookThe site includes data files used in the book and an errata list. http://socserv. mcmaster. ca/jfox/Books/RCommander/Writing-Rcmdr-Plugins. pdf Writing R Commander Plug-in Packages | Using the R Commander A Point-and-Click Interface for R

GBP 175.00
1

R Markdown The Definitive Guide

R Markdown The Definitive Guide

R Markdown: The Definitive Guide is the first official book authored by the core R Markdown developers that provides a comprehensive and accurate reference to the R Markdown ecosystem. With R Markdown you can easily create reproducible data analysis reports presentations dashboards interactive applications books dissertations websites and journal articles while enjoying the simplicity of Markdown and the great power of R and other languages. In this book you will learn Basics: Syntax of Markdown and R code chunks how to generate figures and tables and how to use other computing languages Built-in output formats of R Markdown: PDF/HTML/Word/RTF/Markdown documents and ioslides/Slidy/Beamer/PowerPoint presentations Extensions and applications: Dashboards Tufte handouts xaringan/reveal. js presentations websites books journal articles and interactive tutorials Advanced topics: Parameterized reports HTML widgets document templates custom output formats and Shiny documents. Yihui Xie is a software engineer at RStudio. He has authored and co-authored several R packages including knitr rmarkdown bookdown blogdown shiny xaringan and animation. He has published three other books Dynamic Documents with R and knitr bookdown: Authoring Books and Technical Documents with R Markdown and blogdown: Creating Websites with R Markdown. J. J. Allaire is the founder of RStudio and the creator of the RStudio IDE. He is an author of several packages in the R Markdown ecosystem including rmarkdown flexdashboard learnr and radix. Garrett Grolemund is the co-author of R for Data Science and author of Hands-On Programming with R. He wrote the lubridate R package and works for RStudio as an advocate who trains engineers to do data science with R and the Tidyverse. | R Markdown The Definitive Guide

GBP 31.99
1

The R Student Companion

The R Student Companion

R is the amazing free open-access software package for scientific graphs and calculations used by scientists worldwide. The R Student Companion is a student-oriented manual describing how to use R in high school and college science and mathematics courses. Written for beginners in scientific computation the book assumes the reader has just some high school algebra and has no computer programming background. The author presents applications drawn from all sciences and social sciences and includes the most often used features of R in an appendix. In addition each chapter provides a set of computational challenges: exercises in R calculations that are designed to be performed alone or in groups. Several of the chapters explore algebra concepts that are highly useful in scientific applications such as quadratic equations systems of linear equations trigonometric functions and exponential functions. Each chapter provides an instructional review of the algebra concept followed by a hands-on guide to performing calculations and graphing in R. R is intuitive even fun. Fantastic publication-quality graphs of data equations or both can be produced with little effort. By integrating mathematical computation and scientific illustration early in a student‘s development R use can enhance one's understanding of even the most difficult scientific concepts. While R has gained a strong reputation as a package for statistical analysis The R Student Companion approaches R more completely as a comprehensive tool for scientific computing and graphing.

GBP 175.00
1

Visualizing Surveys in R

Visualizing Surveys in R

Visualizing Surveys in R is about creating static print quality figures from survey data using R. The focus is not for example on statistical analysis of survey data but rather on giving concrete solutions for typical problems in visualizing survey data. While there are many excellent books on data visualization surveys and R the aim of this book is to bring these topics together and offer practical instructions for visualizing surveys in R. Features Introduction to survey data: variables categories and scales Description of a process for visualizing survey data Recommendations for reading survey data into R Advice on building a survey dataset in R to facilitate versatile plotting Step-by-step recipes in R for creating useful plots from survey data The book is intended for researchers who regularly use surveys and are interested in learning how to seize the vast possibilities and the flexibility of R in survey analysis and visualizations. The book is also valuable for psychologists marketeers HR personnel managers and other professionals who wish to standardize and automate the process for visualizing survey data. Finally the book is suitable as a course textbook either more widely on survey studies or more strictly on visualizing survey data in R.

GBP 140.00
1

Learn R As a Language

Learn R As a Language

Learning a computer language like R can be either frustrating fun or boring. Having fun requires challenges that wake up the learner’s curiosity but also provide an emotional reward on overcoming them. This book is designed so that it includes smaller and bigger challenges in what I call playgrounds in the hope that all readers will enjoy their path to R fluency. Fluency in the use of a language is a skill that is acquired through practice and exploration. Although rarely mentioned separately fluency in a computer programming language involves both writing and reading. The parallels between natural and computer languages are many but differences are also important. For students and professionals in the biological sciences humanities and many applied fields recognizing the parallels between R and natural languages should help them feel at home with R. The approach I use is similar to that of a travel guide encouraging exploration and describing the available alternatives and how to reach them. The intention is to guide the reader through the R landscape of 2020 and beyond. Features R as it is currently used Few prescriptive rules—mostly the author’s preferences together with alternatives Explanation of the R grammar emphasizing the R way of doing things Tutoring for programming in the small using scripts The grammar of graphics and the grammar of data described as grammars Examples of data exchange between R and the foreign world using common file formats Coaching for becoming an independent R user capable of both writing original code and solving future challenges What makes this book different from others: Tries to break the ice and help readers from all disciplines feel at home with R Does not make assumptions about what the reader will use R for Attempts to do only one thing well: guide readers into becoming fluent in the R language Pedro J. Aphalo is a PhD graduate from the University of Edinburgh and is currently a lecturer at the University of Helsinki. A plant biologist and agriculture scientist with a passion for data electronics computers and photography in addition to plants Dr. Aphalo has been a user of R for 25 years. He first organized an R course for MSc students 18 years ago and is the author of 13 R packages currently in CRAN. | Learn R As a Language

GBP 56.99
1

Advanced R Second Edition

Advanced R Second Edition

Advanced R helps you understand how R works at a fundamental level. It is designed for R programmers who want to deepen their understanding of the language and programmers experienced in other languages who want to understand what makes R different and special. This book will teach you the foundations of R; three fundamental programming paradigms (functional object-oriented and metaprogramming); and powerful techniques for debugging and optimisingyour code. By reading this book you will learn: The difference between an object and its name and why the distinction is important The important vector data structures how they fit together and how you can pull them apart using subsetting The fine details of functions and environments The condition system which powers messages warnings and errors The powerful functional programming paradigm which can replace many for loops The three most important OO systems: S3 S4 and R6 The tidy eval toolkit for metaprogramming which allows you to manipulate code and control evaluation Effective debugging techniques that you can deploy regardless of how your code is run How to find and remove performance bottlenecks The second edition is a comprehensive update: New foundational chapters: Names and values Control flow and Conditions comprehensive coverage of object oriented programming with chapters on S3 S4 R6 and how to choose between them Much deeper coverage of metaprogramming including the new tidy evaluation framework use of new package like rlang (http://rlang. r-lib. org) which provides a clean interface to low-level operations and purr (http://purrr. tidyverse. org/) for functional programming Use of color in code chunks and figuresHadley Wickham is Chief Scientist at RStudio an Adjunct Professor at Stanford University and the University of Auckland and a member of the R Foundation. He is the lead developer of the tidyverse a collection of R packages including ggplot2 and dplyr designed to support data science. He is also the author of R for Data Science (with Garrett Grolemund) R Packages and ggplot2: Elegant Graphics for Data Analysis. | Advanced R Second Edition

GBP 48.99
1

Computational Methods for Numerical Analysis with R

Computational Methods for Numerical Analysis with R

Computational Methods for Numerical Analysis with R is an overview of traditional numerical analysis topics presented using R. This guide shows how common functions from linear algebra interpolation numerical integration optimization and differential equations can be implemented in pure R code. Every algorithm described is given with a complete function implementation in R along with examples to demonstrate the function and its use. Computational Methods for Numerical Analysis with R is intended for those who already know R but are interested in learning more about how the underlying algorithms work. As such it is suitable for statisticians economists and engineers and others with a computational and numerical background.

GBP 48.99
1

R Markdown Cookbook

R Markdown Cookbook

This new book written by the developers of R Markdown is an essential reference that will help users learn and make full use of the software. Those new to R Markdown will appreciate the short practical examples that address the most common issues users encounter. Frequent users will also benefit from the wide ranging tips and tricks that expose ‘hidden’ features support customization and demonstrate the many new and varied applications of the software. After reading this book users will learn how to: Enhance your R Markdown content with diagrams citations and dynamically generated text Streamline your workflow with child documents code chunk references and caching Control the formatting and layout with Pandoc markdown syntax or by writing custom HTML and LaTeX templates Utilize chunk options and hooks to fine-tune how your code is processed Switch between different language engineers to seamlessly incorporate python D3 and more into your analysis | R Markdown Cookbook

GBP 28.99
1

An R Companion to Linear Statistical Models

An R Companion to Linear Statistical Models

Focusing on user-developed programming An R Companion to Linear Statistical Models serves two audiences: those who are familiar with the theory and applications of linear statistical models and wish to learn or enhance their skills in R; and those who are enrolled in an R-based course on regression and analysis of variance. For those who have never used R the book begins with a self-contained introduction to R that lays the foundation for later chapters. This book includes extensive and carefully explained examples of how to write programs using the R programming language. These examples cover methods used for linear regression and designed experiments with up to two fixed-effects factors including blocking variables and covariates. It also demonstrates applications of several pre-packaged functions for complex computational procedures.

GBP 74.99
1

Rasch Measurement Theory Analysis in R

Rasch Measurement Theory Analysis in R

Rasch Measurement Theory Analysis in R provides researchers and practitioners with a step-by-step guide for conducting Rasch measurement theory analyses using R. It includes theoretical introductions to major Rasch measurement principles and techniques demonstrations of analyses using several R packages that contain Rasch measurement functions and sample interpretations of results. Features: Accessible to users with relatively little experience with R programming Reproducible data analysis examples that can be modified to accommodate users’ own data Accompanying e-book website with links to additional resources and R code updates as needed Features dichotomous and polytomous (rating scale) Rasch models that can be applied to data from a wide range of disciplines This book is designed for graduate students researchers and practitioners across the social health and behavioral sciences who have a basic familiarity with Rasch measurement theory and with R. Readers will learn how to use existing R packages to conduct a variety of analyses related to Rasch measurement theory including evaluating data for adherence to measurement requirements applying the dichotomous Rating Scale Partial Credit and Many-Facet Rasch models examining data for evidence of differential item functioning and considering potential interpretations of results from such analyses.

GBP 56.99
1

Optimization Modelling Using R

Optimization Modelling Using R

This book covers using R for doing optimization a key area of operations research which has been applied to virtually every industry. The focus is on linear and mixed integer optimization. It uses an algebraic modeling approach for creating formulations that pairs naturally with an algebraic implementation in R. With the rapid rise of interest in data analytics a data analytics platform is key. Working technology and business professionals need an awareness of the tools and language of data analysis. R reduces the barrier to entry for people to start using data analytics tools. Philosophically the book emphasizes creating formulations before going intoimplementation. Algebraic representation allows for clear understanding and generalizationof large applications and writing formulations is necessary to explain and convey the modeling decisions made. Appendix A introduces R. Mathematics is used at the level of subscripts and summations Refreshers are provided in Appendix B. This book: • Provides and explains code so examples are relatively clear and self-contained. • Emphasizes creating algebraic formulations before implementing. • Focuses on application rather than algorithmic details. • Embodies the philosophy of reproducible research. • Uses open-source tools to ensure access to powerful optimization tools. • Promotes open-source: all materials are available on the author’s github repository. • Demonstrates common debugging practices with a troubleshooting emphasis specific to optimization modeling using R. • Provides code readers can adapt to their own applications. This book can be used for graduate and undergraduate courses for students without a background in optimization and with varying mathematical backgrounds. | Optimization Modelling Using R

GBP 82.99
1

Exploratory Data Analysis Using R

Exploratory Data Analysis Using R

Exploratory Data Analysis Using R provides a classroom-tested introduction to exploratory data analysis (EDA) and introduces the range of interesting – good bad and ugly – features that can be found in data and why it is important to find them. It also introduces the mechanics of using R to explore and explain data. The book begins with a detailed overview of data exploratory analysis and R as well as graphics in R. It then explores working with external data linear regression models and crafting data stories. The second part of the book focuses on developing R programs including good programming practices and examples working with text data and general predictive models. The book ends with a chapter on keeping it all together that includes managing the R installation managing files documenting and an introduction to reproducible computing. The book is designed for both advanced undergraduate entry-level graduate students and working professionals with little to no prior exposure to data analysis modeling statistics or programming. it keeps the treatment relatively non-mathematical even though data analysis is an inherently mathematical subject. Exercises are included at the end of most chapters and an instructor's solution manual is available. About the Author:Ronald K. Pearson holds the position of Senior Data Scientist with GeoVera a property insurance company in Fairfield California and he has previously held similar positions in a variety of application areas including software development drug safety data analysis and the analysis of industrial process data. He holds a PhD in Electrical Engineering and Computer Science from the Massachusetts Institute of Technology and has published conference and journal papers on topics ranging from nonlinear dynamic model structure selection to the problems of disguised missing data in predictive modeling. Dr. Pearson has authored or co-authored books including Exploring Data in Engineering the Sciences and Medicine (Oxford University Press 2011) and Nonlinear Digital Filtering with Python. He is also the developer of the DataCamp course on base R graphics and is an author of the datarobot and GoodmanKruskal R packages available from CRAN (the Comprehensive R Archive Network).

GBP 44.99
1

Geocomputation with R

Geocomputation with R

Geocomputation with R is for people who want to analyze visualize and model geographic data with open source software. It is based on R a statistical programming language that has powerful data processing visualization and geospatial capabilities. The book equips you with the knowledge and skills to tackle a wide range of issues manifested in geographic data including those with scientific societal and environmental implications. This book will interest people from many backgrounds especially Geographic Information Systems (GIS) users interested in applying their domain-specific knowledge in a powerful open source language for data science and R users interested in extending their skills to handle spatial data. The book is divided into three parts: (I) Foundations aimed at getting you up-to-speed with geographic data in R (II) extensions which covers advanced techniques and (III) applications to real-world problems. The chapters cover progressively more advanced topics with early chapters providing strong foundations on which the later chapters build. Part I describes the nature of spatial datasets in R and methods for manipulating them. It also covers geographic data import/export and transforming coordinate reference systems. Part II represents methods that build on these foundations. It covers advanced map making (including web mapping) bridges to GIS sharing reproducible code and how to do cross-validation in the presence of spatial autocorrelation. Part III applies the knowledge gained to tackle real-world problems including representing and modeling transport systems finding optimal locations for stores or services and ecological modeling. Exercises at the end of each chapter give you the skills needed to tackle a range of geospatial problems. Solutions for each chapter and supplementary materials providing extended examples are available at https://geocompr. github. io/geocompkg/articles/. Dr. Robin Lovelace is a University Academic Fellow at the University of Leeds where he has taught R for geographic research over many years with a focus on transport systems. Dr. Jakub Nowosad is an Assistant Professor in the Department of Geoinformation at the Adam Mickiewicz University in Poznan where his focus is on the analysis of large datasets to understand environmental processes. Dr. Jannes Muenchow is a Postdoctoral Researcher in the GIScience Department at the University of Jena where he develops and teaches a range of geographic methods with a focus on ecological modeling statistical geocomputing and predictive mapping. All three are active developers and work on a number of R packages including stplanr sabre and RQGIS.

GBP 44.99
1

R Companion for Sampling Design and Analysis Third Edition

R Companion for Sampling Design and Analysis Third Edition

The R Companion for Sampling: Design and Analysis designed to be read alongside Sampling: Design and Analysis Third Edition by Sharon L. Lohr (SDA; 2022 CRC Press) shows how to use functions in base R and contributed packages to perform calculations for the examples in SDA. No prior experience with R is needed. Chapter 1 tells you how to obtain R and RStudio introduces basic features of the R statistical software environment and helps you get started with analyzing data. Each subsequent chapter provides step-by-step guidance for working through the data examples in the corresponding chapter of SDA with code output and interpretation. Tips and warnings help you develop good programming practices and avoid common survey data analysis errors. R features and functions are introduced as they are needed so you can see how each type of sample is selected and analyzed. Each chapter builds on the knowledge developed earlier for simpler designs; after finishing the book you will know how to use R to select and analyze almost any type of probability sample. All R code and data sets used in this book are available online to help you develop your skills analyzing survey data from social and public opinion research public health crime education business agriculture and ecology. | R Companion for Sampling Design and Analysis Third Edition

GBP 28.99
1

Applied Meta-Analysis with R and Stata

Applied Meta-Analysis with R and Stata

Review of the First Edition: The authors strive to reduce theory to a minimum which makes it a self-learning text that is comprehensible for biologists physicians etc. who lack an advanced mathematics background. Unlike in many other textbooks R is not introduced with meaningless toy examples; instead the reader is taken by the hand and shown around some analyses graphics and simulations directly relating to meta-analysis… A useful hands-on guide for practitioners who want to familiarize themselves with the fundamentals of meta-analysis and get started without having to plough through theorems and proofs. —Journal of Applied Statistics Statistical Meta-Analysis with R and Stata Second Edition provides a thorough presentation of statistical meta-analyses (MA) with step-by-step implementations using R/Stata. The authors develop analysis step by step using appropriate R/Stata functions which enables readers to gain an understanding of meta-analysis methods and R/Stata implementation so that they can use these two popular software packages to analyze their own meta-data. Each chapter gives examples of real studies compiled from the literature. After presenting the data and necessary background for understanding the applications various methods for analyzing meta-data are introduced. The authors then develop analysis code using the appropriate R/Stata packages and functions. What’s New in the Second Edition: Adds Stata programs along with the R programs for meta-analysis Updates all the statistical meta-analyses with R/Stata programs Covers fixed-effects and random-effects MA meta-regression MA with rare-event and MA-IPD vs MA-SS Adds five new chapters on multivariate MA publication bias missing data in MA MA in evaluating diagnostic accuracy and network MA Suitable as a graduate-level text for a meta-data analysis course the book is also a valuable reference for practitioners and biostatisticians (even those with little or no experience in using R or Stata) in public health medical research governmental agencies and the pharmaceutical industry. | Applied Meta-Analysis with R and Stata

GBP 44.99
1

Applications of Regression for Categorical Outcomes Using R

Applications of Regression for Categorical Outcomes Using R

This book covers the main models within the GLM (i. e. logistic Poisson negative binomial ordinal and multinomial). For each model estimations interpretations model fit diagnostics and how to convey results graphically are provided. There is a focus on graphic displays of results as these are a core strength of using R for statistical analysis. Many in the social sciences are transitioning away from using Stata SPSS and SAS to using R and this book uses statistical models which are relevant to the social sciences. Social Science Applications of Regression for Categorical Outcomes Using R will be useful for graduate students in the social sciences who are looking to expand their statistical knowledge and for Quantitative social scientists due to it’s ability to act as a practitioners guide. Key Features: Applied- in the sense that we will provide code that others can easily adapt Flexible- R is basically just a fancy calculator. Our programs will enable users to derive quantities that they can use in their work Timely- many in the social sciences are currently transitioning to R or are learning it now. Our book will be a useful resource Versatile- we will write functions into an R package that can be applied to all of the regression models we will cover in the book Aesthetically pleasing- one advantage of R relative to other software packages is that graphs are fully customizable. We will leverage this feature to yield high-end graphical displays of results Affordability- R is free. R packages are free. There is no need to purchase site licenses or updates.

GBP 59.99
1

bookdown Authoring Books and Technical Documents with R Markdown

bookdown Authoring Books and Technical Documents with R Markdown

bookdown: Authoring Books and Technical Documents with R Markdown presents a much easier way to write books and technical publications than traditional tools such as LaTeX and Word. The bookdown package inherits the simplicity of syntax and flexibility for data analysis from R Markdown and extends R Markdown for technical writing so that you can make better use of document elements such as figures tables equations theorems citations and references. Similar to LaTeX you can number and cross-reference these elements with bookdown. Your document can even include live examples so readers can interact with them while reading the book. The book can be rendered to multiple output formats including LaTeX/PDF HTML EPUB and Word thus making it easy to put your documents online. The style and theme of these output formats can be customized. We used books and R primarily for examples in this book but bookdown is not only for books or R. Most features introduced in this book also apply to other types of publications: journal papers reports dissertations course handouts study notes and even novels. You do not have to use R either. Other choices of computing languages include Python C C plus plus SQL Bash Stan JavaScript and so on although R is best supported. You can also leave out computing for example to write a fiction. This book itself is an example of publishing with bookdown and R Markdown and its source is fully available on GitHub. | bookdown Authoring Books and Technical Documents with R Markdown

GBP 89.99
1

Data Mining with R Learning with Case Studies Second Edition

Data Mining with R Learning with Case Studies Second Edition

Data Mining with R: Learning with Case Studies Second Edition uses practical examples to illustrate the power of R and data mining. Providing an extensive update to the best-selling first edition this new edition is divided into two parts. The first part will feature introductory material including a new chapter that provides an introduction to data mining to complement the already existing introduction to R. The second part includes case studies and the new edition strongly revises the R code of the case studies making it more up-to-date with recent packages that have emerged in R. The book does not assume any prior knowledge about R. Readers who are new to R and data mining should be able to follow the case studies and they are designed to be self-contained so the reader can start anywhere in the document. The book is accompanied by a set of freely available R source files that can be obtained at the book’s web site. These files include all the code used in the case studies and they facilitate the do-it-yourself approach followed in the book. Designed for users of data analysis tools as well as researchers and developers the book should be useful for anyone interested in entering the world of R and data mining. About the AuthorLuís Torgo is an associate professor in the Department of Computer Science at the University of Porto in Portugal. He teaches Data Mining in R in the NYU Stern School of Business’ MS in Business Analytics program. An active researcher in machine learning and data mining for more than 20 years Dr. Torgo is also a researcher in the Laboratory of Artificial Intelligence and Data Analysis (LIAAD) of INESC Porto LA. | Data Mining with R Learning with Case Studies Second Edition

GBP 44.99
1

Stochastic Processes with R An Introduction

Stochastic Processes with R An Introduction

Stochastic Processes with R: An Introduction cuts through the heavy theory that is present in most courses on random processes and serves as practical guide to simulated trajectories and real-life applications for stochastic processes. The light yet detailed text provides a solid foundation that is an ideal companion for undergraduate statistics students looking to familiarize themselves with stochastic processes before going on to more advanced courses. Key Features Provides complete R codes for all simulations and calculations Substantial scientific or popular applications of each process with occasional statistical analysis Helpful definitions and examples are provided for each process End of chapter exercises cover theoretical applications and practice calculations | Stochastic Processes with R An Introduction

GBP 82.99
1

Doing Meta-Analysis with R A Hands-On Guide

Doing Meta-Analysis with R A Hands-On Guide

Doing Meta-Analysis with R: A Hands-On Guide serves as an accessible introduction on how meta-analyses can be conducted in R. Essential steps for meta-analysis are covered including calculation and pooling of outcome measures forest plots heterogeneity diagnostics subgroup analyses meta-regression methods to control for publication bias risk of bias assessments and plotting tools. Advanced but highly relevant topics such as network meta-analysis multi-three-level meta-analyses Bayesian meta-analysis approaches and SEM meta-analysis are also covered. A companion R package dmetar is introduced at the beginning of the guide. It contains data sets and several helper functions for the meta and metafor package used in the guide. The programming and statistical background covered in the book are kept at a non-expert level making the book widely accessible. Features• Contains two introductory chapters on how to set up an R environment and do basic imports/manipulations of meta-analysis data including exercises• Describes statistical concepts clearly and concisely before applying them in R• Includes step-by-step guidance through the coding required to perform meta-analyses and a companion R package for the book | Doing Meta-Analysis with R A Hands-On Guide

GBP 74.99
1

Nonparametric Statistical Methods Using R

Nonparametric Statistical Methods Using R

A Practical Guide to Implementing Nonparametric and Rank-Based ProceduresNonparametric Statistical Methods Using R covers traditional nonparametric methods and rank-based analyses including estimation and inference for models ranging from simple location models to general linear and nonlinear models for uncorrelated and correlated responses. The authors emphasize applications and statistical computation. They illustrate the methods with many real and simulated data examples using R including the packages Rfit and npsm. The book first gives an overview of the R language and basic statistical concepts before discussing nonparametrics. It presents rank-based methods for one- and two-sample problems procedures for regression models computation for general fixed-effects ANOVA and ANCOVA models and time-to-event analyses. The last two chapters cover more advanced material including high breakdown fits for general regression models and rank-based inference for cluster correlated data. The book can be used as a primary text or supplement in a course on applied nonparametric or robust procedures and as a reference for researchers who need to implement nonparametric and rank-based methods in practice. Through numerous examples it shows readers how to apply these methods using R.

GBP 39.99
1

Statistical Computing with R Second Edition

Statistical Computing with R Second Edition

Praise for the First Edition: . the book serves as an excellent tutorial on the R language providing examples that illustrate programming concepts in the context of practical computational problems. The book will be of great interest for all specialists working on computational statistics and Monte Carlo methods for modeling and simulation. – Tzvetan Semerdjiev Zentralblatt Math Computational statistics and statistical computing are two areas within statistics that may be broadly described as computational graphical and numerical approaches to solving statistical problems. Like its bestselling predecessor Statistical Computing with R Second Edition covers the traditional core material of these areas with an emphasis on using the R language via an examples-based approach. The new edition is up-to-date with the many advances that have been made in recent years. Features Provides an overview of computational statistics and an introduction to the R computing environment. Focuses on implementation rather than theory. Explores key topics in statistical computing including Monte Carlo methods in inference bootstrap and jackknife permutation tests Markov chain Monte Carlo (MCMC) methods and density estimation. Includes new sections exercises and applications as well as new chapters on resampling methods and programming topics. Includes coverage of recent advances including R Studio the tidyverse knitr and ggplot2 Accompanied by online supplements available on GitHub including R code for all the exercises as well as tutorials and extended examples on selected topics. Suitable for an introductory course in computational statistics or for self-study Statistical Computing with R Second Edition provides a balanced accessible introduction to computational statistics and statistical computing. About the Author Maria Rizzo is Professor in the Department of Mathematics and Statistics at Bowling Green State University in Bowling Green Ohio where she teaches statistics actuarial science computational statistics statistical programming and data science. Prior to joining the faculty at BGSU in 2006 she was Assistant Professor in the Department of Mathematics at Ohio University in Athens Ohio. Her main research area is energy statistics and distance correlation. She is the software developer and maintainer of the energy package for R. She also enjoys writing books including a forthcoming joint research monograph on energy statistics.

GBP 66.99
1

Robust Statistical Methods with R Second Edition

Robust Statistical Methods with R Second Edition

The second edition of Robust Statistical Methods with R provides a systematic treatment of robust procedures with an emphasis on new developments and on the computational aspects. There are many numerical examples and notes on the R environment and the updated chapter on the multivariate model contains additional material on visualization of multivariate data in R. A new chapter on robust procedures in measurement error models concentrates mainly on the rank procedures less sensitive to errors than other procedures. This book will be an invaluable resource for researchers and postgraduate students in statistics and mathematics. Features• Provides a systematic practical treatment of robust statistical methods• Offers a rigorous treatment of the whole range of robust methods including the sequential versions of estimators their moment convergence and compares their asymptotic and finite-sample behavior• The extended account of multivariate models includes the admissibility shrinkage effects and unbiasedness of two-sample tests• Illustrates the small sensitivity of the rank procedures in the measurement error model• Emphasizes the computational aspects supplies many examples and illustrations and provides the own procedures of the authors in the R software on the book’s website | Robust Statistical Methods with R Second Edition

GBP 39.99
1

Computational Genomics with R

Computational Genomics with R

Computational Genomics with R provides a starting point for beginners in genomic data analysis and also guides more advanced practitioners to sophisticated data analysis techniques in genomics. The book covers topics from R programming to machine learning and statistics to the latest genomic data analysis techniques. The text provides accessible information and explanations always with the genomics context in the background. This also contains practical and well-documented examples in R so readers can analyze their data by simply reusing the code presented. As the field of computational genomics is interdisciplinary it requires different starting points for people with different backgrounds. For example a biologist might skip sections on basic genome biology and start with R programming whereas a computer scientist might want to start with genome biology. After reading: You will have the basics of R and be able to dive right into specialized uses of R for computational genomics such as using Bioconductor packages. You will be familiar with statistics supervised and unsupervised learning techniques that are important in data modeling and exploratory analysis of high-dimensional data. You will understand genomic intervals and operations on them that are used for tasks such as aligned read counting and genomic feature annotation. You will know the basics of processing and quality checking high-throughput sequencing data. You will be able to do sequence analysis such as calculating GC content for parts of a genome or finding transcription factor binding sites. You will know about visualization techniques used in genomics such as heatmaps meta-gene plots and genomic track visualization. You will be familiar with analysis of different high-throughput sequencing data sets such as RNA-seq ChIP-seq and BS-seq. You will know basic techniques for integrating and interpreting multi-omics datasets. Altuna Akalin is a group leader and head of the Bioinformatics and Omics Data Science Platform at the Berlin Institute of Medical Systems Biology Max Delbrück Center Berlin. He has been developing computational methods for analyzing and integrating large-scale genomics data sets since 2002. He has published an extensive body of work in this area. The framework for this book grew out of the yearly computational genomics courses he has been organizing and teaching since 2015.

GBP 42.99
1

R for Political Data Science A Practical Guide

R for Political Data Science A Practical Guide

R for Political Data Science: A Practical Guide is a handbook for political scientists new to R who want to learn the most useful and common ways to interpret and analyze political data. It was written by political scientists thinking about the many real-world problems faced in their work. The book has 16 chapters and is organized in three sections. The first on the use of R is for those users who are learning R or are migrating from another software. The second section on econometric models covers OLS binary and survival models panel data and causal inference. The third section is a data science toolbox of some the most useful tools in the discipline: data imputation fuzzy merge of large datasets web mining quantitative text analysis network analysis mapping spatial cluster analysis and principal component analysis. Key features: Each chapter has the most up-to-date and simple option available for each task assuming minimal prerequisites and no previous experience in R Makes extensive use of the Tidyverse the group of packages that has revolutionized the use of R Provides a step-by-step guide that you can replicate using your own data Includes exercises in every chapter for course use or self-study Focuses on practical-based approaches to statistical inference rather than mathematical formulae Supplemented by an R package including all data As the title suggests this book is highly applied in nature and is designed as a toolbox for the reader. It can be used in methods and data science courses at both the undergraduate and graduate levels. It will be equally useful for a university student pursuing a PhD political consultants or a public official all of whom need to transform their datasets into substantive and easily interpretable conclusions. | R for Political Data Science A Practical Guide

GBP 44.99
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