David Cran's Books

Discrete Data Analysis with R

Visualization and Modeling Techniques for Categorical and Count Data

By: Michael Friendly,David Meyer

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Discrete Data Analysis with R

Visualization and Modeling Techniques for Categorical and Count Data

By: Michael Friendly,David Meyer

An Applied Treatment of Modern Graphical Methods for Analyzing Categorical DataDiscrete Data Analysis with R: Visualization and Modeling Techniques for Categorical and Count Data presents an applied treatment of modern methods for the analysis of categorical data, both discrete response data and frequency data. It explains how to use graphical meth

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564

Publisher

CRC Press

Published Date

ISBN 10

1498725856

ISBN 13

9781498725859

Computerized Adaptive Testing

From Concept to Implementation

By: David J. Weiss,Alper Sahin

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Computerized Adaptive Testing

From Concept to Implementation

By: David J. Weiss,Alper Sahin

Used worldwide in assessment and professional certification contexts, computerized adaptive testing (CAT) offers a powerful means to measure individual differences or make classifications. This authoritative work from CAT pioneer David J. Weiss and Alper Şahin provides a complete how-to guide for planning and implementing an effective CAT to create a test unique to each person in real time. The book reviews the history of CAT and the basics of item response theory used in CAT. It walks the reader through developing an item bank, pretesting and linking items, selecting required CAT options, and using simulations to design a CAT. Available software for CAT delivery is described, including links to free and commercial options. Engaging multidisciplinary examples illustrate applications of CAT for measuring ability, achievement, proficiency, personality, attitudes, perceptions, patients’ reports of their symptoms, and academic or clinical progress.

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388

Publisher

Guilford Publications

Published Date

ISBN 10

1462554520

ISBN 13

9781462554522

R for Data Science Cookbook

By: Yu-Wei, Chiu (David Chiu)

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R for Data Science Cookbook

By: Yu-Wei, Chiu (David Chiu)

Over 100 hands-on recipes to effectively solve real-world data problems using the most popular R packages and techniques About This Book Gain insight into how data scientists collect, process, analyze, and visualize data using some of the most popular R packages Understand how to apply useful data analysis techniques in R for real-world applications An easy-to-follow guide to make the life of data scientist easier with the problems faced while performing data analysis Who This Book Is For This book is for those who are already familiar with the basic operation of R, but want to learn how to efficiently and effectively analyze real-world data problems using practical R packages. What You Will Learn Get to know the functional characteristics of R language Extract, transform, and load data from heterogeneous sources Understand how easily R can confront probability and statistics problems Get simple R instructions to quickly organize and manipulate large datasets Create professional data visualizations and interactive reports Predict user purchase behavior by adopting a classification approach Implement data mining techniques to discover items that are frequently purchased together Group similar text documents by using various clustering methods In Detail This cookbook offers a range of data analysis samples in simple and straightforward R code, providing step-by-step resources and time-saving methods to help you solve data problems efficiently. The first section deals with how to create R functions to avoid the unnecessary duplication of code. You will learn how to prepare, process, and perform sophisticated ETL for heterogeneous data sources with R packages. An example of data manipulation is provided, illustrating how to use the “dplyr” and “data.table” packages to efficiently process larger data structures. We also focus on “ggplot2” and show you how to create advanced figures for data exploration. In addition, you will learn how to build an interactive report using the “ggvis” package. Later chapters offer insight into time series analysis on financial data, while there is detailed information on the hot topic of machine learning, including data classification, regression, clustering, association rule mining, and dimension reduction. By the end of this book, you will understand how to resolve issues and will be able to comfortably offer solutions to problems encountered while performing data analysis. Style and approach This easy-to-follow guide is full of hands-on examples of data analysis with R. Each topic is fully explained beginning with the core concept, followed by step-by-step practical examples, and concluding with detailed explanations of each concept used.

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452

Publisher

Packt Publishing Ltd

Published Date

ISBN 10

1784392049

ISBN 13

9781784392048

R In Finance And Economics: A Beginner's Guide

By: Abhay Kumar Singh,David Edmund Allen

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R In Finance And Economics: A Beginner's Guide

By: Abhay Kumar Singh,David Edmund Allen

This book provides an introduction to the statistical software R and its application with an empirical approach in finance and economics. It is specifically targeted towards undergraduate and graduate students. It provides beginner-level introduction to R using RStudio and reproducible research examples. It will enable students to use R for data cleaning, data visualization and quantitative model building using statistical methods like linear regression, econometrics (GARCH etc), Copulas, etc. Moreover, the book demonstrates latest research methods with applications featuring linear regression, quantile regression, panel regression, econometrics, dependence modelling, etc. using a range of data sets and examples.

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264

Publisher

World Scientific Publishing Company

Published Date

ISBN 10

9813144483

ISBN 13

9789813144484

R Visualizations

Derive Meaning from Data

By: David Gerbing

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R Visualizations

Derive Meaning from Data

By: David Gerbing

R Visualizations: Derive Meaning from Data focuses on one of the two major topics of data analytics: data visualization, a.k.a., computer graphics. In the book, major R systems for visualization are discussed, organized by topic and not by system. Anyone doing data analysis will be shown how to use R to generate any of the basic visualizations with the R visualization systems. Further, this book introduces the author’s lessR system, which always can accomplish a visualization with less coding than the use of other systems, sometimes dramatically so, and also provides accompanying statistical analyses. Key Features Presents thorough coverage of the leading R visualization system, ggplot2. Gives specific guidance on using base R graphics to attain visualizations of the same quality as those provided by ggplot2. Shows how to create a wide range of data visualizations: distributions of categorical and continuous variables, many types of scatterplots including with a third variable, time series, and maps. Inclusion of the various approaches to R graphics organized by topic instead of by system. Presents the recent work on interactive visualization in R. David W. Gerbing received his PhD from Michigan State University in 1979 in quantitative analysis, and currently is a professor of quantitative analysis in the School of Business at Portland State University. He has published extensively in the social and behavioral sciences with a focus on quantitative methods. His lessR package has been in development since 2009.

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334

Publisher

CRC Press

Published Date

ISBN 10

0429894910

ISBN 13

9780429894916

Methods in Comparative Plant Population Ecology

By: David J. Gibson

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Methods in Comparative Plant Population Ecology

By: David J. Gibson

This second edition provides authoritative guidance on research methodology for plant population ecology. Practical advice is provided to assist senior undergraduates and post-graduate students, and all researchers, design their own field and greenhouse experiments and establish a research programme in plant population ecology.

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319

Publisher

Oxford University Press

Published Date

ISBN 10

019967146X

ISBN 13

9780199671465

Data Analytics for the Social Sciences

Applications in R

By: G. David Garson

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Data Analytics for the Social Sciences

Applications in R

By: G. David Garson

Data Analytics for the Social Sciences is an introductory, graduate-level treatment of data analytics for social science. It features applications in the R language, arguably the fastest growing and leading statistical tool for researchers. The book starts with an ethics chapter on the uses and potential abuses of data analytics. Chapters 2 and 3 show how to implement a broad range of statistical procedures in R. Chapters 4 and 5 deal with regression and classification trees and with random forests. Chapter 6 deals with machine learning models and the "caret" package, which makes available to the researcher hundreds of models. Chapter 7 deals with neural network analysis, and Chapter 8 deals with network analysis and visualization of network data. A final chapter treats text analysis, including web scraping, comparative word frequency tables, word clouds, word maps, sentiment analysis, topic analysis, and more. All empirical chapters have two "Quick Start" exercises designed to allow quick immersion in chapter topics, followed by "In Depth" coverage. Data are available for all examples and runnable R code is provided in a "Command Summary". An appendix provides an extended tutorial on R and RStudio. Almost 30 online supplements provide information for the complete book, "books within the book" on a variety of topics, such as agent-based modeling. Rather than focusing on equations, derivations, and proofs, this book emphasizes hands-on obtaining of output for various social science models and how to interpret the output. It is suitable for all advanced level undergraduate and graduate students learning statistical data analysis.

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704

Publisher

Routledge

Published Date

ISBN 10

1000467082

ISBN 13

9781000467086

R: Recipes for Analysis, Visualization and Machine Learning

By: Viswa Viswanathan,Shanthi Viswanathan,Atmajitsinh Gohil,Yu-Wei, Chiu (David Chiu)

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R: Recipes for Analysis, Visualization and Machine Learning

By: Viswa Viswanathan,Shanthi Viswanathan,Atmajitsinh Gohil,Yu-Wei, Chiu (David Chiu)

Get savvy with R language and actualize projects aimed at analysis, visualization and machine learning About This Book Proficiently analyze data and apply machine learning techniques Generate visualizations, develop interactive visualizations and applications to understand various data exploratory functions in R Construct a predictive model by using a variety of machine learning packages Who This Book Is For This Learning Path is ideal for those who have been exposed to R, but have not used it extensively yet. It covers the basics of using R and is written for new and intermediate R users interested in learning. This Learning Path also provides in-depth insights into professional techniques for analysis, visualization, and machine learning with R – it will help you increase your R expertise, regardless of your level of experience. What You Will Learn Get data into your R environment and prepare it for analysis Perform exploratory data analyses and generate meaningful visualizations of the data Generate various plots in R using the basic R plotting techniques Create presentations and learn the basics of creating apps in R for your audience Create and inspect the transaction dataset, performing association analysis with the Apriori algorithm Visualize associations in various graph formats and find frequent itemset using the ECLAT algorithm Build, tune, and evaluate predictive models with different machine learning packages Incorporate R and Hadoop to solve machine learning problems on big data In Detail The R language is a powerful, open source, functional programming language. At its core, R is a statistical programming language that provides impressive tools to analyze data and create high-level graphics. This Learning Path is chock-full of recipes. Literally! It aims to excite you with awesome projects focused on analysis, visualization, and machine learning. We'll start off with data analysis – this will show you ways to use R to generate professional analysis reports. We'll then move on to visualizing our data – this provides you with all the guidance needed to get comfortable with data visualization with R. Finally, we'll move into the world of machine learning – this introduces you to data classification, regression, clustering, association rule mining, and dimension reduction. This Learning Path combines some of the best that Packt has to offer in one complete, curated package. It includes content from the following Packt products: R Data Analysis Cookbook by Viswa Viswanathan and Shanthi Viswanathan R Data Visualization Cookbook by Atmajitsinh Gohil Machine Learning with R Cookbook by Yu-Wei, Chiu (David Chiu) Style and approach This course creates a smooth learning path that will teach you how to analyze data and create stunning visualizations. The step-by-step instructions provided for each recipe in this comprehensive Learning Path will show you how to create machine learning projects with R.

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958

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Packt Publishing Ltd

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ISBN 10

178728879X

ISBN 13

9781787288799

From Sadat to Saddam

The Decline of American Diplomacy in the Middle East

By: David J. Dunford

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From Sadat to Saddam

The Decline of American Diplomacy in the Middle East

By: David J. Dunford

From Sadat to Saddam offers a fresh perspective on the politicization of the U.S. diplomatic corps and the militarization of U.S. foreign policy in the Middle East. This book begins with the 1981 assassination of Egyptian president Anwar Sadat, continues through two Gulf wars, and ends with the U.S. withdrawal of combat troops from Iraq in 2011. This firsthand account of thirty years in the diplomatic trenches of U.S. foreign policy in the Middle East addresses the basic questions of how and why we find ourselves today in endless military conflict and argues that it is directly related to the decline in reliance on our diplomatic skills. From Sadat to Saddam offers an in-depth look by a career diplomat at how U.S. soft power has been allowed to atrophy. It chronicles three decades of dealing not just with foreign policy challenges and opportunities but also with the frustrations of working with bureaucrats and politicians who don't understand the world and are unwilling to listen to those who do. The book makes clear that the decline of our diplomatic capability began well before the election of Donald Trump. It recommends that instead of trying to make soldiers into diplomats and diplomats into soldiers, we invest in a truly professional diplomatic service.

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347

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U of Nebraska Press

Published Date

ISBN 10

1640122478

ISBN 13

9781640122475

Statistics in Engineering

With Examples in MATLAB® and R, Second Edition

By: Andrew Metcalfe,David Green,Tony Greenfield,Mayhayaudin Mansor,Andrew Smith,Jonathan Tuke

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Statistics in Engineering

With Examples in MATLAB® and R, Second Edition

By: Andrew Metcalfe,David Green,Tony Greenfield,Mayhayaudin Mansor,Andrew Smith,Jonathan Tuke

Engineers are expected to design structures and machines that can operate in challenging and volatile environments, while allowing for variation in materials and noise in measurements and signals. Statistics in Engineering, Second Edition: With Examples in MATLAB and R covers the fundamentals of probability and statistics and explains how to use these basic techniques to estimate and model random variation in the context of engineering analysis and design in all types of environments. The first eight chapters cover probability and probability distributions, graphical displays of data and descriptive statistics, combinations of random variables and propagation of error, statistical inference, bivariate distributions and correlation, linear regression on a single predictor variable, and the measurement error model. This leads to chapters including multiple regression; comparisons of several means and split-plot designs together with analysis of variance; probability models; and sampling strategies. Distinctive features include: All examples based on work in industry, consulting to industry, and research for industry Examples and case studies include all engineering disciplines Emphasis on probabilistic modeling including decision trees, Markov chains and processes, and structure functions Intuitive explanations are followed by succinct mathematical justifications Emphasis on random number generation that is used for stochastic simulations of engineering systems, demonstration of key concepts, and implementation of bootstrap methods for inference Use of MATLAB and the open source software R, both of which have an extensive range of statistical functions for standard analyses and also enable programing of specific applications Use of multiple regression for times series models and analysis of factorial and central composite designs Inclusion of topics such as Weibull analysis of failure times and split-plot designs that are commonly used in industry but are not usually included in introductory textbooks Experiments designed to show fundamental concepts that have been tested with large classes working in small groups Website with additional materials that is regularly updated Andrew Metcalfe, David Green, Andrew Smith, and Jonathan Tuke have taught probability and statistics to students of engineering at the University of Adelaide for many years and have substantial industry experience. Their current research includes applications to water resources engineering, mining, and telecommunications. Mahayaudin Mansor worked in banking and insurance before teaching statistics and business mathematics at the Universiti Tun Abdul Razak Malaysia and is currently a researcher specializing in data analytics and quantitative research in the Health Economics and Social Policy Research Group at the Australian Centre for Precision Health, University of South Australia. Tony Greenfield, formerly Head of Process Computing and Statistics at the British Iron and Steel Research Association, is a statistical consultant. He has been awarded the Chambers Medal for outstanding services to the Royal Statistical Society; the George Box Medal by the European Network for Business and Industrial Statistics for Outstanding Contributions to Industrial Statistics; and the William G. Hunter Award by the American Society for Quality.

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792

Publisher

CRC Press

Published Date

ISBN 10

1439895481

ISBN 13

9781439895481

Bayesian Statistics for the Social Sciences

By: David Kaplan

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Bayesian Statistics for the Social Sciences

By: David Kaplan

Since the publication of the first edition, Bayesian statistics is, arguably, still not the norm in the formal quantitative methods training of social scientists. Typically, the only introduction that a student might have to Bayesian ideas is a brief overview of Bayes' theorem while studying probability in an introductory statistics class. This is not surprising. First, until relatively recently, it was not feasible to conduct statistical modeling from a Bayesian perspective owing to its complexity and lack of available software. Second, Bayesian statistics represents a powerful alternative to frequentist (conventional) statistics and, therefore, can be controversial, especially in the context of null hypothesis significance testing. However, over the last 20 years, or so, considerably progress has been made in the development and application of complex Bayesian statistical methods, due mostly to developments and availability of proprietary and open-source statistical software tools. And, although Bayesian statistics is not quite yet an integral part of the quantitative training of social scientists, there has been increasing interest in the application of Bayesian methods, and it is not unreasonable to say that in terms of theoretical developments and substantive applications, Bayesian statistics has arrived. Because of extensive developments in Bayesian theory and computation since the publication of the first edition of this book, there was a pressing need for a thorough update of the material to reflect new developments in Bayesian methodology and software. The basic foundations of Bayesian statistics remain more or less the same, but this second edition encompasses many new extensions"--

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274

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Guilford Publications

Published Date

ISBN 10

1462553540

ISBN 13

9781462553549

Doing Meta-Analysis with R

A Hands-On Guide

By: Mathias Harrer,Pim Cuijpers,Toshi A. Furukawa,David D. Ebert

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Doing Meta-Analysis with R

A Hands-On Guide

By: Mathias Harrer,Pim Cuijpers,Toshi A. Furukawa,David D. Ebert

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

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500

Publisher

CRC Press

Published Date

ISBN 10

1000435636

ISBN 13

9781000435634

Applications of Regression for Categorical Outcomes Using R

By: David Melamed,Long Doan

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Applications of Regression for Categorical Outcomes Using R

By: David Melamed,Long Doan

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.

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239

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CRC Press

Published Date

ISBN 10

1000907864

ISBN 13

9781000907865

Extending Excel with Python and R

Unlock the potential of analytics languages for advanced data manipulation and visualization

By: Steven Sanderson,David Kun

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Extending Excel with Python and R

Unlock the potential of analytics languages for advanced data manipulation and visualization

By: Steven Sanderson,David Kun

Seamlessly integrate the Python and R programming languages with spreadsheet-based data analysis to maximize productivity Key Features Perform advanced data analysis and visualization techniques with R and Python on Excel data Use exploratory data analysis and pivot table analysis for deeper insights into your data Integrate R and Python code directly into Excel using VBA or API endpoints Purchase of the print or Kindle book includes a free PDF eBook Book Description– Extending Excel with Python and R is a game changer resource written by experts Steven Sanderson, the author of the healthyverse suite of R packages, and David Kun, co-founder of Functional Analytics. – This comprehensive guide transforms the way you work with spreadsheet-based data by integrating Python and R with Excel to automate tasks, execute statistical analysis, and create powerful visualizations. – Working through the chapters, you’ll find out how to perform exploratory data analysis, time series analysis, and even integrate APIs for maximum efficiency. – Both beginners and experts will get everything you need to unlock Excel's full potential and take your data analysis skills to the next level. – By the end of this book, you’ll be able to import data from Excel, manipulate it in R or Python, and perform the data analysis tasks in your preferred framework while pushing the results back to Excel for sharing with others as needed. What you will learn Read and write Excel files with R and Python libraries Automate Excel tasks with R and Python scripts Use R and Python to execute Excel VBA macros Format Excel sheets using R and Python packages Create graphs with ggplot2 and Matplotlib in Excel Analyze Excel data with statistical methods and time series analysis Explore various methods to call R and Python functions from Excel Who this book is for – If you’re a data analyst or data scientist, or a quants, actuaries, or data practitioner looking to enhance your Excel skills and expand your data analysis capabilities with R and Python, this book is for you. – The comprehensive approach to the topics covered makes it suitable for both beginners and intermediate learners. – A basic understanding of Excel, Python, and R is all you need to get started.

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345

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Packt Publishing Ltd

Published Date

ISBN 10

1804615544

ISBN 13

9781804615546

Analyzing Spatial Models of Choice and Judgment

By: David A. Armstrong,Ryan Bakker,Royce Carroll,Christopher Hare,Keith T. Poole,Howard Rosenthal

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Analyzing Spatial Models of Choice and Judgment

By: David A. Armstrong,Ryan Bakker,Royce Carroll,Christopher Hare,Keith T. Poole,Howard Rosenthal

With recent advances in computing power and the widespread availability of preference, perception and choice data, such as public opinion surveys and legislative voting, the empirical estimation of spatial models using scaling and ideal point estimation methods has never been more accessible.The second edition of Analyzing Spatial Models of Choice and Judgment demonstrates how to estimate and interpret spatial models with a variety of methods using the open-source programming language R. Requiring only basic knowledge of R, the book enables social science researchers to apply the methods to their own data. Also suitable for experienced methodologists, it presents the latest methods for modeling the distances between points. The authors explain the basic theory behind empirical spatial models, then illustrate the estimation technique behind implementing each method, exploring the advantages and limitations while providing visualizations to understand the results. This second edition updates and expands the methods and software discussed in the first edition, including new coverage of methods for ordinal data and anchoring vignettes in surveys, as well as an entire chapter dedicated to Bayesian methods. The second edition is made easier to use by the inclusion of an R package, which provides all data and functions used in the book. David A. Armstrong II is Canada Research Chair in Political Methodology and Associate Professor of Political Science at Western University. His research interests include measurement, Democracy and state repressive action. Ryan Bakker is Reader in Comparative Politics at the University of Essex. His research interests include applied Bayesian modeling, measurement, Western European politics, and EU politics. Royce Carroll is Professor in Comparative Politics at the University of Essex. His research focuses on measurement of ideology and the comparative politics of legislatures and political parties. Christopher Hare is Assistant Professor in Political Science at the University of California, Davis. His research focuses on ideology and voting behavior in US politics, political polarization, and measurement. Keith T. Poole is Philip H. Alston Jr. Distinguished Professor of Political Science at the University of Georgia. His research interests include methodology, US political-economic history, economic growth and entrepreneurship. Howard Rosenthal is Professor of Politics at NYU and Roger Williams Straus Professor of Social Sciences, Emeritus, at Princeton. Rosenthal’s research focuses on political economy, American politics and methodology.

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302

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CRC Press

Published Date

ISBN 10

1351770500

ISBN 13

9781351770507

Computerized Adaptive and Multistage Testing with R

Using Packages catR and mstR

By: David Magis,Duanli Yan,Alina A. von Davier

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Computerized Adaptive and Multistage Testing with R

Using Packages catR and mstR

By: David Magis,Duanli Yan,Alina A. von Davier

The goal of this guide and manual is to provide a practical and brief overview of the theory on computerized adaptive testing (CAT) and multistage testing (MST) and to illustrate the methodologies and applications using R open source language and several data examples. Implementation relies on the R packages catR and mstR that have been already or are being developed by the first author (with the team) and that include some of the newest research algorithms on the topic. The book covers many topics along with the R-code: the basics of R, theoretical overview of CAT and MST, CAT designs, CAT assembly methodologies, CAT simulations, catR package, CAT applications, MST designs, IRT-based MST methodologies, tree-based MST methodologies, mstR package, and MST applications. CAT has been used in many large-scale assessments over recent decades, and MST has become very popular in recent years. R open source language also has become one of the most useful tools for applications in almost all fields, including business and education. Though very useful and popular, R is a difficult language to learn, with a steep learning curve. Given the obvious need for but with the complex implementation of CAT and MST, it is very difficult for users to simulate or implement CAT and MST. Until this manual, there has been no book for users to design and use CAT and MST easily and without expense; i.e., by using the free R software. All examples and illustrations are generated using predefined scripts in R language, available for free download from the book's website.

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183

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Springer

Published Date

ISBN 10

3319692186

ISBN 13

9783319692180

Using Geodata and Geolocation in the Social Sciences

Mapping our Connected World

By: David Abernathy

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Using Geodata and Geolocation in the Social Sciences

Mapping our Connected World

By: David Abernathy

Abernathy provides a truly accessible and interdisciplinary introduction to geodata and geolocation covering both the conceptual and the practical. It is a must read for students or researchers looking to make the most of the spatial elements of their data" - Luke Sloan, Senior Lecturer in Quantitative Methods, Cardiff University Using Geodata and Geolocation in the Social Sciences: Mapping our Connected World provides an engaging and accessible introduction to the Geoweb with clear, step-by-step guides for: Capturing Geodata from sources including GPS, sensor networks and Twitter Visualizing Geodata using programmes including QGIS, GRASS and R Featuring colour images, practical exercises walking you through using data sources, and a companion website packed with resources, this book is the perfect guide for students and teachers looking to incorporate location-based data into their social science research.

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437

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SAGE

Published Date

ISBN 10

1473965780

ISBN 13

9781473965782

Over the Edge

By: Jean-David Morvan,Bachan

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Over the Edge

By: Jean-David Morvan,Bachan

This political Sci-Fi action adventure sees the survivors of a peacekeeping spaceship forced to make planet-fall on the alien world they had seemingly left at peace.

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Humanoids, Inc.

Published Date

ISBN 10

164337804X

ISBN 13

9781643378046

Working with Dynamic Crop Models

Methods, Tools and Examples for Agriculture and Environment

By: Daniel Wallach,David Makowski,James W. Jones,Francois Brun

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Working with Dynamic Crop Models

Methods, Tools and Examples for Agriculture and Environment

By: Daniel Wallach,David Makowski,James W. Jones,Francois Brun

This second edition of Working with Dynamic Crop Models is meant for self-learning by researchers or for use in graduate level courses devoted to methods for working with dynamic models in crop, agricultural, and related sciences. Each chapter focuses on a particular topic and includes an introduction, a detailed explanation of the available methods, applications of the methods to one or two simple models that are followed throughout the book, real-life examples of the methods from literature, and finally a section detailing implementation of the methods using the R programming language. The consistent use of R makes this book immediately and directly applicable to scientists seeking to develop models quickly and effectively, and the selected examples ensure broad appeal to scientists in various disciplines. - 50% new content – 100% reviewed and updated - Clearly explains practical application of the methods presented, including R language examples - Presents real-life examples of core crop modeling methods, and ones that are translatable to dynamic system models in other fields

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504

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Academic Press

Published Date

ISBN 10

0444594469

ISBN 13

9780444594464

Analyzing Spatial Models of Choice and Judgment with R

By: David A. Armstrong, II,Ryan Bakker,Royce Carroll,Christopher Hare,Keith T. Poole,Howard Rosenthal

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Analyzing Spatial Models of Choice and Judgment with R

By: David A. Armstrong, II,Ryan Bakker,Royce Carroll,Christopher Hare,Keith T. Poole,Howard Rosenthal

Modern Methods for Evaluating Your Social Science Data With recent advances in computing power and the widespread availability of political choice data, such as legislative roll call and public opinion survey data, the empirical estimation of spatial models has never been easier or more popular. Analyzing Spatial Models of Choice and Judgment with R demonstrates how to estimate and interpret spatial models using a variety of methods with the popular, open-source programming language R. Requiring basic knowledge of R, the book enables researchers to apply the methods to their own data. Also suitable for expert methodologists, it presents the latest methods for modeling the distances between points—not the locations of the points themselves. This distinction has important implications for understanding scaling results, particularly how uncertainty spreads throughout the entire point configuration and how results are identified. In each chapter, the authors explain the basic theory behind the spatial model, then illustrate the estimation techniques and explore their historical development, and finally discuss the advantages and limitations of the methods. They also demonstrate step by step how to implement each method using R with actual datasets. The R code and datasets are available on the book’s website.

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351

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CRC Press

Published Date

ISBN 10

1466517166

ISBN 13

9781466517165

From Experimental Network to Meta-analysis

Methods and Applications with R for Agronomic and Environmental Sciences

By: David Makowski,François Piraux,François Brun

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From Experimental Network to Meta-analysis

Methods and Applications with R for Agronomic and Environmental Sciences

By: David Makowski,François Piraux,François Brun

This book has been designed as a methodological guide and shows the interests and limitations of different statistical methods to analyze data from experimental networks and to perform meta-analyses. It is intended for engineers, students and researchers involved in data analysis in agronomy and environmental science.

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160

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Springer

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ISBN 10

940241696X

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9789402416961

Environmental Valuation with Discrete Choice Experiments

Guidance on Design, Implementation and Data Analysis

By: Petr Mariel,David Hoyos,Jürgen Meyerhoff,Mikolaj Czajkowski,Thijs Dekker,Klaus Glenk,Jette Bredahl Jacobsen,Ulf Liebe,Søren Bøye Olsen,Julian Sagebiel,Mara Thiene

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Environmental Valuation with Discrete Choice Experiments

Guidance on Design, Implementation and Data Analysis

By: Petr Mariel,David Hoyos,Jürgen Meyerhoff,Mikolaj Czajkowski,Thijs Dekker,Klaus Glenk,Jette Bredahl Jacobsen,Ulf Liebe,Søren Bøye Olsen,Julian Sagebiel,Mara Thiene

This open access book offers up-to-date advice and practical guidance on how to undertake a discrete choice experiment as a tool for environmental valuation. It discusses crucial issues in designing, implementing and analysing choice experiments. Compiled by leading experts in the field, the book promotes discrete choice analysis in environmental valuation through a more solid scientific basis for research practice. Instead of providing strict guidelines, the book helps readers avoid common mistakes often found in applied work. It is based on the collective reflections of the scientific network of researchers using discrete choice modelling in the field of environmental valuation (www.envecho.com).

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136

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Springer Nature

Published Date

ISBN 10

3030626695

ISBN 13

9783030626693

R Data Analysis without Programming

Explanation and Interpretation

By: David W. Gerbing

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R Data Analysis without Programming

Explanation and Interpretation

By: David W. Gerbing

The new edition of this innovative book, R Data Analysis without Programming, prepares the readers to quickly analyze data and interpret statistical results using R. Professor Gerbing has developed lessR, a ground-breaking method in alleviating the challenges of R programming. The lessR extends R, removing the need for programming. This edition expands upon the first edition’s introduction to R through lessR, which enables the readers to learn how to organize data for analysis, read the data into R, and generate output without performing numerous functions and programming exercises first. With lessR, readers can select the necessary procedure and change the relevant variables with simple function calls. The text reviews and explains basic statistical procedures with the lessR enhancements added to the standard R environment. Using lessR, data analysis with R becomes immediately accessible to the novice user and easier to use for the experienced user. Highlights along with content new to this edition include: Explanation and Interpretation of all data analysis techniques; much more than a computer manual, this book shows the reader how to explain and interpret the results. Introduces the concepts and commands reviewed in each chapter. Clear, relaxed writing style more effectively communicates the underlying concepts than more stilted academic writing. Extensive margin notes highlight, define, illustrate, and cross-reference the key concepts. When readers encounter a term previously discussed, the margin notes identify the page number for the initial introduction. Scenarios that highlight the use of a specific analysis followed by the corresponding R/lessR input, output, and an interpretation of the results. Numerous examples of output from psychology, business, education, and other social sciences, that demonstrate the analysis and how to interpret results. Two data sets are analyzed multiple times in the book, provide continuity throughout. Comprehensive: A wide range of data analysis techniques are presented throughout the book. Integration with machine learning as regression analysis is presented from both the traditional perspective and from the modern machine learning perspective. End of chapter problems help readers test their understanding of the concepts. A website at www.lessRstats.com that features the data sets referenced in both standard text and SPSS formats so readers can practice using R/lessR by working through the text examples and worked problems, R/lessR videos to help readers better understand the program, and more. This book is ideal for graduate and undergraduate courses in statistics beyond the introductory course, research methods, and/or any data analysis course, taught in departments of psychology, business, education, and other social and health sciences; this book is also appreciated by researchers doing data analysis. Prerequisites include basic statistical knowledge, though the concepts are explained from the beginning in the book. Previous knowledge of R is not assumed.

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591

Publisher

Taylor & Francis

Published Date

ISBN 10

1000812901

ISBN 13

9781000812909

Genetic Epidemiology

Methods and Applications

By: Melissa A. Austin,Terri H. Beaty,W. David Dotson

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Genetic Epidemiology

Methods and Applications

By: Melissa A. Austin,Terri H. Beaty,W. David Dotson

Genetic epidemiology plays a key role in discovering genetic factors influencing health and disease, and in understanding how genes and environmental risk factors interact. There is growing interest in this field within public health, with the goal of translating the results into promoting health and preventing disease in both families and populations. This textbook provides graduate students with a working knowledge of genetic epidemiology research methods. Following an overview of the field, the book reviews key genetic concepts, provides an update on relevant genomic technology, including genome-wide chips and DNA sequencing, and describes methods for assessing the magnitude of genetic influences on diseases and risk factors. The book focuses on research study designs for discovering disease susceptibility genes, including family-based linkage analysis, candidate gene and genome-side association studies, assessing gene-environment interactions and epistasis, studies of Non-Mendelian inheritance, and statistical analyses of data from these studies. Specific applications of each research method are illustrated using a variety of diseases and risk factors relevant to public health, and useful web-based genetic analysis software, human reference panels, and repositories, that can greatly facilitate this work, are described.

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223

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CABI

Published Date

ISBN 10

1780641818

ISBN 13

9781780641812

Time Series Analysis and Its Applications

With R Examples

By: Robert H. Shumway,David S. Stoffer

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Time Series Analysis and Its Applications

With R Examples

By: Robert H. Shumway,David S. Stoffer

The second edition marks a substantial change to the ?rst edition. P- haps the most signi?cant change is the introduction of examples based on the freeware R package. The package, which runs on most operating systems, can be downloaded from The Comprehensive R Archive Network (CRAN) at http://cran. r-project. org/ or any one of its mirrors. Readers who have experience with the S-PLUS R package will have no problem working with R. For novices, R installs some help manuals, and CRAN supplies links to contributed tutorials such as R for Beginners. In our examples, we assume the reader has downloaded and installed R and has downloaded the nec- sary data ?les. The data ?les can be downloaded from the website for the text,http://www. stat. pitt. edu/stoffer/tsa2/ or any one of its mirrors. We will also provide additional code and other information of interest on the text’s website. Most of the material that would be given in an introductory course on time series analysis has associated R code. Although examples are given in R, the material is not R-dependent. In courses we have given using a preliminary version of the new edition of the text, students were allowed to use any package of preference. Although most students used R (or S-PLUS), a number of them completed the course successfully using other programs such R R R as ASTSA, MATLAB ,SAS , and SPSS . Another substantial change from the ?rst edition is that the material has beendividedintosmallerchapters.

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589

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Springer Science & Business Media

Published Date

ISBN 10

0387362762

ISBN 13

9780387362762

The First Lady of Mulberry Walk

By: David Llewellyn

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The First Lady of Mulberry Walk

By: David Llewellyn

Anne Acheson was a sensitive, caring and extremely talented sculptress, whose gift to the world at large has remained unacknowledged for almost a century. During voluntary work in the First World War, she applied her artistic skills to invent the first anatomically accurate splints, which helped the injured Tommies’ limbs heal better. These prototype splints were constructed from papier mâché.After this work with her pioneering team at the Surgical Requisites Association (headquarters Number 17 Mulberry Walk in Chelsea), Anne retrained as a precision engineer and draughtswoman in order to do voluntary work during the Second World War (she also worked for the Red Cross during this time). Her eye-witness accounts of the Blitz in London are particularly vivid, capturing the much-talked of ‘Spirit of the Blitz’.Either side of each of the global conflicts, she pursued her chosen career in sculpting, becoming very popular and successful during the 1920s and 1930s. She was one of the first women to be elected to the Royal Society of British Sculptors, before becoming the organisation’s first female Fellow.She was appointed a CBE in 1919 for her work with the SRA at Mulberry Walk, and her bust of the female explorer and Arabist, Gertrude Bell, is in the National Museum in Baghdad – presently stored in a basement for safekeeping – while a replica stands in the entrance to the Royal Geographical Society in Kensington Gore.

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249

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Troubador Publishing Ltd

Published Date

ISBN 10

1848766009

ISBN 13

9781848766006

Encyclopedia of Mind Enhancing Foods, Drugs and Nutritional Substances, 2d ed.

By: David W. Group

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Encyclopedia of Mind Enhancing Foods, Drugs and Nutritional Substances, 2d ed.

By: David W. Group

This greatly expanded and revised edition contains more than 1,000 entries, covering everything from common everyday foods to the latest in genetic research--and beyond. Each substance's properties are objectively described based on the latest scientific evidence. Included are substances that not only claim to improve thinking and other brain functions, but those that are thought to help prevent brain damage and degeneration, prevent aging and extend the human lifespan, enhance social skills and moral behavior, induce altered states of consciousness, and improve mood, stamina, and energy.

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409

Publisher

McFarland

Published Date

ISBN 10

1476619158

ISBN 13

9781476619156

Book's by David Cran

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