Nature didn't design human beings to be statisticians, and in fact our minds are more naturally attuned to spotting the saber-toothed tiger than seeing the jungle he springs from. Yet scienti?c discovery in practice is often more jungle than tiger. Those of us who devote our scienti?c lives to the deep and satisfying subject of statistical inference usually do so in the face of a certain under-appreciation from the public, and also (though less so these days) from the wider scienti?c world. With this in mind, it feels very nice to be over-appreciated for a while, even at the expense of weathering a 70th birthday. (Are we certain that some terrible chronological error hasn't been made?) Carl Morris and Rob Tibshirani, the two colleagues I've worked most closely with, both 't my ideal pro?le of the statistician as a mathematical scientist working seamlessly across wide areas of theory and application. They seem to have chosen the papers here in the same catholic spirit, and then cajoled an all-star cast of statistical savants to comment on them.

This book presents some recent developments in correlated data analysis. It utilizes the class of dispersion models as marginal components in the formulation of joint models for correlated data. This enables the book to handle a broader range of data types than those analyzed by traditional generalized linear models. One example is correlated angular data. This book provides a systematic treatment for the topic of estimating functions. Under this framework, both generalized estimating equations (GEE) and quadratic inference functions (QIF) are studied as special cases. In addition to marginal models and mixed-effects models, this book covers topics on joint regression analysis based on Gaussian copulas and generalized state space models for longitudinal data from long time series. Various real-world data examples, numerical illustrations and software usage tips are presented throughout the book. This book has evolved from lecture notes on longitudinal data analysis, and may be considered suitable as a textbook for a graduate course on correlated data analysis. This book is inclined more towards technical details regarding the underlying theory and methodology used in software-based applications. Therefore, the book will serve as a useful reference for those who want theoretical explanations to puzzles arising from data analyses or deeper understanding of underlying theory related to analyses.

Probabilistic Methods for Financial and Marketing Informatics aims to provide students with insights and a guide explaining how to apply probabilistic reasoning to business problems. Rather than dwelling on rigor, algorithms, and proofs of theorems, the authors concentrate on showing examples and using the software package Netica to represent and solve problems. The book contains unique coverage of probabilistic reasoning topics applied to business problems, including marketing, banking, operations management, and finance. It shares insights about when and why probabilistic methods can and cannot be used effectively. This book is recommended for all R&D professionals and students who are involved with industrial informatics, that is, applying the methodologies of computer science and engineering to business or industry information. This includes computer science and other professionals in the data management and data mining field whose interests are business and marketing information in general, and who want to apply AI and probabilistic methods to their problems in order to better predict how well a product or service will do in a particular market, for instance. Typical fields where this technology is used are in advertising, venture capital decision making, operational risk measurement in any industry, credit scoring, and investment science.

Combines recent developments in resampling technology (including the bootstrap) with new methods for multiple testing that are easy to use, convenient to report and widely applicable. Software from SAS Institute is available to execute many of the methods and programming is straightforward for other applications. Explains how to summarize results using adjusted p -values which do not necessitate cumbersome table look-ups. Demonstrates how to incorporate logical constraints among hypotheses, further improving power.

This book is dedicated to Prof. Peter Young on his 70th birthday. Professor Young has been a pioneer in systems and control, and over the past 45 years he has influenced many developments in this field. This volume comprises a collection of contributions by leading experts in system identification, time-series analysis, environmetric modelling and control system design - modern research in topics that reflect important areas of interest in Professor Young's research career. Recent theoretical developments in and relevant applications of these areas are explored treating the various subjects broadly and in depth. The authoritative and up-to-date research presented here will be of interest to academic researcher in control and disciplines related to environmental research, particularly those to with water systems. The tutorial style in which many of the contributions are composed also makes the book suitable as a source of study material for graduate students in those areas.

This volume presents a concise and practical overview of statistical methods and tables not readily available in other publications. It begins with a review of the commonly used continuous and discrete probability distributions. Several useful distributions that are not so common and less understood are described with examples and applications in full detail: discrete normal, left-partial, right-partial, left-truncated normal, right-truncated normal, lognormal, bivariate normal, and bivariate lognormal. Table values are provided with examples that enable researchers to easily apply the distributions to real applications and sample data. The left- and right-truncated normal distributions offer a wide variety of shapes in contrast to the symmetrically shaped normal distribution, and a newly developed spread ratio enables analysts to determine which of the three distributions best fits a particular set of sample data. The book will be highly useful to anyone who does statistical and probability analysis. This includes scientists, economists, management scientists, market researchers, engineers, mathematicians, and students in many disciplines.

This volume contains the proceedings of the XII Symposium of Probability and Stochastic Processes which took place at Universidad Autonoma de Yucatan in Merida, Mexico, on November 16-20, 2015. This meeting was the twelfth meeting in a series of ongoing biannual meetings aimed at showcasing the research of Mexican probabilists as well as promote new collaborations between the participants. The book features articles drawn from different research areas in probability and stochastic processes, such as: risk theory, limit theorems, stochastic partial differential equations, random trees, stochastic differential games, stochastic control, and coalescence. Two of the main manuscripts survey recent developments on stochastic control and scaling limits of Markov-branching trees, written by Kazutoshi Yamasaki and Benedicte Haas, respectively. The research-oriented manuscripts provide new advances in active research fields in Mexico. The wide selection of topics makes the book accessible to advanced graduate students and researchers in probability and stochastic processes.

Regression Analysis for Social Sciences presents methods of regression analysis in an accessible way, with each method having illustrations and examples. A broad spectrum of methods are included: multiple categorical predictors, methods for curvilinear regression, and methods for symmetric regression. This book can be used for courses in regression analysis at the advanced undergraduate and beginning graduate level in the social and behavioral sciences. Most of the techniques are explained step-by-step enabling students and researchers to analyze their own data. Examples include data from the social and behavioral sciences as well as biology, making the book useful for readers with biological and biometrical backgrounds. Sample command and result files for SYSTAT are included in the text.
Key Features
* Presents accessible methods of regression analysis
* Includes a broad spectrum of methods
* Techniques are explained step-by-step
* Provides sample command and result files for SYSTAT

This volume collects a selection of refereed papers of the more than one hundred presented at the InternationalConference MAF 2008 - Mathematicaland Statistical Methods for Actuarial Sciences and Finance. The conference was organised by the Department of Applied Mathematics and theDepartment ofStatisticsoftheUniversityCa'Foscari Venice(Italy), withthec- laborationofthe Department ofEconomics and StatisticalSciences ofthe University ofSalerno(Italy).Itwas heldinVenice, fromMarch 26to28,2008, attheprestigious CavalliFranchettipalace, alongGrand Canal, oftheIstitutoVenetodiScienze, Lettere ed Arti. This conference was the ?rst international edition of a biennial national series begunin2004, whichwas bornof thebrilliantbeliefofthe colleagues -and friends- oftheDepartmentofEconomicsandStatisticalSciences oftheUniversityofSalerno: the idea following which the cooperation between mathematicians and statisticians in working in actuarial sciences, in insurance and in ?nance can improve research on these topics. The proof of this consists in the wide participation in these events. In particular, with reference to the 2008 internationaledition: - More than 150 attendants, both academicians and practitioners; - More than 100 accepted communications, organised in 26 parallel sessions, from authors coming from about twenty countries (namely: Canada, Colombia, Czech Republic, France, Germany, Great Britain, Greece, Hungary, Ireland, Israel, Italy, Japan, Poland, Spain, Sweden, Switzerland, Taiwan, USA); - two plenary guest-organised sessions; and - aprestigiouskeynotelecturedeliveredbyProfessorWolfgangHa ]rdleoftheH- boldt Universityof Berlin (Germany)

This monograph focuses on the construction of regression models with linear and non-linear constrain inequalities from the theoretical point of view. Unlike previous publications, this volume analyses the properties of regression with inequality constrains, investigating the flexibility of inequality constrains and their ability to adapt in the presence of additional a priori information The implementation of inequality constrains improves the accuracy of models, and decreases the likelihood of errors. Based on the obtained theoretical results, a computational technique for estimation and prognostication problems is suggested. This approach lends itself to numerous applications in various practical problems, several of which are discussed in detail The book is useful resource for graduate students, PhD students, as well as for researchers who specialize in applied statistics and optimization. This book may also be useful to specialists in other branches of applied mathematics, technology, econometrics and finance

This book lays the foundations for a theory on almost periodic stochastic processes and their applications to various stochastic differential equations, functional differential equations with delay, partial differential equations, and difference equations. It is in part a sequel of authors recent work on almost periodic stochastic difference and differential equations and has the particularity to be the first book that is entirely devoted to almost periodic random processes and their applications. The topics treated in it range from existence, uniqueness, and stability of solutions for abstract stochastic difference and differential equations.

Analyzing observed or measured data is an important step in applied sciences. The recent increase in computer capacity has resulted in a revolution both in data collection and data analysis. An increasing number of scientists, researchers and students are venturing into statistical data analysis; hence the need for more guidance in this field, which was previously dominated mainly by statisticians.

This handbook fills the gap in the range of textbooks on data analysis. Written in a dictionary format, it will serve as a comprehensive reference book in a rapidly growing field. However, this book is more structured than an ordinary dictionary, where each entry is a separate, self-contained entity. The authors provide not only definitions and short descriptions, but also offer an overview of the different topics. Therefore, the handbook can also be used as a companion to textbooks for undergraduate or graduate courses.

1700 entries are given in alphabetical order grouped into 20 topics and each topic is organized in a hierarchical fashion. Additional specific entries on a topic can be easily found by following the cross-references in a top-down manner. Several figures and tables are provided to enhance the comprehension of the topics and a list of acronyms helps to locate the full terminologies. The bibliography offers suggestions for further reading.

This book provides an overview of the current state-of-the-art of nonlinear time series analysis, richly illustrated with examples, pseudocode algorithms and real-world applications. Avoiding a "theorem-proof" format, it shows concrete applications on a variety of empirical time series. The book can be used in graduate courses in nonlinear time series and at the same time also includes interesting material for more advanced readers. Though it is largely self-contained, readers require an understanding of basic linear time series concepts, Markov chains and Monte Carlo simulation methods. The book covers time-domain and frequency-domain methods for the analysis of both univariate and multivariate (vector) time series. It makes a clear distinction between parametric models on the one hand, and semi- and nonparametric models/methods on the other. This offers the reader the option of concentrating exclusively on one of these nonlinear time series analysis methods. To make the book as user friendly as possible, major supporting concepts and specialized tables are appended at the end of every chapter. In addition, each chapter concludes with a set of key terms and concepts, as well as a summary of the main findings. Lastly, the book offers numerous theoretical and empirical exercises, with answers provided by the author in an extensive solutions manual.

These proceedings from the 37th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering (MaxEnt 2017), held in Sao Carlos, Brazil, aim to expand the available research on Bayesian methods and promote their application in the scientific community. They gather research from scholars in many different fields who use inductive statistics methods and focus on the foundations of the Bayesian paradigm, their comparison to objectivistic or frequentist statistics counterparts, and their appropriate applications. Interest in the foundations of inductive statistics has been growing with the increasing availability of Bayesian methodological alternatives, and scientists now face much more difficult choices in finding the optimal methods to apply to their problems. By carefully examining and discussing the relevant foundations, the scientific community can avoid applying Bayesian methods on a merely ad hoc basis. For over 35 years, the MaxEnt workshops have explored the use of Bayesian and Maximum Entropy methods in scientific and engineering application contexts. The workshops welcome contributions on all aspects of probabilistic inference, including novel techniques and applications, and work that sheds new light on the foundations of inference. Areas of application in these workshops include astronomy and astrophysics, chemistry, communications theory, cosmology, climate studies, earth science, fluid mechanics, genetics, geophysics, machine learning, materials science, medical imaging, nanoscience, source separation, thermodynamics (equilibrium and non-equilibrium), particle physics, plasma physics, quantum mechanics, robotics, and the social sciences. Bayesian computational techniques such as Markov chain Monte Carlo sampling are also regular topics, as are approximate inferential methods. Foundational issues involving probability theory and information theory, as well as novel applications of inference to illuminate the foundations of physical theories, are also of keen interest.

In April 2007, the Deutsche Forschungsgemeinschaft (DFG) approved the Priority Program 1324 "Mathematical Methods for Extracting Quantifiable Information from Complex Systems." This volume presents a comprehensive overview of the most important results obtained over the course of the program. Mathematical models of complex systems provide the foundation for further technological developments in science, engineering and computational finance. Motivated by the trend toward steadily increasing computer power, ever more realistic models have been developed in recent years. These models have also become increasingly complex, and their numerical treatment poses serious challenges. Recent developments in mathematics suggest that, in the long run, much more powerful numerical solution strategies could be derived if the interconnections between the different fields of research were systematically exploited at a conceptual level. Accordingly, a deeper understanding of the mathematical foundations as well as the development of new and efficient numerical algorithms were among the main goals of this Priority Program. The treatment of high-dimensional systems is clearly one of the most challenging tasks in applied mathematics today. Since the problem of high-dimensionality appears in many fields of application, the above-mentioned synergy and cross-fertilization effects were expected to make a great impact. To be truly successful, the following issues had to be kept in mind: theoretical research and practical applications had to be developed hand in hand; moreover, it has proven necessary to combine different fields of mathematics, such as numerical analysis and computational stochastics. To keep the whole program sufficiently focused, we concentrated on specific but related fields of application that share common characteristics and as such, they allowed us to use closely related approaches.

This Handbook covers latent variable models, which are a flexible class of models for modeling multivariate data to explore relationships among observed and latent variables.
- Covers a wide class of important models
- Models and statistical methods described provide tools for analyzing a wide spectrum of complicated data
- Includes illustrative examples with real data sets from business, education, medicine, public health and sociology.
- Demonstrates the use of a wide variety of statistical, computational, and mathematical techniques.

This book presents basic aspects for a theory of statistics with fuzzy data, together with a set of practical applications. Theories of fuzzy logic and of random closed sets are used as basic ingredients in building statistical concepts and procedures in the context of imprecise data, including coarse data analysis. The book aims at motivating statisticians to examine fuzzy statistics to enlarge the domain of applicability of statistics in general.

Limit theorems and asymptotic results form a central topic in probability theory and mathematical statistics. New and non-classical limit theorems have been discovered for processes in random environments, especially in connection with random matrix theory and free probability. These questions and the techniques for answering them combine asymptotic enumerative combinatorics, particle systems and approximation theory, and are important for new approaches in geometric and metric number theory as well. Thus, the contributions in this book include a wide range of applications with surprising connections ranging from longest common subsequences for words, permutation groups, random matrices and free probability to entropy problems and metric number theory.

The book is the product of a conference that took place in August 2011 in Bielefeld, Germany to celebrate the 60th birthday of Friedrich Gotze, a noted expert in this field."

Thisbookisintendedformolecularbiologistswhoperformquantitativeanalysesondata emanatingfromtheir?eldandforthestatisticianswhoworkwithmolecularbiologists andotherbiomedicalresearchers. Therearemanyexcellenttextbooksthatprovidefun- mentalcomponentsforstatisticaltrainingcurricula. Therearealsomany"byexpertsfor experts"booksinstatisticsandmolecularbiologywhichrequirein-depthknowledgein bothsubjectstobetakenfulladvantageof. Sofar,nobookinstatisticshasbeenpublished thatprovidesthebasicprinciplesofproperstatisticalanalysesandprogressestoamore advancedstatisticsinresponsetorapidlydevelopingtechnologiesandmethodologiesin the?eldofmolecularbiology. Respondingtothissituation,ourbookaimsatbridgingthegapbetweenthesetwo extremes. Molecularbiologistswillbene?tfromtheprogressivestyleofthebookwhere basicstatisticalmethodsareintroducedandgraduallyelevatedtoanintermediatelevel. Similarly,statisticianswillbene?tfromlearningthevariousbiologicaldatageneratedfrom the?eldofmolecularbiology,thetypesofquestionsofinteresttomolecularbiologists, andthestatisticalapproachestoanalyzingthedata. Thestatisticalconceptsandmethods relevanttostudiesinmolecularbiologyarepresentedinasimpleandpracticalmanner. Speci?cally,thebookcoversbasicandintermediatestatisticsthatareusefulforclassical and molecular biology settings and advanced statistical techniques that can be used to helpsolveproblemscommonlyencounteredinmodernmolecularbiologystudies,such assupervisedandunsupervisedlearning,hiddenMarkovmodels,manipulationandan- ysisofdatafromhigh-throughputmicroarrayandproteomicplatform,andsynthesisof these evidences. A tutorial-type format is used to maximize learning in some chapters. Advicefromjournaleditorsonpeer-reviewedpublicationandsomeusefulinformationon softwareimplementationarealsoprovided. Thisbookisrecommendedforuseassupplementarymaterialbothinsideandoutside classroomsorasaself-learningguideforstudents,scientists,andresearcherswhodealwith numericdatainmolecularbiologyandrelated?elds. Thosewhostartasbeginners,but desiretobeatanintermediatelevel,will?ndthisbookespeciallyusefulintheirlearning pathway. WewanttothankJohnWalker(serieseditor),PatrickMarton,DavidCasey,andAnne Meagher,(editorsatSpringerandHumana)andShanthyJaganathan(Integra-India). The followingpersonsprovidedusefuladviceandcommentsonselectionoftopics,referralto expertsineachtopic,and/orchapterreviewsthatwetrulyappreciate:StephenLooney(a former editor of this book), Stan Young, Dmitri Zaykin, Douglas Hawkins, Wei Pan, Alexandre Almeida, John Ho, Rebecca Doerge, Paula Trushin, Kevin Morgan, Jason Osborne,PeterWestfall,JennyXiang,Ya-linChiu,YolandaBarron,HuiboShao,Alvin Mushlin,andRonaldFanta. Drs. Bang,Zhou,andMazumdarwerepartiallysupported byClinicalTranslationalScienceCenter(CTSC)grant(UL1-RR024996). HeejungBang vii Contents Preface...vii Contributors...xi PARTIBASICSTATISTICS...1 1. ExperimentalStatisticsforBiologicalSciences...3 HeejungBangandMarieDavidian 2. NonparametricMethodsforMolecularBiology...105 KnutM. WittkowskiandTingtingSong 3. BasicsofBayesianMethods...155 SujitK. Ghosh 4. TheBayesiant-TestandBeyond ...179 MithatGonen PARTII DESIGNSANDMETHODSFORMOLECULARBIOLOGY...201 5. SampleSizeandPowerCalculationforMolecularBiologyStudies...203 Sin-HoJung 6. DesignsforLinkageAnalysisandAssociationStudiesofComplexDiseases...219 YuehuaCui,GengxinLi,ShaoyuLi,andRonglingWu 7. IntroductiontoEpigenomicsandEpigenome-WideAnalysis...243 MelissaJ. FazzariandJohnM. Greally 8. Exploration,Visualization,andPreprocessingofHigh-DimensionalData...267 ZhijinWuandZhiqiangWu PARTIII STATISTICALMETHODSFORMICROARRAYDATA ...285 9. IntroductiontotheStatisticalAnalysisofTwo-ColorMicroarrayData...287 MartinaBremer,EdwardHimelblau,andAndreasMadlung 10. BuildingNetworkswithMicroarrayData...315 BradleyM. Broom,WareeRinsurongkawong,LajosPusztai, andKim-AnhDo PARTIV ADVANCEDORSPECIALIZEDMETHODSFORMOLECULARBIOLOGY. . 345 11. SupportVectorMachinesforClassi?cation:AStatisticalPortrait...347 YoonkyungLee 12. AnOverviewofClusteringAppliedtoMolecularBiology ...369 RebeccaNugentandMarinaMeila ix xContents 13. HiddenMarkovModelandItsApplicationsinMotifFindings...405 JingWuandJunXie 14. DimensionReductionforHigh-DimensionalData...417 LexinLi 15. IntroductiontotheDevelopmentandValidationofPredictiveBiomarker ModelsfromHigh-ThroughputDataSets ...435 XutaoDengandFabienCampagne 16. Multi-geneExpression-basedStatisticalApproachestoPredicting Patients'ClinicalOutcomesandResponses...471 FengCheng,Sang-HoonCho,andJaeK. Lee 17. Two-StageTestingStrategiesforGenome-WideAssociationStudies inFamily-BasedDesigns ...485 AmyMurphy,ScottT. Weiss,andChristophLange 18. StatisticalMethodsforProteomics ...497 KlausJung PARTVMETA-ANALYSISFORHIGH-DIMENSIONALDATA ...509 19. StatisticalMethodsforIntegratingMultipleTypesofHigh-ThroughputData. . 511 YangXieandChulAhn 20. ABayesianHierarchicalModelforHigh-DimensionalMeta-analysis...531 FeiLiu 21. MethodsforCombiningMultipleGenome-WideLinkageStudies...541 TreciaA. KippolaandStephanieA. Santorico PARTVI OTHERPRACTICALINFORMATION ...561 22. ImprovedReportingofStatisticalDesignandAnalysis:Guidelines, Education,andEditorialPolicies...5 63 MadhuMazumdar,SampritBanerjee,andHeatherL. VanEpps 23. StataCompanion...599 JenniferSousaBrennan SubjectIndex...627 Contributors CHULAHN* Division of Biostatistics, Department of Clinical Sciences, The Harold C.

Geostatistics is a common tool in reservoir characterization. Written from the basics of statistics, this book covers only those topics that are needed for the two goals of the text: to exhibit the diagnostic potential of statistics and to introduce the important features of statistical modelling. This revised edition contains expanded discussions of some materials, in particular conditional probabilities, Bayes Theorem, correlation, and Kriging. The coverage of estimation, variability, and modelling applications have been updated. Seventy examples illustrate concepts and show the role of geology for providing important information for data analysis and model building. Four reservoir case studies conclude the presentation, illustrating the application and importance of the earlier material. This book aims to help petroleum professionals develop more accurate models, leading to lower sampling costs. It is an ideal book for petroleum engineers, geoscientists, hydrologists, and faculty and students in these and related fields.

The first edition of this classic book has become the authoritative reference for physicists desiring to master the finer points of statistical data analysis. This second edition contains all the important material of the first, much of it unavailable from any other sources. In addition, many chapters have been updated with considerable new material, especially in areas concerning the theory and practice of confidence intervals, including the important Feldman?Cousins method. Both frequentist and Bayesian methodologies are presented, with a strong emphasis on techniques useful to physicists and other scientists in the interpretation of experimental data and comparison with scientific theories. This is a valuable textbook for advanced graduate students in the physical sciences as well as a reference for active researchers.

Long-rangedependent, or long-memory,time seriesarestationarytime series displaying a statistically signi?cant dependence between very distant obs- vations. We formalize this dependence by assuming that the autocorrelation function of these stationary series decays very slowly, hyperbolically, as a function of the time lag. Many economic series display these empirical features: volatility of asset prices returns, future interest rates, etc. There is a huge statistical literature on long-memory processes, some of this research is highly technical, so that it is cited, but often misused in the applied econometrics and empirical e- nomics literature. The ?rst purpose of this book is to present in a formal and pedagogical way some statistical methods for studying long-range dependent processes. Furthermore, the occurrence of long-memory in economic time series might be a statistical artefact as the hyperbolic decay of the sample autoc- relation function does not necessarily derive from long-range dependent p- cesses. Indeed, the realizations of non-homogeneous processes, e.g., switching regime and change-point processes, display the same empirical features. We thus also present in this book recent statistical methods able to discriminate between the long-memory and change-point alternatives. Going beyond the purely statistical analysis of economic series, it is of interest to determine which economic mechanisms are generating the strong dependence properties of economic series, whether they are genuine, or spu- ous. The regularities of the long-memory and change-point properties across economic time series, e.g., common degree of long-range dependence and/or common change-points, suggest the existence of a common economic cause.

The state-of-the-art of multivariate analysis in sensory science is described in this volume. Both methods for aggregated and individual sensory profiles are discussed. Processes and results are presented in such a way that they can be understood not only by statisticians but also by experienced sensory panel leaders and users of sensory analysis.

The techniques presented are focused on examples and interpretation rather than on the technical aspects, with an emphasis on new and important methods which are possibly not so well known to scientists in the field. Important features of the book are discussions on the relationship among the methods with a strong accent on the connection between problems and methods. All procedures presented are described in relation to sensory data and not as completely general statistical techniques.

Sensory scientists, applied statisticians, chemometricians, those working in consumer science, food scientists and agronomers will find this book of value.

This volume, dedicated to Carl Pearcy on the occasion of his 60th birthday, presents recent results in operator theory, nonselfadjoint operator algebras, measure theory and the theory of moments. The articles on these subjects have been contributed by leading area experts, many of whom were associated with Carl Pearcy as students or collaborators. The book testifies to his multifaceted interests and includes a biographical sketch and a list of publications.

This book is designed as a gentle introduction to the fascinating field of choice modeling and its practical implementation using the R language. Discrete choice analysis is a family of methods useful to study individual decision-making. With strong theoretical foundations in consumer behavior, discrete choice models are used in the analysis of health policy, transportation systems, marketing, economics, public policy, political science, urban planning, and criminology, to mention just a few fields of application. The book does not assume prior knowledge of discrete choice analysis or R, but instead strives to introduce both in an intuitive way, starting from simple concepts and progressing to more sophisticated ideas. Loaded with a wealth of examples and code, the book covers the fundamentals of data and analysis in a progressive way. Readers begin with simple data operations and the underlying theory of choice analysis and conclude by working with sophisticated models including latent class logit models, mixed logit models, and ordinal logit models with taste heterogeneity. Data visualization is emphasized to explore both the input data as well as the results of models. This book should be of interest to graduate students, faculty, and researchers conducting empirical work using individual level choice data who are approaching the field of discrete choice analysis for the first time. In addition, it should interest more advanced modelers wishing to learn about the potential of R for discrete choice analysis. By embedding the treatment of choice modeling within the R ecosystem, readers benefit from learning about the larger R family of packages for data exploration, analysis, and visualization.