Springer, Paris, Although many authors have recently advocated for the use of bayesian statistics in psychology Wagenmaker et al. This manual provides a full bayesian toolbox for commonly encountered problems in psychology and social sciences, for comparing proportions, variances and means, and discusses the advantages. But all foundations of the frequentist approach are also provided, from data description to probability and density, through combinatorics and set algebra. A special emphasis has been put on the analysis of categorical data and contingency tables.

Binomial and multinomial models with beta and Dirichlet priors are presented, and their use for making between rows or between cells contrasts in contingency tables is detailed on real data. In addition to classical and Bayesian inference on means, direct and Bayesian inference on effect size and standardized effects are presented, in agreement with recent APA recommendations. Dynamic Documents with R and knitr. The reports range from homework, projects, exams, books, blogs, and web pages to any documents related to statistical graphics, computing, and data analysis.

For beginners, the text provides enough features to get started on basic applications. For power users, the last several chapters enable an understanding of the extensibility of the knitr package. Learn R in a Day. The book assumes no prior knowledge of computer programming and progressively covers all the essential steps needed to become confident and proficient in using R within a day.

Topics include how to input, manipulate, format, iterate loop , query, perform basic statistics on, and plot data, via a step-by-step technique and demonstrations using in-built datasets which the reader is encouraged to replicate on their computer. Each chapter also includes exercises with solutions to practice key skills and empower the reader to build on the essentials gained during this introductory course. It focuses on scalar financial time series with applications.

High-frequency data and volatility models are discussed. The book also uses case studies to illustrate the application of modeling financial data.

Analyse von Genexpressionsdaten mit R und Bioconductor. Ventus Publishing ApS, London, See web site , March R is almost limitlessly flexible and powerful, hence its appeal, but can be very difficult for the novice user. There are no easy pull-down menus, error messages are often cryptic and simple tasks like importing your data or exporting a graph can be difficult and frustrating.

Introductory R is written for the novice user who knows a bit about statistics but who hasn't yet got to grips with the ways of R. This book: walks you through the basics of R's command line interface; gives a set of simple rules to follow to make sure you import your data properly; introduces the script editor and gives advice on workflow; contains a detailed introduction to drawing graphs in R and gives advice on how to deal with some of the most common errors that you might encounter.

The techniques of statistical analysis in R are illustrated by a series of chapters where experimental and survey data are analysed. There is a strong emphasis on using real data from real scientific research, with all the problems and uncertainty that implies, rather than well-behaved made-up data that give ideal and easy to analyse results. Methods of Statistical Model Estimation. Designed for R users, the book is also ideal for anyone wanting to better understand the algorithms used for statistical model fitting.

The text presents algorithms for the estimation of a variety of regression procedures using maximum likelihood estimation, iteratively reweighted least squares regression, the EM algorithm, and MCMC sampling. Fully developed, working R code is constructed for each method. The book starts with OLS regression and generalized linear models, building to two-parameter maximum likelihood models for both pooled and panel models.

It then covers a random effects model estimated using the EM algorithm and concludes with a Bayesian Poisson model using Metropolis-Hastings sampling. The book's coverage is innovative in several ways. First, the authors use executable computer code to present and connect the theoretical content. Therefore, code is written for clarity of exposition rather than stability or speed of execution.

## Epub A Modern Theory Of Factorial Design (Springer Series In Statistics)

Second, the book focuses on the performance of statistical estimation and downplays algebraic niceties. In both senses, this book is written for people who wish to fit statistical models and understand them. Introduction to R for Quantitative Finance. Packt Publishing, November Each chapter briefly presents the theory behind specific concepts and deals with solving a diverse range of problems using R with the help of practical examples. Reproducible Research with R and RStudio.

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Suitable for researchers in any quantitative empirical discipline, it presents practical tools for data collection, data analysis, and the presentation of results. The book takes you through a reproducible research workflow, showing you how to use: R for dynamic data gathering and automated results presentation knitr for combining statistical analysis and results into one document LaTeX for creating PDF articles and slide shows, and Markdown and HTML for presenting results on the web Cloud storage and versioning services that can store data, code, and presentation files; save previous versions of the files; and make the information widely available Unix-like shell programs for compiling large projects and converting documents from one markup language to another RStudio to tightly integrate reproducible research tools in one place.

Use R! Springer, New York, Applied Meta-Analysis with R. Filling this knowledge gap, Applied Meta-Analysis with R shows how to implement statistical meta-analysis methods to real data using R.

Drawing on their extensive research and teaching experiences, the authors provide detailed, step-by-step explanations of the implementation of meta-analysis methods using R. 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 packages and functions.

- The Testing of Dottie Oxbridge and Other Grand Adventures.
- Salam, maman (Einaudi tascabili. Scrittori Vol. 1616) (Italian Edition)!
- Course Descriptions | Department of Mathematics & Statistics;
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This systematic approach helps readers thoroughly understand the analysis methods and R implementation, enabling them to use R and the methods to analyze their own meta-data. 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 in public health, medical research, governmental agencies, and the pharmaceutical industry.

Moderni analyza biologickych dat. Linear Models with Correlations in R]. Masaryk University Press, Brno, In Czech. Tedy linearni metody, ktere jsou vhodnym nastrojem analyzy dat s casovymi, prostorovymi a fylogenetickymi zavislostmi v datech. Text knihy je praktickou priruckou analyzy dat v prostredi jednoho z nejrozsahlejsich statistickych nastroju na svete, volne dostupneho softwaru R.

Je sestaven z 19 vzorove vyresenych a okomentovanych prikladu, ktere byly vybrany tak, aby ukazaly spravnou konstrukci modelu a upozornily na problemy a chyby, ktere se mohou v prubehu analyzy dat vyskytnout. Text je psan jednoduchym jazykem srozumitelnym pro ctenare bez specialniho matematickeho vzdelani. Kniha je predevsim urcena studentum i vedeckym pracovnikum biologickych, zemedelskych, veterinarnich, lekarskych a farmaceutickych oboru, kteri potrebuji korektne analyzovat vysledky svych pozorovani ci experimentu s komplikovanejsi strukturou danou zavislostmi mezi opakovanymi merenimi stejneho subjektu.

Soetaert, J. Cash, and F.

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Solving Differential Equations in R. This book deals with the numerical solution of differential equations, a very important branch of mathematics. Our aim is to give a practical and theoretical account of how to solve a large variety of differential equations, comprising ordinary differential equations, initial value problems and boundary value problems, differential algebraic equations, partial differential equations and delay differential equations. The solution of differential equations using R is the main focus of this book.

- Big data need big theory too.
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It is therefore intended for the practitioner, the student and the scientist, who wants to know how to use R for solving differential equations. However, it has been our goal that non-mathematicians should at least understand the basics of the methods, while obtaining entrance into the relevant literature that provides more mathematical background.

Therefore, each chapter that deals with R examples is preceded by a chapter where the theory behind the numerical methods being used is introduced.

## Books by Rahul Mukerjee

In the sections that deal with the use of R for solving differential equations, we have taken examples from a variety of disciplines, including biology, chemistry, physics, pharmacokinetics. Many examples are well-known test examples, used frequently in the field of numerical analysis. Jotunheim Publishing, It is suitable for anyone that is familiar with basic statistics and wants to begin using R to analyse data and create statistical plots. No prior knowledge of R or of programming is assumed, making this book ideal if you are more accustomed to using point-and-click style statistical packages.

Wiley, Chichester, UK, Graduate and postgraduate students in finance, economics, risk management as well as practitioners in finance and portfolio optimisation will find this book beneficial. It also serves well as an accompanying text in computer-lab classes and is therefore suitable for self-study. Authored by the team that originally developed this software, The BUGS Book provides a practical introduction to this program and its use. The text presents complete coverage of all the functionalities of BUGS, including prediction, missing data, model criticism, and prior sensitivity.

It also features a large number of worked examples and a wide range of applications from various disciplines. The book introduces regression models, techniques for criticism and comparison, and a wide range of modelling issues before going into the vital area of hierarchical models, one of the most common applications of Bayesian methods.

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## Big data need big theory too

It deals with essentials of modelling without getting bogged down in complexity. Many of the examples are biostatistical, but they do not require domain knowledge and are generalisable to a wide range of other application areas. Full code and data for examples, exercises, and some solutions can be found on the book's website. Programming Graphical User Interfaces in R. With examples woven through the text as well as stand-alone demonstrations of simple yet reasonably complete applications, the book features topics especially relevant to statisticians who aim to provide a practical interface to functionality implemented in R.

The accompanying package, ProgGUIinR, includes the complete code for all examples as well as functions for browsing the examples from the respective chapters. Accessible to seasoned, novice, and occasional R users, this book shows that for many purposes, adding a graphical interface to one's work is not terribly sophisticated or time consuming. Event History Analysis with R. Keeping mathematical details to a minimum, the book covers key topics, including both discrete and continuous time data, parametric proportional hazards, and accelerated failure times.

A much-needed primer, Event History Analysis with R is a didactically excellent resource for students and practitioners of applied event history and survival analysis. These models constitute an attractive paradigm for the analysis of follow-up data that is mainly applicable in two settings: First, when focus is on a survival outcome and we wish to account for the effect of an endogenous time-dependent covariate measured with error, and second, when focus is on the longitudinal outcome and we wish to correct for nonrandom dropout.

Aimed at applied researchers and graduate students, this text provides a comprehensive overview of the framework of random effects joint models.