The apparent contradiction between statistical significance and biological relevance has diminished the value of statistical methods as a whole in toxicology. Moreover, recommendations for statistical analysis are imprecise in most toxicological guidelines. Addressing these dilemmas, Statistics in Toxicology Using R explains the statistical analysi

Analyzing Baseball Data with R Second Edition introduces R to sabermetricians, baseball enthusiasts, and students interested in exploring the richness of baseball data. It equips you with the necessary skills and software tools to perform all the analysis steps, from importing the data to transforming them into an appropriate format to visualizing the data via graphs to performing a statistical analysis. The authors first present an overview of publicly available baseball datasets and a gentle introduction to the type of data structures and exploratory and data management capabilities of R. They also cover the ggplot2 graphics functions and employ a tidyverse-friendly workflow throughout. Much of the book illustrates the use of R through popular sabermetrics topics, including the Pythagorean formula, runs expectancy, catcher framing, career trajectories, simulation of games and seasons, patterns of streaky behavior of players, and launch angles and exit velocities. All the datasets and R code used in the text are available online. New to the second edition are a systematic adoption of the tidyverse and incorporation of Statcast player tracking data (made available by Baseball Savant). All code from the first edition has been revised according to the principles of the tidyverse. Tidyverse packages, including dplyr, ggplot2, tidyr, purrr, and broom are emphasized throughout the book. Two entirely new chapters are made possible by the availability of Statcast data: one explores the notion of catcher framing ability, and the other uses launch angle and exit velocity to estimate the probability of a home run. Through the book’s various examples, you will learn about modern sabermetrics and how to conduct your own baseball analyses. Max Marchi is a Baseball Analytics Analyst for the Cleveland Indians. He was a regular contributor to The Hardball Times and Baseball Prospectus websites and previously consulted for other MLB clubs. Jim Albert is a Distinguished University Professor of statistics at Bowling Green State University. He has authored or coauthored several books including Curve Ball and Visualizing Baseball and was the editor of the Journal of Quantitative Analysis of Sports. Ben Baumer is an assistant professor of statistical & data sciences at Smith College. Previously a statistical analyst for the New York Mets, he is a co-author of The Sabermetric Revolution and Modern Data Science with R.

Generate and Analyze Multi-Level Data Spatial microsimulation involves the generation, analysis, and modeling of individual-level data allocated to geographical zones. Spatial Microsimulation with R is the first practical book to illustrate this approach in a modern statistical programming language. Get Insight into Complex Behaviors The book progresses from the principles underlying population synthesis toward more complex issues such as household allocation and using the results of spatial microsimulation for agent-based modeling. This equips you with the skills needed to apply the techniques to real-world situations. The book demonstrates methods for population synthesis by combining individual and geographically aggregated datasets using the recent R packages ipfp and mipfp. This approach represents the "best of both worlds" in terms of spatial resolution and person-level detail, overcoming issues of data confidentiality and reproducibility. Implement the Methods on Your Own Data Full of reproducible examples using code and data, the book is suitable for students and applied researchers in health, economics, transport, geography, and other fields that require individual-level data allocated to small geographic zones. By explaining how to use tools for modeling phenomena that vary over space, the book enhances your knowledge of complex systems and empowers you to provide evidence-based policy guidance.

Currently there are many introductory textbooks on educational measurement and psychometrics as well as R. However, there is no single book that covers important topics in measurement and psychometrics as well as their applications in R. The Handbook of Educational Measurement and Psychometrics Using R covers a variety of topics, including classical test theory; generalizability theory; the factor analytic approach in measurement; unidimensional, multidimensional, and explanatory item response modeling; test equating; visualizing measurement models; measurement invariance; and differential item functioning. This handbook is intended for undergraduate and graduate students, researchers, and practitioners as a complementary book to a theory-based introductory or advanced textbook in measurement. Practitioners and researchers who are familiar with the measurement models but need to refresh their memory and learn how to apply the measurement models in R, would find this handbook quite fulfilling. Students taking a course on measurement and psychometrics will find this handbook helpful in applying the methods they are learning in class. In addition, instructors teaching educational measurement and psychometrics will find our handbook as a useful supplement for their course.

Reproducible Finance with R: Code Flows and Shiny Apps for Portfolio Analysis is a unique introduction to data science for investment management that explores the three major R/finance coding paradigms, emphasizes data visualization, and explains how to build a cohesive suite of functioning Shiny applications. The full source code, asset price data and live Shiny applications are available at reproduciblefinance.com. The ideal reader works in finance or wants to work in finance and has a desire to learn R code and Shiny through simple, yet practical real-world examples. The book begins with the first step in data science: importing and wrangling data, which in the investment context means importing asset prices, converting to returns, and constructing a portfolio. The next section covers risk and tackles descriptive statistics such as standard deviation, skewness, kurtosis, and their rolling histories. The third section focuses on portfolio theory, analyzing the Sharpe Ratio, CAPM, and Fama French models. The book concludes with applications for finding individual asset contribution to risk and for running Monte Carlo simulations. For each of these tasks, the three major coding paradigms are explored and the work is wrapped into interactive Shiny dashboards.

Statistical Models in Toxicology presents an up-to-date and comprehensive account of mathematical statistics problems that occur in toxicology. This is as an exciting time in toxicology because of the attention given by statisticians to the problem of estimating the human health risk for environmental and occupational exposures. The development of modern statistical techniques with solid mathematical foundations in the 20th century and the advent of modern computers in the latter part of the century gave way to development of many statistical models and methods to describe toxicological processes and attempts to solve the associated problems. Not only have the models enjoyed a high level of elegance and sophistication mathematically, they are widely used by industry and government regulatory agencies. Features: Focuses on describing the statistical models in environmental toxicology that facilitate the assessment of risk mainly in humans. The properties and shortfalls of each model are discussed and its impact in the process of risk assessment is examined. Discusses models that assess the risk of mixtures of chemicals. Presents statistical models that are developed for risk estimation in different aspects of environmental toxicology including cancer and carcinogenic substances. Includes models for developmental and reproductive toxicity risk assessment, risk assessment in continuous outcomes and developmental neurotoxicity. Contains numerous examples and exercises. Statistical Models in Toxicology introduces a wide variety of statistical models that are currently utilized for dose-response modeling and risk analysis. These models are often developed based on design and regulatory guidelines of toxicological experiments. The book is suitable for practitioners or as use as a textbook for advanced undergraduate or graduate students of mathematics and statistics.

Newcomers to R are often intimidated by the command-line interface, the vast number of functions and packages, or the processes of importing data and performing a simple statistical analysis. The R Primer provides a collection of concise examples and solutions to R problems frequently encountered by new users of this statistical software. This new edition adds coverage of R Studio and reproducible research.

This new fifth edition of Information Resources in Toxicology offers a consolidated entry portal for the study, research, and practice of toxicology. Both volumes represents a unique, wide-ranging, curated, international, annotated bibliography, and directory of major resources in toxicology and allied fields such as environmental and occupational health, chemical safety, and risk assessment. The editors and authors are among the leaders of the profession sharing their cumulative wisdom in toxicology's subdisciplines. This edition keeps pace with the digital world in directing and linking readers to relevant websites and other online tools.Due to the increasing size of the hardcopy publication, the current edition has been divided into two volumes to make it easier to handle and consult. Volume 1: Background, Resources, and Tools, arranged in 5 parts, begins with chapters on the science of toxicology, its history, and informatics framework in Part 1. Part 2 continues with chapters organized by more specific subject such as cancer, clinical toxicology, genetic toxicology, etc. The categorization of chapters by resource format, for example, journals and newsletters, technical reports, organizations constitutes Part 3. Part 4 further considers toxicology's presence via the Internet, databases, and software tools. Among the miscellaneous topics in the concluding Part 5 are laws and regulations, professional education, grants and funding, and patents. Volume 2: The Global Arena offers contributed chapters focusing on the toxicology contributions of over 40 countries, followed by a glossary of toxicological terms and an appendix of popular quotations related to the field.The book, offered in both print and electronic formats, is carefully structured, indexed, and cross-referenced to enable users to easily find answers to their questions or serendipitously locate useful knowledge they were not originally aware they needed. Among the many timely topics receiving increased emphasis are disaster preparedness, nanotechnology, -omics, risk assessment, societal implications such as ethics and the precautionary principle, climate change, and children's environmental health. - Introductory chapters provide a backdrop to the science of toxicology, its history, the origin and status of toxicoinformatics, and starting points for identifying resources - Offers an extensive array of chapters organized by subject, each highlighting resources such as journals, databases,organizations, and review articles - Includes chapters with an emphasis on format such as government reports, general interest publications, blogs, and audiovisuals - Explores recent internet trends, web-based databases, and software tools in a section on the online environment - Concludes with a miscellany of special topics such as laws and regulations, chemical hazard communication resources, careers and professional education, K-12 resources, funding, poison control centers, and patents - Paired with Volume Two, which focuses on global resources, this set offers the most comprehensive compendium of print, digital, and organizational resources in the toxicological sciences with over 120 chapters contributions by experts and leaders in the field

Learn how to write R code with fewer bugs. The problem with programming is that you are always one typo away from writing something silly. Likewise with data analysis, a small mistake in your model can lead to a big mistake in your results. Combining the two disciplines means that it is all too easy for a missed minus sign to generate a false prediction that you don’t spot until it’s too late. Testing is the only way to be sure that your code, and your results, are correct. Testing R Code teaches you how to perform development-time testing using the testthat package, allowing you to ensure that your code works as intended. The book also teaches run-time testing using the assertive package; enabling your users to correctly run your code. After beginning with an introduction to testing in R, the book explores more advanced cases such as integrating tests into R packages; testing code that accesses databases; testing C++ code with Rcpp; and testing graphics. Each topic is explained with real-world examples, and has accompanying exercises for readers to practise their skills — only a small amount of experience with R is needed to get started!

This book explains how the computer programs work and why and when they can be applied to problems in toxicology. It discusses the statistical models used and their applications in a general fashion. The book overviews the problems that can arise from the blind use of the statistical models.