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Turn your R code into packages that others can easily download and use. This practical book shows you how to bundle reusable R functions, sample data, and documentation together by applying author Hadley Wickham's package development philosophy. In the process, you'll work with devtools, roxygen, and testthat, a set of R packages that automate common development tasks. Devtools encapsulates best practices that Hadley has learned from years of working with this programming language.Ideal for developers, data scientists, and programmers with various backgrounds, this book starts you with the basics and shows you how to improve your package writing over time. You'll learn to focus on what you want your package to do, rather than think about package structure. Ideal for developers, data scientists, and programmers with various backgrounds, this book starts with the basics and shows you how to improve your package writing over time. You'll learn to focus on what you want your package to do, rather than think about package structure

Part I. Finance -- 1. Financial econometrics and big data: a survey of volatility estimators and tests for the presence of jumps and co-jumps / Arpita Mukherjee, Weijia Peng, Norman R. Swanson, Xiye Yang -- 2. Real time monitoring of asset markets: bubbles and crises / Peter C.B. Phillips, Shuping Shi -- 3. Component-wise AdaBoost algorithms for high-dimensional binary classification and class probability prediction / Jianghao Chu, Tae-Hwy Lee, Aman Ullah -- Part II. Macro Econometrics -- 4. Mixed data sampling (MIDAS) regression models / Eric Ghysels, Virmantas Kvedaras, Vaidotas Zemlys-Balevičius -- 5. Encouraging private corporate investment in India / Hrishikesh Vinod, Honey Karun, Lekha S. Chakraborty -- 6. High-mixed frequency forecasting methods in R -- With applications to Philippine GDP and inflation / Roberto S. Mariano, Suleyman Ozmucur -- 7. Nonlinear time series in R: threshold cointegration with tsDyn / Matthieu Stigler -- Part III. Micro Econometrics -- 8. Econometric analysis of productivity: theory and implementation in R / Robin C. Sickles, Wonho Song, Valentin Zelenyuk -- 9. Stochastic frontier models using R / Giancarlo Ferrara.

Production function and regression methods using R -- Univariate time series analysis with R -- Bivariate time series analysis including stochastic diffusion -- Utility theory and empirical implications -- Vector models for multivariate problems -- Simultaneous equation models -- Limited dependent variable (GLM) models -- Consumption and demand : kernel regressions and machine learning -- Single, double, and maximum entropy bootstrap and inference -- Generalized least squares, VARMA, and estimating functions -- Box-Cox, Loess, projection pursuit, quantile and threshold regression -- Miscellany : dependence, correlations, information entropy, causality, panel data, and exact stochastic dominance.

"Joint Species Distribution Modelling (JSDM) is a fastdeveloping field and promises to revolutionise how data on ecological communities are analysed and interpreted. Written for both readers with a limited statistical background, and those with statistical expertise, this book provides a comprehensive account of JSDM. It enables readers to integrate data on species abundances, environmental covariates, species traits, phylogenetic relationships, and the spatio-temporal context in which the data have been acquired. Step-by-step coverage of the full technical detail of statistical methods is provided, as well as advice on interpreting results of statistical analyses in the broader context of modern community ecology theory. With the advantage of numerous example R-scripts, this is an ideal guide to help graduate students and researchers learn how to conduct and interpret statistical analyses in practice with the R package HMSC-R, providing a fast starting point for applying joint species distribution modelling to their own data"--

1. Econometric computing with "R" -- 2. Additive models for quantile regression: an analysis of risk factors for malnutrition in India -- 3. Toward better R defaults for graphics: example of voter turnouts in U.S. elections -- 4. Superior estimation and inference avoiding heteroscedasticity and flawed pivots: R-example of inflation unemployment trade-off -- 5. Bubble plots as a model-free graphical tool for continuous variables -- 6. Combinatorial fusion for improving portfolio performance -- 7. Reference growth charts for Saudi Arabian children and adolescents -- 8. Causal mediation analysis using R -- 9. Statistical validation of functional form in multiple regression using R -- 10. Fitting multinomial models in R: a program based on Bock's multinomial response relation model -- 11. A Bayesian analysis of Leukemia incidence surrounding an inactive hazardous waste site -- 12. Stochastic volatility model with jumps in returns and volatility: an R-package implementation

"The Third Edition of An R Companion to Political Analysis by Philip H. Pollock III and Barry C. Edwards teaches your students to conduct political research with R, the open source programming language and software environment for statistical computing and graphics. This workbook offers the same easy-to-use and effective style as other software Companions, tailored for R. With this comprehensive workbook, students analyze research-quality data to learn descriptive statistics, data transformations, bivariate analysis (such as cross-tabulations and mean comparisons), controlled comparisons, correlation and bivariate regression, interaction effects, and logistic regression. The clear explanations and instructions are aided by the use of many annotated and labeled screen shots, as well as QR codes linking to demonstration videos. The many end-of-chapter exercises allow students to apply their new skills. The Third Edition includes new and revised exercises, along with new and updated datasets from the 2020 American National Election Study, an experiment dataset, and two aggregate datasets, one on 50 U.S. states and one based on countries of the world. A new structure helps break up individual elements of political analysis for deeper explanation while an updated suite of R functions makes using R even easier. Students will gain valuable skills learning to analyze political data in R"--