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Preface. I. THE R LANGUAGE. 1. Basics of R.1.1 What is R?1.2 Installing R.1.3 R Documentation. 1.4 Basics. 1.5 Getting Help. 1.6 Data Entry. 1.7 Tidying Up. 1.8 Saving and Retrieving the Workspace. 2. Summarising Statistical Data. 2.1 Measures of Central Tendency. 2.2 Measures of Dispersion. 2.3 Overall Summary Statistics. 2.4 Programming in R.3. Graphical Displays. 3.1 Boxplots. 3.2 Histograms. 3.3 Stem and Leaf. 3.4 Scatter Plots. 3.5 Graphical Display vs Summary Statistics. II: FUNDAMENTALS OF PROBABILITY. 4. Basics. 4.1 Experiments, Sample Spaces and Events. 4.2 Classical Approach to Probability. 4.3 Permutations and Combinations. 4.4 The Birthday Problem. 4.5 Balls and Bins. 4.6 Relative Frequency Approach to Probability. 4.7 Simulating Probabilities. 5. Rules of Probability. 5.1 Probability and Sets. 5.2 Mutually Exclusive Events. 5.3 Complementary Events. 5.4 Axioms of Probability. 5.5 Properties of Probability. 6. Conditional Probability. 6.1 Multiplication Law of Probability. 6.2 Independent Events. 6.3 The Intel Fiasco. 6.4 Law of Total Probability. 6.5 Trees. 7. Posterior Probability and Bayes. 7.1 Bayes' Rule. 7.2 Hardware Fault Diagnosis. 7.3 Machine Learning. 7.4 The Fundamental Equation of Machine Translation. 8. Reliability. 8.1 Series Systems. 8.2 Parallel Systems. 8.3 Reliability of a System. 8.4 Series-Parallel Systems. 8.5 The Design of Systems. 8.6 The General System. III: DISCRETE DISTRIBUTIONS. 9. Discrete Distributions. 9.1 Discrete Random Variables. 9.2 Cumulative Distribution Function. 9.3 Some Simple Discrete Distributions. 9.4 Benford's Law. 9.5 Summarising Random Variables: Expectation. 9.6 Properties of Expectations. 9.7 Simulating Expectation for Discrete Random Variables. 10. The Geometric Distribution. 10.1 Geometric Random Variables. 10.2 Cumulative Distribution Function. 10.3 The Quantile Function. 10.4 Geometric Expectations. 10.5 Simulating Geometric Probabilities and Expectations. 10.6 Amnesia. 10.7 Project. 11. The Binomial Distribution. 11.1 Binomial Probabilities. 11.2 Binomial Random Variables. 11.3 Cumulative Distribution Function. 11.4 The Quantile Function. 11.5 Machine Learning and the Binomial Distribution. 11.6 Binomial Expectations. 11.7 Simulating Binomial Probabilities and Expectations. 11.8 Project. 12. The Hypergeometric Distribution. 12.1 Hypergeometric Random Variables. 12.2 Cumulative Distribution Function. 12.3 The Lottery. 12.4 Hypergeometric or Binomial?.12.5 Project. 13. The Poisson Distribution. 13.1 Death by Horse Kick. 13.2 Limiting Binomial Distribution. 13.3 Random Events in Time and Space. 13.4 Probability Density Function. 13.5 Cumulative Distribution Function. 13.6 The Quantile Function. 13.7 Estimating Software Reliability. 13.8 Modelling Defects in Integrated Circuits. 13.9 Simulating Poisson Probabilities. 13.10Projects. 14. Sampling Inspection Schemes. 14.1 Introduction. 14.2 Single Sampling Inspection Schemes. 14.3 Acceptance Probabilities. 14.4 Simulating Sampling Inspections Schemes. 14.5 Operating Characteristic Curve. 14.6 Producer's and Consumer's Risks. 14.7 Design of Sampling Schemes. 14.8 Rectifying Sampling Inspection Schemes. 14.9 Average Outgoing Quality. 14.10Double Sampling Inspection Schemes. 14.11Average Sample Size. 14.12Single vs Double Schemes. 14.13Project. IV. CONTINUOUS DISTRIBUTIONS. 15. Continuous Distributions. 15.1 Continuous Random Variables. 15.2 Probability Density Function. 15.3 Cumulative Distribution Function. 15.4 The Uniform Distribution. 15.5 Expectation of a Continuous Random Variable. 15.6 Simulating Continuous Variables. 16. The Exponential Distribution. 16.1 Probability Density Function Of Waiting Times. 16.2 Cumulative Distribution Function. 16.3 Quantiles. 16.4 Exponential Expectations. 16.5 Simulating the Exponential Distribution. 16.6 Amnesia. 16.7 Simulating Markov. 17. Applications of the Exponential Distribution. 17.1 Failure Rate and Reliability. 17.2 Modelling Response Times. 17.3 Queue Lengths. 17.4 Average Response Time. 17.5 Extensions of the M/M/1 queue. 18. The Normal Distribution. 18.1 The Normal Probability Density Function. 18.2 The Cumulative Distribution Function. 18.3 Quantiles. 18.4 The Standard Normal Distribution. 18.5 Achieving Normality; Limiting Distributions. 18.6 Project in R.19. Process Control. 19.1 Control Charts. 19.2 Cusum Charts. 19.3 Charts for Defective Rates. 19.4 Project. V. TAILING OFF. 20. Markov and Chebyshev Bound. 20.1 Markov's Inequality. 20.2 Algorithm Run-Time. 20.3 Chebyshev's Inequality. Appendix 1: Variance derivations. Appendix 2: Binomial approximation to the hypergeometric. Appendix 3:. Standard Normal Tables.

"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"--

"How to learn both applied statistics (econometrics) and free, open-source software R? This book allows students to have a sense of accomplishment by copying and pasting many hands-on templates provided here. The textbook is essential for anyone wishing to have a practical understanding of an extensive range of topics in Econometrics. No other text provides software snippets to learn so many new statistical tools with hands-on examples. The explicit knowledge of inputs and outputs of each new method allows the student to know which algorithm is worth studying. The book offers sufficient theoretical and algorithmic details about a vast range of statistical techniques. The second edition's preface lists the following topics generally absent in other textbooks. (i) Iteratively reweighted least squares, (ii) Pillar charts to represent 3D data. (iii) Stochastic frontier analysis (SFA) (iv) model selection with Mallows' Cp criterion. (v) Hodrick-Prescott (HP) filter. (vi) Automatic ARIMA models. (vi) Nonlinear Granger-causality using kernel regressions and bootstrap confidence intervals. (vii) new Keynesian Phillips curve (NKPC). (viii) Market-neutral pairs trading using two cointegrated stocks. (ix) Artificial neural network (ANN) for product-specific forecasting. (x) Vector AR and VARMA models. (xi) New tools for diagnosing the endogeneity problem. (xii) The elegant set-up of k-class estimators and identification. (xiii) Probit-logit models and Heckman selection bias correction. (xiv) Receiver operating characteristic (ROC) curves and areas under them. (xv) Confusion matrix. (xvi) Quantile regression (xvii) Elastic net estimator. (xviii) generalized Correlations (xix) maximum entropy bootstrap for time series. (xx) Convergence concepts quantified. (xxi) Generalized partial correlation coefficients (xxii) Panel data and duration (survival) models"--

Cover -- Half Title -- Series Page -- Title Page -- Copyright Page -- Contents -- Preface -- Who will find this book useful? -- About the book -- What to expect from the book -- Book structure -- Prerequisites -- How to use the textbook in a methods course? -- Contributors -- Part I: Introduction to R -- 1. Basic R -- 1.1 Installation -- 1.2 Console -- 1.3 Script -- 1.4 Objects (and functions) -- 2. Data Management -- 2.1 Introduction to data management -- 2.2 Describing a dataset -- 2.3 Basic operations -- 2.4 Chain commands -- 2.5 Recode values -- 3. Data Visualization -- 3.1 Why visualize my data? -- 3.2 First steps -- 3.3 Applied example: Local elections and data visualization -- 3.4 To continue learning -- 4. Data Loading -- 4.1 Introduction -- 4.2 Different dataset formats -- 4.3 Files separated by delimiters (.csv and .tsv) -- 4.4 Large tabular datasets -- Part II: Models -- 5. Linear Models -- 5.1 OLS in R -- 5.2 Bivariate model: simple linear regression -- 5.3 Multivariate model: multiple regression -- 5.4 Model adjustment -- 5.5 Inference in multiple linear models -- 5.6 Testing OLS assumptions -- 6. Case Selection Based on Regressions -- 6.1 Which case study should I select for qualitative research? -- 6.2 The importance of combining methods -- 7. Panel Data -- 7.1 Introduction -- 7.2 Describing your panel dataset -- 7.3 Modelling group-level variation -- 7.4 Fixed vs. random effects -- 7.5 Testing for unit roots -- 7.6 Robust and panel-corrected standard errors -- 8. Logistic Models -- 8.1 Introduction -- 8.2 Use of logistic models -- 8.3 How are probabilities estimated? -- 8.4 Model estimation -- 8.5 Creating tables -- 8.6 Visual representation of results -- 8.7 Measures to evaluate the fit of the models -- 9. Survival Models -- 9.1 Introduction -- 9.2 How do we interpret hazard rates? -- 9.3 Cox's model of proportional hazards.

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.

This example-based general introduction to the statistical computing environment does not assume any previous familiarity with R or other software packages. R functions are compellingly presented in the context of interesting applications with real data.

"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"--