Computational statistics and statistical computing are two areas within statistics that may be broadly described as computational, graphical, and numerical approaches to solving statistical problems. Statistical Computing with R, Second Edition covers the traditional core material of these areas with an emphasis on using the R language via an examples-based approach. The new edition is up-to-date with the many advances that have been made in recent years.
Features:
- Provides an overview of computational statistics and an introduction to the R computing environment.
- Focuses on implementation rather than theory.
- Explores key topics in statistical computing including Monte Carlo methods in inference, bootstrap and jackknife, permutation tests, Markov chain Monte Carlo (MCMC) methods, and density estimation.
- Includes new sections, exercises and applications as well as new chapters on resampling methods and programming topics.
- Includes coverage of recent advances including R Studio, the tidyverse, knitr and ggplot2
- Accompanied by online supplements available on GitHub including R code for all the exercises as well as tutorials and extended examples on selected topics.