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Focusing on implementation rather than theory, Statistical Computing with R serves as a valuable tutorial, providing examples that illustrate programming concepts in the context of practical computational problems. This book presents an overview of computational statistics with an introduction to the R computing environment. Reviewing basic concepts in probability and classical statistical inference, the text demonstrates every algorithm through fully implemented examples coded in R. Chapters cover topics such as Monte Carlo methods, clustering, bootstrap, nonparametric regression, density estimation, and goodness-of-fit. Many exercises are included for the students while a solutions manual is included for the instructor.