This thoroughly updated and expanded second edition of Nonparametric Statistical Methods Using R covers traditional nonparametric methods and rank-based analyses. Two new chapters covering multivariate analyses and Big Data have been added. Core classical nonparametrics chapters on one- and two-sample problems have been expanded to include discussions on ties as well as power and sample size determination. Common Machine Learning topics – including k-nearest neighbors and trees – have also been included in this new edition. We have provided numerous examples of real and simulated datasets that illustrate the methods and their computation in R. Hence, we feel that this book also serves as an informative handbook for the researcher wishing to implement nonparametric and rank-based methods in practice. Given that R has continued to grow in popularity, we think that many readers will have already taken a course in R or an applied course that used R extensively. For the vast majority of the book, we have used core R (or base R) rather than add-on packages such as Tidyverse.

Nonparametric Statistical Methods Using R, 2E PDF

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