Discover Nonparametric Statistical Methods Using R, the essential guide for mastering rank-based analyses and traditional nonparametric techniques. This thoroughly updated second edition from Chapman & Hall/CRC Texts in Statistical Science is perfect for statisticians, data scientists, and researchers. Explore expanded chapters on one- and two-sample problems, including ties and sample size determination. Dive into new content on multivariate analyses and big data, plus machine learning topics like k-nearest neighbors and decision trees. With numerous R-based examples and available packages, this book offers robust methods for linear models, time-to-event data, time series, and more. Ideal for advanced students and practitioners seeking practical applications of nonparametric statistics.