I have tried to avoid including any books that are available without permission from the authors or publishers.
Books by John Marder, (Department of Statistics, University of Illinois)
- Mathematical Statistics: Old School (PDF)
- Multivariate Statistics: Old School (PDF)
- Notes on Statistical Learning (PDF)
- Notes on Analysis of Variance: Old School (PDF)
Statistical learning books by Trevor Hastie, Robert Tibshirani, and others
- Introductory: An Introduction to Statistical Learning with Applications in R by Gareth James, Daniela Witten, and TH & RT
NOTE: The website for this book indicates that a Python version will be published in summer 2023. Jonathan Taylor is a new co-author of that version. - Advanced: The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd ed (PDF) by Jerome Friedman and TH & RT
Time Series Analysis and Its Applications With R Examples, 4th Edition (PDF) by Robert H. Shumway (University of California, Davis) and David S. Stoffer (University of Pittsburgh)
The R Inferno (PDF) by Patrick Burns
Forecasting: Principles and Practice (HTML) by Rob J Hyndman and George Athanasopoulos
Books by Hadley Wickham (RStudio) and others
- R for Data Science (bookdown) by Garrett Grolemund and HW
- Advanced R (bookdown) by HW
bookdown is an R-package, and most of the books written with bookdown are R-related.
Old and New Matrix Algebra Useful for Statistics (PDF) by Thomas Minka, (Microsoft Research Lab – Cambridge)