R for Statistical Learning
Introduction
1 Probability Review
2 R Introduction
3 RStudio and RMarkdown
- 3.1 Template
4 Regression Basics in R
5 Regression for Statistical Learning
6 Simulating the Bias–Variance Tradeoff
7 Classification
8 Logistic Regression
9 Generative Models
10 k-Nearest Neighbors
11 Resampling
12 Classification Overview
13 The caret Package
- 13.1 External Links
- 13.2 RMarkdown
14 Subset Selection
15 Shrinkage Methods
16 Elastic Net
17 Regularized Discriminant Analysis
18 Non-Linear Models
19 Trees
20 Ensemble Methods
21 Support Vector Machines
22 Unsupervised Learning
© 2017 David Dalpiaz

R for Statistical Learning

Chapter 2 `R` Introduction

Materials that were previously found here can now be found in Applied Statistics with R.