a FIRST COURSE IN STATISTICAL PROGRAMMING WITH R.

✍ Scribed by W. JOHN. MURDOCH DUNCAN J. BRAUN

• Publisher: CAMBRIDGE UNIV PRESS
• Year: 2021
• Tongue: English
• Leaves: 282
• Edition: 3
• Category: Library

✦ Table of Contents

Cover
Half-title
Title page
Copyright information
Contents
Expanded contents
Preface to the third edition
Preface to the second edition
Preface to the first edition
1 Getting started
1.1 What is statistical programming?
1.2 Outline of this book
1.3 The R package
1.4 Why use a command line?
1.5 Font conventions
1.6 Installation of R and RStudio
1.7 Getting started in RStudio
1.8 Going further
2 Introduction to the R language
2.1 First steps
2.2 Basic features of R
2.3 Vectors in R
2.4 Data storage in R
2.5 Packages, libraries, and repositories
2.6 Getting help
2.7 Useful R features
2.8 Logical vectors and relational operators
2.9 Data frames, tibbles, and lists
2.10 Data input and output
3 Programming statistical graphics
3.1 Simple high level plots
3.2 Choosing a high level graphic
3.3 Low level graphics functions
3.4 Graphics as a language: ggplot2
3.5 Other graphics systems
4 Programming with R
4.1 Flow control
4.2 Managing complexity through functions
4.3 The replicate() function
4.4 Miscellaneous programming tips
4.5 Some general programming guidelines
4.6 Debugging and maintenance
4.7 Efficient programming
5 Complex programming in the tidyverse
5.1 The tidyverse principles
5.2 The tibble package: a data frame improvement
5.3 The readr package: reading data in the tidyverse
5.4 The stringr package for manipulating strings
5.5 The dplyr package for manipulating data sets
5.6 Other tidyverse packages
6 Simulation
6.1 Monte Carlo simulation
6.2 Generation of pseudorandom numbers
6.3 Simulation of other random variables
6.4 Multivariate random number generation
6.5 Markov chain simulation
6.6 Monte Carlo integration
6.7 Advanced simulation methods
7 Computational linear algebra
7.1 Vectors and matrices in R
7.2 Matrix multiplication and inversion
7.3 Eigenvalues and eigenvectors
7.4 Other matrix decompositions
7.5 Other matrix operations
8 Numerical optimization
8.1 The golden section search method
8.2 Newton–Raphson
8.3 The Nelder–Mead simplex method
8.4 Built-in functions
8.5 Linear programming
Appendix A Review of random variables and distributions
Appendix B Base graphics details
B.1 The plotting region and margins
B.2 Adjusting axis tick labels
B.3 Setting graphical parameters
Index