Statistical Computing with R, Second Edition (Hardcover, 2)

Introduction
Computational Statistics and Statistical Computing
The R Environment
Getting Started with R
Using the R Online Help System
Functions
Arrays, Data Frames, and Lists
Workspace and Files
Using Scripts
Using Packages
Graphics

Probability and Statistics Review
Random Variables and Probability
Some Discrete Distributions
Some Continuous Distributions
Multivariate Normal Distribution
Limit Theorems
Statistics
Bayes’ Theorem and Bayesian Statistics
Markov Chains

Methods for Generating Random Variables
Introduction
The Inverse Transform Method
The Acceptance-Rejection Method
Transformation Methods
Sums and Mixtures
Multivariate Distributions
Stochastic Processes

Visualization of Multivariate Data
Introduction
Panel Displays
Surface Plots and 3D Scatter Plots
Conditioning Plots
The Grammar of Graphics and ggplot2
Other 2D Representations of Data
Projection Pursuit
Grand Tour
Other Approaches to Data Visualization

Monte Carlo Integration and Variance Reduction
Introduction
Monte Carlo Integration
Variance Reduction
Antithetic Variables
Control Variates
Importance Sampling
Stratified Sampling
Stratified Importance Sampling

Monte Carlo Methods in Inference
Introduction
Monte Carlo Methods for Estimation
Monte Carlo Methods for Hypothesis Tests
Application

Bootstrap and Jackknife
The Bootstrap
Bootstrapping Linear Models
Generalized Bootstrap
The Jackknife
Jackknife-After-Bootstrap
Bootstrap Confidence Intervals
Better Bootstrap Confidence Intervals
Application

Permutation Tests
Introduction
Tests for Equal Distributions
Multivariate Tests for Equal Distributions
Application

Markov Chain Monte Carlo Methods
Introduction
The Metropolis-Hastings Algorithm
The Gibbs Sampler
Monitoring Convergence
Application

Probability Density Estimation
Univariate Density Estimation
Kernel Density Estimation
Bivariate and Multivariate Density Estimation
Other Methods of Density Estimation

Smoothing and Nonparametric Regression
Introduction
Smoothing
Kernel Regression

High Dimensional Data
Introduction
Methods

Numerical Methods in R
Introduction
Root-Finding in One Dimension
Numerical Integration
Linear Programming?The Simplex Method
Application

Optimization
Introduction
Maximum Likelihood Problems
One-Dimensional Optimization
Two-Dimensional Optimization
The EM Algorithm
Stochastic Optimization

Exercises appear at the end of most chapters.