Statistical Computing with R

preface 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 Exercises Visualization of Multivariate Data Introduction Panel Displays Surface Plots and 3D Scatter Plots Contour Plots Other 2D Representations of Data Other Approaches to Data Visualization Exercises Monte Carlo Integration and Variance Reduction Introduction Monte Carlo Integration Variance Reduction Antithetic Variables Control Variates Importance Sampling Stratified Sampling Stratified Importance Sampling Exercises R Code Monte Carlo Methods in Inference Introduction Monte Carlo Methods for Estimation Monte Carlo Methods for Hypothesis Tests Application Exercises Bootstrap and Jackknife The Bootstrap The Jackknife Jackknife-after-Bootstrap Bootstrap Confidence Intervals Better Bootstrap Confidence Intervals Application Exercises Permutation Tests Introduction Tests for Equal Distributions Multivariate Tests for Equal Distributions Application Exercises Markov Chain Monte Carlo Methods Introduction The Metropolis-Hastings Algorithm The Gibbs Sampler Monitoring Convergence Application Exercises R Code Probability Density Estimation Univariate Density Estimation Kernel Density Estimation Bivariate and Multivariate Density Estimation Other Methods of Density Estimation Exercises R Code Numerical Methods in R Introduction Root-Finding in One Dimension Numerical Integration Maximum Likelihood Problems 1D Optimization 2D Optimization The EM Algorithm Linear Programming-The Simplex Method Application Exercises APPENDIX A: Notation APPENDIX B: Working with Data Frames and Arrays Resampling and Data Partitioning Subsetting and Reshaping Data Data Entry and Data Analysis References Index.