R Programming

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• R Basics
  • Introduction
  • Sample Session
  • Manage your workspace
  • Settings and Installation
  • Documentation
  • Control Structures
  • Working with functions
  • Debugging
  • Using C or Fortran
  • Utilities
  • Estimation utilities
  • Packages
• Data Management
  • Data types
  • Working with data frames
  • Importing and exporting data
  • Text Processing
  • Times and Dates
• Graphics
  • Grammar of graphics
• Publication quality output
• Descriptive Statistics
• Mathematics
  • Optimization
  • Probability Distributions
  • Random Number Generation
• Statistical Core Methods
  • Maximum Likelihood
  • Method of Moments
  • Bayesian Methods
  • Bootstrap
  • Multiple Imputation
  • Nonparametric Methods
• Regression Models
  • Linear Models
  • Quantile Regression
  • Binomial Models
  • Multinomial Models
  • Tobit And Selection Models
  • Count Data Models
  • Duration Analysis
• Time Series
• Factor Analysis
• Classification
  • Ordination
  • Clustering
• Network Analysis
• High Performance Computing
  • Profiling R code
  • Parallel computing with R
• Appendix
  • Sources
  • Index

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Before starting with parallel or high performance computing it is important to analyze and optimize R code. R provides some useful tools to analyze and profile R code. A good and short introduction is provided in the R extension documentation.

Soon we are going to provide some example code: