Georgia R School offers an all-new 6-week course teaching how to program, with R software, unique applications for simulation and Monte-Carlo (MC) methods and processes. R Programming for Simulation and Monte-Carlo Methods instructs with respect to writing R programs for purposes of: (1) simulating probabilistic and numerical processes; (2) implementing simulation experiments; (3) performing Monte Carlo (MC) methods for probabilistic inference; and (4) applying Monte Carlo techniques to integration and variance reduction. Daily course material includes dozens of example R programs that address “real world” processes and mathematical problems. Additionally, there are extended multi-part R software application case studies based on complex simulations of: (1) the spread of major infectious diseases (epidemiology); and (2) inventory management and control.
The course draws heavily from the Comprehensive R Archive Network (CRAN) spuRs package and the excellent reference textbook Introduction to Scientific Programming and Simulation Using R (2nd edition) by Owen Jones, Robert Maillardet and Andrew Robinson (CRC Press, 2009). Course content also includes material from Statistical Computing with R by Maria L. Rizzo (Chapman & Hall/CRC, 2008) and R by Example by Jim Albert and Maria Rizzo (Springer, 2012). It is not necessary to purchase any textbooks to successfully complete this course, all materials are provided. However, if budget is not an issue, these are excellent references for this topic and are recommended.