In many theoretical mathematics courses, little computation is required. However, in both the undergraduate and graduate mathematical statistics courses that I teach, students need to be able do these tasks using software.

1. Graph even very complicated functions, in order to find maxima / minima.
2. Solve equations numerically.
3. Do computations for data analysis and inference for all all the usual statistical techniques in an introductory statistics course.
4. Do simulations, such as simulating the sampling distribution of a statistic.

In this graduate theoretical statistics course, we will discuss some other computational tasks using software. I will provide scripts and instructions in R for these tasks listed above and for other computational tasks in the course. Students are not expected to know R before the course begins, and are not expected to learn to be adept with R during the course. However, I expect students to be able to run the scripts I provide and adapt them in a minimal way. Students will work in groups on any work requiring them to use R and grading on this work will be on just minimal participation.

It is fine - a good thing, indeed - for students to know how to use other software besides R to do any or all of these tasks. I am emphasizing the use of R because I would like for students in the class to be able to work with each other and using the same package is helpful for that. R is freely available so that everyone can have easy access to it, and it takes only minimal setup to run the scripts I will provide.

R is a free package which is available on several different platforms. Information about downloading and setting it up will be provided.