Before we dive into the (other) fun stuff (sampling multi-dimensional probability distributions, using convex optimization to fit data models, and so on), it would be helpful if we review those aspects of R that all subsequent chapters will assume knowledge of.
If you fancy yourself an R guru, you should still, at least, skim through this chapter, because you'll almost certainly find the idioms, packages, and style introduced here to be beneficial for following the rest of the material.
If you don't care much about R (yet), and are just in this for the statistics, you can heave a heavy sigh of relief that, for the most part, you can run the code given in this book in the interactive R interpreter with very little modification and ...
