[R] Suggestions for statistical computing course

[Giovanni Petris]

Dear R-helpers,

I am planning a course on Statistical Computing and Computational
Statistics for the Fall semester, aimed at first year Masters students
in Statistics. Among the topics that I would like to cover are linear
algebra related to least squares calculations, optimization and
root-finding, numerical integration, Monte Carlo methods (possibly
including MCMC), bootstrap, smoothing and nonparametric density
estimation. Needless to say, the software I will be using is R.

1. Does anybody have a suggestion about a book to follow that covers
   (most of) the topics above at a reasonable revel for my audience? 
   Are there any on-line publicly-available manuals, lecture notes,
   instructional documents that may be useful?

2. I do most of my work in R using Emacs and ESS. That means that I
   keep a file in an emacs window and I submit it to R one line at a
   time or one region at a time, making corrections and iterating as
   needed. When I am done, I just save the file with the last,
   working, correct (hopefully!) version of my code. Is there a way of
   doing something like that, or in the same spirit, without using
   Emacs/ESS? What approach would you use to polish and save your code
   in this case? For my course I will be working in a Windows
   environment. 
   
   While I am looking for simple and effective solutions that do not
   require installing emacs in our computer lab, the answer "you
   should teach your students emacs/ess on top of R" is perfecly
   acceptable.
   

Thank you for your consideration, and thank you in advance for the
useful replies.

Have a good day,
Giovanni

-- 

Giovanni Petris  <GPetris at uark.edu>
Department of Mathematical Sciences
University of Arkansas - Fayetteville, AR 72701
Ph: (479) 575-6324, 575-8630 (fax)
http://definetti.uark.edu/~gpetris/