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

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