[R] Suggestions for statistical computing course
Giovanni Petris
GPetris at uark.edu
Fri Apr 20 15:34:28 CEST 2007
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/
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