Hi Giovanni, You may want to consider: "Numerical analysis for statisticians" (Springer) by Ken Lange. We used when I was taking a graduate level (MS and PhD students) course in statistical computing. I really like it and still use it frequently.
Ravi. ---------------------------------------------------------------------------- ------- Ravi Varadhan, Ph.D. Assistant Professor, The Center on Aging and Health Division of Geriatric Medicine and Gerontology Johns Hopkins University Ph: (410) 502-2619 Fax: (410) 614-9625 Email: [EMAIL PROTECTED] Webpage: http://www.jhsph.edu/agingandhealth/People/Faculty/Varadhan.html ---------------------------------------------------------------------------- -------- -----Original Message----- From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] On Behalf Of Giovanni Petris Sent: Friday, April 20, 2007 9:34 AM To: r-help@stat.math.ethz.ch Subject: [R] Suggestions for statistical computing course 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 <[EMAIL PROTECTED]> Department of Mathematical Sciences University of Arkansas - Fayetteville, AR 72701 Ph: (479) 575-6324, 575-8630 (fax) http://definetti.uark.edu/~gpetris/ ______________________________________________ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. ______________________________________________ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.