Dear Prof. Tierney, thank you very much to answer my question. It is good to know that the loss of efficiency can be small.
I came to this question after using R to implement a few low level algorithm: KD-tree and recursive algorithm for conditional Poisson binomial. The R's speed has been slow and even much slower than Ruby. I love R dearly and always tell my students that it is the best thing that ever happened to statistics. R is much more elegant than C or Fortran. Unfortunately Fortran or C is still needed when speed is a concern and a statistician has then to confront the ugly and complex large world. A huge gain in productivity and reduction in mental anguish can be achieved If R's speed can be improved via compilation. I did a little research. The following tool claims to make Python as fast as C http://www-128.ibm.com/developerworks/linux/library/l-psyco.html Recently, a new Ruby implementation makes it several times faster: http://www.antoniocangiano.com/articles/2007/02/19/ruby-implementations-shootout-ruby-vs-yarv-vs-jruby-vs-gardens-point-ruby-net-vs-rubinius-vs-cardinal Jason Liao, http://www.geocities.com/jg_liao Associate Professor of Biostatistics Drexel University School of Public Health 245 N. 15th Street, Mail Stop 660 Philadelphia, PA 19102-1192 phone 215-762-3934 ____________________________________________________________________________________ Expecting? Get great news right away with email Auto-Check. ______________________________________________ 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.
