I have released a (very) preliminary version of my new MCMC software in R, which I’m calling GRIMS, for General R Interface for Markov Sampling. You can get it here. This software differs from other more-or-less general MCMC packages in several respects, all but one of which make it, I think, a much better tool 
This fall, I’ll be teaching a second-year course on Probability with Computer Applications, which is required for Computer Science majors. I’ve taught this before, but that was five years ago, so I’ve been looking to see what new textbooks would be suitable. The course aims not just to use computer science applications as examples, but 
I have now released a new collection of 30 patches to speed up R version 2.13.0. You can get them here Assessing how much these patches speed up R is difficult. First of all, the speedup varies tremendously with the type of program. It also varies quite a bit with the machine and compiler used 
After I realized that some aspects of R’s implementation are rather inefficient, one of the first things I looked at was matrix multiplication. There I found a huge performance penalty for many matrix multiplies, a penalty which remains in the current version, 2.13.0. As discussed below, eliminating this penalty speeds up long vector dot products 
I’ve gotten back to work on speeding up R, starting with improving my suite of speed tests. Among other new features, this suite allows one to easily try out the “byte-code” compiler that is now a standard part of the latest release of R, version 2.13.0. You can get the suite here. I’ve been running 
Following my discovery of two surprising inefficiencies in R, I’ve been inspired to spend much of the last two weeks looking for ways to speed it up. I’ve had quite a bit of success, both at finding ways to speed up particular functions, and at finding ways to reduce general interpretive overhead. You can get 
As I noted here, enclosing sub-expressions in parentheses is slower in R than enclosing them in curly brackets. I now know why, and I’ve modified R to reduce (but not eliminate) the slowness of parentheses. The modification speeds up many other operations in R as well, for an average speedup of something like 5% for 
I see that it’s been over a year since my last post! I have a backlog of blog post ideas, but something else always seems to have higher priority. Today, though, I have some interesting (and useful) things to say about R, which I discovered in the last few days, and which shouldn’t take long 
Unlike the two design flaws I posted about before (here, here, and also here), where one could at least see a reason for the design decision, even if it was unwise, this design flaw is just incomprehensible. For no reason at all that I can see, R allows one to use zero as a subscript 
I’ve previously posted about two design flaws in R. The first post was about how R produces reversed sequences from a:b when a>b, with bad consequences in “for” statements (and elsewhere). The second post was about how R by default drops dimensions in expressions like M when i:j is a sequence only one long (ie, 
Zero Inflated Models and Generalized Linear Mixed Models with R.
Zuur, Saveliev, Ieno (2012).
