Partools 1.1.4 is now on GitHub.

The main change this time is enhancement of the debugging facilities (which work not only for partools but also the cluster-based portion of R’s parallel package in general). As some of you know, I place huge importance on debugging, so much so that I wrote a book on it (The Art of Debugging with GDB, DDD, and Eclipse}, N. Matloff and P. Salzman, NSP, 2008).

But debugging parallel code is hard, especially in the parallel package. The problem is that your R code running on the cluster nodes does so without having a terminal window associated with it. I’ve had various tools for dealing with that in partools from the beginning, but in the latest version their effectiveness is greatly enhanced by adding mechanisms involving R’s dump.frames(). This was Hadley’s idea, for which I am quite grateful. I’ve had a lot of fun using the enhanced debugging tools myself.

This also inspired me to finally add a debugging vignette to the package, which I had long planned to do but hadn’t gotten around to.

I also thank Gábor Csárdi for cleaning up the DESCRIPTION file.

I have more enhancements to partools in the pipeline. One of them involves k-NN nonparametric regression, using Software Alchemy but in a different way than you might think. Actually, I’ve already done this before, in my freqparcoord package with Yingkang Xie, but I’ll do a little tweaking before adding it here. This too is something I’ve been planning to do for a while but hadn’t gotten around to. What inspired me to give it a higher priority was a paper that I recently ran across by some researchers at Stanford and UCB, which establishes nice theoretical properties for a Software Alchemy-type approach for another kind  of nonparametric regression estimation, kernel ridge regression.