Hi, I'm attending summer School at UCLA (IPAM) on "probabilistics models of cognition". I have been an R-user since v. 1.4.1, but was trained in the frequentist tradition (as most psychologists!). I found that all faculty here use matlab and Murphy's bayes net toolbox. I have not had the need to use matlab before, and would love to stick to R for graphics models and bayesian modeling in general (even if it takes me extra time to cross-code the examples in matlab into R).
I'm trying to find an R equivalent to Matlab's Bayes net toolbox. I have found packages 'deal' and 'gR', and played around with: http://www.ci.tuwien.ac.at/gR/ But I cannot really figure out how all these packages are integrated. Also, appendix B of 'bayesian AI' lists gR as "vaporware" (although this could well be outdated by now). Is there any R news article on bayesian networks? It's hard to find, because I don't think the content of R-news is indexed in CRAN. I could download every issue and search the TOC, but it'd be time-consuming. Even though the examples in the documentation in package 'deal' are good, they fall short. A good tutorial would be great. What I'd like to know from you is whether R is a sensible choice or whether BNT is just easier and more mature. Right now I could easily chose R or Matlab, since I have made little investment in any form of bayesian networks modeling; However, since I have a better background in R than in Matlab, I'd love to stay with R. Any resources (mailing lists, books, tutorials) would be greatly appreciated. Thanks a lot in advance, -Jose -- Jose Quesada, PhD. http://www.andrew.cmu.edu/~jquesada ______________________________________________ [email protected] 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.
