Søren Højsgaard <Soren.Hojsgaard <at> agrsci.dk> writes: > > Jose, > I am not entirely sure what Matlabs Bayes net toolbox does, but I guess it implements as propagation > algorithm for Bayesian networks. There is no such package on CRAN - yet - but there will be soon: I've > created a package called gRbayesnet which implements the "Lauritzen- Spiegelhalter" propagation > algorithm. I expect to upload it to CRAN within the next few days. > Best regards > Søren > > ________________________________ > > Fra: r-help-bounces <at> stat.math.ethz.ch på vegne af Jose Quesada > Sendt: ma 16-07-2007 17:58 > Til: r-help <at> lists.r-project.org > Emne: [R] R equivalent to Matlab's Bayes net toolbox > > 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 could well be outdated by now).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 > > ______________________________________________ > R-help <at> 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 <at> 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. > >
Hi Søren, It looks like bnt implements several algorithms for learning both parameters and structure: http://bnt.sourceforge.net MCMC for example is implemented. I didn't know about this Lauritzen- Spiegelhalter" propagation algorithm. The thing that I don't understand in the gR page is why there are so many different packages and why they are not very integrated: boa CoCo coda deal dynamicGraph (core) ggm gRbase (core) mathgraph mimR R2WinBUGS rbugs SIN http://cran.r-project.org/src/contrib/Views/gR.html I tried deal, for example, and it's not really comparable to bnt. In Matlab, there seems to be a package (well, toolbox) only, and there seems to be some of a community around it: http://tech.groups.yahoo.com/group/BayesNetToolbox/ Is there any kind of community doing graphical models stuff on R? Thanks, -Jose ______________________________________________ [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.
