Hi Ben, Before you begin playing with BUGS/JAGS, there are several native R packages that deal with a wide variety of Bayesian models that worth considering. Among many others, I find MCMCpack, DPpackage, and MCMCglmm very useful (and convenient).
Best, Shige On Tue, Apr 13, 2010 at 7:49 PM, Ben <mi...@emerose.org> wrote: > Hi all, > > I would like to start to use R's MCMC abilities to compute answers in > Bayesian statistics. I don't have any specific problems in mind yet, > but I would like to be able to compute/sample posterior probabilities > for low-dimensional custom models, as well as handle "standard" > Bayesian cases like linear regression and hierarchical models. > > R clearly has a lot of abilities in this area: > > http://cran.r-project.org/web/views/Bayesian.html > > --enough to be confusing! For instance, there are apparently three > separate interfaces to JAGS, and I'm not even sure I want/need to > interface to JAGS at all. > > Can someone please get me started? Are there a handful of "must-have" > packages and software that everyone (who uses MCMC regularly) uses? > > Any responses are appreciated, > > -- > Ben > > ______________________________________________ > R-help@r-project.org 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@r-project.org 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.
