Dear R developers, these days I'm working on some R code for fitting completely generic Bayesian Hierarchical Models in R, a la OpenBUGS and JAGS.
A key feature of OpenBUGS and JAGS is that they automatically build an appropriate MCMC sampler from a generic model, specified as a directed acyclic graph (DAG). The spirit of my (would-be) implementation is instead more focused on experimentation and prototyping, i.e. is the user who explicitely assign samplers for each model variable after specifying the model. The sampler can be chosed in a set of predefined samplers, as well as customly specified by the user as an R or C function in a very flexible way. Now I have a prototype scheleton implementation (a bounch of R and C files, together with some base testing scripts) which works at decent speed (w.r.t. JAGS) on some example models, and I'm writing a proof-of-concept, reproducible Sweave file about it, to be published online shortly. What do you think about it in general? What do you think about developing an R package of it as a GSoC project? Best regards, Antonio, Fabio Di Narzo. -- Antonio, Fabio Di Narzo Ph.D. student at Department of Statistical Sciences University of Bologna, Italy ______________________________________________ R-devel@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-devel
