Dear Whit, I have been playing with other examples you provided in the github repository. The one Dirt sent, however, is the only example that I can find from the internet showing how CppBugs works with Rcpp (and R). As I see it, such a combination has great potential providing a flexible yet powerful Bayesian computational tool.
Very nice work, and thanks for the suggestion. Best, Shige On Fri, Sep 30, 2011 at 10:06 PM, Whit Armstrong <[email protected]> wrote: > Shige, > > That example is quite dated at this point. The CppBugs api has > changed a lot since then and is likely to change more in the near > future. > > Please git pull the latest from github, and ping me if you have any issues. > > There are also quite a few pure c++ examples the the 'test' dir to get > you started. > > In the next major release of CppBugs you will be able to declare the > objects directly in R, but give me a few months to get that working. > > -Whit > > > On Fri, Sep 30, 2011 at 9:40 PM, Shige Song <[email protected]> wrote: >> Dear Dirk, >> >> Thank you very much for the suggestions and the upated file. Your file >> actually works flawlessly on my system. It looks really interesting >> and educational. >> >> Thanks also for the great work on Rcpp, really amazing piece of >> software you got there. >> >> Best, >> Shige >> >> On Fri, Sep 30, 2011 at 9:11 PM, Dirk Eddelbuettel <[email protected]> wrote: >>> >>> Shige, >>> >>> There is no way to sugarcoat this: you have to learn to live with, and learn >>> from, the compiler errors and relate them to the actual code. Using Rcpp >>> still means programming in the context of a C++ compiler. >>> >>> >>> You also need Whit's CppBugs repo from github _installed somewhere_ so that >>> >>> #include <cppbugs/cppbugs.hpp> >>> >>> works. Plus the same for Conrad's Armadillo as we have >>> >>> #include <armadillo> >>> >>> And to top it all off, you probably need a bunch of Boost installed as >>> CppBugs uses it. If all that is a given, then you can run the attached file >>> 'whit.r' as I do below. This file served as in example in the Rcpp workshop >>> in April and I just fetched it from my sources. The version posted then is >>> likely a little outdated. But this one works: >>> >>> $ r whit.R >>> Loading required package: methods >>> user system elapsed >>> 0.220 0.020 0.236 >>> $b >>> [1] -0.3303790 0.5276294 >>> >>> $ar >>> [1] 0 >>> >>> $ >>> >>> Whether you use Rscript or r (from littler) does not matter. The updated >>> whit.r is attached. It builds and runs, I have no idea if it makes any >>> sense... I think it regresses y ~ X with both being noise so there. >>> >>> Hope this helps, Dirk >>> >>> >>> >>> >>> -- >>> New Rcpp master class for R and C++ integration is scheduled for >>> San Francisco (Oct 8), more details / reg.info available at >>> http://www.revolutionanalytics.com/products/training/public/rcpp-master-class.php >>> >>> >> _______________________________________________ >> Rcpp-devel mailing list >> [email protected] >> https://lists.r-forge.r-project.org/cgi-bin/mailman/listinfo/rcpp-devel > _______________________________________________ Rcpp-devel mailing list [email protected] https://lists.r-forge.r-project.org/cgi-bin/mailman/listinfo/rcpp-devel
