[Rcpp-devel] Help with an Rcpp and CppBugs example

[Shige Song]

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
<armstrong.whit at gmail.com> 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 <shigesong at gmail.com> 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 <edd at debian.org> 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 likely a little outdated.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
>>>
>>>
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