Hello Dirk,
For my specific purpose I have some reservations about package creation.
Here is a description of the project I have in mind.
I would like to use Rcpp to create a set of generic MCMC algorithms, into
which a useR could easily hook their own target distributions written in
C++ with minimal effort. For example, I would provide the following files:
--------------------------------------
GibbsSampler.cpp
//[[Rcpp::export]]
NumericMatrix GibbsSampler(int nIter, NumericVector xInit, List
tuningParams) {
// run some flavor of the Gibbs sampler
}
---------------------------------------
GibbsSampler.h
double logDensity(NumericVector x);
---------------------------------------
Then the useR would write MyDensityA.cpp, which contains the definition of
logDensity, compile the three files, and have a Gibbs sampler for their
specific density function written in C++ and ready to go in R. However, a
useR might wish to use the GibbsSampler for a different density tomorrow.
They would have a different definition of logDensity in MyDensityB.cpp.
Ideally, the useR would have access to Gibbs samplers for both densities in
the same R session.
I can think of two ways of doing this with Rcpp:
1. Compile with sourceCpp (this is what I'm currently doing). There's the
minor issue of giving separate R names to each Gibbs sampler, but there are
many ways around that. The major issue is that sourceCpp only accepts a
single .cpp file (or at least as far as my attempts were unsuccessful in
the original post). So I'm stuck text-processing a bunch of .cpp and .h
files together (the actual project I'm working on has about a dozen of
each).
2. Compile an entire R package for each of MyDensityA and MyDensityB.
However, it seems somewhat cumbersome to have a package loaded for every
density function in the workspace. Moreover, naming conflicts are a bit
more tricky. Right now (with sourceCpp), I'm using an interface of the form
smpA <- gibbs.sampler(density = MyDensityA, niter = 1e4)
smpB <- gibbs.sampler(density = MyDensityB, niter = 1e4)
This is to align with things like lm(formula = MyModel). However, I can't
quite see how to do this with separate packages loaded. Rather it seems
I'd need something like
smpA <- gibbs.sampler.MyDensityA(niter = 1e4)
smpB <- gibbs.sampler.MyDensityB(niter = 1e4)
However, to do this with packages I feel like I would still have to do some
text replacement, which I'm already doing with the sourceCpp approach.
In summary, I am not opposed to package creation, but I hope you can see my
reservations at taking this route. Perhaps you could please suggest a way
to achieve what I'm after with separate Rcpp packages for each density
function. I take it from your reluctance to answer my original post that
Rcpp only supports compilation of multiple files through the package
creation protocol. I can think of many applications in which the useR
could supply a minimal amount of C++ code (e.g., a log-likelihood function)
to hook in with a large amount of code provided by the developer in order
to speed things up considerably. So in my opinion it would be worthwhile
to devise a mechanism to do this correctly.
Best regards,
Martin Lysy
Assistant Professor of Statistics
Department of Statistics and Actuarial Science
University of Waterloo
On Wed, Apr 13, 2016 at 5:34 PM, Dirk Eddelbuettel <e...@debian.org> wrote:
>
> Martin,
>
> Please please please look into creating a package.
>
> If you use RStudio: File -> New Project -> (New or Existing) Directory ->
> Package and then select Rcpp.
>
> If not, consider install the (very tiny) pkgKitten package and call
> Rcpp.package.skeleton() from Rcpp itself (but enhanced by pkgKitten if
> present) for a saner package.
>
> Cheers, Dirk
>
> --
> http://dirk.eddelbuettel.com | @eddelbuettel | e...@debian.org
>
_______________________________________________
Rcpp-devel mailing list
Rcpp-devel@lists.r-forge.r-project.org
https://lists.r-forge.r-project.org/cgi-bin/mailman/listinfo/rcpp-devel
