Overview

It is particularly useful for C and C++ programs as it provides a standard C interface to a wide range of mathematical routines such as special functions, permutations, combinations, fast fourier transforms, eigensystems, random numbers, quadrature, random distributions, quasi-random sequences, Monte Carlo integration, N-tuples, differential equations, simulated annealing, numerical differentiation, interpolation, series acceleration, Chebyshev approximations, root-finding, discrete Hankel transforms physical constants, basis splines and wavelets. There are over 1000 functions in total with an extensive test suite.

The RcppGSL package provides an easy-to-use interface between GSL data structures and GNU R using concepts from Rcpp which is itself a package that eases the interfaces between R and C++.

What can RcppGSL do?

• templated vector and matrix classes: these are similar to Rcpp's own vector and matrix classes, but really are just smart pointers around the C structure expected by the library
• this means you can transfer data from R to your GSL-using programs in pretty much the same way you would in other C++ programs using Rcpp---by relying on the Rcpp::as() and Rcpp::wrap() converterrs
• at the C++ level you can use these GSL vectors in a more C++-alike way (using eg foo[i] to access an element at index i)
• yet at the same time you can just pass these vector and matrix objects to the GSL functions expecting its C objects: thanks to some cleverness in these classes they pass the right object on (see the example below)
• we also provide the lightweight views for vectors and matrices as the GSL API uses these in many places.

In sum, these features aim to make it a lot easier to use functionality from the (excellent) GNU GSL in a way that is more natural to a user of C++ and R (and of course Rcpp). The package vignette. has a few more details.

So give me an example!

#include <RcppGSL.h>
#include <gsl/gsl_matrix.h>
#include <gsl/gsl_blas.h>

extern "C" SEXP colNorm(SEXP sM) {

  try {

        RcppGSL::matrix<double> M = sM;     // create gsl data structures from SEXP
        int k = M.ncol();
        Rcpp::NumericVector n(k);           // to store results

        for (int j = 0; j < k; j++) {
            RcppGSL::vector_view<double> colview = gsl_matrix_column (M, j);
            n[j] = gsl_blas_dnrm2(colview);
        }
        M.free() ;
        return n;                           // return vector

  } catch( std::exception &ex ) {
        forward_exception_to_r( ex );

  } catch(...) {
        ::Rf_error( "c++ exception (unknown reason)" );
  }
  return R_NilValue; // -Wall
}

To compute the column norms, we loop over all columns. The norm is computed after we extract a col as a view (see the GSL manual for details: this is essentially a lightweight slice referring back to the original object). This column can be passed directly to a GSL function expecting a vector -- very nice. The actual computation is done by a standard GSL function, here gsl_blas_dnrm2, which expects a standard GSL vector -- which our classes transparently provide in this context.

Before returning the result, we then free the memory of the matrix object (something we have to do here as the objects are C-alike and cannot do the reference counting tricks we can do in C++) and return the result vector. All this is wrapped in a try/catch block as is standard with Rcpp.

Another example function for a faster compiled implementation of lm() using the model fitting code in the GSL is also included in the package.

Where do I get it

Authors

License

Last modified: Tue Nov 30 21:31:07 CST 2010