---
title: | 
       | \pkg{RcppGSL}: Easier \pkg{GSL} use from \proglang{R} via \pkg{Rcpp}

# Use letters for affiliations
author:
  - name: Dirk Eddelbuettel
    affiliation: a
  - name: Romain François
    affiliation: b

address:
  - code: a
    address: \url{http://dirk.eddelbuettel.com}
  - code: b
    address: \url{https://romain.rbind.io/}

# For footer text
lead_author_surname: Eddelbuettel and François

# Place DOI URL or CRAN Package URL here
doi: "https://cran.r-project.org/package=RcppGSL"

# Abstract
abstract: |
  The GNU Scientific Library, or \pkg{GSL}, is a collection of numerical
  routines for scientific computing \citep{GSL}. It is particularly useful for
  \proglang{C} and \proglang{C++} programs as it provides a standard
  \proglang{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 \pkg{RcppGSL} package provides an easy-to-use interface between
  \pkg{GSL} and \proglang{R}, with a particular focus on matrix and vector
  data structures. \pkg{RcppGSL} relies on \pkg{Rcpp}
  \citep{JSS:Rcpp,Eddelbuettel:2013:Rcpp,CRAN:Rcpp} which is
  itself a package that eases the interfaces between \proglang{R} and C++.}

# Font size of the document, values of 9pt (default), 10pt, 11pt and 12pt
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one_column: false

# Optional: Enable section numbering, default is unnumbered
numbersections: true

# Optional: Specify the depth of section number, default is 5
secnumdepth: 5

# Optional: Bibliography 
bibliography: Rcpp

# Customize footer, eg by referencing the vignette
footer_contents: "RcppGSL Vignette"

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output: pinp::pinp

# Extra definitions in header
header-includes: >
  \newcommand{\proglang}[1]{\textsf{#1}}
  \newcommand{\pkg}[1]{\textbf{#1}}

# Include bibliography material directly (from .bbl file)
include-after: |
  \begin{thebibliography}{12}
  \newcommand{\enquote}[1]{``#1''}
  \providecommand{\natexlab}[1]{#1}
  \providecommand{\url}[1]{\texttt{#1}}
  \providecommand{\urlprefix}{URL }
  \expandafter\ifx\csname urlstyle\endcsname\relax
    \providecommand{\doi}[1]{doi:\discretionary{}{}{}#1}\else
    \providecommand{\doi}{doi:\discretionary{}{}{}\begingroup
    \urlstyle{rm}\Url}\fi
  \providecommand{\eprint}[2][]{\url{#2}}
  
  \bibitem[{Allaire \emph{et~al.}(2017)Allaire, Eddelbuettel, and
    Fran\c{c}ois}]{CRAN:Rcpp:Attributes}
  Allaire JJ, Eddelbuettel D, Fran\c{c}ois R (2017).
  \newblock \emph{{Rcpp} Attributes}.
  \newblock Vignette included in R package Rcpp,
    \urlprefix\url{http://CRAN.R-Project.org/package=Rcpp}.
  
  \bibitem[{Bates and Eddelbuettel(2013)}]{JSS:RcppEigen}
  Bates D, Eddelbuettel D (2013).
  \newblock \enquote{Fast and Elegant Numerical Linear Algebra Using the
    {RcppEigen} Package.}
  \newblock \emph{Journal of Statistical Software}, \textbf{52}(5), 1--24.
  \newblock \urlprefix\url{http://www.jstatsoft.org/v52/i05/}.
  
  \bibitem[{Bates \emph{et~al.}(2016)Bates, Fran\c{c}ois, and
    Eddelbuettel}]{CRAN:RcppEigen}
  Bates D, Fran\c{c}ois R, Eddelbuettel D (2016).
  \newblock \emph{RcppEigen: Rcpp integration for the Eigen templated linear
    algebra library}.
  \newblock {R} package version 0.3.2.9.0,
    \urlprefix\url{http://CRAN.R-Project.org/package=RcppEigen}.
  
  \bibitem[{Eddelbuettel(2013)}]{Eddelbuettel:2013:Rcpp}
  Eddelbuettel D (2013).
  \newblock \emph{Seamless R and C++ Integration with Rcpp}.
  \newblock Use R! Springer, New York.
  \newblock ISBN 978-1-4614-6867-7.
  
  \bibitem[{Eddelbuettel and Fran\c{c}ois(2011)}]{JSS:Rcpp}
  Eddelbuettel D, Fran\c{c}ois R (2011).
  \newblock \enquote{{Rcpp}: Seamless {R} and {C++} Integration.}
  \newblock \emph{Journal of Statistical Software}, \textbf{40}(8), 1--18.
  \newblock \urlprefix\url{http://www.jstatsoft.org/v40/i08/}.
  
  \bibitem[{Eddelbuettel \emph{et~al.}(2017)Eddelbuettel, Fran\c{c}ois, Allaire,
    Ushey, Kou, Russel, Chambers, and Bates}]{CRAN:Rcpp}
  Eddelbuettel D, Fran\c{c}ois R, Allaire J, Ushey K, Kou Q, Russel N, Chambers
    J, Bates D (2017).
  \newblock \emph{{Rcpp}: Seamless {R} and {C++} Integration}.
  \newblock R package version 0.12.12,
    \urlprefix\url{http://CRAN.R-Project.org/package=Rcpp}.
  
  \bibitem[{Eddelbuettel \emph{et~al.}(2016)Eddelbuettel, Fran\c{c}ois, and
    Bates}]{CRAN:RcppArmadillo}
  Eddelbuettel D, Fran\c{c}ois R, Bates D (2016).
  \newblock \emph{RcppArmadillo: Rcpp integration for Armadillo templated linear
    algebra library}.
  \newblock R package version 0.7.400.2.0,
    \urlprefix\url{http://CRAN.R-Project.org/package=RcppArmadillo}.
  
  \bibitem[{Eddelbuettel and
    Sanderson(2014)}]{Eddelbuettel+Sanderson:2013:RcppArmadillo}
  Eddelbuettel D, Sanderson C (2014).
  \newblock \enquote{{RcppArmadillo}: Accelerating {R} with High-Performance
    {C++} Linear Algebra.}
  \newblock \emph{Computational Statistics and Data Analysis}, \textbf{71},
    1054--1063.
  \newblock \doi{10.1016/j.csda.2013.02.005}.
  \newblock \urlprefix\url{http://dx.doi.org/10.1016/j.csda.2013.02.005}.
  
  \bibitem[{Galassi \emph{et~al.}(2010)Galassi, Davies, Theiler, Gough, Jungman,
    Alken, Booth, and Rossi}]{GSL}
  Galassi M, Davies J, Theiler J, Gough B, Jungman G, Alken P, Booth M, Rossi F
    (2010).
  \newblock \emph{{GNU} {S}cientific {L}ibrary {R}eference {M}anual}, 3rd
    edition.
  \newblock Version 1.14. {ISBN} 0954612078,
    \urlprefix\url{http://www.gnu.org/software/gsl}.
  
  \bibitem[{{R Core Team}(2017)}]{R:Main}
  {R Core Team} (2017).
  \newblock \emph{R: A Language and Environment for Statistical Computing}.
  \newblock R Foundation for Statistical Computing, Vienna, Austria.
  \newblock \urlprefix\url{https://www.R-project.org/}.
  
  \bibitem[{Sanderson(2010)}]{Sanderson:2010:Armadillo}
  Sanderson C (2010).
  \newblock \enquote{{Armadillo}: {An} open source {C++} Algebra Library for Fast
    Prototyping and Computationally Intensive Experiments.}
  \newblock \emph{Technical report}, {NICTA}.
  \newblock \urlprefix\url{http://arma.sf.net}.
  
  \bibitem[{Sklyar \emph{et~al.}(2015)Sklyar, Murdoch, Smith, Eddelbuettel, and
    Fran\c{c}ois}]{CRAN:inline}
  Sklyar O, Murdoch D, Smith M, Eddelbuettel D, Fran\c{c}ois R (2015).
  \newblock \emph{inline: Inline C, C++, Fortran function calls from R}.
  \newblock R package version 0.3.14,
    \urlprefix\url{http://CRAN.R-Project.org/package=inline}.
  
  \end{thebibliography}


# Required: Vignette metadata for inclusion in a package.
vignette: >
  %\VignetteIndexEntry{RcppGSL}
  %\VignetteKeywords{R,GSL,Rcpp,data transfer}
  %\VignetteDepends{RcppGSL}
  %\VignetteEncoding{UTF-8}
  %\VignetteEngine{knitr::rmarkdown}
---

\section{Introduction}

The GNU Scientific Library, or \pkg{GSL}, is a collection of numerical
routines for scientific computing \citep{GSL}. It is a rigourously developed
and tested library providing support for a wide range of scientific or
numerical tasks. Among the topics covered in the \pkg{GSL} are
<!-- from the GSL manual-->
complex numbers, roots of polynomials,
special functions, vector and matrix data structures,
permutations, combinations, sorting, BLAS support,
linear algebra, 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
least-squares fitting,	minimization,
physical constants, basis splines and wavelets.

Support for \proglang{C} programming with the \pkg{GSL} is available as the
\pkg{GSL} itself is written in \proglang{C}, and provides a
\proglang{C}-language Application Programming Interface (API). 
Access from \proglang{C++} is possible, albeit not at an
abstraction level that could be offered by dedicated \proglang{C++}
implementations. Several \proglang{C++} wrappers for the \pkg{GSL} have
been written over the years; none reached a state of completion
comparable to the \pkg{GSL} itself. <!-- Three such wrapping library are
  %\url{http://cholm.home.cern.ch/cholm/misc/gslmm/},
  %\url{http://gslwrap.sourceforge.net/} and
  %\url{http://code.google.com/p/gslcpp/}.-->

The \pkg{GSL} combines broad coverage of scientific topics, serious
implementation effort, and the use of the well-known GNU General Public
License (GPL). This has lead to fairly wide usage of the library.  As a concrete
example, we can consider the Comprehensive R Archive Network (CRAN)
repository network for the \proglang{R} language and environment 
\citep{R:Main}.  CRAN contains over three dozen packages interfacing the
\pkg{GSL}. Of these more than half interface the vector or matrix classes as
shown in Table \ref{tab:useOfGSLatCRAN}. This provides empirical evidence indicating
that the \pkg{GSL} is popular among programmers using either the \proglang{C}
or \proglang{C++} language for solving problems applied science.

\begin{table}
  \centering
  \begin{small}
    \begin{tabular}{lccc}
      \toprule
      Package  & Any \texttt{gsl} header & \texttt{gsl\_vector.h} & \texttt{gsl\_matrix.h}  \\
      \midrule
      abn		 &    $\star$  &       $\star$        & $\star$     \\    
      BayesLogit	 &    $\star$  &                      &             \\ 
      BayesSAE           &    $\star$  &     $\star$          & $\star$     \\  
      BayesVarSel        &    $\star$  &     $\star$          & $\star$     \\
      BH                 &    $\star$  &     $\star$          &             \\ 
      bnpmr              &    $\star$  &                      &             \\ 
      BNSP               &    $\star$  &      $\star$         & $\star$     \\
      cghseg             &    $\star$  &      $\star$         & $\star$     \\
      cit       	 &    $\star$  &                      &             \\ 
      diversitree  	 &    $\star$  &                      & $\star$     \\  
      eco              	 &    $\star$  &                      &              \\
      geoCount   	 &    $\star$  &                      &              \\
      graphscan    	 &    $\star$  &                      &              \\
      gsl                &    $\star$  &    $\star$           &              \\
      gstat     	 &    $\star$  &                      &              \\
      hgm       	 &    $\star$  &                      &              \\
      HiCseg    	 &    $\star$  &                      & $\star$     \\
      igraph  		 &    $\star$  &                      &              \\
      KFKSDS             &    $\star$  &   $\star$            & $\star$      \\
      libamtrack  	 &    $\star$  &                      &              \\ 
      mixcat             &    $\star$  &   $\star$            &  $\star$     \\
      mvabund            &    $\star$  &   $\star$            &  $\star$     \\
      outbreaker         &    $\star$  &   $\star$            &  $\star$     \\
      R2GUESS            &    $\star$  &   $\star$            &  $\star$     \\
      RCA             	 &    $\star$  &                      &              \\   
      RcppGSL            &    $\star$  &   $\star$            &  $\star$     \\
      RcppSMC        	 &    $\star$  &                      &              \\
      RcppZiggurat   	 &    $\star$  &                      &              \\        
      RDieHarder         &    $\star$  &   $\star$            &  $\star$     \\
      ridge              &    $\star$  &    $\star$           &  $\star$     \\
      Rlibeemd           &    $\star$  &    $\star$           &              \\                 
      Runuran            &    $\star$  &                      &              \\
      SemiCompRisks      &    $\star$  &                      & $\star$      \\
      simplexreg         &    $\star$  &    $\star$           & $\star$      \\
      stsm               &    $\star$  &    $\star$           & $\star$      \\
      survSNP        	 &    $\star$  &                      &              \\
      TKF                &    $\star$  &      $\star$         &    $\star$   \\
      topicmodels        &    $\star$  &      $\star$         &   $\star$    \\
      VBLPCM             &    $\star$  &      $\star$         &              \\
      VBmix              &    $\star$  &      $\star$         &   $\star$    \\
      \bottomrule
    \end{tabular}
  \end{small}
  \caption{CRAN Package Usage of \pkg{GSL} outright, for vectors and for matrices.}
  \label{tab:useOfGSLatCRAN}

  \begin{flushleft}
    \footnotesize \textsl{Note:} Data gathered in late July 2015 by use of
    \texttt{grep} searching (recursively) for inclusion of any GSL header, or
    the vector and matrix headers specifically, within the \texttt{src/} or
    \texttt{inst/include/} directories of expanded source code archives of
    the CRAN network.  Convenient (temporary) shell access to such an
    expanded code archive via WU Vienna is gratefully acknowledged.
  \end{flushleft}
\end{table}

At the same time, the \pkg{Rcpp} package
\citep{JSS:Rcpp,Eddelbuettel:2013:Rcpp,CRAN:Rcpp} offers a
higher-level interface between \proglang{R} and \proglang{C++}.
\pkg{Rcpp} permits \proglang{R} objects like
vectors, matrices, lists, functions, environments, $\ldots$, to be
manipulated directly at the \proglang{C++} level, and alleviates the needs for
complicated and error-prone parameter passing and memory allocation. It also
allows compact vectorised expressions similar to what can be written in
\proglang{R} directly at the \proglang{C++} level.

The \pkg{RcppGSL} package discussed here aims to close the gap. It
offers access to \pkg{GSL} functions, in particular via the vector and
matrix data structures used throughout the \pkg{GSL}, while staying closer to the
`whole object model' familar to the \proglang{R} programmer.

The rest of paper is organised as follows. The next section shows a
motivating example of a fast linear model fit routine using \pkg{GSL} functions.
The following section discusses support for \pkg{GSL} vector types, which is
followed by a section on matrices. The following two section discusses error
handling, and then use of \pkg{RcppGSL} in your own package. This is followed
by short discussions of how to use \pkg{RcppGSL} with \pkg{inline} and
\textsl{Rcpp Attributes}, respectively, before a short concluding summary.


\section{Motivation: fastLm}

Fitting linear models is a key building block of analysing and
modeling data. \proglang{R} has a very complete and feature-rich function in
\texttt{lm()} which provides a model fit as well as a number of diagnostic
measure, either directly or via the \texttt{summary()} method for linear
model fits. The \texttt{lm.fit()} function provides a faster alternative
(which is however recommend only for for advanced users) which provides
estimates only and fewer statistics for inference.  This may lead to user
requests for a routine which is both fast and featureful enough.  The
\texttt{fastLm} routine shown here provides such an implementation as part of
the \pkg{RcppGSL} package. It uses
the \pkg{GSL} for the least-squares fitting functions and provides a nice
example for \pkg{GSL} integration with \proglang{R}.

```cpp
#include <RcppGSL.h>

#include <gsl/gsl_multifit.h>
#include <cmath>

// declare a dependency on the RcppGSL package; 
// also activates plugin (but not needed when 
// 'LinkingTo: RcppGSL' is used with a package)
//
// [[Rcpp::depends(RcppGSL)]]

// tell Rcpp to turn this into a callable 
// function called 'fastLm'
// 
// [[Rcpp::export]]
Rcpp::List fastLm(const RcppGSL::Matrix & X, 
                  const RcppGSL::Vector & y) {

    // row and column dimension
    int n = X.nrow(), k = X.ncol();
    double chisq;
    // to hold the coefficient vector
    RcppGSL::Vector coef(k); 
    // and the covariance matrix
    RcppGSL::Matrix cov(k,k);   


    // the actual fit requires working memory 
    // which we allocate and then free
    gsl_multifit_linear_workspace *work = 
        gsl_multifit_linear_alloc (n, k);
    gsl_multifit_linear (X, y, coef, cov, 
                         &chisq, work);
    gsl_multifit_linear_free (work);

    // assign diagonal to a vector, then take 
    // square roots to get std.error
    Rcpp::NumericVector std_err;
    // need two step decl. and assignment
    std_err = gsl_matrix_diagonal(cov); 
    // sqrt() is an Rcpp sugar function
    std_err = Rcpp::sqrt(std_err); 

    return Rcpp::List::create(
        Rcpp::Named("coefficients") = coef,
        Rcpp::Named("stderr")       = std_err,
        Rcpp::Named("df.residual")  = n - k);
}
```

The function interface defines two \pkg{RcppGSL} variables: a matrix and a
vector. Both use the standard numeric type \texttt{double} as discussed
below.  The \pkg{GSL} supports other types ranging from lower precision
floating point to signed and unsigned integers as well as complex numbers.
The vector and matrix classes are templated for use with all these
\proglang{C} / \proglang{C++} types---though \proglang{R} uses only
\texttt{double} and \texttt{int}. For these latter two, we offer a shorthand
definition via a \texttt{typedef} which allows a shorter non-template use.
Having extracted the row and column dimentions, we then reserve another
vector and matrix to hold the resulting coefficient estimates as well as the
estimate of the covariance matrix. Next, we allocate workspace using a
\pkg{GSL} routine, fit the linear model and free the just-allocated
workspace.  The next step involves extracting the diagonal element from the
covariance matrix, and taking the square root (using a vectorised function
from \pkg{Rcpp}). Finally we create a named list with the return values.

In earlier version of the \pkg{RcppGSL} package, we also explicitly called
\texttt{free()} to return temporary memory allocation to the operating
system. This step had to be done as the underlying objects are managed as
\proglang{C} objects. They conform to the \pkg{GSL} interface, and work
without any automatic memory management. But as we provide a \proglang{C++}
data structure for matrix and vector objects, we can manage them using
\proglang{C++} facilities. In particular, the destructor can free the memory
when the object goes out of scope. Explicit \texttt{free()} calls are still
permitted as we keep track the object status so that memory cannot
accidentally be released more than once. Another more recent addition permits
use of \texttt{const \&} in the interface.  This instructs the compiler that
values of the corresponding variable will not be altered, and are passed into
the function by reference rather than by value.

We note that \pkg{RcppArmadillo}
\citep{CRAN:RcppArmadillo,Eddelbuettel+Sanderson:2013:RcppArmadillo}
implements a matching \texttt{fastLm} function using the Armadillo library by
\cite{Sanderson:2010:Armadillo}, and can do so with even more compact code due to
\proglang{C++} features. Moreover, \pkg{RcppEigen}
\citep{CRAN:RcppEigen,JSS:RcppEigen} provides a \texttt{fastLm} implementation with a
comprehensive comparison of matrix decomposition methods.


# Vectors

This section details the different vector represenations, starting with their
definition inside the \pkg{GSL}. We then discuss our layering before showing
how the two types map. A discussion of read-only `vector view' classes
concludes the section.

## \pkg{GSL} Vectors

\pkg{GSL} defines various vector types to manipulate one-dimensionnal
data, similar to \proglang{R} arrays. For example the \verb|gsl_vector| and
\verb|gsl_vector_int| structs are defined as:

```cpp
typedef struct{
    size_t size;
    size_t stride;
    double * data;
    gsl_block * block;
    int owner;
} gsl_vector;

typedef struct {
    size_t size;
    size_t stride;
    int * data;
    gsl_block_int * block;
    int owner;
} gsl_vector_int;
```

A typical use of the \verb|gsl_vector| struct is given below:

```cpp
int i;
// allocate a gsl_vector of size 3
gsl_vector *v = gsl_vector_alloc(3);    

// fill the vector
for (i = 0; i < 3; i++) {        
    gsl_vector_set(v, i, 1.23 + i);
}

// access elements
double sum = 0.0;  
for (i = 0; i < 3; i++) {
    sum += gsl_vector_set(v, i);
}

// free the memory
gsl_vector_free(v); 
```

Note that we have to explicitly free the allocated memory at the end. With
\proglang{C}-style programming, this step is always the responsibility of the
programmer.


## RcppGSL::vector

\pkg{RcppGSL} defines the template \texttt{RcppGSL::vector<T>} to manipulate
\verb|gsl_vector| pointers taking advantage of C++ templates. Using this
template type, the previous example now becomes:

```cpp
int i;
// allocate a gsl_vector of size 3
RcppGSL::vector<double> v(3);

// fill the vector
for (i = 0; i < 3; i++) {
    v[i] = 1.23 + i;
}

// access elements
double sum = 0.0;        
for (i = 0; i < 3; i++) {
    sum += v[i];
}

// (optionally) free the memory
// also automatic when out of scope
v.free();

```

The class \texttt{RcppGSL::vector<double>} is a smart pointer which can be deployed
anywhere where a raw pointer \verb|gsl_vector| can be used, such as the
\verb|gsl_vector_set| and \verb|gsl_vector_get| functions above.

Beyond the convenience of a nicer syntax for allocation (and of course the
managed release of memory either via \texttt{free()} or when going out of
scope), the \texttt{RcppGSL::vector} template faciliates interchange of
\pkg{GSL} vectors with \pkg{Rcpp} objects, and hence \pkg{R} objects. The
following example defines a \texttt{.Call} compatible function called
\verb|sum_gsl_vector_int| that operates on a \verb|gsl_vector_int| through
the \texttt{RcppGSL::vector<int>} template specialization:

```cpp
// [[Rcpp::export]]
int sum_gsl_vector_int(const 
                       RcppGSL::vector<int> & 
                       vec) {
    int res = std::accumulate(vec.begin(), 
                              vec.end(), 0);
    return res;
}
```

Here we no longer need to call \texttt{free()} explicitly as the \texttt{vec}
allocation is returned automatically at the end of the function body when the
variable goes out of scope.

Once the function has created via \texttt{sourceCpp()} or
\texttt{cppFunction()} from \textsl{Rcpp Attributes} (see
section \ref{sec:attributes} for more on this), it can then be called from
\proglang{R} :

```{r inlineex1}
fx <- Rcpp::cppFunction("
int sum_gsl_vector_int(RcppGSL::vector<int> vec) {
    int res = std::accumulate(vec.begin(),
                              vec.end(), 0);
    return res;
}", depends="RcppGSL")
sum_gsl_vector_int(1:10)
```


A second example shows a simple function that grabs elements of an
R list as \verb|gsl_vector| objects using implicit conversion mechanisms
of \pkg{Rcpp}

```cpp
// [[Rcpp::export]]
double gsl_vector_sum_2(const Rcpp::List & data) {
    // grab "x" as a gsl_vector through the 
    // RcppGSL::vector<double> class
    const RcppGSL::vector<double> x = data["x"];

    // grab "y" as a gsl_vector through the 
    // RcppGSL::vector<int> class
    const RcppGSL::vector<int> y = data["y"];
    double res = 0.0;
    for (size_t i=0; i< x->size; i++) {
       res += x[i] * y[i];
    }

    // return result, memory freed automatically
    return res;    
}
```

called from \proglang{R}:

```{r inlinexex2}
Rcpp::cppFunction("
double gsl_vector_sum_2(Rcpp::List data) {
    RcppGSL::vector<double> x = data[\"x\"];
    RcppGSL::vector<int> y = data[\"y\"];
    double res = 0.0;
    for (size_t i=0; i< x->size; i++) {
        res += x[i] * y[i];
    }
    return res;
}", depends= "RcppGSL")
data <- list( x = seq(0,1,length=10), y = 1:10 )
gsl_vector_sum_2(data)
```

## Mapping

Table \ref{tab:mappingVectors} shows the mapping between types defined by the
\pkg{GSL} and their corresponding types in the \pkg{RcppGSL} package.

\begin{table*}[htb]
  \centering
  \begin{small}
    \begin{tabular}{ll}
      \toprule
      GSL vector & RcppGSL \\
      \midrule
      \texttt{gsl\_vector} &            \texttt{RcppGSL::vector<double>} as well as \texttt{RcppGSL::Vector} \\
      \texttt{gsl\_vector\_int} & 	\texttt{RcppGSL::vector<int>} as well as \texttt{RcppGSL::IntVector} \\
      \texttt{gsl\_vector\_float} & 	\texttt{RcppGSL::vector<float>} \\
      \texttt{gsl\_vector\_long} & 	\texttt{RcppGSL::vector<long>} \\
      \texttt{gsl\_vector\_char} & 	\texttt{RcppGSL::vector<char>} \\
      \texttt{gsl\_vector\_complex} & 	\texttt{RcppGSL::vector<gsl\_complex>} \\
      \texttt{gsl\_vector\_complex\_float} & \texttt{RcppGSL::vector<gsl\_complex\_float>} \\
      \texttt{gsl\_vector\_complex\_long\_double} & 	\texttt{RcppGSL::vector<gsl\_complex\_long\_double>} \\
      \texttt{gsl\_vector\_long\_double} & 	\texttt{RcppGSL::vector<long double>} \\
      \texttt{gsl\_vector\_short} & 	\texttt{RcppGSL::vector<short>} \\
      \texttt{gsl\_vector\_uchar} & 	\texttt{RcppGSL::vector<unsigned char>} \\
      \texttt{gsl\_vector\_uint} & 	\texttt{RcppGSL::vector<unsigned int>} \\
      \texttt{gsl\_vector\_ushort} & 	\texttt{RcppGSL::vector<insigned short>} \\
      \texttt{gsl\_vector\_ulong} & 	\texttt{RcppGSL::vector<unsigned long>} \\
      \bottomrule
    \end{tabular}
  \end{small}
  \caption{Correspondance between \pkg{GSL} vector types and templates defined in \pkg{RcppGSL}.}
  \label{tab:mappingVectors}
\end{table*}

As shown, we also define two convenient shortcuts for the very common case of
\texttt{double} and \texttt{int} vectors.  First, \texttt{RcppGSL::Vector} is a
short-hand for the \texttt{RcppGSL::vector<double>} template
instantiation. Second, \texttt{RcppGSL::IntVector} does the same for
integer-valued vectors. Other types still require explicit templates.


## Vector Views

Several \pkg{GSL} algorithms return \pkg{GSL} vector views as their result
type. \pkg{RcppGSL} defines the template class \texttt{RcppGSL::vector\_view}
to handle vector views using \proglang{C++} syntax.

```cpp
// [[Rcpp::export]]
Rcpp::List test_gsl_vector_view() {
    int n = 10;
    RcppGSL::vector<double> v(n);
    for (int i=0 ; i<n; i++) {
        v[i] = i;
    }
    const RcppGSL::vector_view<double> v_even =
      gsl_vector_subvector_with_stride(v,0,2,n/2);
    const RcppGSL::vector_view<double> v_odd  =
      gsl_vector_subvector_with_stride(v,1,2,n/2);

    return Rcpp::List::create(
        Rcpp::Named("even") = v_even,
        Rcpp::Named("odd" ) = v_odd);
}
```

As with vectors, \proglang{C++} objects of type
\texttt{RcppGSL::vector\_view} can be converted implicitly to their
associated \pkg{GSL} view type. Table \ref{tab:mappingVectorViews} displays
the pairwise correspondance so that the \proglang{C++} objects can be passed
to compatible \pkg{GSL} algorithms.

\begin{table*}[htb]
  \centering
  \begin{small}
    \begin{tabular}{ll}
      \toprule
      gsl vector views & RcppGSL \\
      \midrule
      \texttt{gsl\_vector\_view} &      \texttt{RcppGSL::vector\_view<double>}; \texttt{RcppGSL::VectorView} \\
      \texttt{gsl\_vector\_view\_int} & \texttt{RcppGSL::vector\_view<int>}; \texttt{RcppGSL::IntVectorView} \\
      \texttt{gsl\_vector\_view\_float} & 	\texttt{RcppGSL::vector\_view<float>} \\
      \texttt{gsl\_vector\_view\_long} & 	\texttt{RcppGSL::vector\_view<long>} \\
      \texttt{gsl\_vector\_view\_char} & 	\texttt{RcppGSL::vector\_view<char>} \\
      \texttt{gsl\_vector\_view\_complex} & 	\texttt{RcppGSL::vector\_view<gsl\_complex>} \\
      \texttt{gsl\_vector\_view\_complex\_float} & \texttt{RcppGSL::vector\_view<gsl\_complex\_float>} \\
      \texttt{gsl\_vector\_view\_complex\_long\_double} & 	\texttt{RcppGSL::vector\_view<gsl\_complex\_long\_double>} \\
      \texttt{gsl\_vector\_view\_long\_double} & 	\texttt{RcppGSL::vector\_view<long double>} \\
      \texttt{gsl\_vector\_view\_short} & 	\texttt{RcppGSL::vector\_view<short>} \\
      \texttt{gsl\_vector\_view\_uchar} & 	\texttt{RcppGSL::vector\_view<unsigned char>} \\
      \texttt{gsl\_vector\_view\_uint} & 	\texttt{RcppGSL::vector\_view<unsigned int>} \\
      \texttt{gsl\_vector\_view\_ushort} & 	\texttt{RcppGSL::vector\_view<insigned short>} \\
      \texttt{gsl\_vector\_view\_ulong} & 	\texttt{RcppGSL::vector\_view<unsigned long>} \\
      \bottomrule
    \end{tabular}
  \end{small}
  \caption{Correspondance between \pkg{GSL} vector view types and templates defined
    in \pkg{RcppGSL}.}
  \label{tab:mappingVectorViews}
\end{table*}

The vector view class also contains a conversion operator to automatically
transform the data of the view object to a \pkg{GSL} vector object. This
enables use of vector views where \pkg{GSL} would expect a vector. And as
before, \texttt{double} and \texttt{int} types can be accessed via the 
\texttt{typedef} variants \texttt{RcppGSL::VectorView} and
\texttt{RcppGSL::IntVectorView}, respectively.  

Lastly, in order to support \texttt{const \&} behaviour, all
\texttt{gsl\_vector\_XXX\_const\_view} variants are also supported (where
\texttt{XXX} stands for any of the atomistic \proglang{C} and \proglang{C++}
data types).


# Matrices

The \pkg{GSL} also defines a set of matrix data types : \texttt{gsl\_matrix},
\texttt{gsl\_matrix\_int} etc ... for which \pkg{RcppGSL} defines a corresponding
convenience \proglang{C++} wrapper generated by the \texttt{RcppGSL::matrix}
template.

## Creating matrices

The \texttt{RcppGSL::matrix} template exposes three constructors.

```cpp
// convert an R matrix to a GSL matrix
matrix(SEXP x) throw(::Rcpp::not_compatible)

// encapsulate a GSL matrix pointer
matrix(gsl_matrix* x)

// create a new matrix with the given 
// number of rows and columns
matrix(int nrow, int ncol)
```

## Implicit conversion

\texttt{RcppGSL::matrix} defines an implicit conversion to a pointer to the
associated \pkg{GSL} matrix type, as well as dereferencing operators. This
makes the class \texttt{RcppGSL::matrix} look and feel like a pointer to a
\pkg{GSL} matrix type.

```cpp
gsltype* data;
operator gsltype*() { return data; }
gsltype* operator->() const { return data; }
gsltype& operator*() const { return *data; }
```

## Indexing

Indexing of \pkg{GSL} matrices is usually the task of the functions
\texttt{gsl\_matrix\_get}, \texttt{gsl\_matrix\_int\_get}, ... and
\texttt{gsl\_matrix\_set}, \texttt{gsl\_matrix\_int\_set}, ...

\pkg{RcppGSL} takes advantage of both operator overloading and templates
to make indexing a \pkg{GSL} matrix much more convenient.

```cpp
// create a matrix of size 10x10
RcppGSL::matrix<int> mat(10,10);

// fill the diagonal, no need for setter function
for (int i=0; i<10: i++) {
    mat(i,i) = i;
}
```

## Methods

The \texttt{RcppGSL::matrix} type also defines the following member functions:
\begin{quote}
  \begin{itemize}
  \item[\texttt{nrow}] extracts the number of rows
  \item[\texttt{ncol}] extract the number of columns
  \item[\texttt{size}] extracts the number of elements
  \item[\texttt{free}] releases the memory (also called via destructor)
  \end{itemize}
\end{quote}

## Matrix views

Similar to the vector views discussed above, the \pkg{RcppGSL} also provides
an implicit conversion operator which returns the underlying matrix stored in
the matrix view class.

## Error handler

When input values for \pkg{GSL} functions are invalid, the default error
handler will abort the program after printing an error message. This
leads \proglang{R} to an abortion error.  To avoid this behaviour, one needs to avoid it
first by using \texttt{gsl\_set\_error\_handler\_off()}, and then detect
error conditions by checking whether the result is \texttt{NAN} or not.

```cpp
// close the GSL error handler
gsl_set_error_handler_off();

// call GSL function with some invalid values
double res = gsl_sf_hyperg_2F1(1, 1, 1.1467003, 1);

// detect the result is NAN or not
if (ISNAN(res)) {
    Rcpp::Rcout << "Invalid input found!" 
                << std::endl;
}
```

See \url{http://thread.gmane.org/gmane.comp.lang.r.rcpp/7905} for a longer
discussion of the related issues.

Starting with release 0.2.4, two new functions are available:
\texttt{gslSetErrorHandlerOff()} and \texttt{gslResetErrorHandler()} which
allow to turn off the error handler (as discussed above), and to reset to
the prior (default) value. In addition, the package now also calls
\texttt{gslSetErrorHandlerOff()} when being attached, ensuring that the
\pkg{GSL} error handler is turned off by default. 


# Using \pkg{RcppGSL} in your package

The \pkg{RcppGSL} package contains a complete example package providing a single
function \texttt{colNorm} which computes a norm for each column of a
supplied matrix. This example adapts a matrix example from the \pkg{GSL} manual that has
been chosen primarily as a means to showing how to set up a package to use
\pkg{RcppGSL}.

Needless to say, we could compute such a matrix norm easily in \proglang{R}
using existing facilities. One such possibility is a simple
\verb|apply(M, 2, function(x) sqrt(sum(x^2)))| as shown on the corresponding
help page in the example package inside \pkg{RcppGSL}. One point in favour of
using the \pkg{GSL} code is that it employs a BLAS function so on
sufficiently large matrices, and with suitable BLAS libraries installed, this
variant could be faster due to the optimised code in high-performance BLAS
libraries and/or the inherent parallelism a multi-core BLAS variant which
compute compute the vector norm in parallel.  On all `reasonable' matrix
sizes, however, the performance difference should be neglible.

## The \texttt{configure} script

### Using autoconf

Using \pkg{RcppGSL} means employing both the \pkg{GSL} and \proglang{R}. We
may need to find the location of the \pkg{GSL} headers and library, and this
done easily from a \texttt{configure} source script which \texttt{autoconf}
generates from a \texttt{configure.in} source file such as the following:

```sh
AC_INIT([RcppGSLExample], 0.1.0)

## Use gsl-config to find arguments for 
## compiler and linker flags
##
## Check for non-standard programs: gsl-config(1)
AC_PATH_PROG([GSL_CONFIG], [gsl-config])
## If gsl-config was found, let's use it
if test "${GSL_CONFIG}" != ""; then
    # Use gsl-config for header and linker args
    # (without BLAS which we get from R)
    GSL_CFLAGS=`${GSL_CONFIG} --cflags`
    GSL_LIBS=`${GSL_CONFIG} --libs-without-cblas`
else
    AC_MSG_ERROR([gsl-config not found, is 
                  GSL installed?])
fi

# Now substitute these variables in src/Makevars.in to create src/Makevars
AC_SUBST(GSL_CFLAGS)
AC_SUBST(GSL_LIBS)

AC_OUTPUT(src/Makevars)
```

A source file such as this \texttt{configure.in} gets converted into a script
\texttt{configure} by invoking the \texttt{autoconf} program.

We note that many other libraries use a similar (but somewhat newer and
by-now fairly standard) scripting frontend called \texttt{pkg-config} which
be deployed in a very similar by other packages. Calls such as the following
two can be used from \texttt{configure} in a very similar manner:

```sh
pkg-config --cflags libpng
pkg-config --libs libpng
```

where \texttt{libpng} (for the png image format) is used just for illustration.

### Using functions provided by RcppGSL

\pkg{RcppGSL} provides R functions (in the file \texttt{R/inline.R}) that
allow us to retrieve the same information. Therefore the configure script can
also be written as:

```sh
#!/bin/sh

GSL_CFLAGS=`${R_HOME}/bin/Rscript -e \
    "RcppGSL:::CFlags()"`
GSL_LIBS=`${R_HOME}/bin/Rscript -e \
    "RcppGSL:::LdFlags()"`

sed -e "s|@GSL_LIBS@|${GSL_LIBS}|" \
    -e "s|@GSL_CFLAGS@|${GSL_CFLAGS}|" \
    src/Makevars.in > src/Makevars
```

Similarly, the configure.win for windows can be written as:

```sh
GSL_CFLAGS=`${R_HOME}/bin${R_ARCH_BIN}/Rscript.exe\
    -e "RcppGSL:::CFlags()"`
GSL_LIBS=`${R_HOME}/bin${R_ARCH_BIN}/Rscript.exe \
    -e "RcppGSL:::LdFlags()"`

sed -e "s|@GSL_LIBS@|${GSL_LIBS}|" \
    -e "s|@GSL_CFLAGS@|${GSL_CFLAGS}|" \
    src/Makevars.in > src/Makevars.win
```

This allows for a simpler and more direct way to just set the compile and
link options, taking advantage of the installed \pkg{RcppGSL} package. See
the \pkg{RcppZiggurat} package for an example.


## The \texttt{src} directory

The \proglang{C++} source file takes the matrix supplied from \proglang{R}
and applies the \pkg{GSL} function to each column.

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

// [[Rcpp::export]]
Rcpp::NumericVector 
colNorm(const RcppGSL::Matrix & G) {
    int k = G.ncol();
    Rcpp::NumericVector n(k);           // results
    for (int j = 0; j < k; j++) {
        RcppGSL::vector_view<double> colview = 
             gsl_matrix_const_column(G, j);
        n[j] = gsl_blas_dnrm2(colview);
    }
    return n;                           // return
}
```

The \proglang{Makevars.in} file governs the compilation and uses the values
supplied by \texttt{configure} during build-time:

```sh
# set by configure
GSL_CFLAGS = @GSL_CFLAGS@
GSL_LIBS   = @GSL_LIBS@

# combine with standard arguments for R
PKG_CPPFLAGS = $(GSL_CFLAGS)
PKG_LIBS = $(GSL_LIBS)
```

The variables surrounded by \@ will be filled by \texttt{configure} during
package build-time. As discussed above, this can either rely on
\texttt{autoconf} or a possibly-simpler \texttt{Rscript}.

## The \texttt{R} directory

The \proglang{R} source is very simply: it contains a single file created by
the \texttt{Rcpp::compileAttributes()} function implementing the wrapper to
the \texttt{colNorm()} function.


# Using \pkg{RcppGSL} with \pkg{inline}

The \pkg{inline} package \citep{CRAN:inline} is very helpful for prototyping code in
\proglang{C}, \proglang{C++} or \proglang{Fortran} as it takes care of code
compilation, linking and dynamic loading directly from \proglang{R}. It has been
used extensively by \pkg{Rcpp}, for example in the numerous unit tests.

The example below shows how \pkg{inline} can be deployed with
\pkg{RcppGSL}. We implement the same column norm example, but this time as an
\proglang{R} script which is compiled, linked and loaded on-the-fly. Compared
to standard use of \pkg{inline}, we have to make sure to add a short section
declaring which header files from \pkg{GSL} we need to use; the \pkg{RcppGSL}
then communicates with \pkg{inline} to tell it about the location and names
of libraries used to build code against \pkg{GSL}.

```cpp
require(inline)

inctxt='
   #include <gsl/gsl_matrix.h>
   #include <gsl/gsl_blas.h>
'

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

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

foo <- cxxfunction(
    signature(sM="numeric"),
    body=bodytxt, inc=inctxt, plugin="RcppGSL")

# see Section 8.4.13 of the GSL manual: 
# create M as a sum of two outer products
M <- outer(sin(0:9), rep(1,10), "*") + 
     outer(rep(1, 10), cos(0:9), "*")
foo(M)
```

The \texttt{RcppGSL} inline plugin supports creation of a package skeleton
based on the inline function.

```{r exskel, eval=FALSE}
package.skeleton("mypackage", foo)
```

# Using \pkg{RcppGSL} with Rcpp Attributes
\label{sec:attributes}

\textsl{Rcpp Attributes} \citep{CRAN:Rcpp:Attributes} builds on the features
of the \pkg{inline} package described in previous section, and streamlines
the compilation, loading and linking process even further.  It leverages the
existing plugins for \pkg{inline}. We already showed the corresponding
function in the previous section.  Here, we show it again as a self-contained
example used via \texttt{sourceCpp()}. We stress that usage is
\texttt{sourceCpp()} is meant for interactive work at the R command-prompt,
but is not the recommended practice in a package.


```cpp

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

#include <RcppGSL.h>

// declare a dependency on the RcppGSL package; 
// also activates plugin
//
// [[Rcpp::depends(RcppGSL)]]

// declare the function to be 'exported' to R
//
// [[Rcpp::export]]
Rcpp::NumericVector 
colNorm(const RcppGSL::Matrix & M) {
    int k = M.ncol();
    Rcpp::NumericVector n(k);           // results

    for (int j = 0; j < k; j++) {
        RcppGSL::VectorView colview = 
            gsl_matrix_const_column (M, j);
        n[j] = gsl_blas_dnrm2(colview);
    }
    return n;                           // return
}

/*** R
## see Section 8.4.13 of the GSL manual:
## create M as a sum of two outer products
M <- outer(sin(0:9), rep(1,10), "*") +
     outer(rep(1, 10), cos(0:9), "*")
colNorm(M)
*/
```

With the code above stored in a file, say, ``gslNorm.cpp'' one can simply
call \texttt{sourceCpp()} to have the wrapper code added, and all of the
compilation, linking and loading done --- including the execution of the
short \proglang{R} segment at the end:

```{r exnorm, eval=FALSE}
sourceCpp("gslNorm.cpp")
```

The function \texttt{cppFunction()} is also available to convert a simple
character string argument containing a valid C++ function into a eponymous R
function. And like \texttt{sourceCpp()}, it can also use plugins. See the
vignette ``Rcpp-attributes'' \citep{CRAN:Rcpp:Attributes} of the \pkg{Rcpp}
package \citep{CRAN:Rcpp} for full details.

# Summary

The GNU Scientific Library (GSL) by \citet{GSL} offers a very comprehensive
collection of rigorously developed and tested functions for applied
scientific computing under a widely-used and well-understood Open Source
license. This has lead to widespread deployment of \pkg{GSL} among a number
of disciplines.

Using the automatic wrapping and converters offered by the \pkg{RcppGSL}
package presented here, \proglang{R} users and programmers can now deploy
algorithmns provided by the \pkg{GSL} with greater ease.

\newpage
