R-CMD-check ## RcppCGAL: CGAL Headers for R


This package provides access to the Computational Geometry Algorithms Library (CGAL) in R. CGAL provides access to methods like KDtree, Hilbert sorting, convex hull calculation, and many more.

This package allows for the easy linking of the CGAL header files into R packages without having to download and manually add the appropriate CGAL header file into an R package.

Much like the BH package, the RcppCGAL package can be used via the LinkingTo: field in the DESCRIPTION file in R packages. This will allow access to the header files in C/C++ source code.


This package currently includes the version 5.4 stable release of CGAL.


To install this package, you can install the version from CRAN:


Alternatively, you can download or clone the git repository. Then you can install using devtools


You may also install from github directly using the devtools::install_github function.

By default, the package will try to download the header files from the CGAL git repository. If you already have one downloaded that you prefer to use, you can specify the environmental variable CGAL_DIR and R will use that instead:

Sys.setenv("CGAL_DIR" = "path/to/CGAL/include")

Note: this must be done before the package is loaded by R.


We provide an example of how to perform Hilbert sorting using an R matrix:

// [[Rcpp::depends(RcppCGAL)]]
// [[Rcpp::depends(RcppEigen)]]
// [[Rcpp::plugins(cpp14)]]  

#include <RcppEigen.h>
#include <CGAL/basic.h>
#include <CGAL/Cartesian_d.h>
#include <CGAL/spatial_sort.h>
#include <CGAL/Spatial_sort_traits_adapter_d.h>
#include <CGAL/boost/iterator/counting_iterator.hpp>
#include <CGAL/hilbert_sort.h>
#include <CGAL/Spatial_sort_traits_adapter_d.h>

typedef CGAL::Cartesian_d<double>           Kernel;
typedef Kernel::Point_d                     Point_d;

typedef CGAL::Spatial_sort_traits_adapter_d<Kernel, Point_d*>   Search_traits_d;

void hilbert_sort_cgal_fun(const double * A, int D, int N,  int * idx)

  std::vector<Point_d> v;
  double * temp = new double[D];

  for (int n = 0; n < N; n++ ) {
    for (int d = 0; d < D; d ++) {
      temp[d] = A[D * n + d];
    v.push_back(Point_d(D, temp, temp+D));

  std::vector<std::ptrdiff_t> temp_index;

    std::back_inserter(temp_index) );

  CGAL::hilbert_sort (temp_index.begin(), temp_index.end(), Search_traits_d( &(v[0]) ) ) ;

  for (int n = 0; n < N; n++) {
    idx[n] = temp_index[n];

  delete [] temp;

// [[Rcpp::export]]
Rcpp::IntegerVector hilbertSort(const Eigen::MatrixXd & A)
  int K = A.rows();
  int N = A.cols();
  std::vector<int> idx(N);
  hilbert_sort_cgal_fun(A.data(), K, N, &idx[0] );

Saving this code as hilbertSort.cpp and sourcing with Rcpp Rcpp::sourceCpp("hilbertSort.cpp") makes the function hilbertSort(). Note that version 5.4 relies on the C++14 standard. Also, be aware that this example function example assumes that the observations are stored by column rather than by row, that is as the transpose of the usual R matrix or data.frame.


Eric Dunipace


This package is provided under the GPL v3+. Note: if you wish to use CGAL for commercial purposes, you must obtain a license from the GeometryFactory.