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The STL also contains random sampling and shuffling algorithms. We start by looking at random_shuffle.

There are two forms. The first uses an internal RNG with its own seed; the second form allows for a function object conformant to the STL’s requirements (essentially, given N produce a uniform draw greater or equal to zero and less than N). This is useful for us as it lets us tie this to the same RNG which R uses.

#include <Rcpp.h>

// wrapper around R's RNG such that we get a uniform distribution over
// [0,n) as required by the STL algorithm
inline int randWrapper(const int n) { return floor(unif_rand()*n); }

// [[Rcpp::export]]
Rcpp::NumericVector randomShuffle(Rcpp::NumericVector a) {
Rcpp::RNGScope scope;

// clone a into b to leave a alone
Rcpp::NumericVector b = Rcpp::clone(a);

std::random_shuffle(b.begin(), b.end(), randWrapper);

return b;
}


We can illustrate this on a simple example or two:

a <- 1:8
set.seed(42)
randomShuffle(a)

[1] 1 4 3 7 5 8 6 2

set.seed(42)
randomShuffle(a)

[1] 1 4 3 7 5 8 6 2


By tieing the STL implementation of the random permutation to the RNG from R, we are able to compute reproducible permutations, fast and from C++.