Get the same sample of integers from Rcpp as base R

Question

Is it possible to get the same sample of integers from Rcpp as from base R's sample ?

I have tried using Rcpp::sample and Rcpp::RcppArmadillo::sample but they do not return the same values -- example code below. Additionally, the Quick Example section of post https://gallery.rcpp.org/articles/using-the-Rcpp-based-sample-implementation/ returns the same sample from Rcpp and base R, however, I cannot reproduce these results (I attach this code at the end).

Can this be done / what am I doing wrong please?

My attempts:

// [[Rcpp::depends(RcppArmadillo)]]
#include <RcppArmadillo.h>
#include <RcppArmadilloExtensions/sample.h>

// [[Rcpp::export]]
Rcpp::IntegerVector mysamp1( int n) {
  Rcpp::IntegerVector v = Rcpp::sample(n, n);
  return v;
}

// [[Rcpp::export]]
Rcpp::IntegerVector mysamp2(int n) {  
  Rcpp::IntegerVector i = Rcpp::seq(1,n);
  Rcpp::IntegerVector v = wrap(Rcpp::RcppArmadillo::sample(i,n,false));
  return v;
}

// set seed https://stackoverflow.com/questions/43221681/changing-rs-seed-from-rcpp-to-guarantee-reproducibility
// [[Rcpp::export]]
void set_seed(double seed) {
  Rcpp::Environment base_env("package:base");
  Rcpp::Function set_seed_r = base_env["set.seed"];
  set_seed_r(std::floor(std::fabs(seed)));
}

// [[Rcpp::export]]
Rcpp::IntegerVector mysamp3( int n, int seed) {
  set_seed(seed); 
  Rcpp::IntegerVector v = Rcpp::sample(n, n);
  return v;
}


/***R
set.seed(1)
sample(10)
#  [1]  9  4  7  1  2  5  3 10  6  8
set.seed(1)
mysamp1(10)
#  [1]  3  4  5  7  2  8  9  6 10  1
set.seed(1)
mysamp2(10)
#  [1]  3  4  5  7  2  8  9  6 10  1
mysamp3(10, 1)
#  [1]  3  4  5  7  2  8  9  6 10  1

*/

Code from the Using the RcppArmadillo-based Implementation of R's sample() gallery post which return FALSE on my system:

// [[Rcpp::depends(RcppArmadillo)]]
#include <RcppArmadilloExtensions/sample.h>
using namespace Rcpp ;

// [[Rcpp::export]]
CharacterVector csample_char( CharacterVector x, 
                              int size,
                              bool replace, 
                              NumericVector prob = NumericVector::create()) {
  CharacterVector ret = RcppArmadillo::sample(x, size, replace, prob) ;
  return ret ;
}

/*** R
N <- 10
set.seed(7)
sample.r <- sample(letters, N, replace=T)

set.seed(7)
sample.c <- csample_char(letters, N, replace=T)

print(identical(sample.r, sample.c))
# [1] FALSE
*/

Answer 1

Compiling comments into an answer. Akrun noted that by setting RNGkind or RNGversion we can replicate results. From DirkEddelbuettel; there was a "change in R's RNG that came about because someone noticed a bias in, IIRC, use of sampling (at very large N). So thats why you you to turn an option on in R to get the old (matching) behaviour. " And RalfStubner indicates that this is a known issue: https://github.com/RcppCore/RcppArmadillo/issues/250 and https://github.com/RcppCore/Rcpp/issues/945

Presently R uses a different default sampler which leads to different results

RNGkind(sample.kind = "Rejection")
set.seed(1)
sample(10)
# [1]  9  4  7  1  2  5  3 10  6  8
set.seed(1)
mysamp1(10)
# [1]  3  4  5  7  2  8  9  6 10  1

However, an earlier version can be used using

RNGkind(sample.kind = "Rounding")
#Warning message:
#  In RNGkind("Mersenne-Twister", "Inversion", "Rounding") : non-uniform 'Rounding' sampler used

set.seed(1)
sample(10)
# [1]  3  4  5  7  2  8  9  6 10  1
set.seed(1)
mysamp1(10)
# [1]  3  4  5  7  2  8  9  6 10  1