You may use a shell script, that calls perl (do the computations in perl) and then call R. R has a FNN package/module which has Kl.dist function which you may use for K-L distance.

Update: I have written the KL Divergence for discrete probability distribution samples. It is very naive but might be useful.

#!/usr/bin/perl -w

## for Discrete Probability Distribution
## considering that sum of probability values in both arrays equals to
+ 1
 
my $dist = 0;
my @terms = ('a', 'b','c','d');
my @P = (0, 0.3, 0.4, 0.3);
my @Q = (0.2, 0.2, 0.3, 0.3);
        
if ( (scalar(@P) != scalar(@terms) ) && (scalar(@Q) != scalar(@terms))
+ ){
    print " The size should be same \n";
    exit;
}
else{
    for(my $i = 0; $i<= $#P; $i++){
        my $temp = 0 if($P[$i] == 0 || $Q[$i] == 0);
        $temp = $P[$i]*log($P[$i]/$Q[$i]) if($P[$i] != 0 && $Q[$i] != 
+0); 
        $dist = $dist + $temp;
    }
}
print "The Kullback Distance symmetric for discrete Distribution is :"
+, $dist,"\n";
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