First off, a disclaimer: I use Rcpp all the time. In fact, when (having been renamed by the time from Rcpp) RcppTemplate had already been orphaned and without updates for two years, I started to maintain it under its initial name of Rcpp (under which it had been contributed to RQuantLib). That was about a year ago, and I have made a couple of incremental changes that you can find documented in the ChangeLog.
Now RcppTemplate has very recently come back after a full thirty-five months without any update or fix. It contains interesting new code, but it appears that it is not backwards compatible so I won't use it where I already used Rcpp.
Rcppbind was not very actively maintained whenever I checked. Whit Armstrong also has a templated interface package called rabstraction.
Inline is something completely different: it eases the compile / link cycle by 'embedding' your program as an R character string that then gets compiled, linked, and loaded. I have talked to Oleg about having inline support Rcpp which would be nice.
Swig is interesting too. Joe Wang did great work there and wrapped all of QuantLib for R. But when I last tried it, it no longer worked due to some changes in R internals. According to someone from the Swig team, Joe may still work on it. The goal of Swig is larger libraries anyway. This project could probably do with a revival but it is not without technical challenges.
Another mention should go to RInside which works with Rcpp and lets you embed R inside of C++ applications.
So to sum it up: Rcpp works well for me, especially for small exploratory projects where you just want to add a function or two. It's focus is ease of use, and it allows you to 'hide' some of the R internals that are not always fun to work with. I know of a number of other users whom I have helped on and and off via email. So I would say go for this one.
My 'Intro to HPC with R' tutorials have some examples of Rcpp, RInside and inline.
Edit: So let's look at a concrete example (taken from the 'HPC with R Intro' slides and borrowed from Stephen Milborrow who took it from Venables and Ripley). The task is to enumerate all possible combinations of the determinant of a 2x2 matrix containing only single digits in each position. This can be done in clever vectorised ways (as we discuss in the tutorial slides) or by brute force as follows:
#include <Rcpp.h>
RcppExport SEXP dd_rcpp(SEXP v) {
SEXP rl = R_NilValue; // Use this when there is nothing to be returned.
char* exceptionMesg = NULL; // msg var in case of error
try {
RcppVector<int> vec(v); // vec parameter viewed as vector of ints
int n = vec.size(), i = 0;
if (n != 10000)
throw std::length_error("Wrong vector size");
for (int a = 0; a < 9; a++)
for (int b = 0; b < 9; b++)
for (int c = 0; c < 9; c++)
for (int d = 0; d < 9; d++)
vec(i++) = a*b - c*d;
RcppResultSet rs; // Build result set to be returned as list to R
rs.add("vec", vec); // vec as named element with name 'vec'
rl = rs.getReturnList(); // Get the list to be returned to R.
} catch(std::exception& ex) {
exceptionMesg = copyMessageToR(ex.what());
} catch(...) {
exceptionMesg = copyMessageToR("unknown reason");
}
if (exceptionMesg != NULL)
Rf_error(exceptionMesg);
return rl;
}
If you save this as, say, dd.rcpp.cpp and have Rcpp installed, then simply use
PKG_CPPFLAGS=`Rscript -e 'Rcpp:::CxxFlags()'` \
PKG_LIBS=`Rscript -e 'Rcpp:::LdFlags()'` \
R CMD SHLIB dd.rcpp.cpp
to build a shared library. We use Rscript (or r) to ask Rcpp about its header and library locations. Once built, we can load and use this from R as follows:
dyn.load("dd.rcpp.so")
dd.rcpp <- function() {
x <- integer(10000)
res <- .Call("dd_rcpp", x)
tabulate(res$vec)
}
In the same way, you can send vectors, matrics, ... of various R and C++ data types back end forth with ease. Hope this helps somewhat.