**Thinking inside the box**, and kindly contributed to R-bloggers)

Besides the new RcppExamples,

another new package RcppArmadillo got spun out of

Rcpp

with the

recent release 0.7.8 of Rcpp.

Romain and I already had

an example of a simple but fast linear model fit using the (very clever)

Armadillo C++ library by Conrad Sanderson.

In fact, I had used this as a motivational example of why

Rcpp

rocks in a

recent talk to the ACM chapter at U of Chicago which,

thanks to David Smith at REvo,

got some further exposure.

Now this example is more refined as further *glue* got added. Given

that both Armadillo and

Rcpp make use of

C++ templates, the actual amount of code in RcppArmadillo is not that large:

just over 200 lines in a header file, and a little less for some testing accessor

and example functions in a source file. And this makes for some really nice

example code: the ‘fast regression’ example becomes this (where I simply

removed two blocks with conditional on the Armadillo version):

#includeextern "C" SEXP fastLm(SEXP ys, SEXP Xs) { Rcpp::NumericVector yr(ys); // creates Rcpp vector from SEXP Rcpp::NumericMatrix Xr(Xs); // creates Rcpp matrix from SEXP int n = Xr.nrow(), k = Xr.ncol(); arma::mat X(Xr.begin(), n, k, false); // reuses memory and avoids extra copy arma::colvec y(yr.begin(), yr.size(), false); arma::colvec coef = solve(X, y); // fit model y ~ X arma::colvec resid = y - X*coef; // residuals double sig2 = arma::as_scalar( trans(resid)*resid/(n-k) ); // std.error of estimate arma::colvec stderrest = sqrt( sig2 * diagvec( arma::inv(arma::trans(X)*X)) ); Rcpp::Pairlist res(Rcpp::Named( "coefficients", coef), Rcpp::Named( "stderr", stderrest)); return res; }

No extra copies! Armadillo instantiates directly from the underlying R

objects for the vector and matrix, solves the regression equations, computes

the standard error of the estimates and returns the two vectors. Leaving us

to write about eleven lines of code. Moreover, as Armadillo is well designed

and uses template meta-programming to avoid extra copies (see these lecture

notes for details), it is about as efficient as it can be (and will use

Atlas or other BLAS where available).

And, this is just one example. Rcpp should be

suitable for other C++ libraries, and provides an easy to use seamless

interface between C++ and R.

However, we should note that (at about the last minute) we found out about some unit test failures in OS

X as well as some issues in a Debian chroot —

cran2deb ran into some build

issues on i386 and amd64 in the testing chroot even this ‘it all works’

swimmingly on our Debian, Ubuntu and Fedora build environments. A follow-up

with fixes for either Rcpp and/or RcppArmadillo appears likely.

*Update:* The build issues seems to be with 64-bit systems and everything appears cool in 32-bit.

**leave a comment**for the author, please follow the link and comment on their blog:

**Thinking inside the box**.

R-bloggers.com offers

**daily e-mail updates**about R news and tutorials on topics such as: Data science, Big Data, R jobs, visualization (ggplot2, Boxplots, maps, animation), programming (RStudio, Sweave, LaTeX, SQL, Eclipse, git, hadoop, Web Scraping) statistics (regression, PCA, time series, trading) and more...