mlpack is a fabulous project providing countless machine learning algorithms in clean and performant C++ code as a header-only library. This gives both high performance and the ability to run the code in resource-constrained environment such as embedded systems. Bindings to a number of other languages are available, and an examples repo provides examples.
The project also has a mature R package on CRAN which offers the various algorithms directly in R. Sometimes, however, one might want to use the header-only C++ code in another R package. How to do that was not well documented. A user alerted me by email to this fact a few weeks ago, and this lead to both an updated mlpack release at CRAN and this package.
In short, we show via three complete examples how to access the mlpack code in C++ code in this package, offering a re-usable stanza to start a different package from. The only other (header-only) dependencies are provided by CRAN packages RcppArmadillo and RcppEnsmallen wrapping, respectively, the linear algebra and optimization libraries used by mlpack.
Courtesy of my CRANberries, there is also a a ‘new package’ note (no diffstat report yet). More detailed information is on the rcppmlpackexamples page, or the github repo.
This post by Dirk Eddelbuettel originated on his Thinking inside the box blog. If you like this or other open-source work I do, you can sponsor me at GitHub.