rFerns is an extended random ferns implementation for R; in comparison to original, it can handle standard information system containing both categorical and continuous attributes. Moreover, it generates OOB error approximation and permutation-based attribute importance measure similar to randomForest. Here is a paper with all the details.
rFerns is good for doing training fast, in predictable time and utilising many CPU cores; in general it is less accurate than Random Forest, yet not substantially, and obviously there are cases in which it is better.
It is also nice as a very fast variable importance source; in fact it was created to speed-up the Boruta all relevant feature selector, and it did pretty well.
Even more, not it can produce shadow importance, i.e., a heuristic way to reason about significance of importance scores; the package contains a naiveWrapper function which implements a simple feature selector based on it, and you can read all about it here.
Finally, it is a very stochastic method, practically doing no optimisation at all; basically it is crazy that it works.
Hence, it is theoretically interesting (;
There is also a Spark version (not mine), Sparkling Ferns.
Quite fresh version should be on CRAN, you can also install directly from GitLab with:
devtools::install_gitlab('mbq/rFerns')