  In this paper I present an extended implementation of the Random ferns
algorithm contained in the R package rFerns. It differs from the original by
the ability of consuming categorical and numerical attributes instead of only
binary ones. Also, instead of using simple attribute subspace ensemble it
employs bagging and thus produce error approximation and variable importance
measure modelled after Random forest algorithm. I also present benchmarks'
results which show that although Random ferns' accuracy is mostly smaller than
achieved by Random forest, its speed and good quality of importance measure it
provides make rFerns a reasonable choice for a specific applications.
