In this paper I present an extended implementation of the Random ferns algorithm contained in the R package rFerns. It di ers 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 afte...