jForest is a general framework for Machine Learning. It implements tree ensemble based classification methods. It is designed to be very modular and allows easy tuning and modification of the tree induction, classification criterion and feature importance index. It is developed in Java and bundled in the form of an R package. jForest implements the statistically interpretable feature importance index proposed by our colleague Jérôme Paul. You can download jForest code. Learn more on the feature importance index proposed in this package by reading the paper Inferring statistically significant features from random forests published in Neurocomputing in 2015.