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Like many predictive models, random forests provide point predictions for newobservations. Besides the point prediction, it is important to quantify theuncertainty in the prediction. Prediction intervals provide information aboutthe reliability of the point predictions. We have developed a comprehensive Rpackage, RFpredInterval, that integrates 16 methods to build predictionintervals with random forests and boosted forests. The set of methodsimplemented in the package includes a new method to build prediction intervalswith boosted forests (PIBF) and 15 method variations to produce predictionintervals with random forests, as proposed by Roy and Larocque (2020). Weperform an extensive simulation study and apply real data analyses to comparethe performance of the proposed method to ten existing methods for buildingprediction intervals with random forests. The results show that the proposedmethod is very competitive and, globally, outperforms competing methods.

RFpredInterval: An R Package for Prediction Intervals with Random Forests and Boosted Forests