piRF: Prediction Intervals for Random Forests

Implements multiple state-of-the-art prediction interval methodologies for random forests. These include: quantile regression intervals, out-of-bag intervals, bag-of-observations intervals, one-step boosted random forest intervals, bias-corrected intervals, high-density intervals, and split-conformal intervals. The implementations include a combination of novel adjustments to the original random forest methodology and novel prediction interval methodologies. All of these methodologies can be utilized using solely this package, rather than a collection of separate packages. Currently, only regression trees are supported. Also capable of handling high dimensional data. Roy, Marie-Helene and Larocque, Denis (2019) <doi:10.1177/0962280219829885>. Ghosal, Indrayudh and Hooker, Giles (2018) <arXiv:1803.08000>. Zhu, Lin and Lu, Jiaxin and Chen, Yihong (2019) <arXiv:1905.10101>. Zhang, Haozhe and Zimmerman, Joshua and Nettleton, Dan and Nordman, Daniel J. (2019) <doi:10.1080/00031305.2019.1585288>. Meinshausen, Nicolai (2006) <http://www.jmlr.org/papers/volume7/meinshausen06a/meinshausen06a.pdf>. Romano, Yaniv and Patterson, Evan and Candes, Emmanuel (2019) <arXiv:1905.03222>. Tung, Nguyen Thanh and Huang, Joshua Zhexue and Nguyen, Thuy Thi and Khan, Imran (2014) <doi:10.13140/2.1.2500.8002>.

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