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) . Ghosal, Indrayudh and Hooker, Giles (2018) . Zhu, Lin and Lu, Jiaxin and Chen, Yihong (2019) . Zhang, Haozhe and Zimmerman, Joshua and Nettleton, Dan and Nordman, Daniel J. (2019) . Meinshausen, Nicolai (2006) <>. Romano, Yaniv and Patterson, Evan and Candes, Emmanuel (2019) . Tung, Nguyen Thanh and Huang, Joshua Zhexue and Nguyen, Thuy Thi and Khan, Imran (2014) .


Reference manual

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0.1.0 by Chancellor Johnstone, a year ago

Browse source code at

Authors: Chancellor Johnstone [cre, aut, cph] , Haozhe Zhang [aut, cph] , Martin Wright [ctb, cph] , Gregor DeCillia [ctb, cph]

Documentation:   PDF Manual  

GPL-3 license

Imports Rdpack

Suggests testthat, devtools, foreach, doParallel, hdrcde, rfinterval, ranger

See at CRAN