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piRF  

Prediction Intervals for Random Forests
View on CRAN: Click here


Download and install piRF package within the R console
Install from CRAN:
install.packages("piRF")

Install from Github:
library("remotes")
install_github("cran/piRF")

Install by package version:
library("remotes")
install_version("piRF", "0.1.0")



Attach the package and use:
library("piRF")
Maintained by
Chancellor Johnstone
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2020-05-12
Latest Update:
Description:
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) .
How to cite:
Chancellor Johnstone (2020). piRF: Prediction Intervals for Random Forests. R package version 0.1.0, https://cran.r-project.org/web/packages/piRF. Accessed 04 Jun. 2026.
Previous versions and publish date:
0.1.0 (2020-05-12 11:50)
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