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RFpredInterval: Prediction Intervals with Random Forests and Boosted Forests
Implements various prediction interval methods with random forests and boosted forests. The package has two main functions: pibf() produces prediction intervals with boosted forests (PIBF) as described in Alakus et al. (2022) <doi:10.32614/RJ-2022-012> and rfpi() builds 15 distinct variations of prediction intervals with random forests (RFPI) proposed by Roy and Larocque (2020) <doi:10.1177/0962280219829885>.
Getting started
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Package details |
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| Author | Cansu Alakus [aut, cre], Denis Larocque [aut], Aurelie Labbe [aut], Hemant Ishwaran [ctb] (Author of included randomForestSRC codes), Udaya B. Kogalur [ctb] (Author of included randomForestSRC codes) |
| Maintainer | Cansu Alakus <cansu.alakus@hec.ca> |
| License | GPL (>= 3) |
| Version | 1.0.7 |
| URL | https://github.com/calakus/RFpredInterval |
| Package repository | View on CRAN |
| Installation |
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