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>.
| Version: |
1.0.8 |
| Depends: |
R (≥ 3.6.0) |
| Imports: |
ranger, data.table, hdrcde, parallel, data.tree, DiagrammeR |
| Suggests: |
knitr, rmarkdown, testthat |
| Published: |
2023-12-07 |
| DOI: |
10.32614/CRAN.package.RFpredInterval |
| 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 at hec.ca> |
| BugReports: |
https://github.com/calakus/RFpredInterval/issues |
| License: |
GPL (≥ 3) |
| URL: |
https://github.com/calakus/RFpredInterval |
| NeedsCompilation: |
yes |
| Materials: |
README NEWS |
| CRAN checks: |
RFpredInterval results [issues need fixing before 2025-01-10] |