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. (2021) and rfpi() builds 15 distinct variations of prediction intervals with random forests (RFPI) proposed by Roy and Larocque (2020) .


Reference manual

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1.0.4 by Cansu Alakus, 4 months ago

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Authors: 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)

Documentation:   PDF Manual  

GPL (>= 3) license

Imports ranger, data.table, hdrcde, parallel, data.tree, DiagrammeR

Suggests knitr, rmarkdown, testthat

See at CRAN