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)