R/RFpredInterval-package.R

In RFpredInterval: Prediction Intervals with Random Forests and Boosted Forests

#' RFpredInterval: A package for building prediction intervals with random
#' forests and boosted forests
#'
#' \code{RFpredInterval} provides methods to build prediction intervals with
#' random forests. The methods provided in the package are Prediction Intervals
#' with Boosted Forests (PIBF) proposed by Alakus et al. (2022) and 15 distinct
#' variations to build PIs proposed by Roy and Larocque (2020).
#' \code{RFpredInterval} includes two main functions: \code{pibf()} and
#' \code{rfpi()}. \code{pibf()} applies the PIBF method and it uses the
#' \code{ranger} package (Wright and Ziegler, 2017) to fit random forests.
#' \code{rfpi()} applies the 15 variations proposed by Roy and Larocque (2020).
#' For \code{rfpi()}, \code{RFpredInterval} uses \code{randomForestSRC} package.
#' For the least-squares splitting rule, both \code{randomForestSRC} and
#' \code{ranger} packages are applicable.
#'
#' Among 16 methods, ten of them have specialized splitting rules in the random
#' forest growing process. These methods are the ones with L1 and shortest
#' prediction interval (SPI) splitting rules proposed by Roy and Larocque (2020).
#' To implement these methods, the custom split feature of the
#' \code{randomForestSRC} package (Ishwaran and Kogalur, 2021) have been
#' utilised.
#'
#' The \code{randomForestSRC} package allows users to define a custom splitting
#' rule for the tree growing process. The user needs to define the customized
#' splitting rule in the \code{splitCustom.c} file. After modifying the
#' \code{splitCustom.c} file, all C source code files under the \code{src}
#' folder of the package must be recompiled. Finally, the package must be
#' re-installed for the custom split rule to become available.
#' \code{RFpredInterval} uses \code{randomForestSRC} package by freezing at the
#' version 2.11.0.
#'
#' @section RFpredInterval functions:
#'   \code{\link{pibf}}
#'   \code{\link{rfpi}}
#'   \code{\link{piall}}
#'   \code{\link{plot.rfpredinterval}}
#'   \code{\link{print.rfpredinterval}}
#'
#' @references Alakus, C., Larocque, D., & Labbe, A. (2022). RFpredInterval: An
#'   R Package for Prediction Intervals with Random Forests and Boosted Forests.
#'   R JOURNAL, 14(1), 300-319.
#' @references Ishwaran H, Kogalur U (2021). Fast Unified Random Forests for
#'   Survival, Regression, and Classification (RF-SRC). R package version
#'   2.11.0, \url{https://cran.r-project.org/package=randomForestSRC}.
#' @references Roy, M. H., & Larocque, D. (2020). Prediction intervals with
#'   random forests. Statistical methods in medical research, 29(1), 205-229.
#'   doi:10.1177/0962280219829885.
#' @references Wright MN, Ziegler A (2017). “ranger: A Fast Implementation of
#'   Random Forests for High Dimensional Data in C++ and R.” Journal of
#'   Statistical Software, 77(1), 1–17.
#'   doi:10.18637/jss.v077.i01.
#'
#' @docType package
#' @name RFpredInterval-package
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