RLT

Reinforcement Learning Trees is an R package for random forest models with regression, classification, and survival analysis support. The package uses a C++/Rcpp backend with OpenMP for parallel computation.

Website | GitHub | Issues

Highlights

Regression, classification, and survival forests
Reinforcement learning splits with embedded model guidance
Linear combination splits for multivariate split directions
Variable importance and random forest kernel utilities
Survival prediction, confidence bands, and tree inspection tools
Reproducible parallel fitting through recorded random seeds

Installation

Install the released version from CRAN when available:

install.packages("RLT")

Install the development version from GitHub:

# install.packages("remotes")
# remotes::install_github("teazrq/RLT")
install.packages("RLT", repos = c('https://teazrq.r-universe.dev', 'https://cloud.r-project.org'))

On Windows, source installation requires Rtools. On macOS and Linux, source installation requires a working C++ toolchain and OpenMP support.

Quick Example

library(RLT)

set.seed(1)
n <- 200
p <- 6
x <- matrix(rnorm(n * p), n, p)
y <- x[, 1] - x[, 2] + rnorm(n)

fit <- RLT(
  x, y,
  model = "regression",
  ntrees = 100,
  importance = TRUE,
  verbose = FALSE
)

pred <- predict(fit, x[1:5, ])
pred$Prediction

importance(fit)

Documentation

The pkgdown site is built from the package vignettes and website-only articles:

References

Zhu, R., Zeng, D., and Kosorok, M. R. (2015). Reinforcement Learning Trees. Journal of the American Statistical Association, 110(512), 1770-1784. https://doi.org/10.1080/01621459.2015.1036994
Xu, T., Zhu, R., and Shao, X. (2024). On variance estimation of random forests with infinite-order U-statistics. Electronic Journal of Statistics, 18(1), 2135-2207.
Formentini, S., Liang, K., and Zhu, R. (2022). Survival Function Confidence Band Estimation using Random Forests. https://arxiv.org/abs/2204.12038