Package: RLT 6.1.1

RLT: Reinforcement Learning Trees

Random forest with a variety of additional features for regression, classification, and survival analysis. Features include parallel computing with OpenMP, reproducibility with random seeds, variance and confidence band estimations using U-statistics, embedded model for selecting splitting variables and constructing linear combination splits, permutation and distribution-based variable importance, observation and variable weights, and subject tracking across trees.

Authors:Ruoqing Zhu [aut, cre, cph], Sarah Formentini [aut], Haowen Zhou [ctb], Tianning Xu [ctb], Zhechao Huang [ctb]

RLT_6.1.1.tar.gz
RLT_6.1.1.zip(r-4.7)RLT_6.1.1.zip(r-4.6)RLT_6.1.1.zip(r-4.5)
RLT_6.1.1.tgz(r-4.6-x86_64)RLT_6.1.1.tgz(r-4.6-arm64)RLT_6.1.1.tgz(r-4.5-x86_64)RLT_6.1.1.tgz(r-4.5-arm64)
RLT_6.1.1.tar.gz(r-4.7-arm64)RLT_6.1.1.tar.gz(r-4.7-x86_64)RLT_6.1.1.tar.gz(r-4.6-arm64)RLT_6.1.1.tar.gz(r-4.6-x86_64)
RLT_6.1.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
RLT/json (API)

# Install 'RLT' in R:
install.packages('RLT', repos = c('https://teazrq.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/teazrq/rlt/issues

Pkgdown/docs site:https://teazrq.github.io

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library

On CRAN:

Conda:

openblascppopenmp

7.50 score 10 stars 30 scripts 915 downloads 5 mentions 6 exports 5 dependencies

Last updated from:b86125e332. Checks:13 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK423
linux-devel-x86_64OK432
source / vignettesOK861
linux-release-arm64OK453
linux-release-x86_64OK427
macos-release-arm64OK359
macos-release-x86_64OK706
macos-oldrel-arm64OK392
macos-oldrel-x86_64OK704
windows-develOK650
windows-releaseOK665
windows-oldrelOK605
wasm-releaseOK436

Exports:cindexforest.kernelget.one.treeget.surv.bandimportanceRLT

Dependencies:BHdqrngRcppRcppArmadillositmo

RLT Survival Analysis Tutorial
Introduction | Simulated data | Basic usage | Split rules | Observation weights | Linear combination splits | Variable importance | Variance estimation and confidence bands | Confidence bands with get.surv.band() | Reducing the time grid for bands | Inspecting individual trees | Summary
Rendered fromsurvival-tutorial.Rmdusingknitr::rmarkdown

Last update: 2026-05-17
Started: 2026-05-17

RLT Classification Tutorial
Overview | Prerequisites | Data Preparation | Fit a classification model | Predict | Evaluate | Inspect the fitted object
Rendered fromclassification-tutorial.Rmdusingknitr::rmarkdown

Last update: 2026-05-17
Started: 2026-05-17

RLT Regression Tutorial
Overview | Prerequisites | Data | Fit | Predict | Evaluate | Inspect
Rendered fromregression-tutorial.Rmdusingknitr::rmarkdown

Last update: 2026-05-17
Started: 2026-05-17