RLT

This is a new version (>= 4.0.0) of the RLT package. Versions prior to 4.0.0 are written in C (available at RLT-Archive), while newer versions are based on C++. This new version will replace the original CRAN package once it is finished.

The goal of RLT is to provide new functionalities of random forest models. This includes embedded model fit learning a better splitting rule; linear combination splits, confidence intervals, and several other new approaches that are currently being developed.

Installation

You can install this version using

    # install.packages("devtools")
    devtools::install_github("teazrq/RLT")

If you use MacOS, then you need to install a few libraries to be able to compile the package. Please follow this guild.

New features highlight

• Unbiased variance estimation (regression forest) based on Xu, Zhu and Shao (2022+)
• Unbiased survival function confidence band estimation based on Formentini, Liang and Zhu (2022+)
• Reproducibility in parallel tree version with xoshiro256plus random number generator
• Speed and space improvement from earlier c version
• [to be implemented] Graph random forests
• [to be implemented] Python API