Package: lightgbm
Type: Package
Title: Light Gradient Boosting Machine
Version: 4.7.0
Date: 2026-07-18
Authors@R: c(
    person("Yu", "Shi", email = "yushi2@microsoft.com", role = c("aut")),
    person("Guolin", "Ke", email = "guolin.ke@outlook.com", role = c("aut")),
    person("Damien", "Soukhavong", email = "damien.soukhavong@skema.edu", role = c("aut")),
    person("James", "Lamb", email="jaylamb20@gmail.com", role = c("aut", "cre")),
    person("Qi", "Meng", role = c("aut")),
    person("Thomas", "Finley", role = c("aut")),
    person("Taifeng", "Wang", role = c("aut")),
    person("Wei", "Chen", role = c("aut")),
    person("Weidong", "Ma", role = c("aut")),
    person("Qiwei", "Ye", role = c("aut")),
    person("Tie-Yan", "Liu", role = c("aut")),
    person("Nikita", "Titov", role = c("aut")),
    person("Yachen", "Yan", role = c("ctb")),
    person("Microsoft Corporation", role = c("cph")),
    person("Dropbox, Inc.", role = c("cph")),
    person("Alberto", "Ferreira", role = c("ctb")),
    person("Daniel", "Lemire", role = c("ctb")),
    person("Victor", "Zverovich", role = c("cph")),
    person("IBM Corporation", role = c("ctb")),
    person("David", "Cortes", role = c("aut")),
    person("Michael", "Mayer", role = c("ctb"))
    )
Description: Tree based algorithms can be improved by introducing
        boosting frameworks. 'LightGBM' is one such framework, based on
        Ke, Guolin et al. (2017)
        <https://proceedings.neurips.cc/paper/2017/hash/6449f44a102fde848669bdd9eb6b76fa-Abstract.html>.
        This package offers an R interface to work with it. It is
        designed to be distributed and efficient with the following
        advantages: 1. Faster training speed and higher efficiency. 2.
        Lower memory usage. 3. Better accuracy. 4. Parallel learning
        supported. 5. Capable of handling large-scale data. In
        recognition of these advantages, 'LightGBM' has been
        widely-used in many winning solutions of machine learning
        competitions. Comparison experiments on public datasets suggest
        that 'LightGBM' can outperform existing boosting frameworks on
        both efficiency and accuracy, with significantly lower memory
        consumption. In addition, parallel experiments suggest that in
        certain circumstances, 'LightGBM' can achieve a linear speed-up
        in training time by using multiple machines.
Encoding: UTF-8
License: MIT + file LICENSE
URL: https://github.com/lightgbm-org/LightGBM
BugReports: https://github.com/lightgbm-org/LightGBM/issues
NeedsCompilation: yes
Biarch: true
VignetteBuilder: knitr
Suggests: knitr, markdown, RhpcBLASctl, testthat
Depends: R (>= 4.0)
Imports: R6 (>= 2.4.0), data.table (>= 1.9.6), graphics, jsonlite (>=
        1.0), Matrix (>= 1.1-0), methods, parallel, utils
SystemRequirements: C++17
Config/roxygen2/version: 8.0.0
Packaged: 2026-10-16 08:35:01 UTC; root
Author: Yu Shi [aut], Guolin Ke [aut], Damien Soukhavong [aut], James
        Lamb [aut, cre], Qi Meng [aut], Thomas Finley [aut], Taifeng
        Wang [aut], Wei Chen [aut], Weidong Ma [aut], Qiwei Ye [aut],
        Tie-Yan Liu [aut], Nikita Titov [aut], Yachen Yan [ctb],
        Microsoft Corporation [cph], Dropbox, Inc. [cph], Alberto
        Ferreira [ctb], Daniel Lemire [ctb], Victor Zverovich [cph],
        IBM Corporation [ctb], David Cortes [aut], Michael Mayer [ctb]
Maintainer: James Lamb <jaylamb20@gmail.com>
Repository: https://jameslamb.r-universe.dev
Date/Publication: 2026-07-18 22:00:02 UTC
RemoteUrl: https://github.com/cran/lightgbm
RemoteRef: HEAD
RemoteSha: aa2b7244988f0aad12f8df6615616090bdae022d
