Package: lightgbm
Type: Package
Title: Light Gradient Boosting Machine
Version: 4.6.0
Date: 2025-02-13
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://papers.nips.cc/paper/6907-lightgbm-a-highly-efficient-gradient-boosting-decision>.
    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/Microsoft/LightGBM
BugReports: https://github.com/Microsoft/LightGBM/issues
NeedsCompilation: yes
Biarch: true
VignetteBuilder: knitr
Suggests: knitr, markdown, RhpcBLASctl, testthat
Depends: R (>= 3.5)
Imports: R6 (>= 2.0), data.table (>= 1.9.6), graphics, jsonlite (>=
        1.0), Matrix (>= 1.1-0), methods, parallel, utils
SystemRequirements: C++17
RoxygenNote: 7.3.2
Packaged: 2025-02-13 20:29:35 UTC; jlamb
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: CRAN
Date/Publication: 2025-02-13 22:20:09 UTC
Built: R 4.5.2; x86_64-w64-mingw32; 2026-01-13 16:22:07 UTC; windows
Archs: x64
