wsrf/DESCRIPTION at master · SimonYansenZhao/wsrf · GitHub

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Package: wsrf
Type: Package
Title: Weighted Subspace Random Forest for Classification
Version: 1.7.32
Date: 2026-02-21
Authors@R:
    c(person(given = "Qinghan",
             family = "Meng",
             email = "qinghan.meng@gmail.com",
             role="aut"),
      person(given = "He",
             family = "Zhao",
             email = "Simon.Yansen.Zhao@gmail.com",
             role = c("aut", "cre"),
             comment = c(ORCID = "0000-0001-5763-9743")),
      person(given = c("Graham", "J."),
             family = "Williams",
             email = "graham.williams@togaware.com",
             role = "aut",
             comment = c(ORCID = "0000-0001-7041-4127")),
      person(given = "Junchao",
             family = "Lv",
             email = "ljc0711@gmail.com",
             role = "aut"),
      person(given = "Baoxun",
             family = "Xu",
             email = "76276198@qq.com",
             role = "aut"),
      person(given = c("Joshua", "Zhexue"),
             family = "Huang",
             email = "zx.huang@szu.edu.cn",
             role = "aut",
             comment = c(ORCID = "0000-0002-6797-2571")))
Description:
    A parallel implementation of Weighted Subspace Random Forest.  The
    Weighted Subspace Random Forest algorithm was proposed in the
    International Journal of Data Warehousing and Mining by Baoxun Xu,
    Joshua Zhexue Huang, Graham Williams, Qiang Wang, and Yunming Ye
    (2012) <DOI:10.4018/jdwm.2012040103>.  The algorithm can classify
    very high-dimensional data with random forests built using small
    subspaces.  A novel variable weighting method is used for variable
    subspace selection in place of the traditional random variable
    sampling.This new approach is particularly useful in building
    models from high-dimensional data.
License: GPL (>= 3)
URL:
    https://github.com/SimonYansenZhao/wsrf,
    https://togaware.com
BugReports: https://github.com/SimonYansenZhao/wsrf/issues
Depends:
    parallel,
    R (>= 3.3.0),
    Rcpp (>= 0.10.2),
    stats
LinkingTo: Rcpp
Suggests:
    knitr (>= 1.5),
    randomForest (>= 4.6.7),
    stringr (>= 0.6.2),
    rmarkdown (>= 1.6)
VignetteBuilder: knitr
NeedsCompilation: yes
Classification/ACM-2012:
    Computing methodologies ~ Classification and regression trees,
    Computing methodologies ~ Supervised learning by classification,
    Computing methodologies ~ Massively parallel and high-performance
    simulations, Computing methodologies ~ Distributed simulation