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
Repository: https://simonyansenzhao.r-universe.dev
Date/Publication: 2026-02-21 01:39:17 UTC
RemoteUrl: https://github.com/simonyansenzhao/wsrf
RemoteRef: HEAD
RemoteSha: 72f0fe4ca55dd5b09d74404c2a2c7957c891ed9e
Packaged: 2026-10-20 07:22:51 UTC; root
Author: Qinghan Meng [aut],
  He Zhao [aut, cre] (ORCID: <https://orcid.org/0000-0001-5763-9743>),
  Graham J. Williams [aut] (ORCID:
    <https://orcid.org/0000-0001-7041-4127>),
  Junchao Lv [aut],
  Baoxun Xu [aut],
  Joshua Zhexue Huang [aut] (ORCID:
    <https://orcid.org/0000-0002-6797-2571>)
Maintainer: He Zhao <Simon.Yansen.Zhao@gmail.com>