wsrf: Weighted Subspace Random Forest for Classification

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.

Version:	1.7.30
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)
Published:	2023-01-06
Author:	Qinghan Meng [aut], He Zhao [aut, cre], Graham J. Williams [aut], Junchao Lv [aut], Baoxun Xu [aut], Joshua Zhexue Huang [aut]
Maintainer:	He Zhao <Simon.Yansen.Zhao at gmail.com>
BugReports:	https://github.com/SimonYansenZhao/wsrf/issues
License:	GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL:	https://github.com/SimonYansenZhao/wsrf, https://togaware.com
NeedsCompilation:	yes
SystemRequirements:	C++11
Citation:	wsrf citation info
Materials:	README NEWS
In views:	MachineLearning
CRAN checks:	wsrf results

Documentation:

Reference manual:	wsrf.pdf
Vignettes:	A Quick Start Guide for wsrf

Downloads:

Package source:	wsrf_1.7.30.tar.gz
Windows binaries:	r-devel: wsrf_1.7.30.zip, r-release: wsrf_1.7.30.zip, r-oldrel: wsrf_1.7.30.zip
macOS binaries:	r-release (arm64): wsrf_1.7.30.tgz, r-oldrel (arm64): wsrf_1.7.30.tgz, r-release (x86_64): wsrf_1.7.30.tgz, r-oldrel (x86_64): wsrf_1.7.30.tgz
Old sources:	wsrf archive

Linking:

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