steprf: Stepwise Predictive Variable Selection for Random Forest

An introduction to several novel predictive variable selection methods for random forest. They are based on various variable importance methods (i.e., averaged variable importance (AVI), and knowledge informed AVI (i.e., KIAVI, and KIAVI2)) and predictive accuracy in stepwise algorithms. For details of the variable selection methods, please see: Li, J., Siwabessy, J., Huang, Z. and Nichol, S. (2019) <doi:10.3390/geosciences9040180>. Li, J., Alvarez, B., Siwabessy, J., Tran, M., Huang, Z., Przeslawski, R., Radke, L., Howard, F., Nichol, S. (2017). <doi:10.13140/RG.2.2.27686.22085>.

Version:	1.0.2
Depends:	R (≥ 4.0)
Imports:	spm, randomForest, spm2, psy
Suggests:	knitr, rmarkdown, lattice, reshape2
Published:	2022-06-29
DOI:	10.32614/CRAN.package.steprf
Author:	Jin Li [aut, cre]
Maintainer:	Jin Li <jinli68 at gmail.com>
License:	GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation:	no
CRAN checks:	steprf results

Documentation:

Reference manual:

steprf.pdf

Downloads:

Package source:	steprf_1.0.2.tar.gz
Windows binaries:	r-devel: steprf_1.0.2.zip, r-release: steprf_1.0.2.zip, r-oldrel: steprf_1.0.2.zip
macOS binaries:	r-release (arm64): steprf_1.0.2.tgz, r-oldrel (arm64): steprf_1.0.2.tgz, r-release (x86_64): steprf_1.0.2.tgz, r-oldrel (x86_64): steprf_1.0.2.tgz
Old sources:	steprf archive

Reverse dependencies:

Reverse imports:

stepgbm

Linking:

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