CRAN/E | steprf

steprf

Stepwise Predictive Variable Selection for Random Forest

Installation

About

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.

Key Metrics

Version 1.0.2
R ≥ 4.0
Published 2022-06-29 677 days ago
Needs compilation? no
License GPL-2
License GPL-3
CRAN checks steprf results

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Maintainer

Maintainer

Jin Li

jinli68@gmail.com

Authors

Jin Li

aut / cre

Material

Reference manual
Package source

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

r-oldrel

x86_64

Windows

r-devel

x86_64

r-release

x86_64

r-oldrel

x86_64

Old Sources

steprf archive

Depends

R ≥ 4.0

Imports

spm
randomForest
spm2
psy

Suggests

knitr
rmarkdown
lattice
reshape2

Reverse Imports

stepgbm