Package: steprf 1.0.2

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>.

Authors:Jin Li [aut, cre]

steprf_1.0.2.tar.gz
steprf_1.0.2.zip(r-4.7)steprf_1.0.2.zip(r-4.6)steprf_1.0.2.zip(r-4.5)
steprf_1.0.2.tgz(r-4.6-any)steprf_1.0.2.tgz(r-4.5-any)
steprf_1.0.2.tar.gz(r-4.7-any)steprf_1.0.2.tar.gz(r-4.6-any)
steprf_1.0.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html
DESCRIPTION
card.svg |card.png
steprf/json (API)

# Install 'steprf' in R:
install.packages('steprf', repos = c('https://jinli22.r-universe.dev', 'https://cloud.r-project.org'))

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.48 score 1 packages 5 scripts 270 downloads 6 exports 45 dependencies

Last updated from:dbe93cced3. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK145
source / vignettesOK187
linux-release-x86_64OK144
macos-release-arm64OK68
macos-oldrel-arm64OK76
windows-develOK76
windows-releaseOK64
windows-oldrelOK73
wasm-releaseOK159

Exports:RFcv2steprfsteprfAVIsteprfAVI1steprfAVI2steprfAVIPredictors

Dependencies:abindclassclassIntcodetoolsDBIdotCall64e1071fieldsFNNforeachgbmglmnetgstatintervalsiteratorsKernSmoothlatticemapsMASSMatrixnlmeproxypsyrandomForestrangerRColorBrewerRcppRcppEigenrlangs2sfsftimeshapespspacetimespamspmspm2starssurvivalunitsviridisLitewkxtszoo