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
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 169 downloads 6 exports 45 dependencies

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

TargetResultTimeFilesSyslog
linux-devel-x86_64OK123
source / vignettesOK201
linux-release-x86_64OK174
macos-release-arm64OK76
macos-oldrel-arm64OK74
windows-develOK81
windows-releaseOK62
windows-oldrelOK77
wasm-releaseOK134

Exports:RFcv2steprfsteprfAVIsteprfAVI1steprfAVI2steprfAVIPredictors

Dependencies:abindclassclassIntcodetoolsDBIdotCall64e1071fieldsFNNforeachgbmglmnetgstatintervalsiteratorsKernSmoothlatticemapsMASSMatrixnlmeproxypsyrandomForestrangerRColorBrewerRcppRcppEigenrlangs2sfsftimeshapespspacetimespamspmspm2starssurvivalunitsviridisLitewkxtszoo