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-any)steprf_1.0.2.zip(r-4.6-any)steprf_1.0.2.zip(r-4.5-any)
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 2 scripts 309 downloads 6 exports 44 dependencies

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

TargetResultTimeFilesSyslog
linux-develOK147
source / vignettesOK356
linux-releaseOK118
macos-releaseOK105
macos-oldrelOK83
windows-develOK64
windows-releaseOK58
windows-oldrelOK54
wasm-releaseOK139

Exports:RFcv2steprfsteprfAVIsteprfAVI1steprfAVI2steprfAVIPredictors

Dependencies:abindclassclassIntcodetoolsDBIdotCall64e1071fieldsFNNforeachgbmglmnetgstatintervalsiteratorsKernSmoothlatticemapsMatrixnlmeproxypsyrandomForestrangerRColorBrewerRcppRcppEigenrlangs2sfsftimeshapespspacetimespamspmspm2starssurvivalunitsviridisLitewkxtszoo