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:
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')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated from:dbe93cced3. Checks:9 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-x86_64 | OK | 123 | ||
| source / vignettes | OK | 201 | ||
| linux-release-x86_64 | OK | 174 | ||
| macos-release-arm64 | OK | 76 | ||
| macos-oldrel-arm64 | OK | 74 | ||
| windows-devel | OK | 81 | ||
| windows-release | OK | 62 | ||
| windows-oldrel | OK | 77 | ||
| wasm-release | OK | 134 |
Exports:RFcv2steprfsteprfAVIsteprfAVI1steprfAVI2steprfAVIPredictors
Dependencies:abindclassclassIntcodetoolsDBIdotCall64e1071fieldsFNNforeachgbmglmnetgstatintervalsiteratorsKernSmoothlatticemapsMASSMatrixnlmeproxypsyrandomForestrangerRColorBrewerRcppRcppEigenrlangs2sfsftimeshapespspacetimespamspmspm2starssurvivalunitsviridisLitewkxtszoo
