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
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steprf_1.0.2.tgz(r-4.4-any)steprf_1.0.2.tgz(r-4.3-any)
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steprf.pdf |steprf.html
steprf/json (API)

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

Peer review:

On CRAN:

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 128 downloads 6 exports 79 dependencies

Last updated 2 years agofrom:dbe93cced3. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 13 2024
R-4.5-winOKOct 13 2024
R-4.5-linuxOKOct 13 2024
R-4.4-winOKOct 13 2024
R-4.4-macOKOct 13 2024
R-4.3-winOKOct 13 2024
R-4.3-macOKOct 13 2024

Exports:RFcv2steprfsteprfAVIsteprfAVI1steprfAVI2steprfAVIPredictors

Dependencies:abindbiomod2classclassIntclicodetoolscolorspaceDBIdotCall64dplyre1071fansifarverfieldsFNNforeachgbmgenericsggplot2glmnetgluegstatgtableintervalsisobanditeratorsKernSmoothlabelinglatticelifecyclemagrittrmapsMASSMatrixmgcvmunsellnlmepillarpkgconfigplyrPresenceAbsencepROCproxypsyR6randomForestrangerRColorBrewerRcppRcppEigenreshapereshape2rlangrparts2scalessfsftimeshapespspacetimespamspmspm2starsstringistringrsurvivalterratibbletidyselectunitsutf8vctrsviridisLitewithrwkxtszoo