Package: stepgbm 1.0.1

stepgbm: Stepwise Variable Selection for Generalized Boosted Regression Modeling

An introduction to a couple of novel predictive variable selection methods for generalised boosted regression modeling (gbm). They are based on various variable influence methods (i.e., relative variable influence (RVI) and knowledge informed RVI (i.e., KIRVI, and KIRVI2)) that adopted similar ideas as AVI, KIAVI and KIAVI2 in the 'steprf' package, and also based on 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]

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stepgbm/json (API)

# Install 'stepgbm' in R:
install.packages('stepgbm', 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.

1.00 score 1 stars 2 scripts 105 downloads 2 exports 80 dependencies

Last updated 2 years agofrom:916b4a0e04. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 29 2024
R-4.5-winOKOct 29 2024
R-4.5-linuxOKOct 29 2024
R-4.4-winOKOct 29 2024
R-4.4-macOKOct 29 2024
R-4.3-winOKOct 29 2024
R-4.3-macOKOct 29 2024

Exports:stepgbmstepgbmRVI

Dependencies:abindbiomod2classclassIntclicodetoolscolorspaceDBIdotCall64dplyre1071fansifarverfieldsFNNforeachgbmgenericsggplot2glmnetgluegstatgtableintervalsisobanditeratorsKernSmoothlabelinglatticelifecyclemagrittrmapsMASSMatrixmgcvmunsellnlmepillarpkgconfigplyrPresenceAbsencepROCproxypsyR6randomForestrangerRColorBrewerRcppRcppEigenreshapereshape2rlangrparts2scalessfsftimeshapespspacetimespamspmspm2starssteprfstringistringrsurvivalterratibbletidyselectunitsutf8vctrsviridisLitewithrwkxtszoo