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:
stepgbm_1.0.1.tar.gz
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stepgbm.pdf |stepgbm.html✨
stepgbm/json (API)
# Install 'stepgbm' in R: |
install.packages('stepgbm', 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 2 years agofrom:916b4a0e04. Checks:OK: 7. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Oct 29 2024 |
R-4.5-win | OK | Oct 29 2024 |
R-4.5-linux | OK | Oct 29 2024 |
R-4.4-win | OK | Oct 29 2024 |
R-4.4-mac | OK | Oct 29 2024 |
R-4.3-win | OK | Oct 29 2024 |
R-4.3-mac | OK | Oct 29 2024 |
Exports:stepgbmstepgbmRVI
Dependencies:abindbiomod2classclassIntclicodetoolscolorspaceDBIdotCall64dplyre1071fansifarverfieldsFNNforeachgbmgenericsggplot2glmnetgluegstatgtableintervalsisobanditeratorsKernSmoothlabelinglatticelifecyclemagrittrmapsMASSMatrixmgcvmunsellnlmepillarpkgconfigplyrPresenceAbsencepROCproxypsyR6randomForestrangerRColorBrewerRcppRcppEigenreshapereshape2rlangrparts2scalessfsftimeshapespspacetimespamspmspm2starssteprfstringistringrsurvivalterratibbletidyselectunitsutf8vctrsviridisLitewithrwkxtszoo