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>.

Version: 1.0.1
Depends: R (≥ 4.0)
Imports: spm, steprf
Suggests: knitr, rmarkdown, reshape2, lattice
Published: 2023-04-04
DOI: 10.32614/CRAN.package.stepgbm
Author: Jin Li [aut, cre]
Maintainer: Jin Li <jinli68 at>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
CRAN checks: stepgbm results


Reference manual: stepgbm.pdf


Package source: stepgbm_1.0.1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): stepgbm_1.0.1.tgz, r-oldrel (arm64): stepgbm_1.0.1.tgz, r-release (x86_64): stepgbm_1.0.1.tgz, r-oldrel (x86_64): stepgbm_1.0.1.tgz
Old sources: stepgbm archive


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