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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 gmail.com> |
| 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: stepgbm_1.0.1.zip, r-release: stepgbm_1.0.1.zip, r-oldrel: stepgbm_1.0.1.zip |
| 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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