The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.
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 |
Please use the canonical form https://CRAN.R-project.org/package=stepgbm to link to this page.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.