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An introduction to several novel predictive variable selection methods for random forest. They are based on various variable importance methods (i.e., averaged variable importance (AVI), and knowledge informed AVI (i.e., KIAVI, and KIAVI2)) and 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: | 1.0.2 |
| Depends: | R (≥ 4.0) |
| Imports: | spm, randomForest, spm2, psy |
| Suggests: | knitr, rmarkdown, lattice, reshape2 |
| Published: | 2022-06-29 |
| DOI: | 10.32614/CRAN.package.steprf |
| 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: | steprf results |
| Reference manual: | steprf.pdf |
| Package source: | steprf_1.0.2.tar.gz |
| Windows binaries: | r-devel: steprf_1.0.2.zip, r-release: steprf_1.0.2.zip, r-oldrel: steprf_1.0.2.zip |
| macOS binaries: | r-release (arm64): steprf_1.0.2.tgz, r-oldrel (arm64): steprf_1.0.2.tgz, r-release (x86_64): steprf_1.0.2.tgz, r-oldrel (x86_64): steprf_1.0.2.tgz |
| Old sources: | steprf archive |
| Reverse imports: | stepgbm |
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