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An implementation of robust boosting algorithms for regression in R. This includes the RRBoost method proposed in the paper "Robust Boosting for Regression Problems" (Ju X and Salibian-Barrera M. 2020) <doi:10.1016/j.csda.2020.107065> (to appear in Computational Statistics and Data Science). It also implements previously proposed boosting algorithms in the simulation section of the paper: L2Boost, LADBoost, MBoost (Friedman, J. H. (2001) <doi:10.1214/aos/1013203451>) and Robloss (Lutz et al. (2008) <doi:10.1016/j.csda.2007.11.006>).
| Version: | 0.1 |
| Depends: | R (≥ 3.5.0) |
| Imports: | stats, rpart, RobStatTM |
| Published: | 2020-10-13 |
| Author: | Xiaomeng Ju [aut, cre], Matias Salibian-Barrera [aut] |
| Maintainer: | Xiaomeng Ju <xiaomeng.ju at stat.ubc.ca> |
| License: | GPL (≥ 3) |
| NeedsCompilation: | no |
| CRAN checks: | RRBoost results |
| Reference manual: | RRBoost.pdf |
| Package source: | RRBoost_0.1.tar.gz |
| Windows binaries: | r-devel: RRBoost_0.1.zip, r-release: RRBoost_0.1.zip, r-oldrel: RRBoost_0.1.zip |
| macOS binaries: | r-release (arm64): RRBoost_0.1.tgz, r-oldrel (arm64): RRBoost_0.1.tgz, r-release (x86_64): RRBoost_0.1.tgz, r-oldrel (x86_64): RRBoost_0.1.tgz |
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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.
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