irboost: Iteratively Reweighted Boosting for Robust Analysis
Fit a predictive model using iteratively reweighted boosting (IRBoost) to minimize robust loss functions within the CC-family (concave-convex). This constitutes an application of iteratively reweighted convex optimization (IRCO), where convex optimization is performed using the functional descent boosting algorithm. IRBoost assigns weights to facilitate outlier identification. Applications include robust generalized linear models and robust accelerated failure time models. Wang (2021) <doi:10.48550/arXiv.2101.07718>.
| Version: | 0.1-1.5 |
| Depends: | R (≥ 3.5.0) |
| Imports: | mpath (≥ 0.4-2.21), xgboost |
| Suggests: | R.rsp, DiagrammeR, survival, Hmisc |
| Published: | 2024-04-18 |
| Author: | Zhu Wang  [aut,
cre] |
| Maintainer: | Zhu Wang <zhuwang at gmail.com> |
| License: | GPL (≥ 3) |
| NeedsCompilation: | no |
| Citation: | irboost citation info |
| Materials: | README NEWS |
| CRAN checks: | irboost results |
Documentation:
| Reference manual: | irboost.pdf |
| Vignettes: |
An Introduction to irboost |
Downloads:
| Package source: | irboost_0.1-1.5.tar.gz |
| Windows binaries: | r-prerel: irboost_0.1-1.5.zip, r-release: irboost_0.1-1.3.zip, r-oldrel: irboost_0.1-1.3.zip |
| macOS binaries: | r-prerel (arm64): irboost_0.1-1.5.tgz, r-release (arm64): irboost_0.1-1.5.tgz, r-oldrel (arm64): irboost_0.1-1.5.tgz, r-prerel (x86_64): irboost_0.1-1.5.tgz, r-release (x86_64): irboost_0.1-1.5.tgz |
| Old sources: | irboost archive |
Linking:
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