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
DOI: 10.32614/CRAN.package.irboost
Author: Zhu Wang ORCID iD [aut, cre]
Maintainer: Zhu Wang <zhuwang at>
License: GPL (≥ 3)
NeedsCompilation: no
Citation: irboost citation info
Materials: README NEWS
CRAN checks: irboost results


Reference manual: irboost.pdf
Vignettes: An Introduction to irboost


Package source: irboost_0.1-1.5.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): irboost_0.1-1.5.tgz, r-oldrel (arm64): irboost_0.1-1.5.tgz, r-release (x86_64): irboost_0.1-1.5.tgz, r-oldrel (x86_64): irboost_0.1-1.5.tgz
Old sources: irboost archive


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