Package: irboost 0.2-1.0

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 (2025) <doi:10.6339/24-JDS1138>.

Authors:Zhu Wang [aut, cre]

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NEWS

# Install 'irboost' in R:
install.packages('irboost', repos = c('https://zhuwang46.r-universe.dev', 'https://cloud.r-project.org'))

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

3.00 score 335 downloads 4 exports 22 dependencies

Last updated 1 months agofrom:1bbe2e61c4. Checks:9 OK. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKMar 06 2025
R-4.5-winOKMar 06 2025
R-4.5-macOKMar 06 2025
R-4.5-linuxOKMar 06 2025
R-4.4-winOKMar 06 2025
R-4.4-macOKMar 06 2025
R-4.4-linuxOKMar 06 2025
R-4.3-winOKMar 06 2025
R-4.3-macOKMar 06 2025

Exports:dataLSirb.trainirb.train_aftirboost

Dependencies:bstcodetoolsdata.tabledoParallelforeachgbmglmnetiteratorsjsonlitelatticeMASSMatrixmpathnumDerivpsclRcppRcppEigenrpartshapesurvivalWeightSVMxgboost

An Introduction to irboost

Rendered fromstatic_irbst.pdf.asisusingR.rsp::asison Mar 06 2025.

Last update: 2024-04-19
Started: 2022-02-16