CRAN/E | irboost

irboost

Iteratively Reweighted Boosting for Robust Analysis

Installation

About

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.

Citation irboost citation info

Key Metrics

Version 0.1-1.5
R ≥ 3.5.0
Published 2024-04-18 2 days ago
Needs compilation? no
License GPL (≥ 3)
CRAN checks irboost results

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Maintainer

Maintainer

Zhu Wang

zhuwang@gmail.com

Authors

Zhu Wang

aut / cre

Material

README
NEWS
Reference manual
Package source

Vignettes

An Introduction to irboost

macOS

r-prerel

arm64

r-release

arm64

r-oldrel

arm64

r-prerel

x86_64

r-release

x86_64

Windows

r-prerel

x86_64

r-release

x86_64

r-oldrel

x86_64

Old Sources

irboost archive

Depends

R ≥ 3.5.0

Imports

mpath ≥ 0.4-2.21
xgboost

Suggests

R.rsp
DiagrammeR
survival
Hmisc