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irboost  

Iteratively Reweighted Boosting for Robust Analysis
View on CRAN: Click here


Download and install irboost package within the R console
Install from CRAN:
install.packages("irboost")

Install from Github:
library("remotes")
install_github("cran/irboost")

Install by package version:
library("remotes")
install_version("irboost", "0.2-1.0")



Attach the package and use:
library("irboost")
Maintained by
Zhu Wang
[Scholar Profile | Author Map]
First Published: 2022-02-16
Latest Update: 2023-06-25
Description:
Fit a predictive model with the iteratively reweighted boosting (IRBoost) that minimizes the robust loss functions in the CC-family (concave-convex). The convex optimization is conducted by functional descent boosting algorithm in the R package 'xgboost'. The IRBoost reduces the weight of the observation that leads to a large loss; it also provides weights to help identify outliers. Applications include the robust generalized linear models and extensions, where the mean is related to the predictors by boosting, and robust accelerated failure time models. The package supersedes the R package 'ccboost'. Wang (2021) .
How to cite:
Zhu Wang (2022). irboost: Iteratively Reweighted Boosting for Robust Analysis. R package version 0.2-1.0, https://cran.r-project.org/web/packages/irboost. Accessed 30 Apr. 2025.
Previous versions and publish date:
0.1-1.1 (2022-02-16 21:10), 0.1-1.2 (2022-12-22 19:30), 0.1-1.3 (2023-06-25 05:20), 0.1-1.5 (2024-04-18 19:53)
Other packages that cited irboost R package
View irboost citation profile
Other R packages that irboost depends, imports, suggests or enhances
Complete documentation for irboost
Functions, R codes and Examples using the irboost R package
Some associated functions: dataLS . irboost . irboost_aft . 
Some associated R codes: aftloss.R . dataLS.R . irboost.R . irboost_aft.R . probability_distribution.R .  Full irboost package functions and examples
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