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irboost

Fit a predictive model using the 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.

How to generate the vignette document?

R CMD Sweave --pdf --clean irbst.Rnw

This requires jss.bst in the same folder.

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Version

Install

install.packages('irboost')

Monthly Downloads

223

Version

0.1-1.5

License

GPL (>= 3)

Maintainer

Zhu Wang

Last Published

April 18th, 2024

Functions in irboost (0.1-1.5)

irb.train_aft

fit a robust accelerated failure time model with iteratively reweighted boosting algorithm
dataLS

generate random data for classification as in Long and Servedio (2010)
irb.train

fit a robust predictive model with iteratively reweighted boosting algorithm
irboost

fit a robust predictive model with iteratively reweighted boosting algorithm