Package: irboost 0.1-1.5

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>.

Authors:Zhu Wang [aut, cre]

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Last updated 5 months agofrom:7bd4598beb. Checks:OK: 7. Indexed: yes.

Exports:dataLSirb.trainirb.train_aftirboost

Dependencies:bstcodetoolsdata.tabledoParallelforeachgbmglmnetiteratorsjsonlitelatticeMASSMatrixmpathnumDerivpsclRcppRcppEigenrpartshapesurvivalWeightSVMxgboost