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>.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.2-1.1 |
rolling linux/jammy R-4.5 | irboost_0.2-1.1.tar.gz |
1.6 MiB |
0.2-1.1 |
rolling linux/noble R-4.5 | irboost_0.2-1.1.tar.gz |
1.6 MiB |
0.2-1.1 |
rolling source/ R- | irboost_0.2-1.1.tar.gz |
1.5 MiB |
0.2-1.1 |
latest linux/jammy R-4.5 | irboost_0.2-1.1.tar.gz |
1.6 MiB |
0.2-1.1 |
latest linux/noble R-4.5 | irboost_0.2-1.1.tar.gz |
1.6 MiB |
0.2-1.1 |
latest source/ R- | irboost_0.2-1.1.tar.gz |
1.5 MiB |
0.2-1.1 |
2026-04-26 source/ R- | irboost_0.2-1.1.tar.gz |
1.5 MiB |
0.2-1.1 |
2026-04-23 source/ R- | irboost_0.2-1.1.tar.gz |
1.5 MiB |
0.2-1.1 |
2026-04-09 windows/windows R-4.5 | irboost_0.2-1.1.zip |
1.6 MiB |
0.2-1.0 |
2025-04-20 source/ R- | irboost_0.2-1.0.tar.gz |
735.2 KiB |