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.
- Version0.2-1.0
- R versionR (≥ 3.5.0)
- LicenseGPL (≥ 3)
- Needs compilation?No
- irboost citation info
- Last release02/04/2025
Documentation
Team
Zhu Wang
MaintainerShow author details
Insights
Last 30 days
This package has been downloaded 176 times in the last 30 days. More than a random curiosity, but not quite a blockbuster. Still, it's gaining traction! The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 5 times.
The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.
Last 365 days
This package has been downloaded 2,626 times in the last 365 days. That's enough downloads to impress a room full of undergrads. A commendable achievement indeed. The day with the most downloads was Feb 05, 2025 with 44 downloads.
The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.
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Dependencies
- Imports2 packages
- Suggests4 packages