mboost
Model-Based Boosting
Functional gradient descent algorithm (boosting) for optimizing general risk functions utilizing component-wise (penalised) least squares estimates or regression trees as base-learners for fitting generalized linear, additive and interaction models to potentially high-dimensional data. Models and algorithms are described in <doi:10.1214/07-STS242>, a hands-on tutorial is available from <doi:10.1007/s00180-012-0382-5>. The package allows user-specified loss functions and base-learners.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
2.9-11 |
rolling linux/jammy R-4.5 | mboost_2.9-11.tar.gz |
2.2 MiB |
2.9-11 |
rolling linux/noble R-4.5 | mboost_2.9-11.tar.gz |
2.2 MiB |
2.9-11 |
rolling source/ R- | mboost_2.9-11.tar.gz |
1.7 MiB |
2.9-11 |
latest linux/jammy R-4.5 | mboost_2.9-11.tar.gz |
2.2 MiB |
2.9-11 |
latest linux/noble R-4.5 | mboost_2.9-11.tar.gz |
2.2 MiB |
2.9-11 |
latest source/ R- | mboost_2.9-11.tar.gz |
1.7 MiB |
2.9-11 |
2026-04-26 source/ R- | mboost_2.9-11.tar.gz |
1.7 MiB |
2.9-11 |
2026-04-23 source/ R- | mboost_2.9-11.tar.gz |
1.7 MiB |
2.9-11 |
2026-04-09 windows/windows R-4.5 | mboost_2.9-11.zip |
2.2 MiB |
2.9-11 |
2025-04-20 source/ R- | mboost_2.9-11.tar.gz |
1.7 MiB |