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.
- Version2.9-11
- R versionunknown
- LicenseGPL-2
- Needs compilation?Yes
- mboost citation info
- Last release08/22/2024
Documentation
Team
Torsten Hothorn
Fabian Scheipl
Show author detailsRolesContributorFabian Otto-Sobotka
Show author detailsRolesContributorBenjamin Hofner
Show author detailsRolesAuthorThomas Kneib
Show author detailsRolesAuthorMatthias Schmid
Show author detailsRolesAuthorPeter Buehlmann
Andreas Mayr
Show author detailsRolesContributor
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- Depends1 package
- Imports6 packages
- Suggests12 packages
- Reverse Depends7 packages
- Reverse Imports13 packages
- Reverse Suggests17 packages