Functional gradient descent algorithms (boosting) for optimizing general loss functions utilizing componentwise least squares, either of parametric linear form or smoothing splines, or regression trees as base learners for fitting generalized linear, additive and interaction models to potentially high-dimensional data.
| Version: | 1.0-1 |
| Depends: | R (≥ 2.4.0), methods, modeltools (≥ 0.2.10), party (≥ 0.9-10), splines |
| Suggests: | mlbench, ipred, Biobase |
| Date: | $Date: 2007/07/08 15:52:23 $ |
| Author: | Torsten Hothorn and Peter Buhlmann with contributions by Thomas Kneib and Matthias Schmid |
| Maintainer: | Torsten Hothorn <Torsten.Hothorn at R-project.org> |
| License: | GPL-2 |
| In views: | MachineLearning |
| CRAN checks: | mboost results |
Downloads:
| Package source: | mboost_1.0-1.tar.gz |
| MacOS X binary: | mboost_1.0-1.tgz |
| Windows binary: | mboost_1.0-1.zip |
| Reference manual: | mboost.pdf |
| Vignettes: |
Survival Ensembles mboost Illustrations |
| Old sources: | mboost archive |