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.
| Version: | 2.1-1 |
| Depends: | R (≥ 2.10.0), methods, stats |
| Imports: | Matrix, survival, splines, lattice |
| Suggests: | multicore, party (≥ 0.9-9993), ipred, MASS, fields, BayesX, gbm |
| Published: | 2011-11-29 |
| Author: | Torsten Hothorn, Peter Buehlmann, Thomas Kneib, Matthias Schmid and Benjamin Hofner |
| Maintainer: | Torsten Hothorn <Torsten.Hothorn at R-project.org> |
| License: | GPL-2 |
| In views: | MachineLearning, Survival |
| CRAN checks: | mboost results |
| Package source: | mboost_2.1-1.tar.gz |
| MacOS X binary: | mboost_2.1-1.tgz |
| Windows binary: | mboost_2.1-1.zip |
| Reference manual: | mboost.pdf |
| Vignettes: |
Survival Ensembles mboost mboost Illustrations |
| News/ChangeLog: | NEWS |
| Old sources: | mboost archive |
| Reverse depends: | bujar, expectreg, gamboostLSS, globalboosttest, stratasphere |
| Reverse suggests: | caret, catdata, Daim, HSAUR2, multcomp, spikeSlabGAM |