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: | 2.2-2 |
| Depends: | R (≥ 2.14.0), methods, stats, parallel |
| Imports: | Matrix, survival, splines, lattice |
| Suggests: | party (≥ 1.0-3), ipred, MASS, fields, BayesX, gbm, mlbench, RColorBrewer, rpart (≥ 4.0-3) |
| Published: | 2013-02-08 |
| Author: | Torsten Hothorn [aut, cre], Peter Buehlmann [aut], Thomas Kneib [aut], Matthias Schmid [aut], Benjamin Hofner [aut], Fabian Sobotka [ctb], Fabian Scheipl [ctb] |
| Maintainer: | Torsten Hothorn <Torsten.Hothorn at R-project.org> |
| License: | GPL-2 |
| URL: | http://r-forge.r-project.org/projects/mboost/ |
| NeedsCompilation: | yes |
| In views: | MachineLearning, Survival |
| CRAN checks: | mboost results |
| Package source: | mboost_2.2-2.tar.gz |
| MacOS X binary: | mboost_2.2-2.tgz |
| Windows binary: | mboost_2.2-2.zip |
| Reference manual: | mboost.pdf |
| Vignettes: |
Survival Ensembles mboost mboost Illustrations mboost Tutorial |
| News/ChangeLog: | NEWS |
| Old sources: | mboost archive |
| Reverse depends: | expectreg, gamboostLSS, globalboosttest, stratasphere |
| Reverse suggests: | caret, catdata, Daim, fscaret, HSAUR2, multcomp, spikeSlabGAM |