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: | 2.3-0 |
| Depends: | R (≥ 2.14.0), methods, stats, parallel |
| Imports: | Matrix, survival, splines, lattice, nnls, quadprog, utils |
| Suggests: | party (≥ 1.0-3), TH.data, MASS, fields, BayesX, gbm, mlbench, RColorBrewer, rpart (≥ 4.0-3) |
| Published: | 2014-06-26 |
| 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 |
| Materials: | NEWS |
| In views: | MachineLearning, Survival |
| CRAN checks: | mboost results |
| Reference manual: | mboost.pdf |
| Vignettes: |
Survival Ensembles mboost mboost Illustrations mboost Tutorial |
| Package source: | mboost_2.3-0.tar.gz |
| Windows binaries: | r-devel: mboost_2.3-0.zip, r-release: mboost_2.3-0.zip, r-oldrel: mboost_2.3-0.zip |
| OS X Snow Leopard binaries: | r-release: mboost_2.3-0.tgz, r-oldrel: mboost_2.3-0.tgz |
| OS X Mavericks binaries: | r-release: mboost_2.3-0.tgz |
| Old sources: | mboost archive |
| Reverse depends: | expectreg, FDboost, gamboostLSS, globalboosttest, parboost |
| Reverse imports: | bujar, gamboostMSM, imputeR |
| Reverse suggests: | catdata, Daim, fscaret, HSAUR2, HSAUR3, mlr, multcomp, spikeSlabGAM |