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.

Version: 2.2-3
Depends: R (≥ 2.14.0), methods, stats, parallel, survival
Imports: Matrix, splines, lattice
Suggests: party (≥ 1.0-3), TH.data, MASS, fields, BayesX, gbm, mlbench, RColorBrewer, rpart (≥ 4.0-3)
Published: 2013-09-09
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

Downloads:

Reference manual: mboost.pdf
Vignettes: Survival Ensembles
mboost
mboost Illustrations
mboost Tutorial
Package source: mboost_2.2-3.tar.gz
OS X binary: mboost_2.2-3.tgz
Windows binary: mboost_2.2-3.zip
Old sources: mboost archive

Reverse dependencies:

Reverse depends: expectreg, gamboostLSS, globalboosttest, parboost
Reverse imports: gamboostMSM
Reverse suggests: catdata, Daim, fscaret, HSAUR2, HSAUR3, mlr, multcomp, spikeSlabGAM