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: 2.5-0
Depends: R (≥ 2.14.0), methods, stats, parallel, stabs (≥ 0.5-0)
Imports: Matrix, survival, splines, lattice, nnls, quadprog, utils, graphics, grDevices
Suggests: party (≥ 1.0-3), TH.data, MASS, fields, BayesX, gbm, mlbench, RColorBrewer, rpart (≥ 4.0-3), randomForest, nnet
Published: 2015-08-14
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>
BugReports: https://github.com/hofnerb/mboost/issues
License: GPL-2
URL: http://mboost.r-forge.r-project.org/, https://github.com/hofnerb/mboost
NeedsCompilation: yes
Citation: mboost citation info
Materials: NEWS
In views: MachineLearning, Survival
CRAN checks: mboost results


Reference manual: mboost.pdf
Vignettes: Survival Ensembles
mboost Illustrations
mboost Tutorial
Package source: mboost_2.5-0.tar.gz
Windows binaries: r-devel: mboost_2.5-0.zip, r-release: mboost_2.5-0.zip, r-oldrel: mboost_2.5-0.zip
OS X Snow Leopard binaries: r-release: mboost_2.5-0.tgz, r-oldrel: mboost_2.4-2.tgz
OS X Mavericks binaries: r-release: mboost_2.5-0.tgz
Old sources: mboost archive

Reverse dependencies:

Reverse depends: CAM, expectreg, FDboost, gamboostLSS, globalboosttest, InvariantCausalPrediction, parboost
Reverse imports: bujar, DIFboost, gamboostMSM, SurvRank
Reverse suggests: catdata, Daim, fscaret, HSAUR2, HSAUR3, mlr, RBPcurve, spikeSlabGAM
Reverse enhances: stabs