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: 2.9-3
Depends: R (≥ 3.2.0), methods, stats, parallel, stabs (≥ 0.5-0)
Imports: Matrix, survival, splines, lattice, nnls, quadprog, utils, graphics, grDevices, partykit (≥ 1.2-1)
Suggests: TH.data, MASS, fields, BayesX, gbm, mlbench, RColorBrewer, rpart (≥ 4.0-3), randomForest, nnet, testthat (≥ 0.10.0), kangar00
Published: 2020-08-06
Author: Torsten Hothorn ORCID iD [aut], Peter Buehlmann [aut], Thomas Kneib [aut], Matthias Schmid [aut], Benjamin Hofner ORCID iD [aut, cre], Fabian Sobotka [ctb], Fabian Scheipl [ctb], Andreas Mayr [ctb]
Maintainer: Benjamin Hofner <benjamin.hofner at pei.de>
BugReports: https://github.com/boost-R/mboost/issues
License: GPL-2
URL: https://github.com/boost-R/mboost
NeedsCompilation: yes
Citation: mboost citation info
Materials: README NEWS
In views: MachineLearning, Survival
CRAN checks: mboost results


Reference manual: mboost.pdf
Vignettes: Survival Ensembles
mboost Illustrations
mboost Tutorial
Package source: mboost_2.9-3.tar.gz
Windows binaries: r-devel: mboost_2.9-3.zip, r-release: mboost_2.9-3.zip, r-oldrel: mboost_2.9-3.zip
macOS binaries: r-release: mboost_2.9-3.tgz, r-oldrel: mboost_2.9-3.tgz
Old sources: mboost archive

Reverse dependencies:

Reverse depends: betaboost, FDboost, gamboostLSS, globalboosttest, InvariantCausalPrediction, parboost, tbm
Reverse imports: biospear, bujar, carSurv, DIFboost, gamboostMSM, geoGAM
Reverse suggests: catdata, CompareCausalNetworks, compboost, fscaret, HSAUR2, HSAUR3, imputeR, MachineShop, MLInterfaces, mlr, pre, spikeSlabGAM, sqlscore, stabs


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