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. Models and algorithms are described in <doi:10.1214/07-STS242>, a hands-on tutorial is available from <doi:10.1007/s00180-012-0382-5>. The package allows user-specified loss functions and base-learners.

Version: 2.9-10
Depends: R (≥ 3.2.0), methods, stats, parallel, stabs (≥ 0.5-0)
Imports: Matrix, survival (≥ 3.2-10), 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: 2024-04-29
DOI: 10.32614/CRAN.package.mboost
Author: Torsten Hothorn ORCID iD [cre, aut], Peter Buehlmann ORCID iD [aut], Thomas Kneib ORCID iD [aut], Matthias Schmid ORCID iD [aut], Benjamin Hofner ORCID iD [aut], Fabian Otto-Sobotka ORCID iD [ctb], Fabian Scheipl ORCID iD [ctb], Andreas Mayr ORCID iD [ctb]
Maintainer: Torsten Hothorn <Torsten.Hothorn at R-project.org>
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: NEWS
In views: MachineLearning, Survival
CRAN checks: mboost results


Reference manual: mboost.pdf
Vignettes: Survival Ensembles
mboost Illustrations
mboost Tutorial


Package source: mboost_2.9-10.tar.gz
Windows binaries: r-devel: mboost_2.9-10.zip, r-release: mboost_2.9-10.zip, r-oldrel: mboost_2.9-10.zip
macOS binaries: r-release (arm64): mboost_2.9-10.tgz, r-oldrel (arm64): mboost_2.9-10.tgz, r-release (x86_64): mboost_2.9-10.tgz, r-oldrel (x86_64): mboost_2.9-10.tgz
Old sources: mboost archive

Reverse dependencies:

Reverse depends: boostrq, expectreg, FDboost, gamboostLSS, gfboost, InvariantCausalPrediction, tbm
Reverse imports: biospear, bujar, carSurv, censored, DIFboost, EnMCB, gamboostMSM, GeDS, geoGAM, mgwrsar, sgboost, visaOTR
Reverse suggests: catdata, CompareCausalNetworks, familiar, flowml, fscaret, HSAUR2, HSAUR3, imputeR, MachineShop, MLInterfaces, mlr, pre, spikeSlabGAM, sqlscore, stabs, survex, tidyfit


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