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: | 2.9-11 |
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: | 2024-08-22 |
DOI: | 10.32614/CRAN.package.mboost |
Author: | Torsten Hothorn [cre, aut], Peter Buehlmann [aut], Thomas Kneib [aut], Matthias Schmid [aut], Benjamin Hofner [aut], Fabian Otto-Sobotka [ctb], Fabian Scheipl [ctb], Andreas Mayr [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 (source, R code) mboost (source, R code) mboost Illustrations (source, R code) mboost Tutorial (source, R code) |
Package source: | mboost_2.9-11.tar.gz |
Windows binaries: | r-devel: mboost_2.9-11.zip, r-release: mboost_2.9-11.zip, r-oldrel: mboost_2.9-11.zip |
macOS binaries: | r-release (arm64): mboost_2.9-11.tgz, r-oldrel (arm64): mboost_2.9-11.tgz, r-release (x86_64): mboost_2.9-11.tgz, r-oldrel (x86_64): mboost_2.9-11.tgz |
Old sources: | mboost archive |
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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