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: | 2.9-7 |
| 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: | 2022-04-26 |
| 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 |
Documentation:
| Reference manual: | mboost.pdf |
| Vignettes: |
Survival Ensembles mboost mboost Illustrations mboost Tutorial |
Downloads:
| Package source: | mboost_2.9-7.tar.gz |
| Windows binaries: | r-devel: mboost_2.9-7.zip, r-release: mboost_2.9-7.zip, r-oldrel: mboost_2.9-7.zip |
| macOS binaries: | r-release (arm64): mboost_2.9-7.tgz, r-oldrel (arm64): mboost_2.9-7.tgz, r-release (x86_64): mboost_2.9-7.tgz, r-oldrel (x86_64): mboost_2.9-7.tgz |
| Old sources: | mboost archive |
Reverse dependencies:
| Reverse depends: | expectreg, FDboost, gamboostLSS, gfboost, InvariantCausalPrediction, tbm |
| Reverse imports: | biospear, bujar, carSurv, censored, DIFboost, EnMCB, gamboostMSM, visaOTR |
| Reverse suggests: | catdata, CompareCausalNetworks, familiar, fscaret, HSAUR2, HSAUR3, imputeR, MachineShop, MLInterfaces, mlr, pre, spikeSlabGAM, sqlscore, stabs |
Linking:
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