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 | 
| 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.html , 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, FDboost, gamboostLSS, gfboost, InvariantCausalPrediction, mermboost, tbm | 
| Reverse imports: | biospear, bujar, carSurv, censored, DIFboost, EnMCB, gamboostMSM, GeDS, geoGAM, mgwrsar, RobustPrediction, sgboost, survML, visaOTR | 
| Reverse suggests: | catdata, CompareCausalNetworks, familiar, flowml, HSAUR2, HSAUR3, imputeR, MachineShop, MLInterfaces, mlr, mlr3fda, pathMED, pre, spikeSlabGAM, sqlscore, stabs, survex, tidyfit | 
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