Model-Based Boosting R package mboost version 2.9-13 CRAN Task View: MachineLearning (https://CRAN.R-project.org/view=MachineLearning) CRAN Task View: Survival (https://CRAN.R-project.org/view=Survival) 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. Software 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, kangar00, bibtex, lme4, nlme, cobs Torsten Hothorn <Torsten.Hothorn@R-project.org> Comprehensive R Archive Network (CRAN) Torsten Hothorn [cre, aut] (ORCID: <https://orcid.org/0000-0001-8301-0471>), Peter Buehlmann [aut] (ORCID: <https://orcid.org/0000-0002-1782-6015>), Thomas Kneib [aut] (ORCID: <https://orcid.org/0000-0003-3390-0972>), Matthias Schmid [aut] (ORCID: <https://orcid.org/0000-0002-0788-0317>), Benjamin Hofner [aut] (ORCID: <https://orcid.org/0000-0003-2810-3186>), Fabian Otto-Sobotka [ctb] (ORCID: <https://orcid.org/0000-0002-9874-1311>), Fabian Scheipl [ctb] (ORCID: <https://orcid.org/0000-0001-8172-3603>), Andreas Mayr [ctb] (ORCID: <https://orcid.org/0000-0001-7106-9732>) GPL-2 2026-07-17 application/tgz https://CRAN.R-project.org/package=mboost doi:10.32614/CRAN.package.mboost