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.


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mboost implements boosting algorithms for fitting generalized linear, additive and interaction models to potentially high-dimensional data.

For installation instructions see below.

Instructions on how to use mboost can be found in various places:

Issues & Feature Requests

For issues, bugs, feature requests etc. please use the GitHub Issues.

Installation Instructions

  • Current version (from CRAN):

  • Latest patch version (patched version of CRAN package; under development) from GitHub:

  • Latest development version (version with new features; under development) from GitHub:

    install_github("boost-R/mboost", ref = "devel")

    To be able to use the install_github() command, one needs to install devtools first:



Reference manual

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2.9-0 by Benjamin Hofner, 12 days ago

Report a bug at

Browse source code at

Authors: Torsten Hothorn [aut] (<>), Peter Buehlmann [aut], Thomas Kneib [aut], Matthias Schmid [aut], Benjamin Hofner [aut, cre] (<>), Fabian Sobotka [ctb], Fabian Scheipl [ctb], Andreas Mayr [ctb]

Documentation:   PDF Manual  

Task views: Machine Learning & Statistical Learning, Survival Analysis

GPL-2 license

Imports Matrix, survival, splines, lattice, nnls, quadprog, utils, graphics, grDevices, partykit

Depends on methods, stats, parallel, stabs

Suggests, MASS, fields, BayesX, gbm, mlbench, RColorBrewer, rpart, randomForest, nnet, testthat, kangar00

Imported by DIFboost, SurvRank, bujar, carSurv, gamboostMSM, geoGAM, imputeR.

Depended on by CAM, FDboost, InvariantCausalPrediction, betaboost, gamboostLSS, globalboosttest, parboost.

Suggested by CompareCausalNetworks, Daim, HSAUR2, HSAUR3, catdata, fscaret, mlr, spikeSlabGAM, sqlscore, stabs.

See at CRAN