mboost: Model-Based Boosting (original) (raw)
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:
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:
URL:
https://github.com/boost-R/mboost
NeedsCompilation:
yes
Citation:
Materials:
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