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 [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 resultsRetroSearch is an open source project built by @garambo | Open a GitHub Issue
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