mboost: 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. 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.

Package details

AuthorTorsten Hothorn [cre, aut] (<https://orcid.org/0000-0001-8301-0471>), Peter Buehlmann [aut] (<https://orcid.org/0000-0002-1782-6015>), Thomas Kneib [aut] (<https://orcid.org/0000-0003-3390-0972>), Matthias Schmid [aut] (<https://orcid.org/0000-0002-0788-0317>), Benjamin Hofner [aut] (<https://orcid.org/0000-0003-2810-3186>), Fabian Otto-Sobotka [ctb] (<https://orcid.org/0000-0002-9874-1311>), Fabian Scheipl [ctb] (<https://orcid.org/0000-0001-8172-3603>), Andreas Mayr [ctb] (<https://orcid.org/0000-0001-7106-9732>)
MaintainerTorsten Hothorn <Torsten.Hothorn@R-project.org>
URL https://github.com/boost-R/mboost
Package repositoryView on CRAN
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mboost documentation built on Sept. 8, 2023, 6:15 p.m.