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Bioinformatics Advance Access originally published online on August 29, 2006
Bioinformatics 2006 22(22):2828-2829; doi:10.1093/bioinformatics/btl462
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© The Author 2006. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Model-based boosting in high dimensions

Torsten Hothorn 1,* and Peter Bühlmann 2

1 Institut für Medizininformatik, Biometrie und Epidemiologie, Friedrich-Alexander-Universität Erlangen-Nürnberg Waldstraße 6, D-91054 Erlangen, Germany
2 Seminar für Statistik, ETH Zürich CH-8092 Zürich, Switzerland

*To whom correspondence should be addressed.

Summary: The R add-on package mboost implements functional gradient descent algorithms (boosting) for optimizing general loss functions utilizing componentwise least squares, either of parametric linear form or smoothing splines, or regression trees as base learners for fitting generalized linear, additive and interaction models to potentially high-dimensional data.

Availability: Package mboost is available from the Comprehensive R Archive Network (http://CRAN.R-project.org) under the terms of the General Public Licence (GPL).

Contact: Torsten.Hothorn{at}R-project.org

Received on July 9, 2006; revised on August 22, 2006; accepted on August 24, 2006

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