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Bioinformatics Advance Access published online on August 29, 2006
Bioinformatics, 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
Received July 9, 2006
Revised August 22, 2006
Accepted August 24, 2006

Applications note

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.
Torsten Hothorn, E-mail: Torsten.Hothorn{at}R-project.org


  Abstract

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).

Associate Editor: Keith A Crandall
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