Model-based boosting in high dimensions.
CONCLUSIONS
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.


BACKGROUND
Package mboost is available from the Comprehensive R Archive Network (http://CRAN.R-project.org) under the terms of the General Public Licence (GPL).
Hothorn
Torsten
T
Institut für Medizininformatik, Biometrie und Epidemiologie, Friedrich-Alexander-Universität Erlangen-Nürnberg Waldstrasse 6, D-91054 Erlangen, Germany. Torsten.Hothorn@R-project.org
Bühlmann
Peter
P
D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D003196:Computer Graphics; D020869:Gene Expression Profiling; D006801:Humans; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis; D011381:Programming Languages; D012984:Software