title: Model-based boosting in high dimensions.
abstract: 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).
authors.affiliation: Institut für Medizininformatik, Biometrie und Epidemiologie, Friedrich-Alexander-Universität Erlangen-Nürnberg Waldstrasse 6, D-91054 Erlangen, Germany. Torsten.Hothorn@R-project.org
authors.forename: Torsten
authors.initials: T
authors.lastname: Hothorn
authors.forename: Peter
authors.initials: P
authors.lastname: Bühlmann
mesh_terms: 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