Model-Based Boosting CRAN Task View: MachineLearning (http://CRAN.R-project.org/view=MachineLearning) CRAN Task View: Survival (http://CRAN.R-project.org/view=Survival) 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. Software Depends: R (>= 2.10.0), methods, stats Imports: Matrix, survival, splines, lattice Suggests: multicore, party (>= 0.9-9993), ipred, MASS, fields, BayesX, gbm, mlbench, RColorBrewer Torsten Hothorn <Torsten.Hothorn@R-project.org> Torsten Hothorn [aut, cre], Peter Buehlmann [aut], Thomas Kneib [aut], Matthias Schmid [aut], Benjamin Hofner [aut] GPL-2 2012-02-29 application/tgz http://CRAN.R-project.org/package=mboost