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.
| Version: |
1.1-4 |
| Depends: |
R (≥ 2.4.0), methods, modeltools (≥ 0.2.10), party (≥
0.9-993), splines |
| Suggests: |
mlbench, ipred, multicore |
| Published: |
2009-11-18 |
| Author: |
Torsten Hothorn, Peter Buhlmann, Thomas Kneib, Matthias Schmid
and Benjamin Hofner |
| Maintainer: |
Torsten Hothorn <Torsten.Hothorn at R-project.org> |
| License: |
GPL-2 |
| In views: |
MachineLearning, Survival |
| CRAN checks: |
mboost results |