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.
| Version: |
2.0-0 |
| Depends: |
R (≥ 2.9.0), methods, stats |
| Imports: |
Matrix, survival, splines, lattice |
| Suggests: |
multicore, party, ipred, MASS |
| Published: |
2010-02-01 |
| Author: |
Torsten Hothorn, Peter Buehlmann, 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 |