Packaged: Mon Jun 19 13:51:45 2006; hothorn

Packaged: Wed Jul  5 14:25:32 2006; hothorn

Packaged: Mon Jul 17 21:32:00 2006; hothorn

Packaged: Thu Feb  1 11:30:30 2007; hothorn

Packaged: Fri Feb  2 14:28:46 2007; hothorn

Packaged: Wed Feb 28 15:47:10 2007; hothorn

Packaged: Fri Mar 23 13:21:04 2007; hothorn

Packaged: Thu Apr 19 17:55:13 2007; hothorn

Packaged: Wed Apr 25 10:10:53 2007; hothorn

Packaged: Mon May  7 13:16:58 2007; hothorn

Packaged: Wed May 30 12:13:51 2007; hothorn

Packaged: Thu May 31 11:41:20 2007; hothorn

Author: Torsten Hothorn and Peter Buhlmann with
contributions by Thomas Kneib and Matthias Schmid
Packaged: Tue Nov 13 18:14:52 2007; hothorn

Packaged: Sun Dec  9 15:15:45 2007; hothorn

Author: Torsten Hothorn, Peter Buhlmann, Thomas Kneib, Matthias Schmid and
Packaged: Thu Nov  6 11:49:15 2008; hothorn

Packaged: Fri Nov  7 09:29:37 2008; hothorn

Packaged: Thu Nov 13 05:02:12 2008; theussl

Packaged: Tue Dec  2 09:13:03 2008; hothorn

Packaged: Fri Jan  9 13:01:40 2009; hothorn

Author: Torsten Hothorn, Peter Buhlmann, Thomas Kneib, Matthias Schmid
Description: 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
Depends: R (>= 2.4.0), methods, modeltools (>= 0.2.10), party (>=
Packaged: Fri Mar 27 17:22:47 2009; hothorn

Author: Torsten Hothorn, Peter Buehlmann, Thomas Kneib, Matthias Schmid
Description: 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.
Imports: Matrix, survival, splines, lattice

Suggests: multicore, party (>= 0.9-9993), ipred, MASS, gbm

Suggests: multicore, party (>= 0.9-9993), ipred, MASS

Author: Torsten Hothorn, Peter Buehlmann, Thomas Kneib, Matthias Schmid and
Description: 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.
Suggests: multicore, party (>= 0.9-9993), ipred, MASS, fields, BayesX,