Torsten Hothorn and Peter Buhlmann with
contributions by Thomas Kneib and Matthias Schmid

Torsten Hothorn, Peter Buhlmann, Thomas Kneib, Matthias Schmid and
Benjamin Hofner

Torsten Hothorn, Peter Buhlmann, Thomas Kneib, Matthias Schmid
and Benjamin Hofner
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.

Torsten Hothorn, Peter Buehlmann, Thomas Kneib, Matthias Schmid
and Benjamin Hofner
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.

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

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

Torsten Hothorn, Peter Buehlmann, Thomas Kneib, Matthias Schmid and
Benjamin Hofner
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.
multicore, party (>= 0.9-9993), ipred, MASS, fields, BayesX,
gbm

c(person("Torsten", "Hothorn", role = c("aut", "cre"), email
= "Torsten.Hothorn@R-project.org"), person("Peter",
"Buehlmann", role = "aut"), person("Thomas", "Kneib", role =
"aut"), person("Matthias", "Schmid", role = "aut"),
person("Benjamin", "Hofner", role = "aut"), person("Fabian",
"Sobotka", role = "ctr"), person("Fabian", "Scheipl", role =
"ctr"))
multicore, party (>= 0.9-9993), ipred, MASS, fields, BayesX,
gbm, mlbench, RColorBrewer
Torsten Hothorn [aut, cre], Peter Buehlmann [aut], Thomas Kneib
[aut], Matthias Schmid [aut], Benjamin Hofner [aut]