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DESCRIPTION
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Package: mboost
Title: Model-Based Boosting
Version: 2.9-7
Date: 2022-04-25
Authors@R: c(person("Torsten", "Hothorn", role = c("cre", "aut"),
email = "[email protected]",
comment = c(ORCID = "0000-0001-8301-0471")),
person("Peter", "Buehlmann", role = "aut",
comment = c(ORCID = "0000-0002-1782-6015")),
person("Thomas", "Kneib", role = "aut",
comment = c(ORCID = "0000-0003-3390-0972")),
person("Matthias", "Schmid", role = "aut",
comment = c(ORCID = "0000-0002-0788-0317")),
person("Benjamin", "Hofner", role = "aut",
comment = c(ORCID = "0000-0003-2810-3186")),
person("Fabian", "Otto-Sobotka", role = "ctb",
comment = c(ORCID = "0000-0002-9874-1311")),
person("Fabian", "Scheipl", role = "ctb",
comment = c(ORCID = "0000-0001-8172-3603")),
person("Andreas", "Mayr", role = "ctb",
comment = c(ORCID = "0000-0001-7106-9732")))
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.
Models and algorithms are described in <doi:10.1214/07-STS242>,
a hands-on tutorial is available from <doi:10.1007/s00180-012-0382-5>.
The package allows user-specified loss functions and base-learners.
Depends: R (>= 3.2.0), methods, stats, parallel, stabs (>= 0.5-0)
Imports: Matrix, survival (>= 3.2-10), splines, lattice, nnls, quadprog, utils,
graphics, grDevices, partykit (>= 1.2-1)
Suggests: TH.data, MASS, fields, BayesX, gbm, mlbench,
RColorBrewer, rpart (>= 4.0-3), randomForest, nnet,
testthat (>= 0.10.0), kangar00
License: GPL-2
BugReports: https://github.com/boost-R/mboost/issues
URL: https://github.com/boost-R/mboost