Imports: circlize, data.table, doParallel, dplyr, DT, ggplot2,
graphics, grDevices, foreach, lattice , parallel, RColorBrewer,
rtracklayer, stats, stringr, unbalhaar, utils, webshot
Suggests: Hmisc, knitr, rmarkdown, testthat
Packaged: 2021-04-08 15:52:18 UTC; pablofonseca

Packaged: 2021-11-09 18:02:43 UTC; pablofonseca

Title: Generalized additive models by likelihood based boosting
Description: This package provides routines for fitting generalized additive models by likelihood based boosting, using penalized B-splines
Packaged: Mon Jul 10 07:56:10 2006; hornik

Packaged: Mon Oct  2 21:18:33 2006; binderh

Title: Generalized linear and additive models by likelihood based boosting
Description: This package provides routines for fitting generalized linear and and generalized additive models by likelihood based boosting, using penalized B-splines
Packaged: Tue Dec 16 19:34:58 2008; harald

Packaged: Mon Feb 16 14:35:38 2009; harald

Title: Generalized linear and additive models by likelihood based
Description: This package provides routines for fitting generalized
linear and and generalized additive models by likelihood based

Package: GAMens
Title: Applies GAMbag, GAMrsm and GAMens ensemble classifiers for
binary classification
Version: 1.0
Author: Koen W. De Bock, Kristof Coussement and Dirk Van den Poel
Maintainer: Koen W. De Bock <Koen.DeBock@UGent.be>
Depends: R (>= 2.4.0), splines, akima, gam
Description: This package implements the GAMbag, GAMrsm and GAMens
ensemble classifiers for binary classification (De Bock et al.,
2010). The ensembles implement Bagging (Breiman, 1996), the
Random Subspace Method (Ho, 1998), or both, and use Hastie and
Tibshirani's (1990) generalized additive models (GAMs) as base
classifiers. Once an ensemble classifier has been trained, it
can be used for predictions on new data. A function for cross
validation is also included.
License: GPL (>= 2)
Packaged: Mon Jan 11 11:20:11 2010; kwdebock
Repository: CRAN
Date/Publication: 2010-01-11 11:11:08

Package: GAMens
Title: Applies GAMbag, GAMrsm and GAMens ensemble classifiers for
binary classification
Version: 1.1.1
Author: Koen W. De Bock, Kristof Coussement and Dirk Van den Poel
Maintainer: Koen W. De Bock <Koen.DeBock@UGent.be>
Depends: R (>= 2.4.0), splines, akima, gam
Description: This package implements the GAMbag, GAMrsm and GAMens
ensemble classifiers for binary classification (De Bock et al.,
2010). The ensembles implement Bagging (Breiman, 1996), the
Random Subspace Method (Ho, 1998), or both, and use Hastie and
Tibshirani's (1990) generalized additive models (GAMs) as base
classifiers. Once an ensemble classifier has been trained, it
can be used for predictions on new data. A function for cross
validation is also included.
License: GPL (>= 2)
Packaged: Thu Feb 25 11:18:38 2010; kwdebock
Repository: CRAN
Date/Publication: 2010-02-25 11:29:32