BiocStyle, Hmisc, knitr, rmarkdown, testthat

circlize, DT, data.table, doParallel, dplyr, dynamicTreeCut,
ggplot2, graphics, grDevices, foreach, lattice , magick,
parallel, RColorBrewer, rtracklayer, stats, stringr, unbalhaar,
utils

circlize, data.table, doParallel, dplyr, DT, ggplot2,
graphics, grDevices, foreach, lattice , parallel, RColorBrewer,
rtracklayer, stats, stringr, unbalhaar, utils, webshot

Generalized additive models by likelihood based boosting
This package provides routines for fitting generalized additive models by likelihood based boosting, using penalized B-splines

Generalized linear and additive models by likelihood based boosting
This package provides routines for fitting generalized linear and and generalized additive models by likelihood based boosting, using penalized B-splines

Generalized linear and additive models by likelihood based
boosting
This package provides routines for fitting generalized
linear and and generalized additive models by likelihood based
boosting, using penalized B-splines

Applies GAMbag, GAMrsm and GAMens ensemble classifiers for
binary classification
Koen W. De Bock, Kristof Coussement and Dirk Van den Poel
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.

R (>= 2.4.0), splines, akima, gam, mlbench, caTools

Applies GAMbag, GAMrsm and GAMens Ensemble Classifiers for
Binary Classification
R (>= 2.4.0), splines, gam, mlbench, caTools
Implements the GAMbag, GAMrsm and GAMens ensemble
classifiers for binary classification (De Bock et al., 2010) <doi:10.1016/j.csda.2009.12.013>. The ensembles
implement Bagging (Breiman, 1996) <doi:10.1023/A:1010933404324>, the Random Subspace Method (Ho, 1998) <doi:10.1109/34.709601>
, or both, and use Hastie and Tibshirani's (1990, ISBN:978-0412343902) 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.