Package: GAMens
Type: Package
Title: Applies GAMbag, GAMrsm and GAMens Ensemble Classifiers for
Binary Classification
Version: 1.2
Date: 2016-02-26
Author: Koen W. De Bock, Kristof Coussement and Dirk Van den Poel
Maintainer: Koen W. De Bock <K.DeBock@ieseg.fr>
Depends: R (>= 2.4.0), splines, gam, mlbench, caTools
Description: Ensemble classifiers based upon generalized additive models 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:1018054314350>), the Random Subspace Method (Ho (1998) <DOI:10.1109/34.709601>), 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)
NeedsCompilation: no
Packaged: 2016-03-01 18:33:17 UTC; k.debock
Repository: CRAN
Date/Publication: 2016-03-02 01:56:37
Package: adabag
Type: Package
Title: Applies Multiclass AdaBoost.M1, SAMME and Bagging
Version: 4.1
Date: 2015-10-14
Author: Alfaro, Esteban; Gamez, Matias and Garcia, Noelia; with contributions from Li Guo
Maintainer: Esteban Alfaro <Esteban.Alfaro@uclm.es>
Depends: rpart, mlbench, caret
Description: It implements Freund and Schapire's Adaboost.M1 algorithm and Breiman's Bagging
algorithm using classification trees as individual classifiers. Once these classifiers have been
trained, they can be used to predict on new data. Also, cross validation estimation of the error can
be done. Since version 2.0 the function margins() is available to calculate the margins for these
classifiers. Also a higher flexibility is achieved giving access to the rpart.control() argument
of 'rpart'. Four important new features were introduced on version 3.0, AdaBoost-SAMME (Zhu
et al., 2009) is implemented and a new function errorevol() shows the error of the ensembles as
a function of the number of iterations. In addition, the ensembles can be pruned using the option
'newmfinal' in the predict.bagging() and predict.boosting() functions and the posterior probability of
each class for observations can be obtained. Version 3.1 modifies the relative importance measure
to take into account the gain of the Gini index given by a variable in each tree and the weights of
these trees. Version 4.0 includes the margin-based ordered aggregation for Bagging pruning (Guo
and Boukir, 2013) and a function to auto prune the 'rpart' tree. Moreover, three new plots are also
available importanceplot(), plot.errorevol() and plot.margins(). Version 4.1 allows to predict on
unlabeled data.
License: GPL (>= 2)
LazyLoad: yes
NeedsCompilation: no
Packaged: 2015-10-14 20:14:49 UTC; Esteban
Repository: CRAN
Date/Publication: 2015-10-14 23:41:31
Package: trimTrees
Type: Package
Title: Trimmed opinion pools of trees in a random forest
Version: 1.2
Date: 2014-08-1
Depends: R (>= 2.5.0), stats, randomForest, mlbench
Author: Yael Grushka-Cockayne, Victor Richmond R. Jose, Kenneth C. Lichtendahl Jr. and Huanghui Zeng, based on the source code from the randomForest package by Andy Liaw and Matthew Wiener and on the original Fortran code by Leo Breiman and Adele Cutler.
Maintainer: Yael Grushka-Cockayne <grushkay@darden.virginia.edu>
Description: Creates point and probability forecasts from the trees in a random forest using a trimmed opinion pool.
Suggests: MASS
License: GPL (>= 2)
Packaged: 2014-08-13 21:48:25 UTC; lichtendahlc
NeedsCompilation: yes
Repository: CRAN
Date/Publication: 2014-08-14 07:11:17
providing 
.