R Graphical Manual

Last data update: 2014.03.03

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Results 1 - 3 of 3 found.
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llama : Leveraging Learning to Automatically Manage Algorithms

Package: llama
Type: Package
Title: Leveraging Learning to Automatically Manage Algorithms
Version: 0.9.1
Date: 2015-12-04
Author: Lars Kotthoff [aut,cre],
Bernd Bischl [aut],
Barry Hurley [ctb],
Talal Rahwan [ctb]
Maintainer: Lars Kotthoff <larsko@cs.ubc.ca>
Description: Provides functionality to train and evaluate algorithm selection models for portfolios.
Depends: R (>= 2.10), mlr (>= 2.5)
Imports: rJava, parallelMap, ggplot2, checkmate, BBmisc, plyr
Suggests: testthat, ParamHelpers
License: BSD_3_clause + file LICENSE
URL: https://bitbucket.org/lkotthoff/llama
NeedsCompilation: no
Packaged: 2015-12-04 20:39:14 UTC; larsko
Repository: CRAN
Date/Publication: 2015-12-05 15:28:10

● Data Source: CranContrib
● 0 images, 20 functions, 1 datasets
● Reverse Depends: 0

RBPcurve : The Residual-Based Predictiveness Curve

Package: RBPcurve
Type: Package
Title: The Residual-Based Predictiveness Curve
Version: 1.0-33
Date: 2015-11-23
Author: Giuseppe Casalicchio, Bernd Bischl
URL: https://github.com/giuseppec/RBPcurve
BugReports: https://github.com/giuseppec/RBPcurve/issues
Maintainer: Giuseppe Casalicchio <giuseppe.casalicchio@stat.uni-muenchen.de>
Description: The RBP curve is a visual tool to assess the
performance of prediction models.
License: GPL-3
Depends: R (>= 3.0.2), mlr (>= 2.5)
Imports: BBmisc (>= 1.9), checkmate (>= 1.6.2), shape, TeachingDemos
Suggests: testthat, mlbench, mboost
RoxygenNote: 5.0.0
NeedsCompilation: no
Packaged: 2015-11-23 11:56:27 UTC; Admin2
Repository: CRAN
Date/Publication: 2015-11-23 13:09:04

● Data Source: CranContrib
● 1 images, 7 functions, 0 datasets
● Reverse Depends: 0

unbalanced : Racing for Unbalanced Methods Selection

Package: unbalanced
Type: Package
Title: Racing for Unbalanced Methods Selection
Version: 2.0
Date: 2015-06-25
Author: Andrea Dal Pozzolo, Olivier Caelen and Gianluca Bontempi
Maintainer: Andrea Dal Pozzolo <adalpozz@ulb.ac.be>
Description: A dataset is said to be unbalanced when the class of interest (minority class) is much rarer than normal behaviour (majority class). The cost of missing a minority class is typically much higher that missing a majority class. Most learning systems are not prepared to cope with unbalanced data and several techniques have been proposed. This package implements some of most well-known techniques and propose a racing algorithm to select adaptively the most appropriate strategy for a given unbalanced task.
License: GPL (>= 3)
URL: http://mlg.ulb.ac.be
Depends: mlr, foreach, doParallel
Imports: FNN, RANN
Suggests: randomForest, ROCR
Packaged: 2015-06-26 09:54:30 UTC; Andrea
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2015-06-26 13:34:37

● Data Source: CranContrib
● 0 images, 12 functions, 1 datasets
● Reverse Depends: 0