Last data update: 2014.03.03

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conformal : Conformal Prediction for Regression and Classification

Package: conformal
Type: Package
Title: Conformal Prediction for Regression and Classification
Version: 0.2
Date: 2016-03-06
Author: Isidro Cortes <isidrolauscher@gmail.com>
Maintainer: Isidro Cortes <isidrolauscher@gmail.com>
Depends: R (>= 2.12.0), caret, ggplot2, e1071, methods, grid
Imports: randomForest
Suggests: kernlab
Description: Implementation of conformal prediction using caret models for classification and regression.
License: GPL
LazyLoad: yes
Packaged: 2016-03-07 00:24:40 UTC; icortes
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2016-03-07 07:59:51

● Data Source: CranContrib
● 0 images, 14 functions, 0 datasets
● Reverse Depends: 0

fscaret : Automated Feature Selection from 'caret'

Package: fscaret
Type: Package
Title: Automated Feature Selection from 'caret'
Version: 0.9.4
Date: 2015-04-18
Depends: R (>= 3.1.0), caret, gsubfn, hmeasure, utils, parallel
Suggests: ada, arm, Boruta, bst, C50, car, caTools, class, Cubist,
e1071, earth (>= 2.2-3), elasticnet, ellipse, evtree,
extraTrees, fastICA, foba, gam, gbm (>= 2.1), glmnet (>= 1.8),
hda, HDclassif, Hmisc, ipred, kernlab, kknn, klaR, kohonen,
KRLS, lars, leaps, LogicReg, MASS, mboost, mda, mgcv, mlbench,
neuralnet, nnet, nodeHarvest, obliqueRF, pamr, partDSA, party
(>= 0.9-99992), penalized, penalizedLDA, pls, pROC, proxy,
qrnn, quantregForest, randomForest, RANN, relaxo, rFerns, rocc,
rpart, rrcov, RRF, rrlda, RSNNS, RWeka (>= 0.4-1), sda,
sparseLDA (>= 0.1-1), spls, stepPlr, superpc
Authors@R: c(
person("Jakub","Szlek",email="j.szlek@uj.edu.pl",role=c("aut","cre")),
person("Aleksander", "Mendyk",role="ctb"))
Maintainer: Jakub Szlek <j.szlek@uj.edu.pl>
License: GPL-2 | GPL-3
Description: Automated feature selection using variety of models
provided by 'caret' package.
This work was funded by Poland-Singapore bilateral cooperation
project no 2/3/POL-SIN/2012.
Packaged: 2015-04-19 20:28:35 UTC; kuba
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2015-04-20 17:50:40
Author: Jakub Szlek [aut, cre],
Aleksander Mendyk [ctb]

● Data Source: CranContrib
● 0 images, 11 functions, 5 datasets
● Reverse Depends: 0

hsdar : Manage, Analyse and Simulate Hyperspectral Data

Package: hsdar
Type: Package
Title: Manage, Analyse and Simulate Hyperspectral Data
Version: 0.4.1
Date: 2016-02-23
Author: Lukas W. Lehnert [cre, aut],
Hanna Meyer [aut],
Joerg Bendix [aut]
Maintainer: Lukas W. Lehnert <lukaslehnert@googlemail.com>
Depends: R (>= 2.10.0), raster, rgdal, rootSolve, signal, methods,
caret
Suggests: rgl, RCurl, pracma
Description: Transformation of reflectance spectra, calculation of vegetation indices and red edge parameters, spectral resampling for hyperspectral remote sensing, simulation of reflectance and transmittance using the leaf reflectance model PROSPECT and the canopy reflectance model PROSAIL.
License: GPL
LazyLoad: yes
BuildVignettes: yes
Copyright: see file COPYRIGHTS
URL: http://lcrs.geographie.uni-marburg.de
http://teledetection.ipgp.jussieu.fr/prosail/
Packaged: 2016-02-23 10:25:14 UTC; lehnert
NeedsCompilation: yes
Repository: CRAN
Date/Publication: 2016-02-23 13:46:01

● Data Source: CranContrib
● 0 images, 81 functions, 3 datasets
● Reverse Depends: 0

adabag : Applies Multiclass AdaBoost.M1, SAMME and Bagging

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

● Data Source: CranContrib
● 0 images, 15 functions, 0 datasets
● Reverse Depends: 0

ordBTL : Modelling comparison data with ordinal response

Package: ordBTL
Type: Package
Title: Modelling comparison data with ordinal response
Version: 0.8
Date: 2014-05-07
Author: Giuseppe Casalicchio
Maintainer: Giuseppe Casalicchio <giuseppe.casalicchio@campus.lmu.de>
Description: This package extends the Bradley-Terry-Luce model for fitting pair
comparison models with an ordinal response. It is also possible to
incorporate an order effect, or, equivalently, an effect for the home
advantage.
License: GPL-3
Depends: caret, VGAM, wikibooks, gtools
Suggests: BradleyTerry2, prefmod, psychotree, psychotools, psychomix
LazyData: TRUE
Packaged: 2014-05-28 08:51:17 UTC; admin2
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2014-05-28 18:39:18

● Data Source: CranContrib
● 0 images, 6 functions, 2 datasets
● Reverse Depends: 0