Title: Automated Feature Selection from 'caret'
Depends: R (>= 3.1.0), caret, gsubfn, hmeasure, utils, parallel
This work was funded by Poland-Singapore bilateral cooperation

Package: fscaret
Type: Package
Title: Automated caret feature selection
Version: 0.9
Date: 2014-11-5
Depends: R (>= 3.0.0), 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
Author: Jakub Szlek <j.szlek@uj.edu.pl>, acknowledgments to Aleksander
Mendyk, contributions from stackoverflow and
r-help@r-project.org mailing list community.
Acknowledgments: This work was funded by Poland-Singapore bilateral cooperation
project no 2/3/POL-SIN/2012.
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
Packaged: 2015-01-11 11:35:28 UTC; kuba
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2015-01-11 13:15:37

Title: Robust Data Analysis Through Monitoring and Dynamic
Author: Marco Riani<fsda@unipr.it>, Andrea Cerioli <fsda@unipr.it>, Aldo Corbellini<fsda@unipr.it>,
Domenico Perrotta <fsda@unipr.it>, Francesca Torti <fsda@unipr.it>, Emmanuele Sordini<fsda@unipr.it>,
Description: Provides interface to the 'MATLAB' toolbox 'Flexible Statistical Data Analysis
(FSDA)' which is a comprehensive and computationally efficient
software package for robust statistics in regression, multivariate
and categorical data analysis. The current R version implements tools
for regression: (forward search, S- and MM-estimation, least trimmed
squares (LTS) and least median of squares (LMS)). The distinctive
feature of our regression package relies in the possibility of
monitoring the statistics of interest as function of breakdown point,
efficiency or subset size, depending on the estimator. This is
accompanied by a rich set of graphical features, such as dynamic
brushing, linking, particularly useful for exploratory data analysis.