classyfire: Robust multivariate classification using highly optimised SVM ensembles

A collection of functions for the creation and application of highly optimised, robustly evaluated ensembles of support vector machines (SVMs). The package takes care of training individual SVM classifiers using a fast parallel heuristic algorithm, and combines individual classifiers into ensembles. Robust metrics of classification performance are offered by bootstrap resampling and permutation testing.

Version: 0.1-2
Depends: R (≥ 3.0.0), snowfall (≥ 1.84-6), e1071 (≥ 1.6-3), boot (≥ 1.3-11), neldermead (≥ 1.0-9)
Imports: ggplot2 (≥ 1.0-0), optimbase (≥ 1.0-9)
Suggests: RUnit, knitr
Published: 2015-01-12
Author: Eleni Chatzimichali and Conrad Bessant
Maintainer: Eleni Chatzimichali <ea.chatzimichali at gmail.com>
BugReports: https://github.com/eaHat/classyfire/issues
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
Materials: NEWS
CRAN checks: classyfire results

Downloads:

Reference manual: classyfire.pdf
Vignettes: Classyfire Cheat Sheet
Package source: classyfire_0.1-2.tar.gz
Windows binaries: r-devel: classyfire_0.1-2.zip, r-release: classyfire_0.1-2.zip, r-oldrel: classyfire_0.1-2.zip
OS X El Capitan binaries: r-release: classyfire_0.1-2.tgz
OS X Mavericks binaries: r-oldrel: classyfire_0.1-2.tgz
Old sources: classyfire archive

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