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.

Documentation

Manual: classyfire.pdf
Vignette: Classyfire Cheat Sheet

Maintainer: Eleni Chatzimichali <ea.chatzimichali at gmail.com>

Author(s): Eleni Chatzimichali <ea.chatzimichali at gmail.com> and Conrad Bessant <c.bessant at qmul.ac.uk>

Install package and any missing dependencies by running this line in your R console:

install.packages("classyfire")

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
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Package classyfire
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Version 0.1-2
Published 2015-01-12
License GPL (>= 2)
BugReports https://github.com/eaHat/classyfire/issues
SystemRequirements
NeedsCompilation no
Citation
CRAN checks classyfire check results
Package source classyfire_0.1-2.tar.gz