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 |
| 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 binaries: | r-release: classyfire_0.1-2.tgz, r-oldrel: classyfire_0.1-2.tgz |
| Old sources: | classyfire archive |
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