RTextTools: Automatic Text Classification via Supervised Learning

RTextTools is a machine learning package for automatic text classification that makes it simple for novice users to get started with machine learning, while allowing experienced users to easily experiment with different settings and algorithm combinations. The package includes nine algorithms for ensemble classification (svm, slda, boosting, bagging, random forests, glmnet, decision trees, neural networks, maximum entropy), comprehensive analytics, and thorough documentation.

Version: 1.4.2
Depends: R (≥ 2.15.0), SparseM
Imports: methods, randomForest, tree, nnet, tm, e1071, ipred, caTools, maxent, glmnet, tau
Published: 2014-01-19
Author: Timothy P. Jurka, Loren Collingwood, Amber E. Boydstun, Emiliano Grossman, Wouter van Atteveldt
Maintainer: Timothy P. Jurka <tpjurka at ucdavis.edu>
License: GPL-3
URL: http://www.rtexttools.com/
NeedsCompilation: yes
Materials: ChangeLog
In views: NaturalLanguageProcessing
CRAN checks: RTextTools results


Reference manual: RTextTools.pdf
Package source: RTextTools_1.4.2.tar.gz
Windows binaries: r-devel: RTextTools_1.4.2.zip, r-release: RTextTools_1.4.2.zip, r-oldrel: RTextTools_1.4.2.zip
OS X Mavericks binaries: r-release: RTextTools_1.4.2.tgz, r-oldrel: RTextTools_1.4.2.tgz
Old sources: RTextTools archive

Reverse dependencies:

Reverse suggests: RNewsflow