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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 eight algorithms for ensemble classification (svm, slda, boosting, bagging, random forests, glmnet, decision trees, neural networks), comprehensive analytics, and thorough documentation.
| Version: | 1.4.3 |
| Depends: | R (≥ 3.6.0), SparseM |
| Imports: | methods, randomForest, tree, nnet, tm, e1071, ipred, caTools, glmnet, tau |
| Published: | 2020-04-26 |
| DOI: | 10.32614/CRAN.package.RTextTools |
| Author: | Timothy P. Jurka, Loren Collingwood, Amber E. Boydstun, Emiliano Grossman, Wouter van Atteveldt |
| Maintainer: | Loren Collingwood <loren.collingwood at gmail.com> |
| License: | GPL-3 |
| URL: | http://www.rtexttools.com/ |
| NeedsCompilation: | yes |
| Materials: | ChangeLog |
| CRAN checks: | RTextTools results |
| Reference manual: | RTextTools.pdf |
| Package source: | RTextTools_1.4.3.tar.gz |
| Windows binaries: | r-devel: RTextTools_1.4.3.zip, r-release: RTextTools_1.4.3.zip, r-oldrel: RTextTools_1.4.3.zip |
| macOS binaries: | r-release (arm64): RTextTools_1.4.3.tgz, r-oldrel (arm64): RTextTools_1.4.3.tgz, r-release (x86_64): RTextTools_1.4.3.tgz, r-oldrel (x86_64): RTextTools_1.4.3.tgz |
| Old sources: | RTextTools archive |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.