Easily build and evaluate machine learning models on a dataset. Machine learning models supported include penalized linear models, penalized linear models with interactions, random forest, support vector machines, neural networks, and deep neural networks.
| Version: | 0.1.0 |
| Depends: | R (≥ 3.3.1) |
| Imports: | caret, corrplot, darch, dummies, e1071, futile.logger, ggplot2, glinternet, glmnet, parallel, pbapply, pbmcapply, pROC, nnet, randomForest, scales, scorer |
| Suggests: | covr, lintr, testthat, knitr, rmarkdown |
| Published: | 2017-06-26 |
| Author: | Woo-Young Ahn [aut, cre], Paul Hendricks [aut], OSU-CCSL [cph] |
| Maintainer: | Woo-Young Ahn <ahn.280 at osu.edu> |
| BugReports: | https://github.com/CCS-Lab/easyml/issues |
| License: | MIT + file LICENSE |
| URL: | https://github.com/CCS-Lab/easyml |
| NeedsCompilation: | no |
| Materials: | README NEWS |
| CRAN checks: | easyml results |
| Reference manual: | easyml.pdf |
| Package source: | easyml_0.1.0.tar.gz |
| Windows binaries: | r-devel: easyml_0.1.0.zip, r-release: easyml_0.1.0.zip, r-oldrel: easyml_0.1.0.zip |
| OS X El Capitan binaries: | r-release: easyml_0.1.0.tgz |
| OS X Mavericks binaries: | r-oldrel: easyml_0.1.0.tgz |
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