easyml: Easily Build and Evaluate Machine Learning Models

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

Downloads:

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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