Methods to unify the different ways of creating predictive models and their different predictive formats. It includes methods such as K-Nearest Neighbors, Decision Trees, ADA Boosting, Extreme Gradient Boosting, Random Forest, Neural Networks, Deep Learning, Support Vector Machines and Bayesian Methods.
| Version: | 1.0.0 |
| Depends: | R (≥ 3.5) |
| Imports: | neuralnet (≥ 1.44.2), rpart (≥ 4.1-13), xgboost (≥ 0.81.0.1), randomForest (≥ 4.6-14), e1071 (≥ 1.7-0.1), kknn (≥ 1.3.1), dplyr (≥ 0.8.0.1), ada (≥ 2.0-5), nnet (≥ 7.3-12), dummies (≥ 1.5.6), stringr (≥ 1.4.0) |
| Suggests: | knitr, rmarkdown, rpart.plot |
| Published: | 2019-10-07 |
| Author: | Oldemar Rodriguez R. [aut, cre], Andres Navarro D. [ctb, prg] |
| Maintainer: | Oldemar Rodriguez R. <oldemar.rodriguez at ucr.ac.cr> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| URL: | http://www.promidat.com |
| NeedsCompilation: | no |
| CRAN checks: | traineR results |
| Reference manual: | traineR.pdf |
| Vignettes: |
traineR |
| Package source: | traineR_1.0.0.tar.gz |
| Windows binaries: | r-devel: traineR_1.0.0.zip, r-release: traineR_1.0.0.zip, r-oldrel: traineR_1.0.0.zip |
| macOS binaries: | r-release: traineR_1.0.0.tgz, r-oldrel: traineR_1.0.0.tgz |
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