traineR: Predictive Models Homologator

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

Downloads:

Reference manual: traineR.pdf
Vignettes: traineR
Package source: traineR_1.0.0.tar.gz
Windows binaries: r-devel: traineR_1.0.0.zip, r-devel-gcc8: traineR_1.0.0.zip, r-release: traineR_1.0.0.zip, r-oldrel: traineR_1.0.0.zip
OS X binaries: r-release: traineR_1.0.0.tgz, r-oldrel: traineR_1.0.0.tgz

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