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, Bayesian Methods, Linear Discriminant Analysis and Quadratic Discriminant Analysis, Logistic Regression, Penalized Logistic Regression.

Version: 1.6.2
Depends: R (≥ 3.5)
Imports: neuralnet (≥ 1.44.2), rpart (≥ 4.1-13), xgboost (≥, randomForest (≥ 4.6-14), e1071 (≥ 1.7-0.1), kknn (≥ 1.3.1), dplyr (≥, MASS (≥ 7.3-53), ada (≥ 2.0-5), nnet (≥ 7.3-12), dummies (≥ 1.5.6), stringr (≥ 1.4.0), adabag, glmnet, ROCR, ggplot2, scales, glue, grDevices
Suggests: knitr, rmarkdown, rpart.plot
Published: 2021-06-03
Author: Oldemar Rodriguez R. [aut, cre], Andres Navarro D. [ctb, prg], Ariel Arroyo S. [ctb, prg]
Maintainer: Oldemar Rodriguez R. <oldemar.rodriguez at>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
CRAN checks: traineR results


Reference manual: traineR.pdf
Vignettes: traineR
Package source: traineR_1.6.2.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): traineR_1.6.2.tgz, r-release (x86_64): traineR_1.6.2.tgz, r-oldrel: traineR_1.6.2.tgz
Old sources: traineR archive


Please use the canonical form to link to this page.