Predictive (Classification and Regression) Models Homologator R package traineR version 2.2.12 Methods to unify the different ways of creating predictive models and their different predictive formats for classification and regression. It includes methods such as K-Nearest Neighbors Schliep, K. P. (2004) <doi:10.5282/ubm/epub.1769>, Decision Trees Leo Breiman, Jerome H. Friedman, Richard A. Olshen, Charles J. Stone (2017) <doi:10.1201/9781315139470>, ADA Boosting Esteban Alfaro, Matias Gamez, Noelia García (2013) <doi:10.18637/jss.v054.i02>, Extreme Gradient Boosting Chen & Guestrin (2016) <doi:10.1145/2939672.2939785>, Random Forest Breiman (2001) <doi:10.1023/A:1010933404324>, Neural Networks Venables, W. N., & Ripley, B. D. (2002) <ISBN:0-387-95457-0>, Support Vector Machines Bennett, K. P. & Campbell, C. (2000) <doi:10.1145/380995.380999>, Bayesian Methods Gelman, A., Carlin, J. B., Stern, H. S., & Rubin, D. B. (1995) <doi:10.1201/9780429258411>, Linear Discriminant Analysis Venables, W. N., & Ripley, B. D. (2002) <ISBN:0-387-95457-0>, Quadratic Discriminant Analysis Venables, W. N., & Ripley, B. D. (2002) <ISBN:0-387-95457-0>, Logistic Regression Dobson, A. J., & Barnett, A. G. (2018) <doi:10.1201/9781315182780> and Penalized Logistic Regression Friedman, J. H., Hastie, T., & Tibshirani, R. (2010) <doi:10.18637/jss.v033.i01>. Software Depends: R (>= 4.1) Imports: neuralnet (>= 1.44.2), rpart (>= 4.1-13), xgboost (>= 0.81.0.1), randomForest (>= 4.6-14), e1071 (>= 1.7-0.1), kknn (>= 1.4.1), dplyr (>= 1.0.0), MASS (>= 7.3-53), nnet (>= 7.3-12), stringr (>= 1.4.0), rlang, adabag, glmnet, ROCR, gbm, ggplot2 Suggests: rgl Oldemar Rodriguez R. <oldemar.rodriguez@ucr.ac.cr> Comprehensive R Archive Network (CRAN) Oldemar Rodriguez R. [aut, cre], Andres Navarro D. [aut], Ariel Arroyo S. [aut], Diego Jimenez A. [aut], Jennifer Lobo V. [aut] GPL (>= 2) 2026-03-03 application/tgz https://CRAN.R-project.org/package=traineR doi:10.32614/CRAN.package.traineR