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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>.
| Version: | 2.2.0 |
| 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), MASS (≥ 7.3-53), ada (≥ 2.0-5), nnet (≥ 7.3-12), stringr (≥ 1.4.0), adabag, glmnet, ROCR, gbm, ggplot2 |
| Published: | 2023-11-09 |
| Author: | Oldemar Rodriguez R. [aut, cre], Andres Navarro D. [aut], Ariel Arroyo S. [aut], Diego Jimenez A. [aut] |
| Maintainer: | Oldemar Rodriguez R. <oldemar.rodriguez at ucr.ac.cr> |
| BugReports: | https://github.com/PROMiDAT/traineR/issues |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| URL: | https://promidat.website/,https://github.com/PROMiDAT/traineR |
| NeedsCompilation: | no |
| CRAN checks: | traineR results |
| Reference manual: | traineR.pdf |
| Package source: | traineR_2.2.0.tar.gz |
| Windows binaries: | r-devel: traineR_2.2.0.zip, r-release: traineR_2.2.0.zip, r-oldrel: traineR_2.2.0.zip |
| macOS binaries: | r-release (arm64): traineR_2.2.0.tgz, r-oldrel (arm64): traineR_2.2.0.tgz, r-release (x86_64): traineR_2.2.0.tgz, r-oldrel (x86_64): traineR_2.2.0.tgz |
| Old sources: | traineR archive |
| Reverse imports: | predictoR, regressoR |
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These binaries (installable software) and packages are in development.
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