The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.

traineR: Predictive (Classification and Regression) Models Homologator

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
DOI: 10.32614/CRAN.package.traineR
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

Documentation:

Reference manual: traineR.pdf

Downloads:

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 dependencies:

Reverse imports: predictoR, regressoR

Linking:

Please use the canonical form https://CRAN.R-project.org/package=traineR to link to this page.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.