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traineR
View on CRAN: Click
here
Download and install traineR package within the R console
Install from CRAN:
install.packages("traineR")
Install from Github:
library("remotes")
install_github("cran/traineR")
Install by package version:
library("remotes")
install_version("traineR", "2.2.0")
Attach the package and use:
library("traineR")
Maintained by
Oldemar Rodriguez R.
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
10.32614/CRAN.package.traineR . https://github.com/PROMiDAT/traineR/issues . https://github.com/PROMiDAT/traineR . traineR results . traineR.pdf . traineR_2.2.0.tar.gz . traineR_2.2.0.zip . traineR_2.2.0.zip . traineR_2.2.0.zip . traineR_2.2.0.tgz . traineR_2.2.0.tgz . traineR_2.2.0.tgz . traineR_2.2.0.tgz . traineR archive . https://CRAN.R-project.org/package=traineR .
First Published: 2019-10-07
Latest Update: 2022-08-22
Description:
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>.
How to cite:
Oldemar Rodriguez R. (2019). traineR: Predictive (Classification and Regression) Models Homologator. R package version 2.2.0, https://cran.r-project.org/web/packages/traineR. Accessed 09 May. 2025.
Previous versions and publish date:
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Other R packages that traineR depends,
imports, suggests or enhances
Complete documentation for traineR
Functions, R codes and Examples using
the traineR R package
Some associated functions: ROC.area . ROC.plot . categorical.predictive.power . confusion.matrix . contr.dummy . contr.metric . contr.ordinal . create.model . create.prediction . dummy.data.frame . general.indexes . get.default.parameters . get_test_less_predict . gg_color . importance.plot . max_col . numeric_to_predict . numerical.predictive.power . original_model . plot.prmdt . predict.ada.prmdt . predict.adabag.prmdt . predict.bayes.prmdt . predict.gbm.prmdt . predict.glm.prmdt . predict.glmnet.prmdt . predict.knn.prmdt . predict.lda.prmdt . predict.neuralnet.prmdt . predict.nnet.prmdt . predict.qda.prmdt . predict.randomForest.prmdt . predict.rpart.prmdt . predict.svm.prmdt . predict.xgb.Booster.prmdt . prediction.variable.balance . print.indexes.prmdt . print.prediction.prmdt . print.prmdt . scaler . select_on_class . train.ada . train.adabag . train.bayes . train.gbm . train.glm . train.glmnet . train.knn . train.lda . train.neuralnet . train.nnet . train.qda . train.randomForest . train.rpart . train.svm . train.xgboost . traineR . type_correction . varplot .
Some associated R codes: Utilities.R . confusion.matrix.R . general.indexes.R . predict.R . threshold.R . train.R . traineR.R . variables.R . Full traineR package functions and examples
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