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tidylearn
View on CRAN: Click
here
Download and install tidylearn package within the R console
Install from CRAN:
install.packages("tidylearn")
Install from Github:
library("remotes")
install_github("cran/tidylearn") Install by package version:
library("remotes")
install_version("tidylearn", "0.5.0") Attach the package and use:
library("tidylearn")
Maintained by
Cesaire Tobias
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2026-02-06
Latest Update: 2026-02-06
Description:
Provides a unified tidyverse-compatible interface to R's machine learning packages. Wraps established implementations from 'glmnet', 'randomForest', 'xgboost', 'e1071', 'rpart', 'gbm', 'nnet', 'cluster', 'dbscan', and others - providing consistent function signatures, tidy tibble output, and unified 'ggplot2'-based visualization. The underlying algorithms are unchanged; 'tidylearn' simply makes them easier to use together. Access raw model objects via the $fit slot for package-specific functionality. Methods include random forests Breiman (2001) <doi:10.1023/A:1010933404324>, LASSO regression Tibshirani (1996) <doi:10.1111/j.2517-6161.1996.tb02080.x>, elastic net Zou and Hastie (2005) <doi:10.1111/j.1467-9868.2005.00503.x>, support vector machines Cortes and Vapnik (1995) <doi:10.1007/BF00994018>, and gradient boosting Friedman (2001) <doi:10.1214/aos/1013203451>.
How to cite:
Cesaire Tobias (2026). tidylearn: A Unified Tidy Interface to R's Machine Learning Ecosystem. R package version 0.5.0, https://cran.r-project.org/web/packages/tidylearn. Accessed 26 Oct. 2026.
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