A Unified Tidy Interface to R's Machine Learning Ecosystem R package tidylearn version 0.4.0 Provides a unified tidyverse-compatible interface to R's machine learning ecosystem - from data ingestion to model publishing. The tl_read() family reads data from files ('CSV', 'Excel', 'Parquet', 'JSON'), databases ('SQLite', 'PostgreSQL', 'MySQL', 'BigQuery'), and cloud sources ('S3', 'GitHub', 'Kaggle'). The tl_model() function wraps established implementations from 'glmnet', 'randomForest', 'xgboost', 'e1071', 'rpart', 'gbm', 'nnet', 'cluster', 'dbscan', and others with consistent function signatures and tidy tibble output. Results flow into unified 'ggplot2'-based visualization and optional formatted 'gt' tables via the tl_table() family. 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>. Software Depends: R (>= 3.6.0) Imports: dplyr (>= 1.0.0), ggplot2 (>= 3.3.0), tibble (>= 3.0.0), tidyr (>= 1.0.0), purrr (>= 0.3.0), rlang (>= 0.4.0), magrittr, parallel, stats, e1071, gbm, glmnet, nnet, randomForest, rpart, rsample, ROCR, yardstick, cluster (>= 2.1.0), dbscan (>= 1.1.0), MASS, smacof (>= 2.1.0) Suggests: arules, arulesViz, bigrquery, car, DBI, DT, GGally, ggforce, gridExtra, gt, jsonlite, keras, knitr, lmtest, moments, nanoparquet, NeuralNetTools, paws.storage, readr, readxl, RMariaDB, rmarkdown, RPostgres, rpart.plot, RSQLite, scales, shiny, shinydashboard, tensorflow, testthat (>= 3.1.7), xgboost Cesaire Tobias <cesaire@sheetsolved.com> Comprehensive R Archive Network (CRAN) Cesaire Tobias [aut, cre] MIT + file LICENSE (https://CRAN.R-project.org/package=tidylearn/LICENSE) 2026-08-03 application/tgz https://CRAN.R-project.org/package=tidylearn doi:10.32614/CRAN.package.tidylearn