https://ces0491.r-universe.devRecent package updates in ces0491R-universehttps://github.com/ces0491.pnghttps://ces0491.r-universe.devFri, 22 May 2026 07:10:00 GMTcesaire@sheetsolved.com (Cesaire Tobias)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>.https://github.com/r-universe/ces0491/actions/runs/26282792002Fri, 22 May 2026 07:10:00 GMTtidylearn0.3.1.9000successhttps://ces0491.r-universe.devhttps://github.com/ces0491/tidylearnautoml.Rmdautoml.htmlAutomated Machine Learning with tidylearn2025-11-10 09:56:562026-04-09 08:51:26data-ingestion.Rmddata-ingestion.htmlData Ingestion with tidylearn2026-03-18 10:32:302026-04-09 08:51:26getting-started.Rmdgetting-started.htmlGetting Started with tidylearn2025-11-10 09:56:562026-03-18 10:32:30integration-workflows.Rmdintegration-workflows.htmlIntegration Workflows: Combining Supervised and Unsupervised Learning2025-11-10 09:56:562026-04-09 08:51:26reporting.Rmdreporting.htmlReporting with tidylearn2026-03-18 10:32:302026-03-18 10:32:30supervised-learning.Rmdsupervised-learning.htmlSupervised Learning with tidylearn2025-11-10 09:56:562026-03-18 10:32:30unsupervised-learning.Rmdunsupervised-learning.htmlUnsupervised Learning with tidylearn2025-11-10 09:56:562026-03-18 10:32:30