Package: tidylearn
Title: A Unified Tidy Interface to R's Machine Learning Ecosystem
Version: 0.4.0
Authors@R: 
    person("Cesaire", "Tobias", email = "cesaire@sheetsolved.com", role = c("aut", "cre"))
Description: 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>.
License: MIT + file LICENSE
Encoding: UTF-8
RoxygenNote: 7.3.3
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
Config/testthat/edition: 3
URL: https://github.com/ces0491/tidylearn
BugReports: https://github.com/ces0491/tidylearn/issues
VignetteBuilder: knitr
Collate: 'tidylearn-package.R' 'utils.R' 'read.R' 'read-backends.R'
        'core.R' 'preprocessing.R' 'supervised-classification.R'
        'supervised-regression.R' 'supervised-regularization.R'
        'supervised-trees.R' 'supervised-svm.R'
        'supervised-neural-networks.R' 'supervised-deep-learning.R'
        'supervised-xgboost.R' 'unsupervised-distance.R'
        'unsupervised-pca.R' 'unsupervised-mds.R'
        'unsupervised-clustering.R' 'unsupervised-hclust.R'
        'unsupervised-dbscan.R' 'unsupervised-market-basket.R'
        'unsupervised-validation.R' 'integration.R' 'pipeline.R'
        'model-selection.R' 'compute-detection.R' 'compute-advisor.R'
        'compute-routing.R' 'tuning.R' 'interactions.R' 'diagnostics.R'
        'metrics.R' 'visualization.R' 'tables.R' 'workflows.R'
NeedsCompilation: no
Packaged: 2026-08-03 17:26:08 UTC; CesaireTobias
Author: Cesaire Tobias [aut, cre]
Maintainer: Cesaire Tobias <cesaire@sheetsolved.com>
Repository: CRAN
Date/Publication: 2026-08-03 18:00:02 UTC
Built: R 4.5.3; ; 2026-08-24 06:13:39 UTC; windows
