Package: tidylearn
Title: A Unified Tidy Interface to R's Machine Learning Ecosystem
Version: 0.2.0
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 packages. Wraps established implementations from 'glmnet',
    'randomForest', 'xgboost', 'e1071', 'rpart', 'gbm', 'nnet', 'cluster',
    'dbscan', and others - providing consistent function signatures, tidy tibble
    output, unified 'ggplot2'-based visualization, and optional formatted 'gt'
    tables via the tl_table() family of functions. 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,
        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, car, caret, DT, GGally, ggforce,
        gridExtra, gt, keras, knitr, lmtest, mclust, moments,
        NeuralNetTools, onnx, parsnip, recipes, reticulate, rmarkdown,
        rpart.plot, scales, shiny, shinydashboard, tensorflow, testthat
        (>= 3.0.0), workflows, xgboost
Config/testthat/edition: 3
URL: https://github.com/ces0491/tidylearn
BugReports: https://github.com/ces0491/tidylearn/issues
VignetteBuilder: knitr
Collate: 'utils.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' 'tuning.R'
        'interactions.R' 'diagnostics.R' 'metrics.R' 'visualization.R'
        'tables.R' 'workflows.R'
NeedsCompilation: no
Packaged: 2026-03-15 11:49:48 UTC; CesaireTobias
Author: Cesaire Tobias [aut, cre]
Maintainer: Cesaire Tobias <cesaire@sheetsolved.com>
Repository: CRAN
Date/Publication: 2026-03-16 08:00:08 UTC
Built: R 4.6.0; ; 2026-03-26 05:47:11 UTC; windows
