Package: tidylearn
Title: A Unified Tidy Interface to R's Machine Learning Ecosystem
Version: 0.7.0.9000
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 a supervised method, or $fit$model for an
        unsupervised one. 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
Roxygen: list(markdown = TRUE)
RoxygenNote: 7.3.3
Depends: R (>= 4.1.0)
Imports: dplyr (>= 1.0.0), ggplot2 (>= 3.4.0), tibble (>= 3.0.0), tidyr
        (>= 1.0.0), purrr (>= 0.3.0), rlang (>= 1.0.0), 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, bigrquery, car, DBI, DiagrammeR, DT,
        GGally, ggforce, gridExtra, gt, httr2 (>= 1.0.0), jsonlite,
        keras3, knitr, later, lmtest, moments, nanoparquet,
        NeuralNetTools, paws.storage, readr, RhpcBLASctl, readxl,
        RMariaDB, rmarkdown, RPostgres, rpart.plot, RSQLite, shiny,
        shinydashboard, tensorflow, testthat (>= 3.1.7), withr, xgboost
Config/testthat/edition: 3
URL: https://tidylearn.sheetsolved.com,
        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' 'cloud-consent.R' 'cloud-endpoint.R'
        'cloud-cost.R' 'cloud-serialize.R' 'tuning.R' 'interactions.R'
        'diagnostics.R' 'metrics.R' 'coefficients.R' 'visualization.R'
        'tables.R' 'workflows.R'
Config/pak/sysreqs: cmake make libicu-dev libuv1-dev libx11-dev
        zlib1g-dev
Repository: https://ces0491.r-universe.dev
Date/Publication: 2026-11-02 16:41:17 UTC
RemoteUrl: https://github.com/ces0491/tidylearn
RemoteRef: HEAD
RemoteSha: 34e62842202bf2863c8dfb172dda4ab50b9a3344
NeedsCompilation: no
Packaged: 2026-11-02 21:30:00 UTC; root
Author: Cesaire Tobias [aut, cre]
Maintainer: Cesaire Tobias <cesaire@sheetsolved.com>