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  "Title": "A Unified Tidy Interface to R's Machine Learning Ecosystem",
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  "Authors@R": "person(\"Cesaire\", \"Tobias\", email = \"cesaire@sheetsolved.com\", role = c(\"aut\", \"cre\"))",
  "Description": "Provides a unified tidyverse-compatible interface to R's\nmachine learning ecosystem - from data ingestion to model\npublishing. The tl_read() family reads data from files ('CSV',\n'Excel', 'Parquet', 'JSON'), databases ('SQLite', 'PostgreSQL',\n'MySQL', 'BigQuery'), and cloud sources ('S3', 'GitHub',\n'Kaggle'). The tl_model() function wraps established\nimplementations from 'glmnet', 'randomForest', 'xgboost',\n'e1071', 'rpart', 'gbm', 'nnet', 'cluster', 'dbscan', and\nothers with consistent function signatures and tidy tibble\noutput. Results flow into unified 'ggplot2'-based visualization\nand optional formatted 'gt' tables via the tl_table() family.\nThe underlying algorithms are unchanged; 'tidylearn' simply\nmakes them easier to use together. Access raw model objects via\nthe $fit slot for a supervised method, or $fit$model for an\nunsupervised one. Methods include random forests Breiman (2001)\n<doi:10.1023/A:1010933404324>, LASSO regression Tibshirani\n(1996) <doi:10.1111/j.2517-6161.1996.tb02080.x>, elastic net\nZou and Hastie (2005) <doi:10.1111/j.1467-9868.2005.00503.x>,\nsupport vector machines Cortes and Vapnik (1995)\n<doi:10.1007/BF00994018>, and gradient boosting Friedman (2001)\n<doi:10.1214/aos/1013203451>.",
  "License": "MIT + file LICENSE",
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  "Config/pak/sysreqs": "cmake make libicu-dev libuv1-dev libx11-dev\nzlib1g-dev",
  "Repository": "https://ces0491.r-universe.dev",
  "Date/Publication": "2026-11-02 16:41:17 UTC",
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        "find_related_items"
      ]
    },
    {
      "page": "get_pca_loadings",
      "title": "Get PCA Loadings in Wide Format",
      "topics": [
        "get_pca_loadings"
      ]
    },
    {
      "page": "get_pca_variance",
      "title": "Get Variance Explained Summary",
      "topics": [
        "get_pca_variance"
      ]
    },
    {
      "page": "inspect_rules",
      "title": "Inspect Association Rules",
      "topics": [
        "inspect_rules"
      ]
    },
    {
      "page": "optimal_clusters",
      "title": "Find Optimal Number of Clusters",
      "topics": [
        "optimal_clusters"
      ]
    },
    {
      "page": "optimal_hclust_k",
      "title": "Determine Optimal Number of Clusters for Hierarchical Clustering",
      "topics": [
        "optimal_hclust_k"
      ]
    },
    {
      "page": "plot_cluster_comparison",
      "title": "Create Cluster Comparison Plot",
      "topics": [
        "plot_cluster_comparison"
      ]
    },
    {
      "page": "plot_cluster_sizes",
      "title": "Plot Cluster Size Distribution",
      "topics": [
        "plot_cluster_sizes"
      ]
    },
    {
      "page": "plot_clusters",
      "title": "Plot Clusters in 2D Space",
      "topics": [
        "plot_clusters"
      ]
    },
    {
      "page": "plot_dendrogram",
      "title": "Plot Dendrogram with Cluster Highlights",
      "topics": [
        "plot_dendrogram"
      ]
    },
    {
      "page": "plot_distance_heatmap",
      "title": "Create Distance Heatmap",
      "topics": [
        "plot_distance_heatmap"
      ]
    },
    {
      "page": "plot_elbow",
      "title": "Create Elbow Plot for K-Means",
      "topics": [
        "plot_elbow"
      ]
    },
    {
      "page": "plot_gap_stat",
      "title": "Plot Gap Statistic",
      "topics": [
        "plot_gap_stat"
      ]
    },
    {
      "page": "plot_knn_dist",
      "title": "Plot k-NN Distance Plot",
      "topics": [
        "plot_knn_dist"
      ]
    },
    {
      "page": "plot_mds",
      "title": "Plot MDS Configuration",
      "topics": [
        "plot_mds"
      ]
    },
    {
      "page": "plot_silhouette",
      "title": "Plot Silhouette Analysis",
      "topics": [
        "plot_silhouette"
      ]
    },
    {
      "page": "plot_variance_explained",
      "title": "Plot Variance Explained (PCA)",
      "topics": [
        "plot_variance_explained"
      ]
    },
    {
      "page": "plot.tidylearn_eda",
      "title": "Plot EDA results",
      "topics": [
        "plot.tidylearn_eda"
      ]
    },
    {
      "page": "plot.tidylearn_model",
      "title": "Plot method for tidylearn models",
      "topics": [
        "plot.tidylearn_model"
      ]
    },
    {
      "page": "predict.tidylearn_model",
      "title": "Predict using a tidylearn model",
      "topics": [
        "predict.tidylearn_model"
      ]
    },
    {
      "page": "predict.tidylearn_stratified",
      "title": "Predict from stratified models",
      "topics": [
        "predict.tidylearn_stratified"
      ]
    },
    {
      "page": "predict.tidylearn_transfer",
      "title": "Predict with transfer learning model",
      "topics": [
        "predict.tidylearn_transfer"
      ]
    },
    {
      "page": "print.tidy_apriori",
      "title": "Print Method for tidy_apriori",
      "topics": [
        "print.tidy_apriori"
      ]
    },
    {
      "page": "print.tidy_dbscan",
      "title": "Print Method for tidy_dbscan",
      "topics": [
        "print.tidy_dbscan"
      ]
    },
    {
      "page": "print.tidy_gap",
      "title": "Print Method for tidy_gap",
      "topics": [
        "print.tidy_gap"
      ]
    },
    {
      "page": "print.tidy_hclust",
      "title": "Print Method for tidy_hclust",
      "topics": [
        "print.tidy_hclust"
      ]
    },
    {
      "page": "print.tidy_kmeans",
      "title": "Print Method for tidy_kmeans",
      "topics": [
        "print.tidy_kmeans"
      ]
    },
    {
      "page": "print.tidy_mds",
      "title": "Print Method for tidy_mds",
      "topics": [
        "print.tidy_mds"
      ]
    },
    {
      "page": "print.tidy_pam",
      "title": "Print Method for tidy_pam",
      "topics": [
        "print.tidy_pam"
      ]
    },
    {
      "page": "print.tidy_pca",
      "title": "Print Method for tidy_pca",
      "topics": [
        "print.tidy_pca"
      ]
    },
    {
      "page": "print.tidy_silhouette",
      "title": "Print Method for tidy_silhouette",
      "topics": [
        "print.tidy_silhouette"
      ]
    },
    {
      "page": "print.tidylearn_automl",
      "title": "Print auto ML results",
      "topics": [
        "print.tidylearn_automl"
      ]
    },
    {
      "page": "print.tidylearn_compute_advice",
      "title": "Print method for 'tidylearn_compute_advice' objects",
      "topics": [
        "print.tidylearn_compute_advice"
      ]
    },
    {
      "page": "print.tidylearn_data",
      "title": "Print a tidylearn_data object",
      "topics": [
        "print.tidylearn_data"
      ]
    },
    {
      "page": "print.tidylearn_eda",
      "title": "Print EDA results",
      "topics": [
        "print.tidylearn_eda"
      ]
    },
    {
      "page": "print.tidylearn_gpu_check",
      "title": "Print method for 'tidylearn_gpu_check' objects",
      "topics": [
        "print.tidylearn_gpu_check"
      ]
    },
    {
      "page": "print.tidylearn_model",
      "title": "Print method for tidylearn models",
      "topics": [
        "print.tidylearn_model"
      ]
    },
    {
      "page": "print.tidylearn_pipeline",
      "title": "Print a tidylearn pipeline",
      "topics": [
        "print.tidylearn_pipeline"
      ]
    },
    {
      "page": "recommend_products",
      "title": "Generate Product Recommendations",
      "topics": [
        "recommend_products"
      ]
    },
    {
      "page": "standardize_data",
      "title": "Standardize Data",
      "topics": [
        "standardize_data"
      ]
    },
    {
      "page": "suggest_eps",
      "title": "Suggest eps Parameter for DBSCAN",
      "topics": [
        "suggest_eps"
      ]
    },
    {
      "page": "summarize_rules",
      "title": "Summarize Association Rules",
      "topics": [
        "summarize_rules"
      ]
    },
    {
      "page": "summary.tidylearn_model",
      "title": "Summary method for tidylearn models",
      "topics": [
        "summary.tidylearn_model"
      ]
    },
    {
      "page": "summary.tidylearn_pipeline",
      "title": "Summarize a tidylearn pipeline",
      "topics": [
        "summary.tidylearn_pipeline"
      ]
    },
    {
      "page": "tidy_apriori",
      "title": "Tidy Apriori Algorithm",
      "topics": [
        "tidy_apriori"
      ]
    },
    {
      "page": "tidy_clara",
      "title": "Tidy CLARA (Clustering Large Applications)",
      "topics": [
        "tidy_clara"
      ]
    },
    {
      "page": "tidy_cutree",
      "title": "Cut Hierarchical Clustering Tree",
      "topics": [
        "tidy_cutree"
      ]
    },
    {
      "page": "tidy_dbscan",
      "title": "Tidy DBSCAN Clustering",
      "topics": [
        "tidy_dbscan"
      ]
    },
    {
      "page": "tidy_dendrogram",
      "title": "Plot Dendrogram",
      "topics": [
        "tidy_dendrogram"
      ]
    },
    {
      "page": "tidy_dist",
      "title": "Tidy Distance Matrix Computation",
      "topics": [
        "tidy_dist"
      ]
    },
    {
      "page": "tidy_gap_stat",
      "title": "Tidy Gap Statistic",
      "topics": [
        "tidy_gap_stat"
      ]
    },
    {
      "page": "tidy_gower",
      "title": "Gower Distance Calculation",
      "topics": [
        "tidy_gower"
      ]
    },
    {
      "page": "tidy_hclust",
      "title": "Tidy Hierarchical Clustering",
      "topics": [
        "tidy_hclust"
      ]
    },
    {
      "page": "tidy_kmeans",
      "title": "Tidy K-Means Clustering",
      "topics": [
        "tidy_kmeans"
      ]
    },
    {
      "page": "tidy_knn_dist",
      "title": "Compute k-NN Distances",
      "topics": [
        "tidy_knn_dist"
      ]
    },
    {
      "page": "tidy_mds",
      "title": "Tidy Multidimensional Scaling",
      "topics": [
        "tidy_mds"
      ]
    },
    {
      "page": "tidy_mds_classical",
      "title": "Classical (Metric) MDS",
      "topics": [
        "tidy_mds_classical"
      ]
    },
    {
      "page": "tidy_mds_kruskal",
      "title": "Kruskal's Non-metric MDS",
      "topics": [
        "tidy_mds_kruskal"
      ]
    },
    {
      "page": "tidy_mds_sammon",
      "title": "Sammon Mapping",
      "topics": [
        "tidy_mds_sammon"
      ]
    },
    {
      "page": "tidy_mds_smacof",
      "title": "SMACOF MDS (Metric or Non-metric)",
      "topics": [
        "tidy_mds_smacof"
      ]
    },
    {
      "page": "tidy_pam",
      "title": "Tidy PAM (Partitioning Around Medoids)",
      "topics": [
        "tidy_pam"
      ]
    },
    {
      "page": "tidy_pca",
      "title": "Tidy Principal Component Analysis",
      "topics": [
        "tidy_pca"
      ]
    },
    {
      "page": "tidy_pca_biplot",
      "title": "Create PCA Biplot",
      "topics": [
        "tidy_pca_biplot"
      ]
    },
    {
      "page": "tidy_pca_screeplot",
      "title": "Create PCA Scree Plot",
      "topics": [
        "tidy_pca_screeplot"
      ]
    },
    {
      "page": "tidy_rules",
      "title": "Convert Association Rules to Tidy Tibble",
      "topics": [
        "tidy_rules"
      ]
    },
    {
      "page": "tidy_silhouette",
      "title": "Tidy Silhouette Analysis",
      "topics": [
        "tidy_silhouette"
      ]
    },
    {
      "page": "tidy_silhouette_analysis",
      "title": "Silhouette Analysis Across Multiple k Values",
      "topics": [
        "tidy_silhouette_analysis"
      ]
    },
    {
      "page": "tidylearn-classification",
      "title": "Classification Functions for tidylearn",
      "topics": [
        "tidylearn-classification"
      ]
    },
    {
      "page": "tidylearn-cloud-consent",
      "title": "Cloud data-egress consent for tidylearn",
      "topics": [
        "tidylearn-cloud-consent"
      ]
    },
    {
      "page": "tidylearn-cloud-cost",
      "title": "Cost controls for tidylearn cloud compute",
      "topics": [
        "tidylearn-cloud-cost"
      ]
    },
    {
      "page": "tidylearn-cloud-endpoint",
      "title": "Cloud endpoint resolution for tidylearn",
      "topics": [
        "tidylearn-cloud-endpoint"
      ]
    },
    {
      "page": "tidylearn-cloud-serialize",
      "title": "Model serialisation for tidylearn cloud compute",
      "topics": [
        "tidylearn-cloud-serialize"
      ]
    },
    {
      "page": "tidylearn-coefficients",
      "title": "Coefficient Inference for tidylearn",
      "topics": [
        "tidylearn-coefficients"
      ]
    },
    {
      "page": "tidylearn-core",
      "title": "tidylearn: A Unified Tidy Interface to R's Machine Learning Ecosystem",
      "topics": [
        "tidylearn-core"
      ]
    },
    {
      "page": "tidylearn-deep-learning",
      "title": "Deep Learning for tidylearn",
      "topics": [
        "tidylearn-deep-learning"
      ]
    },
    {
      "page": "tidylearn-diagnostics",
      "title": "Advanced Diagnostics Functions for tidylearn",
      "topics": [
        "tidylearn-diagnostics"
      ]
    },
    {
      "page": "tidylearn-interactions",
      "title": "Interaction Analysis Functions for tidylearn",
      "topics": [
        "tidylearn-interactions"
      ]
    },
    {
      "page": "tidylearn-metrics",
      "title": "Metrics Functionality for tidylearn",
      "topics": [
        "tidylearn-metrics"
      ]
    },
    {
      "page": "tidylearn-model-selection",
      "title": "Model Selection Functions for tidylearn",
      "topics": [
        "tidylearn-model-selection"
      ]
    },
    {
      "page": "tidylearn-neural-networks",
      "title": "Neural Networks for tidylearn",
      "topics": [
        "tidylearn-neural-networks"
      ]
    },
    {
      "page": "tidylearn-pipeline",
      "title": "Model Pipeline Functions for tidylearn",
      "topics": [
        "tidylearn-pipeline"
      ]
    },
    {
      "page": "tidylearn-read",
      "title": "Data Reading Functions for tidylearn",
      "topics": [
        "tidylearn-read"
      ]
    },
    {
      "page": "tidylearn-read-backends",
      "title": "Data Reading Backends for tidylearn",
      "topics": [
        "tidylearn-read-backends"
      ]
    },
    {
      "page": "tidylearn-regression",
      "title": "Regression Functions for tidylearn",
      "topics": [
        "tidylearn-regression"
      ]
    },
    {
      "page": "tidylearn-regularization",
      "title": "Regularization Functions for tidylearn",
      "topics": [
        "tidylearn-regularization"
      ]
    },
    {
      "page": "tidylearn-svm",
      "title": "Support Vector Machines for tidylearn",
      "topics": [
        "tidylearn-svm"
      ]
    },
    {
      "page": "tidylearn-tables",
      "title": "Table Functions for tidylearn",
      "topics": [
        "tidylearn-tables"
      ]
    },
    {
      "page": "tidylearn-trees",
      "title": "Tree-based Methods for tidylearn",
      "topics": [
        "tidylearn-trees"
      ]
    },
    {
      "page": "tidylearn-tuning",
      "title": "Hyperparameter Tuning Functions for tidylearn",
      "topics": [
        "tidylearn-tuning"
      ]
    },
    {
      "page": "tidylearn-visualization",
      "title": "Visualization Functions for tidylearn",
      "topics": [
        "tidylearn-visualization"
      ]
    },
    {
      "page": "tidylearn-workflows",
      "title": "High-Level Workflows for Common Machine Learning Patterns",
      "topics": [
        "tidylearn-workflows"
      ]
    },
    {
      "page": "tidylearn-xgboost",
      "title": "XGBoost Functions for tidylearn",
      "topics": [
        "tidylearn-xgboost"
      ]
    },
    {
      "page": "tl_add_cluster_features",
      "title": "Cluster-Based Features",
      "topics": [
        "tl_add_cluster_features"
      ]
    },
    {
      "page": "tl_anomaly_aware",
      "title": "Anomaly-Aware Supervised Learning",
      "topics": [
        "tl_anomaly_aware"
      ]
    },
    {
      "page": "tl_auto_interactions",
      "title": "Find important interactions automatically",
      "topics": [
        "tl_auto_interactions"
      ]
    },
    {
      "page": "tl_auto_ml",
      "title": "Auto ML: Automated Machine Learning Workflow",
      "topics": [
        "tl_auto_ml"
      ]
    },
    {
      "page": "tl_calc_classification_metrics",
      "title": "Calculate classification metrics",
      "topics": [
        "tl_calc_classification_metrics"
      ]
    },
    {
      "page": "tl_check_assumptions",
      "title": "Check model assumptions",
      "topics": [
        "tl_check_assumptions"
      ]
    },
    {
      "page": "tl_check_gpu",
      "title": "Detect local GPU availability for tidylearn methods",
      "topics": [
        "tl_check_gpu"
      ]
    },
    {
      "page": "tl_cloud_allow_host",
      "title": "Allow an additional host for cloud uploads in this R session",
      "topics": [
        "tl_cloud_allow_host"
      ]
    },
    {
      "page": "tl_cloud_allowed_hosts",
      "title": "Hosts tidylearn will currently upload to",
      "topics": [
        "tl_cloud_allowed_hosts"
      ]
    },
    {
      "page": "tl_cloud_consent",
      "title": "Grant or revoke cloud upload consent for this R session",
      "topics": [
        "tl_cloud_consent"
      ]
    },
    {
      "page": "tl_cloud_jobs",
      "title": "Cloud jobs submitted in this R session",
      "topics": [
        "tl_cloud_jobs"
      ]
    },
    {
      "page": "tl_coefficients",
      "title": "Model coefficients as a tibble",
      "topics": [
        "tl_coefficients"
      ]
    },
    {
      "page": "tl_compare_cv",
      "title": "Compare models using cross-validation",
      "topics": [
        "tl_compare_cv"
      ]
    },
    {
      "page": "tl_compare_pipeline_models",
      "title": "Compare models from a pipeline",
      "topics": [
        "tl_compare_pipeline_models"
      ]
    },
    {
      "page": "tl_compute_advisor",
      "title": "Advise on the best compute tier for a tidylearn fit",
      "topics": [
        "tl_compute_advisor",
        "tl_compute_advisor.character",
        "tl_compute_advisor.default",
        "tl_compute_advisor.tidylearn_supervised"
      ]
    },
    {
      "page": "tl_cv",
      "title": "Cross-validation for tidylearn models",
      "topics": [
        "tl_cv"
      ]
    },
    {
      "page": "tl_dashboard",
      "title": "Create interactive visualization dashboard for a model",
      "topics": [
        "tl_dashboard"
      ]
    },
    {
      "page": "tl_default_param_grid",
      "title": "Create pre-defined parameter grids for common models",
      "topics": [
        "tl_default_param_grid"
      ]
    },
    {
      "page": "tl_detect_outliers",
      "title": "Detect outliers in the data",
      "topics": [
        "tl_detect_outliers"
      ]
    },
    {
      "page": "tl_diagnostic_dashboard",
      "title": "Create a diagnostic dashboard",
      "topics": [
        "tl_diagnostic_dashboard"
      ]
    },
    {
      "page": "tl_evaluate",
      "title": "Evaluate a tidylearn model",
      "topics": [
        "tl_evaluate"
      ]
    },
    {
      "page": "tl_explore",
      "title": "Exploratory Data Analysis Workflow",
      "topics": [
        "tl_explore"
      ]
    },
    {
      "page": "tl_get_best_model",
      "title": "Get the best model from a pipeline",
      "topics": [
        "tl_get_best_model"
      ]
    },
    {
      "page": "tl_influence_measures",
      "title": "Calculate influence measures for a linear model",
      "topics": [
        "tl_influence_measures"
      ]
    },
    {
      "page": "tl_interaction_effects",
      "title": "Calculate partial effects based on a model with interactions",
      "topics": [
        "tl_interaction_effects"
      ]
    },
    {
      "page": "tl_load_pipeline",
      "title": "Load a pipeline from disk",
      "topics": [
        "tl_load_pipeline"
      ]
    },
    {
      "page": "tl_model",
      "title": "Create a tidylearn model",
      "topics": [
        "tl_model"
      ]
    },
    {
      "page": "tl_pipeline",
      "title": "Create a modeling pipeline",
      "topics": [
        "tl_pipeline"
      ]
    },
    {
      "page": "tl_plot_cv_comparison",
      "title": "Plot comparison of cross-validation results",
      "topics": [
        "tl_plot_cv_comparison"
      ]
    },
    {
      "page": "tl_plot_cv_results",
      "title": "Plot cross-validation results",
      "topics": [
        "tl_plot_cv_results"
      ]
    },
    {
      "page": "tl_plot_deep_architecture",
      "title": "Plot deep learning model architecture",
      "topics": [
        "tl_plot_deep_architecture"
      ]
    },
    {
      "page": "tl_plot_deep_history",
      "title": "Plot deep learning model training history",
      "topics": [
        "tl_plot_deep_history"
      ]
    },
    {
      "page": "tl_plot_gain",
      "title": "Plot gain chart for a classification model",
      "topics": [
        "tl_plot_gain"
      ]
    },
    {
      "page": "tl_plot_importance_comparison",
      "title": "Plot feature importance across multiple models",
      "topics": [
        "tl_plot_importance_comparison"
      ]
    },
    {
      "page": "tl_plot_importance_regularized",
      "title": "Plot variable importance for a regularized model",
      "topics": [
        "tl_plot_importance_regularized"
      ]
    },
    {
      "page": "tl_plot_influence",
      "title": "Plot influence diagnostics",
      "topics": [
        "tl_plot_influence"
      ]
    },
    {
      "page": "tl_plot_interaction",
      "title": "Plot interaction effects",
      "topics": [
        "tl_plot_interaction"
      ]
    },
    {
      "page": "tl_plot_intervals",
      "title": "Create confidence and prediction interval plots",
      "topics": [
        "tl_plot_intervals"
      ]
    },
    {
      "page": "tl_plot_lift",
      "title": "Plot lift chart for a classification model",
      "topics": [
        "tl_plot_lift"
      ]
    },
    {
      "page": "tl_plot_model_comparison",
      "title": "Plot model comparison",
      "topics": [
        "tl_plot_model_comparison"
      ]
    },
    {
      "page": "tl_plot_nn_architecture",
      "title": "Plot neural network architecture",
      "topics": [
        "tl_plot_nn_architecture"
      ]
    },
    {
      "page": "tl_plot_nn_tuning",
      "title": "Plot a neural network tuning grid",
      "topics": [
        "tl_plot_nn_tuning"
      ]
    },
    {
      "page": "tl_plot_partial_dependence",
      "title": "Plot partial dependence for tree-based models",
      "topics": [
        "tl_plot_partial_dependence"
      ]
    },
    {
      "page": "tl_plot_regularization_cv",
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