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tidylearn 0.3.0

New Features

Data Ingestion (tl_read() Family)

• New tl_read() dispatcher function — auto-detects format from file extension, URL pattern, or connection string and routes to the appropriate reader
• All readers return a tidylearn_data object, a tibble subclass carrying source, format, and timestamp metadata via print.tidylearn_data()

File Format Readers

• tl_read_csv() / tl_read_tsv() — via readr with base R fallback
• tl_read_excel() — .xls, .xlsx, .xlsm files via readxl
• tl_read_parquet() — via nanoparquet
• tl_read_json() — tabular JSON via jsonlite
• tl_read_rds() / tl_read_rdata() — native R formats via base R

Database Readers

• tl_read_db() — query any live DBI connection
• tl_read_sqlite() — auto-connect to SQLite files via RSQLite
• tl_read_postgres() — connection string or named params via RPostgres
• tl_read_mysql() — connection string or named params via RMariaDB
• tl_read_bigquery() — Google BigQuery via bigrquery

Cloud/API Readers

• tl_read_s3() — download and read from S3 URIs via paws.storage
• tl_read_github() — download raw files from GitHub repositories
• tl_read_kaggle() — download datasets via the Kaggle CLI

Multi-File Reading

• tl_read() accepts a character vector of paths — reads each and row-binds with a source_file column
• tl_read_dir() — scan a directory for data files with optional format, pattern, and recursive filtering
• tl_read_zip() — extract and read from zip archives, with optional file selection
• All backend packages are suggested dependencies, checked at call time via tl_check_packages()

New Vignette

• Added “Data Ingestion with tidylearn” vignette covering all readers, databases, cloud sources, multi-file reading, and the full pipeline
• Updated “Getting Started” vignette to include tl_read() in the workflow

Bug Fixes

Workflow and Pipeline Fixes

• Fixed tl_transfer_learning() hanging indefinitely when used with PCA pre-training. The .obs_id row-identifier column from PCA output was being included in the supervised formula, creating a massive dummy-variable matrix. The column is now stripped before both training and prediction.
• Fixed tl_run_pipeline() failing with “attempt to select less than one element” when all cross-validation metrics were NA. Root cause: scale() returned matrix columns instead of vectors, causing downstream metric computation to produce NaN. Added as.vector() wrapper and hardened the best-model selection to handle all-NA metric values gracefully.
• Overhauled tl_auto_ml() time budget enforcement. The budget now controls which models are attempted: budgets under 30s skip slow C-level models (forest, SVM, XGBoost) entirely, and cross-validation is skipped when remaining time is tight. Baseline model order changed to fast-first (tree, logistic/linear, then forest). See ?tl_auto_ml for full details on budget tiers.

Interaction and Prediction Fixes

• Fixed tl_interaction_effects() crashing with “unused argument (se.fit)” because tidylearn’s predict() method does not support se.fit. Now uses stats::predict() on the raw model object for confidence intervals. Also fixed an invalid formula in the internal slope calculation.
• Fixed tl_plot_interaction() expecting fit/lwr/upr columns from predict() output. Now correctly handles tidylearn’s .pred tibble format.

Visualization Fixes

• Fixed tl_plot_intervals() calling non-existent tl_prediction_intervals() function. Now computes confidence and prediction intervals directly via stats::predict(..., interval = "confidence") and stats::predict(..., interval = "prediction").
• Fixed tl_plot_svm_boundary() erroring with “at least two predictor variables required” when using response ~ . formulas. The function now resolves predictors from data column names instead of all.vars(), which does not expand .. Also switched from geom_contour_filled (which failed on discrete class predictions) to geom_raster.
• Fixed tl_plot_svm_tuning() passing NULL entries in the ranges list to e1071::tune(), which caused “NA/NaN/Inf in foreign function call” errors. Tuning ranges are now built conditionally based on the kernel type.
• Fixed tl_plot_xgboost_shap_summary() failing with “arguments imply differing number of rows” when n_samples differed from nrow(data). Sampling is now performed before SHAP computation so that feature values and SHAP values always have the same number of rows.

Other Fixes

• Fixed classification auto-detection silently treating numeric responses with <= 10 unique values as classification. The response must now be a factor or character for classification; a helpful message is emitted when a low-cardinality numeric response is detected.
• Fixed tl_check_assumptions() crashing with “list object cannot be coerced to logical” when some assumption checks returned NULL (e.g., when optional test packages were not installed).
• Fixed SVM default gamma calculation to use predictor count only (1 / (ncol(data) - 1)) instead of including the response column.
• Added missing @return tag to print.tidylearn_data().
• Replaced deprecated ggplot2 size parameter with linewidth in all geom_line() calls across visualization, classification, PCA, DBSCAN, and validation plotting functions.

Tests

• Added test suite for visualization module (26 tests) — plot dispatch, regression/classification plots, lift/gain charts, model comparison, unsupervised visualization, and Shiny dashboard.
• Added test suite for tuning module (49 tests) — tl_default_param_grid, tl_tune_grid, tl_tune_random, tl_plot_tuning_results, and input validation.
• Added test suite for diagnostics module (75 tests) — influence measures, influence plots, assumption checking, and outlier detection across all methods (IQR, z-score, Cook’s, Mahalanobis).

Code Quality

• Package-wide lint cleanup — all R source files, tests, and vignettes now pass lintr with zero issues
• Replaced unsafe 1:n patterns with seq_len() / seq_along()
• Removed unused variables across the codebase
• Renamed non-snake_case variables to follow R conventions
• Added .lintr configuration enforcing %>% pipe consistency

tidylearn 0.2.0

New Features

Formatted gt Tables

• New tl_table() dispatcher function — mirrors plot() but produces formatted gt tables instead of ggplot2 visualisations
• tl_table_metrics() — styled evaluation metrics table from tl_evaluate()
• tl_table_coefficients() — model coefficients with p-values (lm/glm) or sorted by magnitude (glmnet), with conditional highlighting
• tl_table_confusion() — confusion matrix with correct predictions highlighted on the diagonal
• tl_table_importance() — ranked feature importance with colour gradient
• tl_table_variance() — PCA variance explained with cumulative % coloured
• tl_table_loadings() — PCA loadings with diverging red–blue colour scale
• tl_table_clusters() — cluster sizes and mean feature values for kmeans, pam, clara, dbscan, and hclust models
• tl_table_comparison() — side-by-side multi-model comparison table
• All table functions share a consistent gt theme via internal tl_gt_theme() helper
• gt is a suggested dependency — functions error with an install message if gt is not available

New Vignette

• Added “Reporting with tidylearn” vignette covering all plot and table functions

Bug Fixes

• Fixed tl_fit_dbscan() returning a non-existent core_points field instead of summary from the underlying tidy_dbscan() result

tidylearn 0.1.1

Bug Fixes

• Fixed plot() failing on supervised models with “could not find function ‘tl_plot_model’” by implementing the missing tl_plot_model() and tl_plot_unsupervised() internal dispatchers (#1)
• Fixed tl_plot_actual_predicted(), tl_plot_residuals(), and tl_plot_confusion() failing due to accessing a non-existent $prediction column on predict output (correct column is $.pred)
• Fixed the same $prediction column mismatch in the tl_dashboard() predictions table

tidylearn 0.1.0

Initial CRAN Release

• First release of tidylearn - a unified tidy interface to R’s machine learning ecosystem

Features

Unified Interface

• tl_model() - Single function to fit 20+ machine learning models
• Consistent function signatures across all methods
• Tidy tibble output for all results
• Access raw model objects via $fit for package-specific functionality

Supervised Learning Methods

• Linear regression (stats::lm)
• Polynomial regression (stats::lm with poly)
• Logistic regression (stats::glm)
• Ridge, LASSO, elastic net (glmnet)
• Decision trees (rpart)
• Random forests (randomForest)
• Gradient boosting (gbm)
• XGBoost (xgboost)
• Support vector machines (e1071)
• Neural networks (nnet)
• Deep learning (keras, optional)

Unsupervised Learning Methods

• Principal Component Analysis (stats::prcomp)
• Multidimensional Scaling (stats, MASS, smacof)
• K-means clustering (stats::kmeans)
• PAM clustering (cluster::pam)
• CLARA clustering (cluster::clara)
• Hierarchical clustering (stats::hclust)
• DBSCAN (dbscan)

Additional Features

• tl_split() - Train/test splitting with stratification support
• tl_prepare_data() - Data preprocessing (scaling, imputation, encoding)
• tl_evaluate() - Model evaluation with multiple metrics
• tl_auto_ml() - Automated machine learning
• tl_tune() - Hyperparameter tuning with grid and random search
• Unified ggplot2-based visualization functions
• Integration workflows combining supervised and unsupervised learning

Wrapped Packages

tidylearn wraps established R packages including: stats, glmnet, randomForest, xgboost, gbm, e1071, nnet, rpart, cluster, dbscan, MASS, and smacof.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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