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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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