# NBvarsel 0.1.1

* Improved package documentation and roxygen comments for the main selection function and plotting helpers, making the exported API clearer and easier to use.
* Refined the documentation for `all_subset_plot()` and `VIF_plot()` to better describe their inputs, outputs, and examples.
* Extended plotting utilities with optional predictor relabelling via `data_dict`, making it easier to present clinically meaningful variable names in figures.
* Improved the tutorial vignette layout and narrative around the fabricated clinical example and case-study sections, with clearer descriptions of model comparisons and interpretation.
* Cleaned up the vignette workflow to avoid brittle global-environment cleanup during execution.

# NBvarsel 0.1.0

* Initial release.
* Added `adnex_results` dataset: pre-computed exhaustive variable selection results from the IOTA/ADNEX ovarian tumour case study (top 20 models per predictor count from a 65,535-model search). Original patient data are not disclosed.
* Added a tutorial vignette (`nb-varsel-tutorial`) demonstrating the full workflow with fabricated clinical data, comparison with backward elimination and LASSO, and a real-data case study using the shipped ADNEX results.
* `nb_varsel()` performs exhaustive or groupwise variable selection for binary outcome models using cross-validated Net Benefit, with support for predictor costs (per-variable or grouped), restricted cubic splines, interaction terms, permutation importance, and parallel computation.
* `all_subset_plot()` creates a two-panel figure showing model performance and predictor inclusion across all evaluated models. Supports customisable colors via `highlight_color` and `tile_color` arguments.
* `VIF_plot()` creates a horizontal bar chart of average permutation importance (delta Net Benefit) per predictor. Supports a customisable `color` argument.
