Variable Selection via Cross-Validated Net Benefit
Overview
Performs exhaustive or groupwise (backward elimination) variable selection for binary outcome prediction models using cross-validated Net Benefit as the optimization criterion. It supports predictor costs, restricted cubic splines, interaction terms, permutation importance, and parallel computation. It includes visualizations for model comparison and variable importance. References include Vickers AJ & Elkin EB (2006) doi:10.1177/0272989X06295361, Van Calster B et al. (2018) doi:10.1016/j.eururo.2018.08.038, Vickers AJ et al. (2019) doi:10.1186/s41512-019-0064-7, and Baker SG et al. (2012) doi:10.1515/1557-4679.1395.
- 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()andVIF_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.
From NEWS.md in the release
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- ERROR12 OK, 1 ERROR2026-09-07
- OK8 OK2026-08-31
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- Documented parameters
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- Return-value docs
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- 0.1.1Latest2026-08-30 · current release
- RR 4.6.0 released · 2026-04-24
Package metadata
- First published
- 2026-08-30
- Total releases
- 1 / 1 yrs
- License
- GPL (>= 3) OSI
- Minimum R
- ≥ 4.1.0
- Bundled data
- 25 KB / 1 file
- Download size
- 1.2 MB
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