NBvarsel
Variable Selection via Cross-Validated Net Benefit
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>.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.1.1 |
rolling linux/jammy R-4.5 | NBvarsel_0.1.1.tar.gz |
1.1 MiB |
0.1.1 |
rolling linux/noble R-4.5 | NBvarsel_0.1.1.tar.gz |
1.1 MiB |
0.1.1 |
rolling source/ R- | NBvarsel_0.1.1.tar.gz |
1.1 MiB |
0.1.1 |
latest linux/jammy R-4.5 | NBvarsel_0.1.1.tar.gz |
1.1 MiB |
0.1.1 |
latest linux/noble R-4.5 | NBvarsel_0.1.1.tar.gz |
1.1 MiB |
0.1.1 |
latest source/ R- | NBvarsel_0.1.1.tar.gz |
1.1 MiB |
0.1.1 |
2026-04-23 source/ R- | NBvarsel_0.1.1.tar.gz |
0 B |