STOPES
Selection Threshold Optimized Empirically via Splitting
Implements variable selection procedures for low to moderate size generalized linear regressions models. It includes the STOPES functions for linear regression (Capanu M, Giurcanu M, Begg C, Gonen M, Optimized variable selection via repeated data splitting, Statistics in Medicine, 2020, 19(6):2167-2184) as well as subsampling based optimization methods for generalized linear regression models (Marinela Capanu, Mihai Giurcanu, Colin B Begg, Mithat Gonen, Subsampling based variable selection for generalized linear models).
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.2 |
rolling linux/jammy R-4.5 | STOPES_0.2.tar.gz |
42.1 KiB |
0.2 |
rolling linux/noble R-4.5 | STOPES_0.2.tar.gz |
42.1 KiB |
0.2 |
rolling source/ R- | STOPES_0.2.tar.gz |
5.8 KiB |
0.2 |
latest linux/jammy R-4.5 | STOPES_0.2.tar.gz |
42.1 KiB |
0.2 |
latest linux/noble R-4.5 | STOPES_0.2.tar.gz |
42.1 KiB |
0.2 |
latest source/ R- | STOPES_0.2.tar.gz |
5.8 KiB |
0.2 |
2026-04-26 source/ R- | STOPES_0.2.tar.gz |
5.8 KiB |
0.2 |
2026-04-23 source/ R- | STOPES_0.2.tar.gz |
5.8 KiB |
0.2 |
2026-04-09 windows/windows R-4.5 | STOPES_0.2.zip |
44.6 KiB |
0.2 |
2025-04-20 source/ R- | STOPES_0.2.tar.gz |
5.8 KiB |