LINselect
Selection of Linear Estimators
Estimate the mean of a Gaussian vector, by choosing among a large collection of estimators, following the method developed by Y. Baraud, C. Giraud and S. Huet (2014) <doi:10.1214/13-AIHP539>. In particular it solves the problem of variable selection by choosing the best predictor among predictors emanating from different methods as lasso, elastic-net, adaptive lasso, pls, randomForest. Moreover, it can be applied for choosing the tuning parameter in a Gauss-lasso procedure.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
1.1.6 |
rolling linux/jammy R-4.5 | LINselect_1.1.6.tar.gz |
371.3 KiB |
1.1.6 |
rolling linux/noble R-4.5 | LINselect_1.1.6.tar.gz |
371.2 KiB |
1.1.6 |
rolling source/ R- | LINselect_1.1.6.tar.gz |
311.7 KiB |
1.1.6 |
latest linux/jammy R-4.5 | LINselect_1.1.6.tar.gz |
371.3 KiB |
1.1.6 |
latest linux/noble R-4.5 | LINselect_1.1.6.tar.gz |
371.2 KiB |
1.1.6 |
latest source/ R- | LINselect_1.1.6.tar.gz |
311.7 KiB |
1.1.6 |
2026-04-26 source/ R- | LINselect_1.1.6.tar.gz |
311.7 KiB |
1.1.6 |
2026-04-23 source/ R- | LINselect_1.1.6.tar.gz |
311.7 KiB |
1.1.6 |
2026-04-09 windows/windows R-4.5 | LINselect_1.1.6.zip |
374.4 KiB |
1.1.5 |
2025-04-20 source/ R- | LINselect_1.1.5.tar.gz |
310.0 KiB |