autotune
Faster and more Efficient Lasso (than 'glmnet' and 'scalreg') with Data-Driven Tuning
Fits Lasso paths for high-dimensional regression using coordinate descent with automatic, data-driven tuning of the regularization parameter. The implementation is 10 to 50 times faster than the standard 'glmnet' implementation of Lasso and over 100 times faster than scaled Lasso. It also provides a reliable estimate of the regression noise level. For details of the method, see Sadhukhan, Wilms, Smeekes and Basu (2025) "Autotune: fast, accurate, and automatic tuning parameter selection for Lasso" <doi:10.48550/arXiv.2512.11139>.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.1.0 |
rolling linux/jammy R-4.5 | autotune_0.1.0.tar.gz |
1.4 MiB |
0.1.0 |
rolling linux/noble R-4.5 | autotune_0.1.0.tar.gz |
1.4 MiB |
0.1.0 |
rolling source/ R- | autotune_0.1.0.tar.gz |
1.3 MiB |
0.1.0 |
latest linux/jammy R-4.5 | autotune_0.1.0.tar.gz |
1.4 MiB |
0.1.0 |
latest linux/noble R-4.5 | autotune_0.1.0.tar.gz |
1.4 MiB |
0.1.0 |
latest source/ R- | autotune_0.1.0.tar.gz |
1.3 MiB |
0.1.0 |
2026-04-23 source/ R- | autotune_0.1.0.tar.gz |
0 B |