Fast, Accurate and Automatic Tuning Parameter Selection for Lasso
Overview
Fits Lasso paths for high-dimensional regression using coordinate descent with automatic, data-driven tuning of the regularization parameter. The implementation is 10 to 200 times faster than the standard 'glmnet' implementation of Lasso tuned via Cross Validation and over 100 times faster than scaled Lasso. It also provides a reliable estimate of the regression noise level and a new diagnostic for sparsity. 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.
Install
Health
- OK13 OK2026-09-08
- ERROR12 OK, 1 ERROR2026-09-07
- OK7 OK2026-08-22
Documentation
- Help pages
- 6includes a package overview page
- Examples that run
- 100%6 run in a plain check
- Exported functions with examples
- 100%
- Documented parameters
- 100%
- Return-value docs
- 100%
- Help pages citing references
- 17%
- Autotune Lasso HTML
Downloads
Dependencies
Installing autotune adds 1 package (1 with compiled code, 1.8 MB of source tarballs). Heaviest: Rcpp 1.8 MB.
Code & Tests
Datasets
People & History
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- 0.1.1Latest
- 0.1.02026-08-21
- RR 4.6.0 released · 2026-04-24
Package metadata
- First published
- 2026-08-21
- Total releases
- 2 / 1 yrs
- License
- GPL (>= 2) OSI
- Minimum R
- ≥ 2.10
- Bundled data
- 975 KB / 1 file
- Download size
- 1.4 MB
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