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autotune  

Faster and more Efficient Lasso (than 'glmnet' and 'scalreg') with Data-Driven Tuning
View on CRAN: Click here


Download and install autotune package within the R console
Install from CRAN:
install.packages("autotune")

Install from Github:
library("remotes")
install_github("cran/autotune")

Install by package version:
library("remotes")
install_version("autotune", "0.1.0")



Attach the package and use:
library("autotune")
Maintained by
Tathagata Sadhukhan
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2026-08-21
Latest Update: 2026-08-21
Description:
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>.
How to cite:
Tathagata Sadhukhan (2026). autotune: Faster and more Efficient Lasso (than 'glmnet' and 'scalreg') with Data-Driven Tuning. R package version 0.1.0, https://cran.r-project.org/web/packages/autotune. Accessed 20 Sep. 2026.
Previous versions and publish date:
(2026-09-02 18:20), 0.1.0 (2026-08-21 15:40)
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Complete documentation for autotune
Functions, R codes and Examples using the autotune R package
Full autotune package functions and examples
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