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cff-version: 1.2.0
message: 'To cite package "autotune" in publications use:'
type: software
title: 'autotune: Faster and more Efficient Lasso (than ''glmnet'' and ''scalreg'')
  with Data-Driven Tuning'
version: 0.1.0
doi: 10.32614/CRAN.package.autotune
abstract: 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.
authors:
- family-names: Sadhukhan
  given-names: Tathagata
  email: ts767@cornell.edu
- family-names: Wilms
  given-names: Ines
- family-names: Smeekes
  given-names: Stephan
- family-names: Basu
  given-names: Sumanta
repository: https://tathagata-s.r-universe.dev
commit: 9d4d570df560c9f77f89a7b81fb53a9ab4320501
date-released: '2026-08-20'
contact:
- family-names: Sadhukhan
  given-names: Tathagata
  email: ts767@cornell.edu