Package: autotune
Type: Package
Title: Faster and more Efficient Lasso (than 'glmnet' and 'scalreg')
        with Data-Driven Tuning
Version: 0.1.0
Maintainer: Tathagata Sadhukhan <ts767@cornell.edu>
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>.
License: GPL (>= 2)
Encoding: UTF-8
Depends: R (>= 2.10)
Imports: Rcpp (>= 1.0.13)
LinkingTo: Rcpp
RoxygenNote: 7.3.2
Suggests: knitr, rmarkdown, glmnet, AUC, ggplot2, ggExtra, dplyr,
        tidyr, Matrix
VignetteBuilder: knitr
Authors@R: c(person(given = "Tathagata",
                        family = "Sadhukhan",
                        role = c("aut", "cre"),
                        email = "ts767@cornell.edu"),
                 person(given = "Ines",
                        family = "Wilms",
                        role = "aut"),
                 person(given = "Stephan",
                        family = "Smeekes",
                        role = "aut"),
                 person(given = "Sumanta",
                        family = "Basu",
                        role = "aut"))
NeedsCompilation: yes
Packaged: 2026-08-20 00:18:39 UTC; ts767
Author: Tathagata Sadhukhan [aut, cre],
  Ines Wilms [aut],
  Stephan Smeekes [aut],
  Sumanta Basu [aut]
Repository: CRAN
Date/Publication: 2026-08-21 13:40:26 UTC
Built: R 4.5.3; x86_64-w64-mingw32; 2026-08-24 01:34:38 UTC; windows
Archs: x64
