Package: SLOPE
Title: Sorted L1 Penalized Estimation
Version: 2.1.0
Authors@R: 
    c(
      person(
        "Johan",
        "Larsson",
        role = c("aut", "cre"),
        email = "johan@jolars.co",
        comment = c(ORCID = "0000-0002-4029-5945")
      ),
      person(
        "Jonas",
        "Wallin",
        role = "aut",
        email = "jonas.wallin@stat.lu.se",
        comment = c(ORCID = "0000-0003-0381-6593")
      ),
      person(
        "Malgorzata",
        "Bogdan",
        role = "aut",
        comment = c(ORCID = "0000-0002-0657-4342")
      ),
      person("Ewout", "van den Berg", role = "aut"),
      person("Chiara", "Sabatti", role = "aut"),
      person("Emmanuel", "Candes", role = "aut"),
      person("Evan", "Patterson", role = "aut"),
      person("Weijie", "Su", role = "aut"),
      person("Jakub", "Kała", role = "aut"),
      person("Krystyna", "Grzesiak", role = "aut"),
      person("Mathurin", "Massias", role = "aut"),
      person("Quentin", "Klopfenstein", role = "aut"),
      person(
        "Michal",
        "Burdukiewicz",
        comment = c(ORCID = "0000-0001-8926-582X"),
        role = "aut"
      ),
      person(
        "Jerome",
        "Friedman",
        role = "ctb",
        comment = "code adapted from 'glmnet'"
      ),
      person(
        "Trevor",
        "Hastie",
        role = "ctb",
        comment = "code adapted from 'glmnet'"
      ),
      person(
        "Rob",
        "Tibshirani",
        role = "ctb",
        comment = "code adapted from 'glmnet'"
      ),
      person(
        "Balasubramanian",
        "Narasimhan",
        role = "ctb",
        comment = "code adapted from 'glmnet'"
      ),
      person(
        "Noah",
        "Simon",
        role = "ctb",
        comment = "code adapted from 'glmnet'"
      ),
      person(
        "Junyang",
        "Qian",
        role = "ctb",
        comment = "code adapted from 'glmnet'"
      )
    )
Description: Efficient implementations for Sorted L-One Penalized Estimation
    (SLOPE): generalized linear models regularized with the sorted L1-norm
    (Bogdan et al. 2015). Supported models include
    ordinary least-squares regression, binomial regression, multinomial
    regression, and Poisson regression. Both dense and sparse  predictor
    matrices are supported. In addition, the package features predictor
    screening rules that enable fast and efficient solutions to high-dimensional
    problems.
License: GPL-3
LazyData: true
Depends: R (>= 3.5.0)
Imports: Matrix, methods, Rcpp
LinkingTo: Rcpp, RcppEigen (>= 0.3.4.0.0), BH, bigmemory
Suggests: covr, knitr, rmarkdown, spelling, testthat (>= 2.1.0),
        bigmemory
SystemRequirements: C++17
RoxygenNote: 7.3.3
Language: en-US
Encoding: UTF-8
URL: https://jolars.github.io/SLOPE/, https://github.com/jolars/SLOPE
BugReports: https://github.com/jolars/SLOPE/issues
VignetteBuilder: knitr
NeedsCompilation: yes
Packaged: 2026-03-27 22:13:36 UTC; jola
Author: Johan Larsson [aut, cre] (ORCID:
    <https://orcid.org/0000-0002-4029-5945>),
  Jonas Wallin [aut] (ORCID: <https://orcid.org/0000-0003-0381-6593>),
  Malgorzata Bogdan [aut] (ORCID:
    <https://orcid.org/0000-0002-0657-4342>),
  Ewout van den Berg [aut],
  Chiara Sabatti [aut],
  Emmanuel Candes [aut],
  Evan Patterson [aut],
  Weijie Su [aut],
  Jakub Kała [aut],
  Krystyna Grzesiak [aut],
  Mathurin Massias [aut],
  Quentin Klopfenstein [aut],
  Michal Burdukiewicz [aut] (ORCID:
    <https://orcid.org/0000-0001-8926-582X>),
  Jerome Friedman [ctb] (code adapted from 'glmnet'),
  Trevor Hastie [ctb] (code adapted from 'glmnet'),
  Rob Tibshirani [ctb] (code adapted from 'glmnet'),
  Balasubramanian Narasimhan [ctb] (code adapted from 'glmnet'),
  Noah Simon [ctb] (code adapted from 'glmnet'),
  Junyang Qian [ctb] (code adapted from 'glmnet')
Maintainer: Johan Larsson <johan@jolars.co>
Repository: CRAN
Date/Publication: 2026-03-28 06:11:28 UTC
Built: R 4.6.1; x86_64-w64-mingw32; 2026-08-23 02:19:52 UTC; windows
Archs: x64
