Package: SLOPE
Title: Sorted L1 Penalized Estimation
Version: 2.1.1
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
URL: https://jolars.github.io/SLOPE/, https://github.com/jolars/SLOPE
BugReports: https://github.com/jolars/SLOPE/issues
Depends: R (>= 4.1.0)
Imports: Matrix, methods, Rcpp
Suggests: bigmemory, covr, knitr, rmarkdown, spelling, testthat (>=
        2.1.0)
LinkingTo: BH, bigmemory, Rcpp, RcppEigen (>= 0.3.4.0.0)
VignetteBuilder: knitr
Encoding: UTF-8
Language: en-US
LazyData: true
RoxygenNote: 7.3.3
SystemRequirements: C++17
NeedsCompilation: yes
Packaged: 2026-08-28 13:34:56 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: 2026-08-29 12:00:09 UTC
Built: R 4.7.0; x86_64-w64-mingw32; 2026-08-31 23:51:19 UTC; windows
Archs: x64
