Package: SLOPE
Title: Sorted L1 Penalized Estimation
Version: 2.1.0.9000
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
Roxygen: list(markdown = TRUE)
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
Repository: https://jolars.r-universe.dev
Date/Publication: 2026-06-12 12:11:24 UTC
RemoteUrl: https://github.com/jolars/slope
RemoteRef: HEAD
RemoteSha: 13ebb360999370bbe243114ffc8542c3fa0e34ec
NeedsCompilation: yes
Packaged: 2026-06-12 15:01:10 UTC; root
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>