Package: SLOPE
Title: Sorted L1 Penalized Estimation
Version: 0.5.0
Authors@R: 
    c(
      person(
        "Johan",
        "Larsson",
        role = c("aut", "cre"),
        email = "johan.larsson@stat.lu.se",
        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"),
      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(
        "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'"
      ),
      person("Akarsh", "Goyal", role = "ctb")
    )
Description: Efficient implementations for Sorted L-One Penalized Estimation
    (SLOPE): generalized linear models regularized with the sorted L1-norm
    (Bogdan et al. (2015) <doi:10/gfgwzt>). 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: checkmate, foreach, ggplot2, glmnet, Matrix, methods, mice,
        Rcpp
LinkingTo: Rcpp, RcppArmadillo (>= 0.9.850.1.0)
Suggests: bench, caret, covr, dplyr, knitr, rmarkdown, scales,
        spelling, stringr, testthat (>= 2.1.0), tidyr, vdiffr
RoxygenNote: 7.1.2
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: 2022-06-09 15:19:25 UTC; gerd-jln
Author: Johan Larsson [aut, cre] (<https://orcid.org/0000-0002-4029-5945>),
  Jonas Wallin [aut] (<https://orcid.org/0000-0003-0381-6593>),
  Malgorzata Bogdan [aut],
  Ewout van den Berg [aut],
  Chiara Sabatti [aut],
  Emmanuel Candes [aut],
  Evan Patterson [aut],
  Weijie Su [aut],
  Jakub Kała [aut],
  Krystyna Grzesiak [aut],
  Michal Burdukiewicz [aut] (<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'),
  Akarsh Goyal [ctb]
Maintainer: Johan Larsson <johan.larsson@stat.lu.se>
Repository: CRAN
Date/Publication: 2022-06-09 22:40:18 UTC
Built: R 4.4.0; x86_64-apple-darwin20; 2024-06-02 17:49:52 UTC; unix
Archs: SLOPE.so.dSYM
