SLOPE is a package for Sorted L-One Penalized Estimation


Johan Larsson

Jonas Wallin

Małgorzata Bogdan

Ewout van den Berg

Chiara Sabatti

Emmanuel Candés

Evan Patterson

Weijie Su

Jakub Kała

Krystyna Grzesiak

Michal Burdukiewicz

Akarsh Goyal


9 June 2022




BibTeX citation:
  author = {Johan Larsson and Jonas Wallin and Małgorzata Bogdan and
    Ewout van den Berg and Chiara Sabatti and Emmanuel Candés and Evan
    Patterson and Weijie Su and Jakub Kała and Krystyna Grzesiak and
    Michal Burdukiewicz and Akarsh Goyal},
  title = {SLOPE},
  date = {2022-06-09},
  url = {},
  langid = {en},
  abstract = {Efficient implementations for Sorted L-One Penalized
    Estimation (SLOPE): generalized linear models regularized with the
    sorted L1-norm (Bogdan et al. (2015) \textless
    doi:10/gfgwzt\textgreater). 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.}
For attribution, please cite this work as:
Johan Larsson, Jonas Wallin, Małgorzata Bogdan, Ewout van den Berg, Chiara Sabatti, Emmanuel Candés, Evan Patterson, et al. 2022. “SLOPE.” R.