CITATION.cff

cff-version: 1.2.0
title: 'SLOPE: Sorted L1 Penalized Estimation'
message: >-
  If you use this software, please cite it using
  these metadata.
type: software
authors:
  - given-names: Johan
    family-names: Larsson
    email: johanlarsson@outlook.com
    affiliation: 'Department of Statistics, Lund University'
    orcid: 'https://orcid.org/0000-0002-4029-5945'
  - given-names: Jonas
    family-names: Wallin
    email: jonas.wallin@stat.lu.se
    affiliation: 'Department of Statistics, Lund University'
    orcid: 'https://orcid.org/0000-0003-0381-6593'
  - given-names: Malgorzata
    family-names: Bogdan
    orcid: 'https://orcid.org/0000-0002-0657-4342'
    email: malgorzata.bogdan@stat.lu.se
  - given-names: Ewout
    name-particle: van der
    family-names: Berg
  - given-names: Chiara
    family-names: Sabatti
  - given-names: Emmanuel
    family-names: Candes
  - given-names: Evan
    family-names: Patterson
  - given-names: Weijie
    family-names: Su
  - given-names: Jakub
    family-names: Kała
  - given-names: Krystyna
    family-names: Grzesiak
  - given-names: Michal
    family-names: Burdukiewicz
    orcid: 'https://orcid.org/0000-0001-8926-582X'
repository-code: 'https://github.com/jolars/SLOPE'
url: 'https://jolars.github.io/SLOPE/'
abstract: >-
  Efficient implementations for Sorted L-One
  Penalized Estimation (SLOPE): generalized linear
  models regularized with the sorted L1-norm (Bogdan
  et al. (2015) <doi:10.1214/15-AOAS842>). 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.
keywords:
  - SLOPE
  - regularization
  - sparse regression
  - generalized linear models
license: GPL-3.0-or-later