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  "Title": "Sorted L1 Penalized Estimation",
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  "Description": "Efficient implementations for Sorted L-One Penalized\nEstimation (SLOPE): generalized linear models regularized with\nthe sorted L1-norm (Bogdan et al. 2015). Supported models\ninclude ordinary least-squares regression, binomial regression,\nmultinomial regression, and Poisson regression. Both dense and\nsparse predictor matrices are supported. In addition, the\npackage features predictor screening rules that enable fast and\nefficient solutions to high-dimensional problems.",
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  "Author": "Johan Larsson [aut, cre] (ORCID:\n<https://orcid.org/0000-0002-4029-5945>), Jonas Wallin [aut]\n(ORCID: <https://orcid.org/0000-0003-0381-6593>), Malgorzata\nBogdan [aut] (ORCID: <https://orcid.org/0000-0002-0657-4342>),\nEwout van den Berg [aut], Chiara Sabatti [aut], Emmanuel Candes\n[aut], Evan Patterson [aut], Weijie Su [aut], Jakub Kała [aut],\nKrystyna Grzesiak [aut], Mathurin Massias [aut], Quentin\nKlopfenstein [aut], Michal Burdukiewicz [aut] (ORCID:\n<https://orcid.org/0000-0001-8926-582X>), Jerome Friedman [ctb]\n(code adapted from 'glmnet'), Trevor Hastie [ctb] (code adapted\nfrom 'glmnet'), Rob Tibshirani [ctb] (code adapted from\n'glmnet'), Balasubramanian Narasimhan [ctb] (code adapted from\n'glmnet'), Noah Simon [ctb] (code adapted from 'glmnet'),\nJunyang Qian [ctb] (code adapted from 'glmnet')",
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