{
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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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  "Date/Publication": "2026-06-12 12:11:24 UTC",
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  "Author": "Johan Larsson [aut, cre] (ORCID:\n<https://orcid.org/0000-0002-4029-5945>),\nJonas Wallin [aut] (ORCID: <https://orcid.org/0000-0003-0381-6593>),\nMalgorzata Bogdan [aut] (ORCID:\n<https://orcid.org/0000-0002-0657-4342>),\nEwout van den Berg [aut],\nChiara Sabatti [aut],\nEmmanuel Candes [aut],\nEvan Patterson [aut],\nWeijie Su [aut],\nJakub Kała [aut],\nKrystyna Grzesiak [aut],\nMathurin Massias [aut],\nQuentin Klopfenstein [aut],\nMichal Burdukiewicz [aut] (ORCID:\n<https://orcid.org/0000-0001-8926-582X>),\nJerome Friedman [ctb] (code adapted from 'glmnet'),\nTrevor Hastie [ctb] (code adapted from 'glmnet'),\nRob Tibshirani [ctb] (code adapted from 'glmnet'),\nBalasubramanian Narasimhan [ctb] (code adapted from 'glmnet'),\nNoah Simon [ctb] (code adapted from 'glmnet'),\nJunyang Qian [ctb] (code adapted from 'glmnet')",
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