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  "Title": "Robust Regression and Estimation Through Maximum Mean\nDiscrepancy Minimization",
  "Description": "The functions in this package compute robust estimators by\nminimizing a kernel-based distance known as MMD (Maximum Mean\nDiscrepancy) between the sample and a statistical model. Recent\nworks proved that these estimators enjoy a universal\nconsistency property, and are extremely robust to outliers.\nVarious optimization algorithms are implemented: stochastic\ngradient is available for most models, but the package also\nallows gradient descent in a few models for which an exact\nformula is available for the gradient. In terms of distribution\nfit, a large number of continuous and discrete distributions\nare available: Gaussian, exponential, uniform, gamma, Poisson,\ngeometric, etc. In terms of regression, the models available\nare: linear, logistic, gamma, beta and Poisson. Alquier, P. and\nGerber, M. (2024) <doi:10.1093/biomet/asad031>\nCherief-Abdellatif, B.-E. and Alquier, P. (2022)\n<doi:10.3150/21-BEJ1338>.",
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  "Author": "Pierre Alquier [aut, cre] (ORCID:\n<https://orcid.org/0000-0003-4249-7337>), Mathieu Gerber [aut]\n(ORCID: <https://orcid.org/0000-0001-6774-2330>)",
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