Robust Regression and Estimation Through Maximum Mean Discrepancy Minimization R package regMMD version 0.1.0 The functions in this package compute robust estimators by minimizing a kernel-based distance known as MMD (Maximum Mean Discrepancy) between the sample and a statistical model. Recent works proved that these estimators enjoy a universal consistency property, and are extremely robust to outliers. Various optimization algorithms are implemented: stochastic gradient is available for most models, but the package also allows gradient descent in a few models for which an exact formula is available for the gradient. In terms of distribution fit, a large number of continuous and discrete distributions are available: Gaussian, exponential, uniform, gamma, Poisson, geometric, etc. In terms of regression, the models available are: linear, logistic, gamma, beta and Poisson. Alquier, P. and Gerber, M. (2024) <doi:10.1093/biomet/asad031> Cherief-Abdellatif, B.-E. and Alquier, P. (2022) <doi:10.3150/21-BEJ1338>. Software Imports: Rdpack (>= 0.7) Pierre Alquier <pierre.alquier.stat@gmail.com> Comprehensive R Archive Network (CRAN) Pierre Alquier [aut, cre] (ORCID: <https://orcid.org/0000-0003-4249-7337>), Mathieu Gerber [aut] (ORCID: <https://orcid.org/0000-0001-6774-2330>) GPL (>= 3) 2025-11-18 application/tgz https://CRAN.R-project.org/package=regMMD doi:10.32614/CRAN.package.regMMD