Package: regMMD Version: 0.1.0 Title: Robust Regression and Estimation Through Maximum Mean Discrepancy Minimization Description: 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) Cherief-Abdellatif, B.-E. and Alquier, P. (2022) . Authors@R: c( person("Pierre", "Alquier",, email = "pierre.alquier.stat@gmail.com", role = c("aut", "cre"), comment = c(ORCID = "0000-0003-4249-7337")), person("Mathieu", "Gerber",, email = "mathieu.gerber@bristol.ac.uk", role = "aut", comment = c(ORCID = "0000-0001-6774-2330")) ) License: GPL (>= 3) Encoding: UTF-8 RoxygenNote: 7.3.2 RdMacros: Rdpack Imports: Rdpack (>= 0.7) Author: Pierre Alquier [aut, cre] (ORCID: ), Mathieu Gerber [aut] (ORCID: ) Maintainer: Pierre Alquier NeedsCompilation: no Packaged: 2026-10-15 06:41:18 UTC; root Repository: https://pierrealquier.r-universe.dev Date/Publication: 2025-11-18 15:40:02 UTC RemoteUrl: https://github.com/cran/regMMD RemoteRef: HEAD RemoteSha: 834ae1e72e386d4fdcae9c4c7cf402d63585061e