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regMMD  

Robust Regression and Estimation Through Maximum Mean Discrepancy Minimization
View on CRAN: Click here


Download and install regMMD package within the R console
Install from CRAN:
install.packages("regMMD")

Install from Github:
library("remotes")
install_github("cran/regMMD")

Install by package version:
library("remotes")
install_version("regMMD", "0.0.1")



Attach the package and use:
library("regMMD")
Maintained by
Pierre Alquier
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2024-10-25
Latest Update: 2024-10-25
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) <doi:10.1093/biomet/asad031> Cherief-Abdellatif, B.-E. and Alquier, P. (2022) <doi:10.3150/21-BEJ1338>.
How to cite:
Pierre Alquier (2024). regMMD: Robust Regression and Estimation Through Maximum Mean Discrepancy Minimization. R package version 0.0.1, https://cran.r-project.org/web/packages/regMMD. Accessed 22 Dec. 2024.
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