Package: regMMD 0.0.1

regMMD: Robust Regression and Estimation Through Maximum Mean Discrepancy Minimization

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>.

Authors:Pierre Alquier [aut, cre], Mathieu Gerber [aut]

regMMD_0.0.1.tar.gz
regMMD_0.0.1.zip(r-4.5)regMMD_0.0.1.zip(r-4.4)regMMD_0.0.1.zip(r-4.3)
regMMD_0.0.1.tgz(r-4.4-any)regMMD_0.0.1.tgz(r-4.3-any)
regMMD_0.0.1.tar.gz(r-4.5-noble)regMMD_0.0.1.tar.gz(r-4.4-noble)
regMMD_0.0.1.tgz(r-4.4-emscripten)regMMD_0.0.1.tgz(r-4.3-emscripten)
regMMD.pdf |regMMD.html
regMMD/json (API)

# Install 'regMMD' in R:
install.packages('regMMD', repos = c('https://pierrealquier.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.00 score 10 downloads 2 exports 2 dependencies

Last updated 5 days agofrom:816e8edbe2. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 27 2024
R-4.5-winOKOct 27 2024
R-4.5-linuxOKOct 27 2024
R-4.4-winOKOct 27 2024
R-4.4-macOKOct 27 2024
R-4.3-winOKOct 27 2024
R-4.3-macOKOct 27 2024

Exports:mmd_estmmd_reg

Dependencies:rbibutilsRdpack