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. doi:10.1093/biomet/asad031 doi:10.3150/21-BEJ1338.
- Version0.0.1
- R versionunknown
- LicenseGPL (≥ 3)
- Needs compilation?No
- Last release10/25/2024
Team
Pierre Alquier
Mathieu Gerber
Insights
Last 30 days
Last 365 days
The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.
Data provided by CRAN
Binaries
Dependencies
- Imports1 package