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>.

Provides a comprehensive framework for fitting regularized finite mixture regression models via an MM algorithm. The sparse group lasso (sgl) penalty is applied to parameter updates within the MM algorithm for variable selection with respect to groups and covariates. The package provides multiple functions for estimation and allows users to fit models over different lambda-alpha sgl penalties and group counts.

Network-based regularization has achieved success in variable selection for 
    high-dimensional biological data due to its ability to incorporate correlations among 
    genomic features. This package provides procedures of network-based variable selection 
    for generalized linear models (Ren et al. (2017) <doi:10.1186/s12863-017-0495-5> and 
	Ren et al.(2019) <doi:10.1002/gepi.22194>). Continuous, binary, and survival response 
	are supported. Robust network-based methods are available for continuous and survival 
	responses.

Provides R6 classes, methods and utilities to construct,
    analyze, summarize, and visualize regression models.

Focused on linear, quadratic and cubic regression models, it has a function for calculating the models, obtaining a list with their parameters, and a function for making the graphs for the respective models.