Tools for modelling electric vehicle charging sessions into
    generic groups with similar connection patterns called "user profiles",
    using Gaussian Mixture Models clustering. The clustering and profiling
    methodology is described in Cañigueral and Meléndez (2021, ISBN:0142-0615) 
    <doi:10.1016/j.ijepes.2021.107195>.

An implementation of the 'Evidential Neural Network for Regression' model recently introduced in Denoeux (2023) <doi:10.1109/TFUZZ.2023.3268200>. In this model, prediction uncertainty is quantified by Gaussian random fuzzy numbers as introduced in Denoeux (2023) <doi:10.1016/j.fss.2022.06.004>. The package contains functions for training the network, tuning hyperparameters by cross-validation or the hold-out method, and making predictions. It also contains utilities for making calculations with Gaussian random fuzzy numbers (such as, e.g., computing the degrees of belief and plausibility of an interval, or combining Gaussian random fuzzy numbers).

Simulation of Electric Vehicles charging sessions using 
    Gaussian models, together with time-series power demand calculations. 
    The simulation methodology is published in 
    Cañigueral et al. (2023, ISBN:0957-4174) <doi:10.1016/j.eswa.2023.120318>.

The R package proposes extreme value index estimators for heavy tailed models 
             by mean of order p <DOI:10.1016/j.csda.2012.07.019>, peaks over random threshold
             <DOI:10.57805/revstat.v4i3.37> and a bias-reduced estimator 
             <DOI:10.1080/00949655.2010.547196>.
	     The package also computes moment, generalised Hill <DOI:10.2307/3318416> 
	     and mixed moment estimates for the extreme value index.
	     High quantiles and value at risk estimators based on these estimators are implemented.

Two classifiers for open set recognition and novelty detection based on extreme value theory. The first classifier is based on the generalized Pareto distribution (GPD) and the second classifier is based on the generalized extreme value (GEV) distribution. For details, see Vignotto, E., & Engelke, S. (2018) <arXiv:1808.09902>.