Facilitates univariate and multivariate analysis of evolutionary sequences of phenotypic change. The package extends the modeling framework available in the 'paleoTS' package. Please see <https://klvoje.github.io/evoTS/index.html> for information about the package and the implemented models.

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>.

Simulation of Electric Vehicles charging sessions using Gaussian models, together with time-series power demand calculations.

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>.

Commonly used classification and regression tree methods like the CART algorithm
             are recursive partitioning methods that build the model in a forward stepwise search.
	     Although this approach is known to be an efficient heuristic, the results of recursive
	     tree methods are only locally optimal, as splits are chosen to maximize homogeneity at
	     the next step only. An alternative way to search over the parameter space of trees is
	     to use global optimization methods like evolutionary algorithms. The 'evtree' package
	     implements an evolutionary algorithm for learning globally optimal classification and
	     regression trees in R. CPU and memory-intensive tasks are fully computed in C++ while
	     the 'partykit' package is leveraged to represent the resulting trees in R, providing
	     unified infrastructure for summaries, visualizations, and predictions.