Various clustering algorithms that produce a credal partition,
    i.e., a set of Dempster-Shafer mass functions representing the membership of objects
    to clusters. The mass functions quantify the cluster-membership uncertainty of the     
    objects. The algorithms are: Evidential c-Means, Relational Evidential c-Means, 
    Constrained Evidential c-Means, Evidential Clustering, Constrained Evidential 
    Clustering, Evidential K-nearest-neighbor-based Clustering, Bootstrap Model-Based
    Evidential Clustering, Belief Peak Evidential Clustering, Neural-Network-based
    Evidential Clustering.

Combine pieces of evidence in the form of uncertainty representations.

Extends simulation, distribution, quantile and density
        functions to univariate and multivariate parametric extreme
        value distributions, and provides fitting functions which
        calculate maximum likelihood estimates for univariate and
        bivariate maxima models, and for univariate and bivariate
        threshold models.

Provides functions for the Bayesian analysis of extreme
        value models, using Markov chain Monte Carlo methods. Allows
		the construction of both uninformative and informed prior 
		distributions for common statistical models applied to extreme
        event data, including the generalized extreme value distribution.

Functions for setting up and analyzing event history data.

Event dataset repository including both real-life and artificial event logs. They can be used in combination with functionalities provided by the 'bupaR' packages. Janssenswillen et al. (2020) <http://ceur-ws.org/Vol-2703/paperTD7.pdf>.

A graphical user interface for open source event detection.

Detect events in time-series data. Combines multiple well-known R packages like 'forecast' and 'neuralnet' to deliver an easily configurable tool for multivariate event detection.

A user friendly, easy to understand way of doing event
    history regression for marginal estimands of interest,
    including the cumulative incidence and the restricted mean
    survival, using the pseudo observation framework for 
    estimation. For a review of the methodology, see Andersen and
    Pohar Perme (2010) <doi:10.1177/0962280209105020> or Sachs 
    and Gabriel (2022) <doi:10.18637/jss.v102.i09>. The
    interface uses the well known formulation of a generalized
    linear model and allows for features including plotting of 
    residuals, the use of sampling weights, and corrected
    variance estimation.