The function TailClassifier() suggests one of the following types of tail for your discrete data: 1) Power decaying tail; 2) Sub-exponential decaying tail; and 3) Near-exponential decaying tail. The function also provides an estimate of the parameter for the classified-distribution as a reference.

Provides functions implementing minimal distance estimation methods for parametric tail dependence models, as proposed in  
	 Einmahl, J.H.J., Kiriliouk, A., Krajina, A., and Segers, J. (2016) <doi:10.1111/rssb.12114> and 
	 Einmahl, J.H.J., Kiriliouk, A., and Segers, J. (2018) <doi:10.1007/s10687-017-0303-7>.

The goal of 'TailID' is to detect sensitive points in the tail of a dataset using techniques from Extreme Value Theory (EVT). It utilizes the Generalized Pareto Distribution (GPD) for assessing tail behavior and detecting inconsistent points with the Identical Distribution hypothesis of the tail. For more details see Manau (2025)<doi:10.4230/LIPIcs.ECRTS.2025.20>.

Set of tools to estimate the probability in the upper tail of the aggregate loss distribution using different methods: Panjer recursion, Monte Carlo simulations, Markov bound, Cantelli bound, Moment bound, and Chernoff bound.

Postprocessors refine predictions outputted from machine
    learning models to improve predictive performance or better satisfy
    distributional limitations. This package introduces 'tailor' objects,
    which compose iterative adjustments to model predictions. A number of
    pre-written adjustments are provided with the package, such as
    calibration. See Lichtenstein, Fischhoff, and Phillips (1977) 
    <doi:10.1007/978-94-010-1276-8_19>. Other methods and utilities to compose 
    new adjustments are also included. Tailors are tightly integrated with the 
    'tidymodels' framework.

Estimators for two functionals used to detect Gamma, Pareto or Lognormal distributions, as well as distributions exhibiting similar tail behavior, as introduced by Iwashita and Klar (2023) <doi:10.1111/stan.12316> and 
            Klar (2024) <doi:10.1080/00031305.2024.2413081>. 
            One of these functionals, g, originally proposed by Asmussen and Lehtomaa (2017) <doi:10.3390/risks5010010>, distinguishes between log-convex and log-concave tail behavior. 
            Furthermore the characterization of the lognormal distribution is based on the work of Mosimann (1970) <doi:10.2307/2284599>.
            The package also includes methods for visualizing these estimators and their associated confidence intervals across various threshold values.