Fuzzy clustering of species in an ecological community as common or
    rare based on their abundance and occupancy. It also includes functions to
    compute confidence intervals of classification metrics and plot results. See
    Balbuena et al. (2020, <doi:10.1101/2020.08.12.247502>).

Design and simulate fuzzy logic systems using Type-1 and Interval Type-2 Fuzzy Logic.
    This toolkit includes with graphical user interface (GUI) and an adaptive neuro-
    fuzzy inference system (ANFIS). This toolkit is a continuation from the previous
    package ('FuzzyToolkitUoN'). Produced by the Intelligent Modelling & Analysis Group (IMA)
    and Lab for UnCertainty In Data and decision making (LUCID), University of Nottingham. 
    A big thank you to the many people who have contributed to the development/evaluation of the toolbox.
    Please cite the toolbox and the corresponding paper <doi:10.1109/FUZZ48607.2020.9177780> when using it.
    More related papers can be found in the NEWS.

Does fuzzy tests and confidence intervals (following Geyer
    and Meeden, Statistical Science, 2005, <doi:10.1214/088342305000000340>)
    for sign test and Wilcoxon signed rank and rank sum tests.

The classical (i.e. Efron's, see Efron and Tibshirani (1994, ISBN:978-0412042317)  "An Introduction to the Bootstrap") bootstrap is widely used for both the real (i.e. "crisp") and fuzzy data.
 The main aim of the algorithms implemented in this package  is to overcome a problem with repetition of a few distinct values and to create fuzzy numbers, which are "similar" (but not the same) to values from the initial sample.
 To do this, different characteristics of triangular/trapezoidal numbers are kept (like the value, the ambiguity, etc., see Grzegorzewski et al. <doi:10.2991/eusflat-19.2019.68>, Grzegorzewski et al. (2020) <doi:10.2991/ijcis.d.201012.003>, Grzegorzewski et al. (2020) <doi:10.34768/amcs-2020-0022>, Grzegorzewski and Romaniuk (2022) <doi:10.1007/978-3-030-95929-6_3>,  Romaniuk and Hryniewicz (2019) <doi:10.1007/s00500-018-3251-5>).
 Some additional procedures related to these resampling methods are also provided,
 like calculation of the Bertoluzza et al.'s distance (aka the mid/spread distance, see Bertoluzza et al. (1995) "On a new class of distances between fuzzy numbers")
 and estimation of the p-value of the one- and two- sample bootstrapped test for the mean (see Lubiano et al. (2016, <doi:10.1016/j.ejor.2015.11.016>)).
 Additionally, there are procedures which randomly generate trapezoidal fuzzy numbers using some well-known statistical distributions.