Estimators for fuzzy linear regression. The functions estimate parameters of 
    fuzzy linear regression models with crisp or fuzzy independent variables (triangular
    fuzzy numbers are supported). Implements multiple methods for parameter estimation and 
    algebraic operations with triangular fuzzy numbers. Includes functions for 
    summarising, printing and plotting the model fit. Calculates predictions from the 
    model and total error of fit. Individual methods are described in
    Diamond (1988) <doi:10.1016/0020-0255(88)90047-3>,
	Hung & Yang (2006) <doi:10.1016/j.fss.2006.08.004>,
	Lee & Tanaka (1999) <doi:10.15807/jorsj.42.98>,
	Nasrabadi, Nasrabadi & Nasrabady (2005) <doi:10.1016/j.amc.2004.02.008>,
	Skrabanek, Marek & Pozdilkova (2021) <doi:10.3390/math9060685>,
	Tanaka, Hayashi & Watada (1989) <doi:10.1016/0377-2217(89)90431-1>,
	Zeng, Feng & Li (2017) <doi:10.1016/j.asoc.2016.09.029>.

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.