Functions to compute fuzzy versions of species occurrence patterns based on presence-absence data (including inverse distance interpolation, trend surface analysis, and prevalence-independent favourability obtained from probability of presence), as well as pair-wise fuzzy similarity (based on fuzzy logic versions of commonly used similarity indices) among those occurrence patterns. Includes also functions for model consensus and comparison (overlap and fuzzy similarity, loss or gain), and for data preparation, such as obtaining unique abbreviations of species names, cleaning and gridding (thinning) point occurrence data onto raster maps, selecting absences under specified criteria, converting species lists (long format) to presence-absence tables (wide format), transposing part of a data frame, selecting relevant variables for models, assessing the false discovery rate, or analysing and dealing with multicollinearity. Initially described in Barbosa (2015) <doi:10.1111/2041-210X.12372>.

Random simulations of fuzzy numbers are still a challenging problem. The aim of this package is to provide the respective 
 procedures to simulate fuzzy random variables, especially in the case of the piecewise linear fuzzy numbers (PLFNs, 
 see Coroianua et al. (2013) <doi:10.1016/j.fss.2013.02.005> for the further details).
 Additionally, the special resampling algorithms known as the epistemic bootstrap are provided (see Grzegorzewski and Romaniuk
 (2022) <doi:10.34768/amcs-2022-0021>, Grzegorzewski and Romaniuk (2022) <doi:10.1007/978-3-031-08974-9_39>)
 together with the functions to apply statistical tests and estimate various characteristics based on the epistemic bootstrap.
 The package also includes a real-life data set of epistemic fuzzy triangular numbers.
 The fuzzy numbers used in this package are consistent with the 'FuzzyNumbers' package.

An implementation of a method for computing fuzzy numbers representing stationary probabilities of an unknown Markov chain,
        from which a sequence of observations along time has been obtained. The algorithm is based on the proposal presented by James Buckley 
        in his book on Fuzzy probabilities (Springer, 2005), chapter 6. Package 'FuzzyNumbers' is used to represent the output probabilities.