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, fuzzy loss, fuzzy gain), and for data preparation, such as obtaining unique abbreviations of species names, defining the background region, cleaning and gridding (thinning) point occurrence data onto raster maps, selecting among (pseudo)absences to address survey bias, 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>,
 Romaniuk et al. (2024) <doi:10.32614/RJ-2024-016>)
 together with the functions to apply statistical tests and estimate various characteristics based on the epistemic bootstrap.
 The package also includes real-life datasets of epistemic fuzzy triangular and trapezoidal numbers.
 The fuzzy numbers used in this package are consistent with the 'FuzzyNumbers' package.

Implementation of the FVIBES, the Fuzzy Variable-Importance Based Eigenspace Separation algorithm as described in the paper by Ghashti, J.S., Hare, W., and J.R.J. Thompson (2025). Variable-Weighted Adjacency Constructions for Fuzzy Spectral Clustering. Submitted.