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.

The aim of the package is to provide some basic functions
 for doing statistics with trapezoidal fuzzy numbers. In particular,
  the package contains several functions for simulating trapezoidal 
  fuzzy numbers, as well as for calculating some central tendency 
  measures (mean and two types of median), some scale measures 
  (variance, ADD, MDD, Sn, Qn, Tn and some M-estimators) and 
  one diversity index and one inequality index. Moreover, 
  functions for calculating the 1-norm distance, the mid/spr 
  distance and the (phi,theta)-wabl/ldev/rdev distance between 
  fuzzy numbers are included, and a function to calculate the 
  value phi-wabl given a sample of trapezoidal fuzzy numbers.

Perform fuzzy joins on data frames using approximate string
    matching. Implements inner, left, right, full, semi, and anti joins with
    string distance metrics from the 'stringdist' package, including Optimal
    String Alignment, Levenshtein, Damerau-Levenshtein, Jaro-Winkler, q-gram,
    cosine, Jaccard, and Soundex. Uses a 'data.table' backend plus compiled
    'C++' result assembly to reduce overhead in large joins, while adaptive
    candidate planning avoids unnecessary distance evaluations in single-column
    string joins. Suitable for reconciling misspellings, inconsistent labels,
    and other near-match identifiers while optionally returning the computed
    distance for each match. Bibliographic references include the 'stringdist'
    package documentation (2014) <https://CRAN.R-project.org/package=stringdist>
    and Robinson, D. (2015) <https://github.com/dgrtwo/fuzzyjoin>.