Links datasets through fuzzy string matching using pretrained text embeddings. Produces more accurate record linkage when lexical string distance metrics are a poor guide to match quality (e.g., "Patricia" is more lexically similar to "Patrick" than it is to "Trish"). Capable of performing multilingual record linkage. Methods are described in Ornstein (2025) <doi:10.1017/pan.2025.10016>.

Provides methods to solve Fuzzy Linear Programming Problems with
             fuzzy constraints (following different approaches proposed by 
             Verdegay, Zimmermann, Werners and Tanaka), fuzzy costs, and fuzzy
             technological matrix.

Contains functions for operations with fuzzy cognitive maps using t-norm and s-norm operators. T-norms and S-norms are described by Dov M. Gabbay and George Metcalfe (2007) <doi:10.1007/s00153-007-0047-1>. System indicators are described by Cox, Earl D. (1995) <isbn:1886801010>. Executable examples are provided in the "inst/examples" folder.

S4 classes and methods
    to deal with fuzzy numbers. They allow for computing any arithmetic
    operations (e.g., by using the Zadeh extension principle),
    performing approximation of arbitrary fuzzy numbers by trapezoidal
    and piecewise linear ones, preparing plots for publications, computing
    possibility and necessity values for comparisons, etc.

One can easily draw the membership function of f(x,y) by package 'FuzzyNumbers.Ext.2' in which f(.,.) is supposed monotone and x and y are two fuzzy numbers. This work is possible using function f2apply() which is an extension of function fapply() from Package 'FuzzyNumbers' for two-variable monotone functions. Moreover, this package has the ability of computing the core, support and alpha-cuts of the fuzzy-valued final result.

Estimates fuzzy measures of poverty and deprivation. It also estimates the sampling variance of these measures using bootstrap or jackknife repeated replications.

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>).