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.

The main goal of this package is drawing the membership function of the fuzzy p-value which is defined as a fuzzy set on the unit interval for three following problems: (1) testing crisp hypotheses based on fuzzy data, (2) testing fuzzy hypotheses based on crisp data, and (3) testing fuzzy hypotheses based on fuzzy data. In all cases, the fuzziness of data or/and the fuzziness of the boundary of null fuzzy hypothesis transported via the p-value function and causes to produce the fuzzy p-value. If the p-value is fuzzy, it is more appropriate to consider a fuzzy significance level for the problem. Therefore, the comparison of the fuzzy p-value and the fuzzy significance level is evaluated by a fuzzy ranking method in this package.