An interface for training Fuzzy DBScan with both Fuzzy Core and Fuzzy Border.
    Therefore, the package provides a method to initialize and run the algorithm and a
    function to predict new data w.t.h. of 'R6'.
    The package is build upon the paper "Fuzzy Extensions of the DBScan algorithm"
    from Ienco and Bordogna (2018) <doi:10.1007/s00500-016-2435-0>.
    A predict function assigns new data according to the same criteria as the algorithm itself.
    However, the prediction function freezes the algorithm to preserve the trained
    cluster structure and treats each new prediction object individually.

Fuzzy forests, a new algorithm based on random forests,
    is designed to reduce the bias seen in random forest feature selection
    caused by the presence of correlated features.  Fuzzy forests uses
    recursive feature elimination random forests to select
    features from separate blocks of correlated features where the
    correlation within each block of features is high
    and the correlation between blocks of features is low.
    One final random forest is fit using the surviving features.
    This package fits random forests using the 'randomForest' package and
    allows for easy use of 'WGCNA' to split features into distinct blocks.
    See D. Conn, Ngun, T., C. Ramirez, and G. Li (2019) <doi:10.18637/jss.v091.i09>
    for further details.

Join tables together based not on whether columns
  match exactly, but whether they are similar by some comparison.
  Implementations include string distance and regular expression
  matching.

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.