Tree-based classification and soft-clustering method for preference rankings, with tools for external validation of fuzzy clustering, and Kemeny-equivalent augmented unfolding.
    It contains the recursive partitioning algorithm for preference rankings, non-parametric tree-based method for a matrix of preference rankings as a response variable. It contains also the distribution-free soft clustering method for preference rankings, namely the K-median cluster component analysis (CCA). 
    The package depends on the 'ConsRank' R package.
    Options for validate the tree-based method are both test-set procedure and V-fold cross validation.
    The package contains the routines to compute the adjusted concordance index (a fuzzy version of the adjusted rand index) and the normalized degree of concordance (the corresponding fuzzy version of the rand index).
    The package also contains routines to perform the Kemeny-equivalent augmented unfolding. The mds endine is the function 'sacofSym' from the package 'smacof'.
    Essential references:
    D'Ambrosio, A., Vera, J.F., and Heiser, W.J. (2021) <doi:10.1080/00273171.2021.1899892>;
    D'Ambrosio, A., Amodio, S., Iorio, C., Pandolfo, G., and Siciliano, R. (2021) <doi:10.1007/s00357-020-09367-0>;
    D'Ambrosio, A., and Heiser, W.J. (2019) <doi:10.1007/s41237-018-0069-5>;    
    D'Ambrosio, A., and Heiser W.J. (2016) <doi:10.1007/s11336-016-9505-1>;
    Hullermeier, E., Rifqi, M., Henzgen, S., and Senge, R. (2012) <doi:10.1109/TFUZZ.2011.2179303>;
    Marden, J.J. <ISBN:0412995212>.

Fits or generalized linear models either a regression with Autoregressive moving-average (ARMA) errors for time series data. 
       The package makes it easy to incorporate constraints into the model's coefficients. 
          The model is specified by an objective function (Gaussian, Binomial or Poisson) or an ARMA order (p,q), 
          a vector of bound constraints 
          for the coefficients (i.e beta1 > 0) and the possibility to incorporate restrictions
          among coefficients (i.e beta1 > beta2).
          The references of this packages are the same as 'stats' package for glm() and arima() functions.
          See Brockwell, P. J. and Davis, R. A. (1996, ISBN-10: 9783319298528).
          For the different optimizers implemented, it is recommended to consult the documentation of the corresponding packages.