Tools for calculating disclosure risk measures for microdata,
    including record-level and file-level measures. The record-level disclosure
    risk is estimated primarily using exhaustive tabulation. The file-level
    disclosure risk is estimated by fitting loglinear models on the observed 
    sample counts in cells formed by key variables and their interactions. 
    Funded by the National Center for Education Statistics. See Skinner and
    Shlomo (2008) <doi:10.1198/016214507000001328> for a description of the
    file-level risk measures and the loglinear model approach.

Privacy protected raster maps 
  can be created from spatial point data. Protection
  methods include smoothing of dichotomous variables by de Jonge and de Wolf (2016) 
  <doi:10.1007/978-3-319-45381-1_9>, continuous variables by de Wolf and 
  de Jonge (2018) <doi:10.1007/978-3-319-99771-1_23>, suppressing 
  revealing values and a generalization of the quad tree method by 
  Suñé, Rovira, Ibáñez and Farré (2017) <doi:10.2901/EUROSTAT.C2017.001>.

Methods for statistical disclosure control in
    tabular data such as primary and secondary cell suppression as described for example
    in Hundepol et al. (2012) <doi:10.1002/9781118348239> are covered in this package.

Companion package to the book Simulation and Inference for
        Stochastic Differential Equations With R Examples, ISBN
        978-0-387-75838-1, Springer, NY.

Set of functions for Stochastic Data Envelopment Analysis. Chance constrained versions of radial, directional and additive DEA models are implemented, as long as super-efficiency models. See: Cooper, W.W.; Deng, H.; Huang, Z.; Li, S.X. (2002). <doi:10.1057/palgrave.jors.2601433>, Bolós, V.J.; Benítez, R.; Coll-Serrano, V. (2024) <doi:10.1016/j.orp.2024.100307>.

Implementation of evolutionary fuzzy systems for the data mining task called
    "subgroup discovery". In particular, the algorithms presented in this package are:
    M. J. del Jesus, P. Gonzalez, F. Herrera, M. Mesonero (2007) <doi:10.1109/TFUZZ.2006.890662>
    M. J. del Jesus, P. Gonzalez, F. Herrera (2007) <doi:10.1109/MCDM.2007.369416>
    C. J. Carmona, P. Gonzalez, M. J. del Jesus, F. Herrera (2010) <doi:10.1109/TFUZZ.2010.2060200>
    C. J. Carmona, V. Ruiz-Rodado, M. J. del Jesus, A. Weber, M. Grootveld, P. González, D. Elizondo (2015) <doi:10.1016/j.ins.2014.11.030>
    It also provide a Shiny App to ease the analysis. The algorithms work with data sets provided in
    KEEL, ARFF and CSV format and also with data.frame objects.