Data from statistical agencies and other institutions are mostly
    confidential. This package, introduced in Templ, Kowarik and Meindl (2017) <doi:10.18637/jss.v067.i04>, can be used for the generation of anonymized
    (micro)data, i.e. for the creation of public- and scientific-use files.
    The theoretical basis for the methods implemented can be found in Templ (2017) <doi:10.1007/978-3-319-50272-4>.
    Various risk estimation and anonymization methods are included. Note that the package
    includes a graphical user interface published in Meindl and Templ (2019) <doi:10.3390/a12090191> that allows to use various methods of this
    package.

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.

A tidy toolkit for distribution dynamics: analysing how a
    cross-sectional distribution of values evolves over time and where it
    settles in the long run. Provides discrete-time, spatial, rank and local
    indicator of spatial association ('LISA') Markov transition estimation,
    ergodic analysis (steady-state, mean first passage and sojourn times),
    rank-mobility measures (Kendall's tau and the Theta statistic) and Markov
    mobility indices. Methods use long-format 'id'/'time'/'value' data rather
    than transition matrices and build on the distribution-dynamics literature
    (Quah (1993); Rey (2001)
    <doi:10.1111/j.1538-4632.2001.tb00444.x>). Results are validated for
    numerical parity against the reference 'giddy' library.