Provides functionality to generate, (interactively) modify (by adding, removing and renaming nodes) and convert nested hierarchies between different formats.
  These tree like structures can be used to define for example complex hierarchical tables used for statistical disclosure control.

Tools for researchers to explicitly show that their results
    comply to rules for statistical disclosure control imposed by research
    data centers. These tools help in checking descriptive statistics and
    models and in calculating extreme values that are not individual data.
    Also included is a simple function to create log files. The methods
    used here are described in the "Guidelines for the checking of output
    based on microdata research" by Bond, Brandt, and de Wolf (2015)
    <https://cros.ec.europa.eu/system/files/2024-02/Output-checking-guidelines.pdf>.

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.