Includes all the datasets of 'Sampling: Design and Analysis'
    (3rd edition by Sharon Lohr) in R format and additional functions for
    analyzing and graphing probability samples.

Stock-and-flow models are a computational method from the field of 
    system dynamics. They represent how systems change over time and are 
    mathematically equivalent to ordinary differential equations. 'sdbuildR'
    (system dynamics builder) provides an intuitive interface for constructing 
    stock-and-flow models without requiring extensive domain knowledge. Models 
    can quickly be simulated and revised, supporting iterative development. 
    'sdbuildR' simulates models in 'R' and 'Julia', where 'Julia' offers unit
    support and large-scale ensemble simulations. Additionally, 'sdbuildR' can 
    import models created in 'Insight Maker' (<https://insightmaker.com/>).

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.