argus_microaggregation

calls microaggregation code from mu-argus. In case only one variable should be
microaggregated and <code>useOptimal</code> is <code>TRUE</code>, Hansen-Mukherjee polynomial exact method
is applied. In any other case, the Mateo-Domingo method is used.

Data from statistical agencies and other institutions are mostly
confidential. This package (see also 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 anonymisation methods are included. Note that the package
includes a graphical user interface (Meindl and Templ, 2019 <doi:10.3390/a12090191>) that allows to use various methods of this
package.

Matthias Templ

sdcMicro

Statistical Disclosure Control Methods for Anonymization of Data
and Risk Estimation

Bernhard Meindl

Alexander Kowarik

argus_microaggregation function

<dl><dt>df</dt>
<dd>a <code>data.frame</code> with only numerical columns</dd>
<dt>k</dt>
<dd>required group size</dd>
<dt>useOptimal</dt>
<dd>(logical) should optimal microaggregation be applied (ony possible in
in case of one variable)</dd></dl>

Arguments

argus_microaggregation — argus_microaggregation

<dl>

<dt>df</dt>
<dd>a <code>data.frame</code> with only numerical columns</dd>


<dt>k</dt>
<dd>required group size</dd>


<dt>useOptimal</dt>
<dd>(logical) should optimal microaggregation be applied (ony possible in
in case of one variable)</dd>

</dl>

argus_microaggregation: argus_microaggregation

Description

Usage

Value

Arguments

See Also

Examples