Package: synthpop 1.9-3

synthpop: Generating Synthetic Versions of Sensitive Microdata for Statistical Disclosure Control

A tool for producing synthetic versions of microdata containing confidential information so that they are safe to be released to users for exploratory analysis. The key objective of generating synthetic data is to replace sensitive original values with synthetic ones causing minimal distortion of the statistical information contained in the data set. Most synthesising methods available in the package synthesise from conditional distributions where variables, which can be categorical or continuous, are synthesised one-by-one using sequential modelling. Replacements are generated by drawing from conditional distributions fitted to the original data using parametric or classification and regression trees models. Methods that are not sequential, but synthesise all variables at once, are 'sample', 'ipf', and 'catall'. Data are synthesised via the function syn() which can be largely automated, if default settings are used, or with methods defined by the user. Optional parameters can be used to influence the disclosure risk and the analytical quality of the synthesised data. The package also includes functions to assess the utility and disclosure risk of the synthetic data compared to the original. These are described in vignettes (Utility - Assessing, Visualizing and Improving the Utility of Synthetic Data) and (Disclosure - Practical Privacy Metrics for Synthetic Data).

Authors:Beata Nowok [aut, cre], Gillian Raab [aut], Chris Dibben [ctb], Joshua Snoke [ctb], Caspar van Lissa [ctb], Lotte Pater [ctb], Timon Huijser [ctb]