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. 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. 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. For a description of the implemented method see Nowok, Raab and Dibben (2016) <doi:10.18637/jss.v074.i11>.

Version: 1.4-1
Depends: lattice, MASS, methods, nnet, ggplot2
Imports: graphics, stats, utils, rpart, party, foreign, plyr, proto, polspline, randomForest, classInt
Published: 2018-01-06
Author: Beata Nowok, Gillian M Raab, Joshua Snoke and Chris Dibben
Maintainer: Beata Nowok <beata.nowok at>
License: GPL-2 | GPL-3
NeedsCompilation: no
Citation: synthpop citation info
Materials: NEWS
In views: OfficialStatistics
CRAN checks: synthpop results


Reference manual: synthpop.pdf
Vignettes: Inference in synthpop
Using synthpop
Package source: synthpop_1.4-1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
OS X El Capitan binaries: r-release: synthpop_1.4-1.tgz
OS X Mavericks binaries: r-oldrel: synthpop_1.4-1.tgz
Old sources: synthpop archive


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