Package: synthpop
Type: Package
Title: Generating Synthetic Versions of Sensitive Microdata for
        Statistical Disclosure Control
Version: 1.9-3
Date: 2026-04-15
Authors@R: c(person(given = "Beata",
                    family = "Nowok",
                    role = c("aut", "cre"),
                    email = "beata.nowok@gmail.com"),
             person(given = "Gillian",
                    family = "Raab",
                    role = "aut"),
             person(given = "Chris",
                    family = "Dibben",
                    role = "ctb"),
             person(given = "Joshua",
                    family = "Snoke",
                    role = "ctb"),
             person(given = "Caspar",
                    family = "van Lissa",
                    role = "ctb"),
             person(given = "Lotte",
                    family = "Pater",
                    role = "ctb"),
             person(given = "Timon",
                    family = "Huijser",
                    role = "ctb"))
Description: 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).
License: GPL-2 | GPL-3
URL: <https://www.synthpop.org.uk/>
Imports: lattice, MASS, methods, nnet, ggplot2, graphics, stats, utils,
        rpart, party, foreign, plyr, proto, polspline, randomForest,
        ranger, classInt, mipfp, survival, stringr, rmutil, broman,
        forcats
Encoding: UTF-8
LazyData: yes
Config/pak/sysreqs: libicu-dev
Repository: https://bnowok.r-universe.dev
Date/Publication: 2026-04-15 09:10:00 UTC
RemoteUrl: https://github.com/bnowok/synthpop
RemoteRef: HEAD
RemoteSha: 8e51186105491d731eb8f5e2ef403fc93de86048
NeedsCompilation: no
Packaged: 2026-06-14 08:16:38 UTC; root
Author: Beata Nowok [aut, cre],
  Gillian Raab [aut],
  Chris Dibben [ctb],
  Joshua Snoke [ctb],
  Caspar van Lissa [ctb],
  Lotte Pater [ctb],
  Timon Huijser [ctb]
Maintainer: Beata Nowok <beata.nowok@gmail.com>