Synthesize numeric, categorical, mixed and time series data. Data 
    circumstances including mixed (or zero-inflated) distributions and missing
    data patterns are reproduced in the synthetic data. A single parameter allows
    balancing between high-quality synthetic data that represents correlations of
    the original data and lower quality but more privacy safe synthetic data
    without correlations. Tuning can be done per variable or for the whole
    dataset.

Creation of an individual claims simulator which generates various
    features of non-life insurance claims. An initial set of test parameters,
    designed to mirror the experience of an Auto Liability portfolio, were set
    up and applied by default to generate a realistic test data set of
    individual claims (see vignette). The simulated data set then allows
    practitioners to back-test the validity of various reserving models and to
    prove and/or disprove certain actuarial assumptions made in claims
    modelling. The distributional assumptions used to generate this data set can
    be easily modified by users to match their experiences. Reference: Avanzi B,
    Taylor G, Wang M, Wong B (2020) "SynthETIC: an individual insurance claim
    simulator with feature control" <arXiv:2008.05693>.

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>. Functions to assess identity and attribute disclosure for the original and for the synthetic data are included in the package, and their use is illustrated in a vignette on disclosure (Practical Privacy Metrics for Synthetic Data).