Generates synthetic clinical datasets that preserve
    statistical properties while reducing re-identification risk.
    Implements Gaussian copula simulation, bootstrap with noise
    injection, and Laplace noise perturbation, with built-in utility
    and privacy validation metrics. Useful for privacy-aware data
    sharing in multi-site clinical research. Validates synthetic data
    quality via distributional similarity (Kolmogorov-Smirnov),
    discriminative accuracy (real-vs-synthetic classifier), and
    nearest-neighbor privacy ratio. Methods described in Jordon et al.
    (2022) <doi:10.48550/arXiv.2205.03257> and Snoke et al. (2018)
    <doi:10.1111/rssa.12358>.

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).

Implements the revised Synthetic Matching Algorithm of Kreitmeir, Lane, and Raschky (2025) <doi:10.2139/ssrn.3751162>, building on 
  the original approach of Acemoglu, Johnson, Kermani, Kwak, and Mitton (2016) <doi:10.1016/j.jfineco.2015.10.001>, to estimate the cumulative 
  treatment effect of an event on treated firms’ stock returns.