Synthetic Clinical Data Generation and Privacy-Preserving Validation R package syntheticdata version 0.1.0 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>. Software Depends: R (>= 4.1.0) Imports: cli (>= 3.4.0), dplyr (>= 1.1.0), stats, tibble (>= 3.1.0) Suggests: knitr, rmarkdown, testthat (>= 3.0.0) Cuiwei Gao <48gaocuiwei@gmail.com> Comprehensive R Archive Network (CRAN) Cuiwei Gao [aut, cre, cph] MIT + file LICENSE (https://CRAN.R-project.org/package=syntheticdata/LICENSE) 2026-04-02 application/tgz https://CRAN.R-project.org/package=syntheticdata doi:10.32614/CRAN.package.syntheticdata en-US