Synthetic Clinical Data Generation and Privacy-Preserving Validation // 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.