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DPrivStats 0.1.0

• Initial CRAN submission.
• Privacy mechanisms: Laplace (pure ε-DP), Gaussian (classic calibration), analytic Gaussian calibration (Balle & Wang, 2018), exponential mechanism.
• DP descriptive statistics: mean, variance, quantiles, median, histogram.
• DP hypothesis tests: two-sample t-test, chi-square test of independence, Kolmogorov–Smirnov test, one-way ANOVA.
• Regression: closed-form DP linear regression (dp_lm) and DP-SGD for GLMs (dp_glm).
• Privacy-aware confidence intervals: analytical, parametric bootstrap, and privacy-aware bootstrap.
• Privacy budget tracker with basic, advanced, and Rényi/zCDP composition.
• Diagnostics: coverage validation, utility comparison, composition comparison.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.