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.