advanced_composition_epsilon
                        Advanced composition
analytic_gaussian_mechanism
                        Analytic Gaussian mechanism
analytic_gaussian_sigma
                        Analytic Gaussian calibration (Balle & Wang,
                        2018)
basic_composition       Basic (sequential) composition
can_spend               Check whether a release fits in the remaining
                        budget
clip_gradients          Per-sample gradient clipping
compare_composition     Compare composition rules over a workflow
compare_utility         Compare utility of non-private and private fits
confint.dp_lm           Confint interface for dp_lm objects
cpp_clip_gradients      Fast per-sample gradient clipping (C++)
cpp_rlaplace            Fast Laplace sampling (C++)
dp_anova                DP one-way ANOVA F-test
dp_chisq_test           DP chi-square test of independence
dp_confint              Privacy-aware confidence intervals
dp_glm                  DP-SGD for generalized linear models
dp_histogram            DP histogram
dp_ks_test              DP Kolmogorov-Smirnov test
dp_lm                   Differentially private linear regression
dp_lm_sensitivity       Global L2 sensitivity of OLS coefficients
dp_mean                 DP mean
dp_median               DP median via the exponential mechanism
dp_quantile             DP quantile via the exponential mechanism
dp_t_test               DP two-sample t-test
dp_utility_diagnostics
                        Utility diagnostics for DP estimators
dp_variance             DP variance
empirical_delta_from_losses
                        Empirical delta from simulated privacy losses
epsilon_to_rdp          Convert epsilon-DP to zCDP (RDP) parameter
example_microdata       Census-like example microdata
exponential_mechanism   Exponential mechanism sampler
gaussian_mechanism      Gaussian mechanism
gaussian_sigma          Classic Gaussian noise scale
laplace_mechanism       Laplace mechanism
laplace_plr_tail        Privacy loss random variables for the Laplace
                        mechanism
new_privacy_budget      Create a privacy budget object
print.dp_confint        Print method for dp_confint objects
print.dp_estimate       Print method for dp_estimate objects
print.dp_glm            Print method for dp_glm objects
print.dp_htest          Print method for dp_htest objects
print.dp_lm             Print method for dp_lm objects
print.privacy_budget    Print method for privacy_budget objects
print.summary.dp_lm     Print method for summary.dp_lm objects
rdp_composition         RDP composition
rdp_to_epsilon          Convert zCDP (RDP) parameter to (epsilon,
                        delta)-DP
rlaplace                Sample Laplace noise
simulate_data           Simulate synthetic microdata
simulate_gaussian_losses
                        Simulate privacy losses of Gaussian releases
spend                   Spend privacy budget
stat_sensitivities      Sensitivity of common bounded-data statistics
summary.dp_lm           Summarize a dp_lm fit
validate_coverage       Validate confidence-interval coverage by Monte
                        Carlo
