assess_loss_ci          Confidence-interval bound of the relative loss
                        Z
assess_loss_expectation
                        Conditional expectation of the absolute
                        relative loss |Z|
assess_risk_I           Scenario-I (dominance) risk measure
assess_risk_II          Scenario-II (p%-rule) risk measure
assess_risk_diff        Compute the upper bound of the differencing
                        risk.
assess_risk_empirical   Observed disclosure risk
assess_utility_empirical
                        Observed information loss
plot.pm_calib_diff      Plot method for a differencing calibration grid
plot.pm_calib_dominance
                        Plot method for a dominance calibration grid
pm_calib_diff           Calibration of the differencing noise
                        ('sigma_epsilon')
pm_calib_dominance      Calibration of the dominance noise (sigma_nu
                        and n)
pm_commit_diff          Commit the differencing decision into the
                        parameter object
pm_commit_dominance     Commit the dominance decision (sigma_nu and n)
                        into the parameter object
pm_draws                Derive the two Gaussian draws from a cell key
pm_params               Create the perturbation-parameter object
                        containing all the parameters of the noise.
pm_perturb              Perturb an aggregated table
pm_plot_impact          Observed perturbation against dominance
pm_plot_risk_max        Worst-case risk against a mechanism parameter
pm_plot_risk_profile    Risk profile as a function of dominance
pm_plot_tradeoff        Risk-utility trade-off map
pm_sigma_eps            Smallest 'sigma_eps' guaranteeing the
                        differencing-risk ceiling
pm_suggest_n            Largest admissible shape parameter n
summary.pm_calib_dominance
                        Worst-case risk and loss range of a dominance
                        calibration grid
