Local Differential Privacy Tools for Distributional Inference
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Documentation for package ‘ldpdist’ version 0.1.0
Help Pages
| ldpdist-package | Local Differential Privacy Tools for Distributional Inference |
| coef.ldp_quantiles_fit | Jointly estimate multiple quantiles under categorical LDP |
| coef.ldp_quantile_fit | Estimate one quantile with online binary LDP queries |
| confint.ldp_cdf_fit | Estimate a CDF from local threshold reports |
| confint.ldp_quantiles_fit | Jointly estimate multiple quantiles under categorical LDP |
| confint.ldp_quantile_cs | Asymptotic confidence sequence for one LDP quantile |
| confint.ldp_quantile_fit | Estimate one quantile with online binary LDP queries |
| ldpdist | Local Differential Privacy Tools for Distributional Inference |
| ldp_aggregate_surveys | Aggregate CDF estimates from heterogeneous surveys |
| ldp_category_range_probability | Estimate category probabilities over a value range |
| ldp_cdf_variance | Finite-grid variance for LDP CDF estimates |
| ldp_confidence_region | Self-normalized confidence region for many quantiles |
| ldp_dc_critical | Finite-block critical values for D&C CDF inference |
| ldp_decode_category | Categorical local randomizers and unbiased decoders |
| ldp_design | Create an LDP data-collection design |
| ldp_estimate_category_cdf | Estimate category-specific CDFs from censored ULDP reports |
| ldp_estimate_cdf | Estimate a CDF from local threshold reports |
| ldp_estimate_quantile | Estimate one quantile with online binary LDP queries |
| ldp_estimate_quantiles | Jointly estimate multiple quantiles under categorical LDP |
| ldp_estimate_quantile_cs | Asymptotic confidence sequence for one LDP quantile |
| ldp_gaussian_cs_boundary | Gaussian confidence-sequence boundaries |
| ldp_mechanism | Create a local privacy mechanism object |
| ldp_median_efficiency | Median efficiency diagnostic under binary randomized response |
| ldp_msn_critical | Multivariate self-normalized Brownian critical values |
| ldp_pava | Weighted pool-adjacent-violators algorithm |
| ldp_privacy_check | Check analytic likelihood-ratio bounds for LDP mechanisms |
| ldp_randomize | Locally randomize private observations |
| ldp_randomize_category | Categorical local randomizers and unbiased decoders |
| ldp_rng_uniform | Generate uniform random numbers for LDP workflows |
| ldp_sn_critical | Simulate a scalar self-normalized Brownian critical value |
| ldp_supported_methods | List registered mechanisms, tasks, and estimators |
| plot.ldp_category_cdf_fit | Plot ldpdist fitted objects |
| plot.ldp_cdf_fit | Plot ldpdist fitted objects |
| plot.ldp_quantile_cs | Plot ldpdist fitted objects |
| predict.ldp_category_cdf_fit | Estimate category-specific CDFs from censored ULDP reports |
| predict.ldp_cdf_fit | Estimate a CDF from local threshold reports |
| summary.ldp_cdf_fit | Estimate a CDF from local threshold reports |
| summary.ldp_quantiles_fit | Jointly estimate multiple quantiles under categorical LDP |
| summary.ldp_quantile_cs | Asymptotic confidence sequence for one LDP quantile |
| summary.ldp_quantile_fit | Estimate one quantile with online binary LDP queries |
| vcov.ldp_cdf_fit | Estimate a CDF from local threshold reports |
| vcov.ldp_quantile_fit | Estimate one quantile with online binary LDP queries |