fdp
1.0.0f-Differential Privacy and Gaussian Differential Privacy
Overview
Constructs and visualises trade-off functions for f-differential privacy (f-DP) as introduced by Dong et al. (2022) doi:10.1111/rssb.12454. Supports Gaussian differential privacy, the f-DP generalisation of (epsilon, delta)-differential privacy, and accepts user-specified optimal type I / type II errors from which the lower convex hull trade-off function is automatically constructed.
Install
Health
- OK2026-08-0513 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- NOTE2026-08-0112 OK · 1 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- OK2026-03-1014 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
Documentation
- Examples that run
- 100%
- Documented parameters
- 96%
- Return-value docs
- 100%
- References docs
- 70%
Downloads
Repository
Repository practices
4 development-tooling and community-health practices detected across 3 families in the upstream repository
Checks run against github.com/louisaslett/fdp on 2026-08-09.
Dependencies
Nothing depends on this yet.
Code & Tests
People & History
1 release. R releases are shown for context.
- RR 4.6.0 released · 2026-04-24
- 1.0.0Latest2026-03-10 · current release
- RR 4.5.0 released · 2025-04-11
Package metadata
- First published
- 2025-11-04
- Total releases
- 1 / 1 yrs
- License
- GPL (>= 3) OSI
- Minimum R
- ≥ 4.1.0
- Download size
- 206 KB
- Installed size
- not tracked yet
- With dependencies
- not tracked yet
Cite
Cite this package
Run in R for the authors' preferred citation:
citation("fdp")This is what citation() produces when a package has no citation file of its own. If it prints something else, use that.
Cite the R Observatory
For a number measured here: a download total, a coverage figure, an archival date.
From data release v2026-08-15, which the citation names so these numbers can be found later. More on citing and the projects behind them.