FakeDataR
Privacy-Preserving Synthetic Data for 'LLM' Workflows
Generate privacy-preserving synthetic datasets that mirror structure, types, factor levels, and missingness; export bundles for 'LLM' workflows (data plus 'JSON' schema and guidance); and build fake data directly from 'SQL' database tables without reading real rows. Methods are related to approaches in Nowok, Raab and Dibben (2016) <doi:10.32614/RJ-2016-019> and the foundation-model overview by Bommasani et al. (2021) <doi:10.48550/arXiv.2108.07258>.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.2.2 |
rolling source/ R- | FakeDataR_0.2.2.tar.gz |
62.0 KiB |
0.2.2 |
latest source/ R- | FakeDataR_0.2.2.tar.gz |
62.0 KiB |
0.2.2 |
2026-04-23 source/ R- | FakeDataR_0.2.2.tar.gz |
62.0 KiB |
0.2.2 |
2026-04-09 windows/windows R-4.5 | FakeDataR_0.2.2.zip |
150.7 KiB |