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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>.
Getting started
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Package details |
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| Author | Zobaer Ahmed [aut, cre] |
| Maintainer | Zobaer Ahmed <zunnun09@gmail.com> |
| License | MIT + file LICENSE |
| Version | 0.2.2 |
| URL | https://zobaer09.github.io/FakeDataR/ https://github.com/zobaer09/FakeDataR |
| Package repository | View on CRAN |
| Installation |
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