FakeDataR: Privacy-Preserving Synthetic Data for 'LLM' Workflows (original) (raw)
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>.
| Version: | 0.2.2 |
|---|---|
| Imports: | dplyr, jsonlite, zip |
| Suggests: | readr, testthat (≥ 3.0.0), knitr, rmarkdown, DBI, RSQLite, tibble, nycflights13, palmerpenguins, gapminder, arrow, withr |
| Published: | 2025-10-06 |
| DOI: | 10.32614/CRAN.package.FakeDataR |
| Author: | Zobaer Ahmed [aut, cre] |
| Maintainer: | Zobaer Ahmed |
| BugReports: | https://github.com/zobaer09/FakeDataR/issues |
| License: | MIT + file |
| URL: | https://zobaer09.github.io/FakeDataR/,https://github.com/zobaer09/FakeDataR |
| NeedsCompilation: | no |
| Language: | en-US |
| Materials: | README, NEWS |
| CRAN checks: | FakeDataR results |
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