Package: dapper
Title: Data Augmentation for Private Posterior Estimation
Version: 1.1.0
Authors@R: 
    person("Kevin", "Eng", , "kevine1221@gmail.com", role = c("aut", "cre", "cph"))
Description: A data augmentation based sampler for conducting privacy-aware Bayesian inference. The dapper_sample()
             function takes an existing sampler as input and automatically constructs
             a privacy-aware sampler. The process of constructing a sampler is simplified 
             through the specification of four independent modules, allowing for
             easy comparison between different privacy mechanisms by only swapping
             out the relevant modules. Probability mass functions
             for the discrete Gaussian and discrete Laplacian are provided to facilitate
             analyses dealing with privatized count data. The output of dapper_sample()
             can be analyzed using many of the same tools from the 'rstan' ecosystem. For methodological details
             on the sampler see Ju et al. (2022) <doi:10.48550/arXiv.2206.00710>,
             and for details on the discrete Gaussian and discrete Laplacian distributions see
             Canonne et al. (2020) <doi:10.48550/arXiv.2004.00010>.
License: MIT + file LICENSE
Encoding: UTF-8
RoxygenNote: 7.3.3
URL: https://github.com/mango-empire/dapper
BugReports: https://github.com/mango-empire/dapper/issues
Suggests: testthat (>= 3.0.0)
Config/testthat/edition: 3
Imports: bayesplot, checkmate, furrr, memoise, posterior, progressr,
        stats
NeedsCompilation: no
Packaged: 2026-02-01 22:27:22 UTC; kevin
Author: Kevin Eng [aut, cre, cph]
Maintainer: Kevin Eng <kevine1221@gmail.com>
Repository: CRAN
Date/Publication: 2026-02-02 06:30:58 UTC
Built: R 4.6.0; ; 2026-04-26 17:28:06 UTC; unix
