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  "Title": "Data Augmentation for Private Posterior Estimation",
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  "Authors@R": "person(\"Kevin\", \"Eng\", , \"kevine1221@gmail.com\", role = c(\"aut\", \"cre\", \"cph\"))",
  "Description": "A data augmentation based sampler for conducting\nprivacy-aware Bayesian inference. The dapper_sample() function\ntakes an existing sampler as input and automatically constructs\na privacy-aware sampler. The process of constructing a sampler\nis simplified through the specification of four independent\nmodules, allowing for easy comparison between different privacy\nmechanisms by only swapping out the relevant modules.\nProbability mass functions for the discrete Gaussian and\ndiscrete Laplacian are provided to facilitate analyses dealing\nwith privatized count data. The output of dapper_sample() can\nbe analyzed using many of the same tools from the 'rstan'\necosystem. For methodological details on the sampler see Ju et\nal. (2022) <doi:10.48550/arXiv.2206.00710>, and for details on\nthe discrete Gaussian and discrete Laplacian distributions see\nCanonne et al. (2020) <doi:10.48550/arXiv.2004.00010>.",
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  "Encoding": "UTF-8",
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  "URL": "https://github.com/mango-empire/dapper",
  "BugReports": "https://github.com/mango-empire/dapper/issues",
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  "Repository": "https://mango-empire.r-universe.dev",
  "Date/Publication": "2026-02-01 22:11:16 UTC",
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