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# CITATION file created with {cffr} R package
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cff-version: 1.2.0
message: 'To cite package "dapper" in publications use:'
type: software
license: MIT
title: 'dapper: Data Augmentation for Private Posterior Estimation'
version: 1.0.1
doi: 10.32614/CRAN.package.dapper
abstract: 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) <https://doi.org/10.48550/arXiv.2206.00710>,
  and for details on the discrete Gaussian and discrete Laplacian distributions see
  Canonne et al. (2020) <https://doi.org/10.48550/arXiv.2004.00010>.
authors:
- family-names: Eng
  given-names: Kevin
  email: kevine1221@gmail.com
repository: https://CRAN.R-project.org/package=dapper
repository-code: https://github.com/mango-empire/dapper
url: https://github.com/mango-empire/dapper
date-released: '2024-10-29'
contact:
- family-names: Eng
  given-names: Kevin
  email: kevine1221@gmail.com