The R Journal: accepted article

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Multiple Imputation and Synthetic Data Generation with the R Package NPBayesImputeCat PDF download
Jingchen Hu, Olanrewaju Akande and Quanli Wang

Abstract In many contexts, missing data and disclosure control are ubiquitous and challenging issues. In particular at statistical agencies, the respondent-level data they collect from surveys and censuses can suffer from high rates of missingness. Furthermore, agencies are obliged to protect respondents’ privacy when publishing the collected data for public use. The NPBayesImputeCat R package, introduced in this paper, provides routines to i) create multiple imputations for missing data, and ii) create synthetic data for statistical disclosure control, for multivariate categorical data, with or without structural zeros. We describe the Dirichlet process mixture of products of multinomial distributions model used in the package, and illustrate various uses of the package using data samples from the American Community Survey (ACS). We also compare results of the missing data imputation to the mice R package and those of the synthetic data generation to the synthpop R package.

Received: 2020-05-01; online 2021-09-20, supplementary material, (2.5 Kb)
CRAN packages: NPBayesImputeCat, mice, synthpop, bayesplot, tidyverse
CRAN Task Views implied by cited CRAN packages: MissingData, OfficialStatistics, Multivariate, SocialSciences


CC BY 4.0
This article and supplementary materials are licensed under a Creative Commons Attribution 4.0 International license.

@article{RJ-2021-080,
  author = {Jingchen Hu and Olanrewaju Akande and Quanli Wang},
  title = {{Multiple Imputation and Synthetic Data Generation with the R
          Package NPBayesImputeCat}},
  year = {2021},
  journal = {{The R Journal}},
  doi = {10.32614/RJ-2021-080},
  url = {https://journal.r-project.org/archive/2021/RJ-2021-080/index.html}
}