Multiple Imputation and Synthetic Data Generation with the R package NPBayesImputeCat
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In many contexts, missing data and disclosure control are ubiquitous and difficult 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 disclosure control, for multivariate categorical data, with or without structural zeros. We describe the Dirichlet Process mixtures of products of multinomials (DPMPM) models used in the package, and illustrate various uses of the package using data samples from the American Community Survey (ACS).
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