  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.
