  Missing data on response variables are common in clinical studies.
Corresponding to the uncertainty of missing mechanism, theoretical frameworks
on controlled imputation have been developed. In practice, it is recommended to
conduct a statistically valid analysis under the primary assumptions on missing
data, followed by sensitivity analysis under alternative assumptions to assess
the robustness of results. Due to the availability of software, controlled
multiple imputation (MI) procedures, including delta-based and reference-based
approaches, have become popular for analyzing continuous variables under
missing-not-at-random assumptions. Similar tools, however, still limit
application of these methods to categorical data. In this paper, we introduce
the R package \textbf{remiod}, which utilizes the Bayesian framework to perform
imputation in regression models on binary and ordinal outcomes. Following
outlining theoretical backgrounds, usage and features of \textbf{remiod} are
described and illustrated using examples.
