| Summary: | The <i>R</i> packages <i>Dire</i> and <i>EdSurvey</i> allow analysts to make a conditioning model with new variables and then draw new plausible values. This is important because results for a variable not in the conditioning model are biased. For regression-type analyses, users can also use direct estimation to estimate parameters without generating new plausible values. <i>Dire</i> is distinct from other available software in <i>R</i> in that it requires fixed item parameters and simplifies calculation of high-dimensional integrals necessary to calculate composite or subscales. When used with <i>EdSurvey</i>, it is very easy to use published item parameters to estimate a new conditioning model. We show the theory behind the methods in <i>Dire</i> and a coding example where we perform an analysis that includes simple process data variables. Because the process data is not used in the conditioning model, the estimator is biased if a new conditioning model is not added with <i>Dire</i>.
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