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Abstract

Missing data is strongly connected to statistics that is concerned with the collect and pre-processing of data. In this article, we review the different methods that can be used to diagnose and impute missing data. We
also present approaches aiming at evaluating the impact of imputation on subsequent analyses. Finally, we describe available implementations, in R packages, of the presented methods.