Performs multiple tests for the missing always at random assumption in multivariate data. Each of the diagnostic tests also suggest which variables are the most likely to violate the assumptions of the test. Although missing always at random is not a necessary condition to ensure validity under the Bayesian and direct-likelihood paradigms, it is sufficient, and evidence for its violation should encourage the careful statistician to conduct targeted sensitivity analyses. The package also implements several missing data mechanisms which can be useful for checking the performance for methods that handle missing data.
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