Missing not at Random Imputation Models for Multiple Imputation by Chained Equation

Provides imputation models and functions for binary or continuous Missing Not At Random (MNAR) outcomes through the use of the 'mice' package. The mice.impute.hecknorm() function provides imputation model for continuous outcome based on Heckman's model also named sample selection model as described in Galimard et al (2018) and Galimard et al (2016) . The mice.impute.heckprob() function provides imputation model for binary outcome based on bivariate probit model as described in Galimard et al (2018).


Reference manual

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1.0.2 by Jacques-Emmanuel Galimard, 3 years ago

Browse source code at https://github.com/cran/miceMNAR

Authors: Jacques-Emmanuel Galimard [aut, cre] (INSERM , U1153 , ECSTRA team) , Matthieu Resche-Rigon [aut] (INSERM , U1153 , ECSTRA team)

Documentation:   PDF Manual  

Task views: Missing Data

GPL-2 | GPL-3 license

Imports stats, mvtnorm, pbivnorm, GJRM, sampleSelection

Depends on mice

See at CRAN