miceMNAR: 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) <doi:10.1002/sim.6902>. The mice.impute.heckprob() function provides imputation model for binary outcome based on bivariate probit model as described in Galimard et al (2018).

Version: 1.0.2
Depends: R (≥ 3.2.1), mice (≥ 3.0.0)
Imports: stats, mvtnorm, pbivnorm, GJRM, sampleSelection
Published: 2018-08-27
Author: Jacques-Emmanuel Galimard [aut, cre] (INSERM, U1153, ECSTRA team), Matthieu Resche-Rigon [aut] (INSERM, U1153, ECSTRA team)
Maintainer: Jacques-Emmanuel Galimard <jacques-emmanuel.galimard at inserm.fr>
License: GPL-2 | GPL-3
NeedsCompilation: no
In views: MissingData
CRAN checks: miceMNAR results


Reference manual: miceMNAR.pdf


Package source: miceMNAR_1.0.2.tar.gz
Windows binaries: r-devel: miceMNAR_1.0.2.zip, r-release: miceMNAR_1.0.2.zip, r-oldrel: miceMNAR_1.0.2.zip
macOS binaries: r-release (arm64): miceMNAR_1.0.2.tgz, r-release (x86_64): miceMNAR_1.0.2.tgz, r-oldrel: miceMNAR_1.0.2.tgz
Old sources: miceMNAR archive


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