micemd: Multiple Imputation by Chained Equations with Multilevel Data
"The micemd package provides methods to perform multiple imputation using chained equations in the presence of multilevel data. It includes imputation methods that account for both sporadically and systematically missing values of continuous, binary and count variables. Following the recommendations of Audigier et al. (2018), the choice of the imputation method for each variable can be facilitated by a default choice tuned according to the structure of the incomplete dataset. Allows parallel calculation for 'mice'. R package available from CRAN and GitHUB (maintained by Vincent Audigier).
1. Imputation of sporadically and systematically missing values in multilevel data via mice.impute.2l.2stage.bin (binary data), mice.impute.2l.2stage.norm (continous data) and mice.impute.2l.2stage.pois (count data). See Audigier et al. 2018.
2. Imputation of univariate missing data using a Bayesian generalized linear mixed model with non-informative prior distributions via mice.impute.2l.glm.bin (binary data), mice.impute.2l.glm.norm (continous data) and mice.impute.2l.glm.pois (count data). See Jolani, Debray et al. (2015) and Audigier et al. (2018).
3. Predictive mean matching imputation for multilevel data via mice.impute.2l.2stage.pmm (Audigier et al. 2018)."
