Maximum Likelihood Multiple Imputation
Imputation for categorical variables using log linear models
Imputation for a mixture of continuous and categorical variables using...
Multivariate normal model imputation
Normal regression imputation of a single variable
Reference based imputation of repeated measures continuous data
Score based variance estimation for multiple imputation
Within between variance estimation
Implements proper and so-called Maximum Likelihood Multiple Imputation as described by von Hippel and Bartlett (2021) <doi:10.1214/20-STS793>. A number of different imputation methods are available, by utilising the 'norm', 'cat' and 'mix' packages. Inferences can be performed either using Rubin's rules (for proper imputation), or a modified version for maximum likelihood imputation. For maximum likelihood imputations a likelihood score based approach based on theory by Wang and Robins (1998) <doi:10.1093/biomet/85.4.935> is also available.