mlmi implements so called Maximum Likelihood Multiple Imputation as described by von Hippel and Bartlett (2021) . A number of different imputations are available, by utilising the norm, cat and mix packages. Inferences can be performed either using combination rules similar to Rubin’s or using a likelihood score based approach based on theory by Wang and Robins (1998) .

mlmi also implements a maximum likelihood MI version of reference based MNAR imputation for repeatedly measured continuous endpoints.

You can install the released version of bootImpute from CRAN with: install.packages(“mlmi”)

And the development version with install.packages(“devtools”) devtools::install_github(“jwb133/mlmi”)