Maximum Likelihood Multiple Imputation

Implements proper and so-called Maximum Likelihood Multiple Imputation as described by von Hippel and Bartlett (2021) . 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) is also available.

mlmi implements so called Maximum Likelihood Multiple Imputation as described by von Hippel and Bartlett (2021) \doi{10.1214/20-STS793}. 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) \doi{10.1093/biomet/85.4.935}.

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")