Model Based Treatment of Missing Data

Contains model-based treatment of missing data for regression models with missing values in covariates or the dependent variable using maximum likelihood or Bayesian estimation (Ibrahim et al., 2005; ; Luedtke, Robitzsch, & West, 2020a, 2020b; ). The regression model can be nonlinear (e.g., interaction effects, quadratic effects or B-spline functions). Multilevel models with missing data in predictors are available for Bayesian estimation. Substantive-model compatible multiple imputation can be also conducted.

mdmb

Model Based Treatment of Missing Data

If you use mdmb and have suggestions for improvement or have found bugs, please email me at [email protected]. Please always provide a minimal dataset, necessary to demonstrate the problem, a minimal runnable code necessary to reproduce the issue, which can be run on the given dataset, and all necessary information on the used librarys, the R version, and the OS it is run on, perhaps a sessionInfo().

Manual

The manual may be found here https://alexanderrobitzsch.github.io/mdmb/

CRAN version

The official version of mdmb is hosted on CRAN and may be found here. The CRAN version can be installed from within R using:

utils::install.packages("mdmb")

GitHub version

The version hosted here is the development version of mdmb. The GitHub version can be installed using devtools as:

devtools::install_github("alexanderrobitzsch/mdmb")