pan and jomo, as well as several functions for visualizing, managing and analyzing multiply imputed data sets.The main interface to pan is the function panImpute, which allows specification of imputation models for continuous variables with missing data at level 1.
In addition, the function jomoImpute provides an interface to jomo, which extends the functionality of pan to continuous and categorical variables with missing data at level 1 and level 2.
Imputations and parameter chains are stored in objects of class mitml.
To obtain the completed (i.e., imputed) data sets, mitmlComplete is used, producing a list of imputed data sets of class mitml.list that can be used in further analyses.
Several additional functions allow for convenient analysis of multiply imputed data sets, especially when using the R packages lme4 and nlme.
The functions with and within can be used for manipulating the data sets and for model fitting.
Final parameter estimates can be extracted using testEstimates.
Single- and multi-parameter hypotheses tests can be performed using the functions testConstraints and testModels.
In addition, the anova method provides a simple interface to model comparisons with automatic refitting of statistical models.
Data sets can be imported and exported from or to different statistical software packages.
Currently, mids2mitml.list, jomo2mitml.list, and long2mitml.list can be used for importing imputations for other packages in R (e.g., mice and jomo).
In addition, write.mitmlMplus, write.mitmlSAV, and write.mitmlSPSS export data sets to Mplus and SPSS, respectively.
Finally, the package provides tools for summarizing and visualizing imputation models, which is useful for the assessment of convergence and the reporting of results.
The data sets contained in this package are published under the same license as the package itself. They contain simulated data and may be used by anyone free of charge as long as reference to this package is given.