Group Iterative Multiple Model Estimation (GIMME)
The GIMME algorithm is available from the continually maintained R package, gimme. Please select specific topics from the “Tutorial” menu.
The program can be installed directly from R. In addition to the official CRAN documentation, a comprehensive tutorial for using the gimme R package can be found here: gimme R Tutorial. A tutorial on data preparation and other considerations can be found here: GIMME General Tutorial.
Program developers are invited to submit changes to our GitHub repository.
- GIMME can search for patterns of relations according to three models:
- Unified SEM: both lagged and contemporaneous relations are directed
- Vector Autoregression: lagged relations are directed, contemporaneous relations are bidirectional correlations among residuals
- Hybrid-VAR: lagged relations are directed; contemporaneous relations can be either directed among the variables or bidirectional among residuals
- Missing data is not a problem. Simply put “NA” where the data are missing. However – if there are more rows of missing data than there are rows with data, then convergence problems may arise.
- Heterogeneous data is not a problem:
- No “group” or “common” structure will be forced unless it truly describes the majority.
- Individual-level nuances will surface after a group or common structure is fit (provided one exists).
- If desired, subgroups of individuals with similar patterns of effects will be generated to aid the researcher in finding similar patterns among the varied individual models.
- Works well with as little as 3 or as many as 25 variables (between 5 and 15 is recommended).
- Can be freely downloaded by installing the package “gimme” in R.