**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.

**The Basics**

- 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.