Since it incorporates mixed effects models my guess is that it allows you to leverage structure in your data to improve the efficiency (in the statistical sense

Since it incorporates mixed effects models my guess is that it allows you to leverage structure in your data to improve the efficiency (in the statistical sense) of the estimation. That is, you may have a panel of groups. So you might assume observations have in-group and out-group variances (or some other clustering of errors) and furthermore, observations come in time dimensions. Then, obviously, making use of that information will improve your "fit" in the sense that the regression error will be lower when you estimate across the whole sample, since the dependencies are taken into account. Something like that.