Our data have 90% of people observed once and 10% of people at >= 2 time points. Our model includes person “random” effects modeled as `normal(0, sigma)`

. Our dataset is huge, so even with only 10% repeated folks, we can estimate `sigma`

. Are there modeling / computational recommendations for this scenario to improve / speed up model fitting?

Some references:

- K-fold CV in linear regression model with varying intercepts and very small clusters
- https://stats.stackexchange.com/questions/242821/how-will-random-effects-with-only-1-observation-affect-a-generalized-linear-mixe
- https://stats.stackexchange.com/questions/24280/can-i-fit-a-mixed-model-with-subjects-that-only-have-1-observation
- https://stats.stackexchange.com/questions/82637/random-intercepts-model-one-measurement-per-subject