Abstract
Nonlinear random effects models (NREMs) are particularly useful for modeling longitudinal data that follow intrinsically nonlinear trends. However, NREMs assume both random effects and random errors to be normally distributed, which is likely violated when the outcome variable does not appear normal. No existing software under the frequentist framework provides researchers the flexibility to specify non-normal random effects to date. To address this significant gap in the application and research of NREMs, we developed an R routine
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