Thanks @martinmodrak,
Sorry for the late reply, missed the notification.
As you have pointed to test the linear model first, I tried to fit the linear model with two approaches, one with “rstanarm” and other with “brms”.
But, the results from rstanarm’s “stan_lmer()” converged with fewer divergent errors (9) and in lesser time when compared to “brm()”. The “brm()” function was around 4 times slower and resulted in many divergent transitions.
This could be because the group-level standard deviations are modeled differently in both approaches as pointed out by @bgoodri in the post. I am new to brms/stan and I am not confident on how to go about the modeling process (setting priors or re-parameterizing). Any help on how to approach this will be much appreciated. Please let me know if any information is required.