Brms vs rstanarm for ordered logistic

Hi guys! I am new in Stan and I am little confused about ordinal regression.Here are some questions:

1] I was trying to fit for my data an ordered logistic regression (only fixed effects) with brm and stan_polr function and after many trials I figured out that they produce the same results (using as argument the cumulative “logit” and family=“logistic” respectively). Which is the difference between these functions then? Maybe that in brms someone can add random effects or other facilities to his formula?

2] The brm function is the respective of lrm (library rms) from classical statistics like stan_polr which is respective with polr from MASS package?

3] For the two different versions (brms and rstanarm) which is the form of the model? I mean the form of the model is this logit(p_j)=b_{0j}+(b_{1} X_1+...+b_k X_k) or logit(p_j)=b_{0j}-(b_{1} X_1+...+b_k X_k) ?

Because in MASS package the - is used and the lrm library the +.

4] Finally, how can I change the baseline category in ordered in stan?In multinomial logistic the 1st level is used instead of ordered where the last level is used as baseline. I want to adjust it so that I have the baseline in 1st level my baseline .

Yes, `brm` can estimate several types of ordinal models and has more flexibility with the parameterization of the linear predictor while `stan_polr` is essentially just a Bayesian version of `MASS::polr`. They also have different priors.
Yeah, there are different parameterizations of a proportional odds model and `stan_polr` sticks with the one in `MASS::polr`.
Redefine the outcome using the `factor` function and use whatever ordering you want for the factor levels. The first one will be considered the baseline.