Binomial regression with identity link in brms

As suggested here and elsewhere the model:

glm(y~x, family=binomial(link=identity))

gives estimates in risk difference.

I would like to know if the same model in brms, or specifically the model:

mod<-brm(r | trials(n) ~ treat, data=dt, family=binomial(link=identity))

for the data below would also output estimates in risk difference:

dt = read.table(header = TRUE, text = "
n r r/n group treat c2 c1 w
62 3 0.048387097 1 0 0.1438 1.941115288 1.941115288
96 1 0.010416667 1 0 0.237 1.186583128 1.186583128
17 0 0 0 0 0.2774 1.159882668 3.159882668
41 2 0.048780488 1 0 0.2774 1.159882668 3.159882668
212 170 0.801886792 0 0 0.2093 1.133397521 1.133397521
143 21 0.146853147 1 1 0.1206 1.128993008 1.128993008
143 0 0 1 1 0.1707 1.128993008 2.128993008
143 33 0.230769231 0 1 0.0699 1.128993008 1.128993008
73 62 1.260273973 0 1 0.1351 1.121927228 1.121927228
73 17 0.232876712 0 1 0.1206 1.121927228 1.121927228")

I read here that:

"In the following, we list all possible links for each family. (…) The (…) families binomial , bernoulli , Beta , zero_inflated_binomial , zero_inflated_beta , and zero_one_inflated_beta the links logit , probit , probit_approx , cloglog , cauchit , and identity".

I hope what I am proposing is possible in bmrs. Thank you in advance for any help.

Yes, that should work with brms as well, but you may need to specify reasonable priors on the regression coefficients so that the predictions are between 0 and 1 and thus resemble a probability.

Thanks @paul.buerkner. That is very helpful.