How do you compute average marginal effects for glm.cluster models?

Question

I am looking for a way to compute average marginal effects with clustered standard errors which i seem to be having a few problems with. My model is as follows:

cseLogit <- miceadds::glm.cluster(data = data_long, 
                                  formula = follow ~ f1_distance + f2_distance + PolFol + MediaFol,
                                  cluster = "id",
                                  family  = binomial(link = "logit"))

Where the dependent variable is binary (0/1) and all explanatory variables are numeric. I've tried to different ways of getting average marginal effects. The first one is:

marginaleffects <- margins(cseLogit, vcov = your_matrix)

Which gives me the following error:

Error in find_data.default(model, parent.frame()) : 
  'find_data()' requires a formula call

I've also tried this:

marginaleffects <- with(cseLogit, margins(glm_res, vcov=vcov))

which gives me this error:

Error in eval(predvars, data, env) : 
  object 'f1_distance' was not found
In addition: warnings:
1: In dydx.default(X[[i]], ...) :
  Class of variable, f1_distance, is unrecognized. Returning NA.
2: In dydx.default(X[[i]], ...) :
  Class of variable, f2_distance, is unrecognized. Returning NA.

Can you tell me what i'm doing wrong? If i haven't provided enough information, please let me know. Thanks in advance.