Plotting conditional effects for precision parameter in location scale models

I am fitting a mixed-effects beta-binomial model where both the \mu and \phi parameters have predictors.

m <- brm(
  formula = bf(
    y | trials(60) ~ 1 + x + (1 | cluster),
    phi ~ 1 + x + (1 | cluster)
  family = beta_binomial(),

I can generate a nice conditional effects plot showing the relationship between x and \mu like so:

conditional_effects(m, "x")

But I’m struggling to figure out how to plot the relationship between x and \phi.

Nevermind. I found that the ... argument for conditional_effects() goes to posterior_epred() and that has the dpar argument to control the predicted distributional parameter.

So this works:

conditional_effects(m, "x", dpar = "phi")