Cannot check model from mgcv and categorical outcome

Reproducible example:

library(mgcv)
library(performance)

df <- mtcars |>
  tibble::as_tibble() |>
  dplyr::mutate(carb = as.integer(carb))

model <- mgcv::gam(
  carb~s(wt, k=5),
  data = df,
  family = mgcv::ocat(R=8)
)

plot(check_model(model))

Error:

Error in d[, !(names(data) %in% all.varying), drop = FALSE] : 
  incorrect number of dimensions

It seems that there is something wrong when using mgcv and ocat. Nevertheless, I can use MASS and ordinal regression:

df <- mtcars |>
  tibble::as_tibble() |>
  dplyr::mutate(carb = as.factor(carb))
model <- MASS::polr(
  carb~wt,
  data = df,
  Hess = TRUE
)
plot(check_model(model))

Start by running the reproducible mgcv::gam example with performance::check_model() and inspect the handling of categorical outcomes from mgcv::ocat models. Done means check_model(model) completes without the dimensionality error and produces the diagnostic plot shown in the report.