Check if Prior is Informative
Performs a simple test to check whether the prior is informative to the posterior. This idea, and the accompanying heuristics, were discussed in this blogpost.
check_prior(model, method = "gelman", simulate_priors = TRUE, ...)
| model | A  | 
| method | Can be  | 
| simulate_priors | Should prior distributions be simulated using
 | 
| ... | Currently not used. | 
A data frame with two columns: The parameter names and the quality
of the prior (which might be "informative", "uninformative")
or "not determinable" if the prior distribution could not be
determined).
https://statmodeling.stat.columbia.edu/2019/08/10/
## Not run: 
library(bayestestR)
if (require("rstanarm")) {
  model <- stan_glm(mpg ~ wt + am, data = mtcars, chains = 1, refresh = 0)
  check_prior(model, method = "gelman")
  check_prior(model, method = "lakeland")
  # An extreme example where both methods diverge:
  model <- stan_glm(mpg ~ wt,
    data = mtcars[1:3, ],
    prior = normal(-3.3, 1, FALSE),
    prior_intercept = normal(0, 1000, FALSE),
    refresh = 0
  )
  check_prior(model, method = "gelman")
  check_prior(model, method = "lakeland")
  plot(si(model)) # can provide visual confirmation to the Lakeland method
}
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