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library(posterior)
library(priorsense)

priorsense is compatible with models fit with either rstan and cmdstanr. To use priorsense with a Stan model, the log prior and log likelihood evaluations should be added to the model code.

Consider the univariate normal model with unknown mu and sigma available viaexample_powerscale_model("univariate_normal"). In Stan, lprior and log_lik variables can be defined as below. By also defining separate lprior_mu and lprior_sigma variables, it will be possible to check the sensitivity for each prior separately.

model <- example_powerscale_model("univariate_normal")

data {
  int<lower=1> N;
  array[N] real y;
}
parameters {
  real mu;
  real<lower=0> sigma;
}
transformed parameters {
  real lprior; // joint prior
  real lprior_mu = normal_lpdf(mu | 0, 1); // marginal prior density for mu
  real lprior_sigma = normal_lpdf(sigma | 0, 2.5); // marginal prior density for sigma
  lprior = lprior_mu + lprior_sigma;
}
model {
  // priors
  target += lprior;
  // likelihood
  target += normal_lpdf(y | mu, sigma);
}
generated quantities {
  vector[N] log_lik;
  // likelihood
  for (n in 1:N) log_lik[n] = normal_lpdf(y[n] | mu, sigma);
}

We first fit the model using Stan:

fit <- rstan::stan(
  model_code = model$model_code,
  data = model$data,
  refresh = FALSE,
  seed = 123
)

Then the priorsense functions will work as usual.

powerscale_sensitivity(fit)

Sensitivity based on cjs_dist
Prior selection: all priors
Likelihood selection: all data

 variable prior likelihood                           diagnosis
       mu 0.433      0.641 potential prior-likelihood conflict
    sigma 0.359      0.674 potential prior-likelihood conflict

powerscale_sensitivity(fit, prior_selection = "sigma")

Sensitivity based on cjs_dist
Prior selection: sigma
Likelihood selection: all data

 variable prior likelihood diagnosis
       mu 0.006      0.641         -
    sigma 0.010      0.674         -

powerscale_sensitivity(fit, prior_selection = "mu")

Sensitivity based on cjs_dist
Prior selection: mu
Likelihood selection: all data

 variable prior likelihood                           diagnosis
       mu 0.438      0.641 potential prior-likelihood conflict
    sigma 0.369      0.674 potential prior-likelihood conflict

powerscale_plot_dens(fit)

In some cases, setting moment_match = TRUE will improve unreliable estimates at the cost of some further computation. This requires the iwmm package.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.