test_that("simulation_generalized runs correctly with valid inputs", {

  # Set seed for reproducibility
  set.seed(123)

  # Define dimensions
  nsim <- 10      # Number of simulation replications
  n_train <- 54    # Training size
  n_test <- 19      # Testing size
  D <- 2           # Number of margins
  M <- 5           # Number of covariates

  # Generate synthetic data
  params <- init_params_full_G #  parameters
  U_train <-uu        # Pseudo-observations
  Z_train <- zz_train      # Covariate data
  X <- xx_train              # Risk factors
  Y_test <- yy_test           # True future values
  n.iter <- 1000

  # Generate mock BSTS models (for simplicity, we create empty placeholders)
  bsts_Dufferin <- fit_bsts(yy_train[,1], zz_train[,1,], lags = 2, MCMC.iter = n.iter) # Dufferin
  bsts_Wellington <- fit_bsts(yy_train[,2], zz_train[,2,], lags = 2, MCMC.iter = n.iter) # Wellington

  BSTS_list <- list(bsts_Dufferin,bsts_Wellington)
  # Supported copula types
  copulas <- c("Clayton", "Frank", "Gumbel", "Joe", "Gaussian")

  # Run function for each copula type
  for (copula in copulas) {
      result <- simulation_generalized(nsim, n_train, n_test, copula, params,
                                     log_likelihood_Generalized, U_train, Z_train, X, Y_test, BSTS_list)

      # Check that output is a list
      expect_type(result, "list")
      expect_named(result, c("optim_results", "theta_sim", "Y_sim", "MSE"))

      # Check optimization result structure
      expect_type(result$optim_results, "list")

      # Check that theta_sim has the correct structure
      if (copula == "Gaussian" && D > 2) {
        expect_equal(dim(result$theta_sim), c(nsim, n_test, D * (D - 1) / 2))
      } else {
        expect_equal(dim(result$theta_sim), c(nsim, n_test))
      }

      # Check Y_sim structure
      expect_equal(dim(result$Y_sim), c(nsim, n_test, D))

      # Check MSE values
      expect_equal(length(result$MSE), nsim)
      expect_type(result$MSE, "double")
      expect_true(all(result$MSE >= 0))  # MSE should be non-negative

  }
})