# test_that("audit_report renders an HTML report", {
#   skip_if_not_installed("rmarkdown")
#   pandoc_ok <- tryCatch(rmarkdown::pandoc_available(), error = function(e) FALSE)
#   if (!isTRUE(pandoc_ok)) {
#     skip("pandoc not available")
#   }
#
#   set.seed(3)
#   df <- data.frame(
#     subject = rep(1:6, each = 2),
#     outcome = rep(c(0, 1), 6),
#     x1 = rnorm(12),
#     x2 = rnorm(12)
#   )
#   splits <- make_split_plan(df, outcome = "outcome",
#                         mode = "subject_grouped", group = "subject",
#                         v = 3, seed = 1, progress = FALSE)
#
#   custom <- list(
#     glm = list(
#       fit = function(x, y, task, weights, ...) {
#         suppressWarnings(stats::glm(y ~ ., data = as.data.frame(x),
#                                     family = stats::binomial(), weights = weights))
#       },
#       predict = function(object, newdata, task, ...) {
#         as.numeric(suppressWarnings(stats::predict(object,
#                                                    newdata = as.data.frame(newdata),
#                                                    type = "response")))
#       }
#     )
#   )
#
#   fit <- fit_resample(
#     df,
#     outcome = "outcome",
#     splits = splits,
#     learner = "glm",
#     custom_learners = custom,
#     metrics = "auc",
#     refit = FALSE,
#     seed = 1
#   )
#   audit <- audit_leakage(fit, metric = "auc", B = 5, perm_stratify = FALSE)
#
#   out_file <- audit_report(audit, output_dir = tempdir(),
#                            output_file = "bioLeak_audit_report_test.html",
#                            quiet = TRUE, open = FALSE)
#   expect_true(file.exists(out_file))
# })

test_that("audit_report validates inputs and dependencies", {
  expect_error(audit_report(list()), "LeakAudit or LeakFit")

  if (!requireNamespace("rmarkdown", quietly = TRUE)) {
    df <- make_class_df(10)
    splits <- make_split_plan_quiet(df, outcome = "outcome",
                                mode = "subject_grouped", group = "subject",
                                v = 2, seed = 1)
    custom <- make_custom_learners()
    fit <- fit_resample_quiet(df, outcome = "outcome", splits = splits,
                              learner = "glm", custom_learners = custom,
                              metrics = "auc", refit = FALSE, seed = 1)
    expect_error(audit_report(fit), "Install 'rmarkdown'")
  }
})