test_that("scenarios stack runs", {
  sc <- sd_scenarios(agri_adoption_model(),
                     list(base = list(), fast = list(q = 0.6)), stop = 10)
  expect_s3_class(sc, "sd_scenarios")
  expect_equal(nrow(sc), 22)
  s <- summary(sc, vars = "adopters")
  expect_gt(s$final[s$scenario == "fast"], s$final[s$scenario == "base"])
  expect_error(plot(sc, "nope"))
})

test_that("sensitivity analysis returns bands and SRC", {
  sa <- sd_sensitivity(agri_adoption_model(),
                       ranges = list(p = c(0.01, 0.05), q = c(0.2, 0.6)),
                       outputs = "adopters", n = 20, stop = 8, seed = 42)
  expect_s3_class(sa, "sd_sensitivity")
  expect_equal(nrow(sa$samples), 20)
  expect_equal(nrow(sa$src), 2)
  expect_true(all(sa$samples$p >= 0.01 & sa$samples$p <= 0.05))
  expect_output(print(sa), "Standardised")
  expect_error(sd_sensitivity(agri_adoption_model(), list(zz = c(0, 1)),
                              "adopters", n = 5))
})

test_that("fit statistics decompose MSE", {
  f <- sd_fit_stats(c(1, 2, 3, 4), c(1.5, 2.5, 3.5, 4.5))
  expect_equal(f[["um"]], 1)
  expect_equal(f[["um"]] + f[["us"]] + f[["uc"]], 1)
  expect_equal(f[["rmse"]], 0.5)
  p <- sd_fit_stats(1:5, 1:5)
  expect_equal(p[["theil_u"]], 0)
})

test_that("calibration recovers known parameters", {
  true <- sd_simulate(agri_adoption_model(p = 0.02, q = 0.5), stop = 15)
  obs <- data.frame(time = true$time, adopters = true$adopters)
  cal <- sd_calibrate(agri_adoption_model(), obs,
                      params = c(p = 0.05, q = 0.3),
                      lower = c(0.001, 0.01), upper = c(0.2, 1), stop = 15)
  expect_equal(unname(cal$estimates), c(0.02, 0.5), tolerance = 0.02)
  expect_output(print(cal), "Estimates")
})

test_that("NPV discounting", {
  run <- data.frame(time = 0:2, v = c(100, 100, 100))
  expect_equal(sd_npv(run, "v", 0), 200)
  expect_lt(sd_npv(run, "v", 0.1), 200)
  expect_error(sd_npv(run, "w", 0.1))
})