## ---- tsML tests ------------------------------------------------------------

test_that("nrm_arima returns model and AIC", {
  data(nrm_example, package = "NRMstatsML")
  ar <- nrm_arima(nrm_example, value_var = "crop_yield")
  expect_s3_class(ar, "nrm_arima")
  expect_true(is.numeric(ar$aic))
  expect_true(is.numeric(ar$bic))
  expect_length(ar$order, 3)
})

test_that("nrm_forecast returns forecast object", {
  data(nrm_example, package = "NRMstatsML")
  fc <- nrm_forecast(nrm_example, value_var = "crop_yield", horizon = 3)
  expect_s3_class(fc, "nrm_forecast")
  expect_equal(fc$horizon, 3)
  expect_true(is.numeric(fc$accuracy))
})

test_that("nrm_forecast errors on unknown method", {
  data(nrm_example, package = "NRMstatsML")
  expect_error(
    nrm_forecast(nrm_example, value_var = "crop_yield", method = "xgboost"),
    "Unknown method"
  )
})

## ---- uncertaintyML tests ---------------------------------------------------

test_that("nrm_bootstrap returns mean, sd, and CI", {
  data(nrm_example, package = "NRMstatsML")
  bs <- nrm_bootstrap(nrm_example,
          stat_fn = function(d) mean(d$crop_yield),
          n_iter = 200)
  expect_s3_class(bs, "nrm_bootstrap")
  expect_true(is.numeric(bs$mean))
  expect_true(is.numeric(bs$sd))
  expect_length(bs$ci, 2)
  expect_true(bs$ci[1] <= bs$ci[2])
})

test_that("nrm_monte_carlo returns mean, sd, and CI", {
  data(nrm_example, package = "NRMstatsML")
  mc <- nrm_monte_carlo(nrm_example,
          stat_fn = function(d) mean(d$crop_yield),
          n_iter = 200)
  expect_s3_class(mc, "nrm_monte_carlo")
  expect_true(is.numeric(mc$mean))
  expect_length(mc$ci, 2)
})

test_that("nrm_uncertainty dispatches to bootstrap", {
  data(nrm_example, package = "NRMstatsML")
  unc <- nrm_uncertainty(nrm_example,
           stat_fn = function(d) mean(d$crop_yield),
           method = "bootstrap", n_iter = 100)
  expect_true(inherits(unc, "nrm_uncertainty"))
  expect_equal(unc$method, "bootstrap")
})

test_that("nrm_uncertainty dispatches to monte_carlo", {
  data(nrm_example, package = "NRMstatsML")
  unc <- nrm_uncertainty(nrm_example,
           stat_fn = function(d) mean(d$crop_yield),
           method = "monte_carlo", n_iter = 100)
  expect_equal(unc$method, "monte_carlo")
})

test_that("nrm_bootstrap errors when stat_fn is not a function", {
  data(nrm_example, package = "NRMstatsML")
  expect_error(nrm_uncertainty(nrm_example, stat_fn = "mean"),
               "`stat_fn` must be a function")
})