make_ret <- function(n = 200, seed = 99) {
  set.seed(seed)
  # Simple two-regime synthetic return series
  s  <- sample(c(1, 2), n, replace = TRUE, prob = c(0.7, 0.3))
  mu <- c(0, -0.005)[s]
  sg <- c(0.01, 0.04)[s]
  rnorm(n, mean = mu, sd = sg)
}

test_that("fit_hmm_em (Gaussian) returns a valid fit object", {
  ret <- make_ret()
  fit <- fit_hmm_em(ret, K = 2L, model = "gaussian",
                    n_starts = 2L, max_iter = 30L, verbose = FALSE)
  expect_named(fit, c("model","A","delta","params","gamma","xi","logLik","n","K"))
  expect_true(is.finite(fit$logLik))
  expect_equal(dim(fit$A), c(2L, 2L))
  expect_equal(rowSums(fit$A), c(1, 1), tolerance = 1e-8)
})

test_that("fit_hmm_em (Student-t) returns a valid fit object", {
  ret <- make_ret()
  fit <- fit_hmm_em(ret, K = 2L, model = "student",
                    n_starts = 2L, max_iter = 30L, verbose = FALSE,
                    nu_grid = c(4, 8, 15))
  expect_true(is.finite(fit$logLik))
  expect_true(!is.null(fit$params$nu))
  expect_true(all(fit$params$nu >= 2.1))
})

test_that("reorder_fit_by_scale orders states by ascending volatility (Gaussian)", {
  ret <- make_ret()
  fit <- fit_hmm_em(ret, K = 2L, model = "gaussian",
                    n_starts = 2L, max_iter = 30L, verbose = FALSE)
  fit <- KRONX:::reorder_fit_by_scale(fit)
  expect_lte(fit$params$sigma[1], fit$params$sigma[2])
})

test_that("reorder_fit_by_scale orders states by conditional variance (Student-t)", {
  ret <- make_ret()
  fit <- fit_hmm_em(ret, K = 2L, model = "student",
                    n_starts = 2L, max_iter = 30L, verbose = FALSE,
                    nu_grid = c(4, 8, 15))
  fit     <- KRONX:::reorder_fit_by_scale(fit)
  cv      <- (fit$params$nu / (fit$params$nu - 2)) * fit$params$sigma^2
  expect_lte(cv[1], cv[2])
})