test_that("Dependence measures work correctly", {
  xi1 <- 2.0
  xi2 <- 1.5
  delta <- 0.6
  z1 <- 0.5
  z2 <- 0.7
  
  # Local Dependence (gamma)
  loc_dep <- locdepbivteissier(z1, z2, xi1, xi2, delta)
  expect_true(loc_dep > 0) # Positive for delta > 0 (TP2)
  expect_equal(locdepbivteissier(z1, z2, xi1, xi2, 0), 0)
  expect_true(locdepbivteissier(z1, z2, xi1, xi2, -0.6) < 0)
  
  # Clayton-Oakes Measure (l)
  co_val <- claytonoakesbivteissier(z1, z2, xi1, xi2, delta)
  expect_true(co_val > 1) # > 1 for positive association
  expect_equal(claytonoakesbivteissier(z1, z2, xi1, xi2, 0), 1)
  
  # Conditional Probability Measure (psi)
  cp_val <- condprobbivteissier(z1, z2, xi1, xi2, delta)
  expect_true(cp_val > 1)
  expect_equal(condprobbivteissier(z1, z2, xi1, xi2, 0), 1)
  
  # Global Dependence Measures
  dep_pos <- depmeasuresbivteissier(0.8)
  expect_equal(dep_pos$spearman_rho, 0.8 / 3)
  expect_equal(dep_pos$kendall_tau, 2 * 0.8 / 9)
  expect_equal(dep_pos$dependence_type, "Positive Orthant Dependent (POD)")
  expect_true(dep_pos$tp2_property)
  
  dep_neg <- depmeasuresbivteissier(-0.6)
  expect_equal(dep_neg$spearman_rho, -0.6 / 3)
  expect_equal(dep_neg$kendall_tau, 2 * -0.6 / 9)
  expect_equal(dep_neg$dependence_type, "Negative Orthant Dependent (NOD)")
  expect_false(dep_neg$tp2_property)
  
  dep_zero <- depmeasuresbivteissier(0)
  expect_equal(dep_zero$spearman_rho, 0)
  expect_equal(dep_zero$kendall_tau, 0)
  expect_equal(dep_zero$dependence_type, "Independent")
})