test_that("matrix batch agrees with independent fast dip tests", {
  set.seed(912)
  x <- cbind(
    Normal = rnorm(10000),
    Rounded = round(rnorm(10000), 1),
    Mixture = c(rnorm(5000, -1), rnorm(5000, 1))
  )

  batch <- fast_dip_test_matrix(x)
  expect_equal(batch$Column, seq_len(ncol(x)))
  expect_equal(batch$Distance, colnames(x))

  for (j in seq_len(ncol(x))) {
    single <- fast_dip_test(x[, j])
    expect_equal(
      batch$DipStatistic[[j]],
      unname(single$statistic),
      tolerance = 2e-15
    )
    expect_equal(
      batch$DipPValue[[j]],
      single$p.value,
      tolerance = 2e-14
    )
  }
})

test_that("distance-analysis helper retains diptest below the threshold", {
  skip_if_not_installed("diptest")
  set.seed(913)
  features <- cbind(A = rnorm(5000), B = runif(5000))

  observed <- BIDistances:::.distance_distribution_analysis_dip_tests(
    features,
    1:2
  )
  expect_identical(
    observed$DipMethod,
    rep("diptest::dip.test", 2)
  )

  for (j in 1:2) {
    reference <- suppressMessages(diptest::dip.test(features[, j]))
    expect_equal(
      observed$DipStatistic[[j]],
      unname(reference$statistic),
      tolerance = 2e-15
    )
    expect_equal(
      observed$DipPValue[[j]],
      reference$p.value,
      tolerance = 2e-14
    )
  }
})

test_that("distance-analysis helper batches candidates above 72000", {
  set.seed(914)
  features <- cbind(
    A = rnorm(72001),
    B = c(rnorm(36000, -1), rnorm(36001, 1))
  )

  observed <- BIDistances:::.distance_distribution_analysis_dip_tests(
    features,
    1:2
  )
  reference <- fast_dip_test_matrix(
    features,
    columns = 1:2,
    p.value.method = "asymptotic"
  )

  expect_identical(
    observed$DipMethod,
    rep("fast_dip_test_matrix_asymptotic", 2)
  )
  expect_equal(
    observed$DipStatistic,
    reference$DipStatistic,
    tolerance = 0
  )
  expect_equal(
    observed$DipPValue,
    reference$DipPValue,
    tolerance = 0
  )
})

test_that("DistanceDistributionAnalysis reports DipStatistic", {
  skip_if_not_installed("DataVisualizations")
  skip_if_not_installed("diptest")
  skip_if_not_installed("e1071")
  set.seed(915)

  # 102 objects have 102 * 101 / 2 = 5151 upper-triangle pairs.
  pair_count <- 102 * 101 / 2
  features <- cbind(
    Uniform = runif(pair_count),
    HalfNormal = abs(rnorm(pair_count))
  )

  invisible(capture.output(
    result <- DistanceDistributionAnalysis(features, PlotIt = FALSE)
  ))
  expect_true("DipStatistic" %in% names(result$SelectionStatistics))

  for (j in seq_len(ncol(features))) {
    row <- match(
      colnames(features)[[j]],
      result$SelectionStatistics$Distance
    )
    reference <- suppressMessages(diptest::dip.test(features[, j]))
    expect_equal(
      result$SelectionStatistics$DipStatistic[[row]],
      unname(reference$statistic),
      tolerance = 2e-15
    )
    expect_equal(
      result$SelectionStatistics$DipPValue[[row]],
      reference$p.value,
      tolerance = 2e-14
    )
  }
})