test_that("lpbwcde default output", {
  set.seed(42)
  n <- 100
  x_data <- matrix(rnorm(1 * n, mean = 0, sd = 1), ncol = 1)
  y_data <- matrix(rnorm(n, mean = 0, sd = 1))
  y_grid <- stats::quantile(y_data, seq(from = 0.1, to = 0.9, by = 0.1))

  # bw estimation
  model1 <- lpbwcde(x_data = x_data, y_data = y_data, x = 0, bw_type = "imse-rot")
  print(model1)
  summary(model1)
  coef(model1)
  expect_equal(model1$opt$ng, 19)

  model1 <- lpbwcde(x_data = x_data, y_data = y_data, x = 0, bw_type = "mse-rot")
  expect_equal(model1$opt$bw_type, "mse-rot")
})

test_that("lpbwcde multivariate default output", {
  set.seed(42)
  n <- 100
  x_data <- matrix(rnorm(2 * n, mean = 0, sd = 1), ncol = 2)
  y_data <- matrix(rnorm(n, mean = 0, sd = 1))
  y_grid <- stats::quantile(y_data, seq(from = 0.1, to = 0.9, by = 0.1))
  # bw estimation
  model1 <- lpbwcde(x_data = x_data, y_data = y_data, y_grid = y_grid, x = matrix(c(0, 0), ncol = 2), bw_type = "imse-rot")
  summary(model1)
  expect_equal(model1$opt$bw_type, "imse-rot")
})

test_that("lpbwcde default output", {
  set.seed(42)
  n <- 100
  x_data <- matrix(rnorm(1 * n, mean = 0, sd = 1), ncol = 1)
  y_data <- matrix(rnorm(n, mean = 0, sd = 1))
  y_grid <- stats::quantile(y_data, seq(from = 0.1, to = 0.9, by = 0.1))

  # bw estimation
  model1 <- lpbwcde(x_data = x_data, y_data = y_data, y_grid = y_grid, x = 0, mu = 0, p = 3, bw_type = "imse-rot")
  print(model1)
  summary(model1)
  coef(model1)
  expect_equal(model1$opt$bw_type, "imse-rot")
})