dat <- lavaan::HolzingerSwineford1939
mod <- "
    visual  =~ x1 + x2 + x3
    textual =~ x4 + x5 + x6
    speed   =~ x7 + x8 + x9
  "
fit_lav <- lavaan::cfa(mod, dat)
NSAMP <- 3
STDLV <- FALSE

test_that("Method: skewnorm", {
  expect_no_error({
    fit <- acfa(
      mod,
      dat,
      marginal_method = "skewnorm",
      verbose = FALSE,
      nsamp = NSAMP,
      std.lv = STDLV
    )
  })
  expect_no_error(out <- capture.output(summary(fit)))

  expect_s4_class(fit, "INLAvaan")
  expect_equal(coef(fit), coef(fit_lav), tolerance = 0.1)
  expect_equal(fit@optim$dx, rep(0, length(coef(fit))), tolerance = 1e-3)
})

test_that("Method: asymgaus", {
  expect_no_error({
    fit <- acfa(
      mod,
      dat,
      marginal_method = "asymgaus",
      verbose = FALSE,
      nsamp = NSAMP,
      std.lv = STDLV
    )
  })
  expect_no_error(out <- capture.output(summary(fit)))

  expect_s4_class(fit, "INLAvaan")
  expect_equal(coef(fit), coef(fit_lav), tolerance = 0.1)
})

test_that("Method: marggaus", {
  expect_no_error({
    fit <- acfa(
      mod,
      dat,
      marginal_method = "marggaus",
      verbose = FALSE,
      nsamp = NSAMP,
      std.lv = STDLV
    )
  })
  expect_no_error(out <- capture.output(summary(fit)))

  expect_s4_class(fit, "INLAvaan")
  expect_equal(coef(fit), coef(fit_lav), tolerance = 0.1)
})

test_that("Method: sampling", {
  expect_no_error({
    fit <- acfa(
      mod,
      dat,
      marginal_method = "sampling",
      verbose = FALSE,
      nsamp = NSAMP,
      std.lv = STDLV
    )
  })
  expect_no_error(out <- capture.output(summary(fit)))

  expect_s4_class(fit, "INLAvaan")
  expect_equal(coef(fit), coef(fit_lav), tolerance = 0.1)
})

test_that("Gradients are correct (Finite Difference Check)", {
  suppressMessages(
    tmp <- capture.output(fit <- acfa(mod, dat, test = "none", debug = TRUE))
  )
  test_df <- read.table(text = tmp, skip = 1)[, -1]
  colnames(test_df) <- c("fd", "analytic", "diff")

  expect_equal(
    as.numeric(test_df$fd),
    as.numeric(test_df$diff),
    tolerance = 1e-3
  )
  expect_equal(
    as.numeric(test_df$diff),
    rep(0, nrow(test_df)),
    tolerance = 1e-3
  )
})