mod <- "
  # intercept and slope with fixed coefficients
  i =~ 1*t1 + 1*t2 + 1*t3 + 1*t4
  s =~ 0*t1 + 1*t2 + 2*t3 + 3*t4

  # regressions
  i ~ x1 + x2
  s ~ x1 + x2

  # time-varying covariates
  t1 ~ c1
  t2 ~ c2
  t3 ~ c3
  t4 ~ c4
"
dat <- lavaan::Demo.growth
fit_lav <- lavaan::cfa(mod, dat)
NSAMP <- 3

test_that("Method: skewnorm", {
  expect_no_error({
    fit <- agrowth(
      mod,
      dat,
      marginal_method = "skewnorm",
      verbose = FALSE,
      nsamp = NSAMP
    )
  })
  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 <- agrowth(
      mod,
      dat,
      marginal_method = "asymgaus",
      verbose = FALSE,
      nsamp = NSAMP
    )
  })
  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 <- agrowth(
      mod,
      dat,
      marginal_method = "marggaus",
      verbose = FALSE,
      nsamp = NSAMP
    )
  })
  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 <- agrowth(
      mod,
      dat,
      marginal_method = "sampling",
      verbose = FALSE,
      nsamp = NSAMP
    )
  })
  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 <- agrowth(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
  )
})

################################################################################
## CHECK AGAINST MCMC ##########################################################
################################################################################
testthat::skip_on_ci()
testthat::skip_on_cran()
testthat::skip_if_not(interactive())
library(blavaan)
future::plan("multisession", workers = future::availableCores() - 2)

fit_blav <- bgrowth(
  mod,
  dat,
  bcontrol = list(cores = 3),
  burnin = 1000,
  sample = 2000
)
fit_inl1 <- agrowth(
  mod,
  dat,
  marginal_method = "skewnorm",
  debug = TRUE,
  test = "none"
)
fit_inl2 <- agrowth(
  mod,
  dat,
  marginal_method = "asymgaus",
  debug = TRUE,
  test = "none"
)
fit_inl3 <- agrowth(
  mod,
  dat,
  marginal_method = "sampling",
  debug = TRUE,
  test = "none"
)

res <- compare_mcmc(
  fit_blav,
  "skewnorm" = fit_inl1,
  "asymgaus" = fit_inl2,
  "sampling" = fit_inl3
)
print(res$p_compare)
print(res$p_errors)
print(res$metrics_df, n = 1000)