test_that("CLPM", {

  # The following simulation and analysis of a random intercept cross-lagged panel model
  # is based on the syntax from Jeroen D. Mulder & Ellen L. Hamaker (2021) Three Extensions of the Random
  # Intercept Cross-Lagged Panel Model, Structural Equation Modeling: A Multidisciplinary Journal,
  # 28:4, 638-648, DOI: 10.1080/10705511.2020.1784738
  #
  # See https://jeroendmulder.github.io/RI-CLPM/lavaan.html

  library(lavaan)
  library(mxsem)

  set.seed(123)

  # Set up model
  model <- "
# autoregressive and cross-lagged parameters:
eta1_u2 ~ a11_u2*eta1_u1 + a12_u2*eta2_u1
eta2_u2 ~ a21_u2*eta1_u1 + a22_u2*eta2_u1
eta1_u3 ~ a11_u3*eta1_u2 + a12_u3*eta2_u2
eta2_u3 ~ a21_u3*eta1_u2 + a22_u3*eta2_u2
eta1_u4 ~ a11_u4*eta1_u3 + a12_u4*eta2_u3
eta2_u4 ~ a21_u4*eta1_u3 + a22_u4*eta2_u3
eta1_u5 ~ a11_u5*eta1_u4 + a12_u5*eta2_u4
eta2_u5 ~ a21_u5*eta1_u4 + a22_u5*eta2_u4

# covariances
eta1_u1 ~~ initialCov_21*eta2_u1 + initialCov_11*eta1_u1
eta2_u1 ~~ initialCov_22*eta2_u1

eta1_u2 ~~ 0*eta2_u2 + v11*eta1_u2
eta2_u2 ~~ v22*eta2_u2

eta1_u3 ~~ 0*eta2_u3 + v11*eta1_u3
eta2_u3 ~~ v22*eta2_u3

eta1_u4 ~~ 0*eta2_u4 + v11*eta1_u4
eta2_u4 ~~ v22*eta2_u4

eta1_u5 ~~ 0*eta2_u5 + v11*eta1_u5
eta2_u5 ~~ v22*eta2_u5

# Add observations:
eta1_u1 =~ 1*y1_u1
eta2_u1 =~ 1*y2_u1

eta1_u2 =~ 1*y1_u2
eta2_u2 =~ 1*y2_u2

eta1_u3 =~ 1*y1_u3
eta2_u3 =~ 1*y2_u3

eta1_u4 =~ 1*y1_u4
eta2_u4 =~ 1*y2_u4

eta1_u5 =~ 1*y1_u5
eta2_u5 =~ 1*y2_u5

y1_u1 ~~ 0*y1_u1
y2_u1 ~~ 0*y2_u1

y1_u2 ~~ 0*y1_u2
y2_u2 ~~ 0*y2_u2

y1_u3 ~~ 0*y1_u3
y2_u3 ~~ 0*y2_u3

y1_u4 ~~ 0*y1_u4
y2_u4 ~~ 0*y2_u4

y1_u5 ~~ 0*y1_u5
y2_u5 ~~ 0*y2_u5

# random intercepts
RI_eta1 =~ 1*y1_u1
RI_eta2 =~ 1*y2_u1

RI_eta1 =~ 1*y1_u2
RI_eta2 =~ 1*y2_u2

RI_eta1 =~ 1*y1_u3
RI_eta2 =~ 1*y2_u3

RI_eta1 =~ 1*y1_u4
RI_eta2 =~ 1*y2_u4

RI_eta1 =~ 1*y1_u5
RI_eta2 =~ 1*y2_u5

RI_eta1 ~~ vri11*RI_eta1 + vri12*RI_eta2
RI_eta2 ~~ vri22*RI_eta2

RI_eta1 ~~ 0*eta1_u1 + 0*eta2_u1
RI_eta2 ~~ 0*eta1_u1 + 0*eta2_u1
"

  suppressWarnings(data <- lavaan::simulateData(model))

  fit_lavaan <- cfa(model,
                    data = data,
                    missing = "ml",
                    fixed.x = FALSE)

  fit_mx  <- mxsem(model = model,
                   data = data,
                   add_exogenous_latent_covariances = FALSE) |>
    mxTryHard()

  testthat::expect_true(length(unique(names(coef(fit_lavaan)))) == length(omxGetParameters(fit_mx)))

  testthat::expect_true(abs(-2*logLik(fit_lavaan) -
                              fit_mx$fitfunction$result[[1]]) < 1e-4)

  # no intercepts

  fit_lavaan_no_int <- cfa(model,
                           data = data,
                           fixed.x = FALSE)

  fit_mx_no_int  <- mxsem(model = model,
                          data = data,
                          add_intercepts = FALSE,
                          add_exogenous_latent_covariances = FALSE) |>
    mxTryHard()

  testthat::expect_true(length(unique(names(coef(fit_lavaan_no_int)))) == length(omxGetParameters(fit_mx_no_int)))

  testthat::expect_true(
    all(
      abs(
        omxGetParameters(fit_mx)[names(omxGetParameters(fit_mx_no_int))]
        - omxGetParameters(fit_mx_no_int)
      ) < 1e-4
    )
  )

  # test custom

  fit_mx_custom  <- mxsem(model = model,
                          data = mxData(observed = cov(data), type = "cov", means = colMeans(data), numObs = nrow(data)),
                          add_exogenous_latent_covariances = FALSE) |>
    mxTryHard()

  testthat::expect_true(length(unique(names(coef(fit_lavaan)))) == length(omxGetParameters(fit_mx_custom)))

  testthat::expect_true(
    all(
      abs(
        omxGetParameters(fit_mx)[names(omxGetParameters(fit_mx_custom))]
        - omxGetParameters(fit_mx_custom)
      ) < 1e-4
    )
  )
})