if (suppressPackageStartupMessages(require("bayestestR", quietly = TRUE)) && require("testthat")) {
  test_that("blavaan, all", {
    skip_on_cran()
    skip_if_not_installed("blavaan")
    skip_if_not_installed("lavaan")
    skip_if_not_installed("cmdstanr")
    require(blavaan)

    data("PoliticalDemocracy", package = "lavaan")

    model <- "
    # latent variable definitions
    dem60 =~ y1 + a*y2
    dem65 =~ y5 + a*y6

    # regressions
    dem65 ~ dem60

    # residual correlations
    y1 ~~ y5
  "

    model2 <- "
    # latent variable definitions
    dem60 =~ y1 + a*y2
    dem65 =~ y5 + a*y6

    # regressions
    dem65 ~ 0*dem60

    # residual correlations
    y1 ~~ 0*y5
  "
    suppressWarnings(capture.output({
      bfit <- blavaan::bsem(model,
        data = PoliticalDemocracy,
        n.chains = 1, burnin = 50, sample = 100
      )
      bfit2 <- blavaan::bsem(model2,
        data = PoliticalDemocracy,
        n.chains = 1, burnin = 50, sample = 100
      )
    }))

    x <- point_estimate(bfit, centrality = "all", dispersion = TRUE)
    expect_true(all(c("Median", "MAD", "Mean", "SD", "MAP", "Component") %in% colnames(x)))
    expect_equal(nrow(x), 14)

    x <- eti(bfit)
    expect_equal(nrow(x), 14)

    x <- hdi(bfit)
    expect_equal(nrow(x), 14)

    x <- p_direction(bfit)
    expect_equal(nrow(x), 14)

    x <- rope(bfit, range = c(-.1, .1))
    expect_equal(nrow(x), 14)

    x <- p_rope(bfit, range = c(-.1, .1))
    expect_equal(nrow(x), 14)

    x <- p_map(bfit)
    expect_equal(nrow(x), 14)

    x <- p_significance(bfit, threshold = c(-.1, .1))
    expect_equal(nrow(x), 14)

    x <- equivalence_test(bfit, range = c(-.1, .1))
    expect_equal(nrow(x), 14)

    x <- estimate_density(bfit)
    expect_equal(length(unique(x$Parameter)), 14)


    ## Bayes factors ----
    expect_warning(bayesfactor_models(bfit, bfit2))
    x <- suppressWarnings(bayesfactor_models(bfit, bfit2))
    expect_true(x$log_BF[2] < 0)

    expect_warning(weighted_posteriors(bfit, bfit2))
    x <- suppressWarnings(weighted_posteriors(bfit, bfit2))
    expect_equal(ncol(x), 14)

    # bfit_prior <- unupdate(bfit)
    # capture.output(x <- bayesfactor_parameters(bfit, prior = bfit_prior))
    # expect_equal(nrow(x), 14)
    #
    # x <- expect_warning(si(bfit, prior = bfit_prior))
    # expect_equal(nrow(x), 14)
    #
    # ## Prior/posterior checks ----
    # suppressWarnings(x <- check_prior(bfit))
    # expect_equal(nrow(x), 13)
    #
    # x <- check_prior(bfit, simulate_priors = FALSE)
    # expect_equal(nrow(x), 14)

    x <- diagnostic_posterior(bfit)
    expect_equal(nrow(x), 14)

    x <- simulate_prior(bfit)
    expect_equal(ncol(x), 13)
    # YES this is 13! We have two parameters with the same prior.

    x <- describe_prior(bfit)
    expect_equal(nrow(x), 13)
    # YES this is 13! We have two parameters with the same prior.

    # x <- describe_posterior(bfit, test = "all", rope_range = c(-.1, .1))
    # expect_equal(nrow(x), 14)
  })
}