if (requiet("rstanarm") &&
  requiet("BayesFactor") &&
  requiet("testthat") &&
  requiet("insight") &&
  requiet("httr") &&
  requiet("brms")) {
  # bayesfactor_parameters data frame ---------------------------------------

  test_that("bayesfactor_parameters data frame", {
    Xprior <- data.frame(
      x = distribution_normal(1e4),
      y = distribution_normal(1e4)
    )
    Xposterior <- data.frame(
      x = distribution_normal(1e4, mean = 0.5),
      y = distribution_normal(1e4, mean = -0.5)
    )

    # point
    bfsd <- bayesfactor_parameters(Xposterior, prior = Xprior, null = 0, direction = 0, verbose = FALSE)
    expect_equal(bfsd$log_BF, c(0.12, 0.12), tolerance = 0.1)

    bfsd <- bayesfactor_parameters(Xposterior, prior = Xprior, null = 0, direction = 1, verbose = FALSE)
    expect_equal(bfsd$log_BF, c(0.44, -0.35), tolerance = 0.1)

    bfsd <- bayesfactor_parameters(Xposterior, prior = Xprior, null = 0, direction = -1, verbose = FALSE)
    expect_equal(bfsd$log_BF, c(-0.35, 0.44), tolerance = 0.1)

    bfsd <- bayesfactor_parameters(Xposterior, prior = Xprior, null = 0.5, direction = 0, verbose = FALSE)
    expect_equal(bfsd$log_BF, c(-0.12, 0.37), tolerance = 0.1)

    expect_warning(bayestestR::bayesfactor_parameters(Xposterior, Xprior))

    w <- capture_warnings(bfsd <- bayestestR::bayesfactor_parameters(Xposterior))
    expect_match(w, "Prior", all = FALSE)
    expect_match(w, "40", all = FALSE)
    expect_equal(bfsd$log_BF, c(0, 0), tolerance = 0.1)

    # interval
    expect_warning(
      bfsd <- bayesfactor_parameters(Xposterior, prior = Xprior, null = c(-.1, .1), direction = 0),
      regexp = NA
    )
    expect_equal(bfsd$log_BF, c(0.13, 0.13), tolerance = 0.1)

    bfsd <- bayesfactor_parameters(Xposterior, prior = Xprior, null = c(-.1, .1), direction = 1)
    expect_equal(bfsd$log_BF, c(0.47, -0.39), tolerance = 0.1)

    bfsd <- bayesfactor_parameters(Xposterior, prior = Xprior, null = c(-.1, .1), direction = -1)
    expect_equal(bfsd$log_BF, c(-0.39, 0.47), tolerance = 0.1)

    # interval with inf
    bfsd <- bayesfactor_parameters(Xposterior, prior = Xprior, null = c(-.1, Inf))
    expect_equal(bfsd$log_BF, c(-0.81, 0.80), tolerance = 0.1)

    bfsd <- bayesfactor_parameters(Xposterior, prior = Xprior, null = c(-Inf, .1))
    expect_equal(bfsd$log_BF, c(0.80, -0.81), tolerance = 0.1)
  })


  # bayesfactor_parameters RSTANARM -----------------------------------------

  test_that("bayesfactor_parameters RSTANARM", {
    skip_on_cran()

    fit <- stan_glm(mpg ~ ., data = mtcars, refresh = 0)

    set.seed(333)
    fit_p <- unupdate(fit)
    expect_warning(BF2 <- bayesfactor_parameters(fit, fit_p))

    set.seed(333)
    BF1 <- bayesfactor_parameters(fit, verbose = FALSE)

    expect_equal(BF1, BF2)

    model_flat <- stan_glm(extra ~ group, data = sleep, prior = NULL, refresh = 0)
    expect_error(bayesfactor_parameters(model_flat))

    skip_on_ci()
    fit10 <- update(fit, chains = 10, iter = 5100, warmup = 100)
    expect_warning(bayesfactor_parameters(fit10), regexp = NA)
  })


  # bayesfactor_parameters BRMS ---------------------------------------------
  # .runThisTest <- Sys.getenv("RunAllbayestestRTests") == "yes"
  # if (.runThisTest) {
  #   test_that("bayesfactor_parameters BRMS", {
  #     skip_on_cran()
  #
  #     brms_mixed_6 <- insight::download_model("brms_mixed_6")
  #
  #     set.seed(222)
  #     brms_mixed_6_p <- unupdate(brms_mixed_6)
  #     bfsd1 <- bayesfactor_parameters(brms_mixed_6, brms_mixed_6_p, effects = "fixed")
  #
  #     set.seed(222)
  #     bfsd2 <- bayesfactor_parameters(brms_mixed_6, effects = "fixed")
  #
  #     expect_equal(log(bfsd1$BF), log(bfsd2$BF), tolerance = .11)
  #
  #
  #     brms_mixed_1 <- insight::download_model("brms_mixed_1")
  #     expect_error(bayesfactor_parameters(brms_mixed_1))
  #   })
  # }
}