if (require("bayestestR") && require("testthat")) {

  # bayesfactor_models BIC --------------------------------------------------
  test_that("bayesfactor_models BIC", {
    skip_if_not_installed("lme4")
    set.seed(444)
    mo1 <- lme4::lmer(Sepal.Length ~ (1 | Species), data = iris)
    mo2 <- lme4::lmer(Sepal.Length ~ Petal.Length + (1 | Species), data = iris)
    mo3 <- lme4::lmer(Sepal.Length ~ Petal.Length + (Petal.Length | Species), data = iris)
    mo4 <- lme4::lmer(Sepal.Length ~ Petal.Length + Petal.Width + (Petal.Length | Species), data = iris)
    mo5 <- lme4::lmer(Sepal.Length ~ Petal.Length * Petal.Width + (Petal.Length | Species), data = iris)
    mo4_e <- lme4::lmer(Sepal.Length ~ Petal.Length + Petal.Width + (Petal.Length | Species), data = iris[-1, ])

    # both uses of denominator
    BFM1 <<- bayesfactor_models(mo2, mo3, mo4, mo1, denominator = 4)
    BFM2 <- bayesfactor_models(mo2, mo3, mo4, denominator = mo1)
    BFM3 <- bayesfactor_models(mo2, mo3, mo4, mo1, denominator = mo1)
    BFM4 <<- bayesfactor_models(mo2, mo3, mo4, mo5, mo1, denominator = mo1)

    expect_equal(BFM1, BFM2)
    expect_equal(BFM1, BFM3)
    expect_equal(BFM1, bayestestR::bayesfactor_models(list(mo2=mo2, mo3=mo3, mo4=mo4, mo1=mo1), denominator = 4))

    # only on same data!
    expect_error(bayestestR::bayesfactor_models(mo1, mo2, mo4_e))

    # update models
    expect_equal(log(update(BFM2, subset = c(1, 2))$BF), c(1, 57.3, 54.52), tolerance = 0.1)

    # update reference
    expect_equal(log(update(BFM2, reference = 1)$BF),
      c(0, -2.8, -6.2, -57.4),
      tolerance = 0.1
    )
  })

  test_that("bayesfactor_models BIC (unsupported / diff nobs)", {
    skip_on_cran()
    set.seed(444)

    fit1 <- lm(Sepal.Length ~ Sepal.Width + Petal.Length, iris)
    fit2a <- lm(Sepal.Length ~ Sepal.Width, iris[-1, ]) # different number of objects
    fit2b <- lm(Sepal.Length ~ Sepal.Width, iris) # not supported
    class(fit2b) <- "NOTLM"
    logLik.NOTLM <<- function(...) {
      stats:::logLik.lm(...)
    }

    # Should fail
    expect_error(bayesfactor_models(fit1, fit2a))

    # Should warn, but still work
    expect_warning(res <- bayesfactor_models(fit1, fit2b))
    expect_equal(log(res$BF), c(0, -133.97), tolerance = 0.1)
  })


  # bayesfactor_models STAN ---------------------------------------------
  test_that("bayesfactor_models STAN", {
    skip_on_cran()
    skip_if_not_installed("rstanarm")
    skip_if_not_installed("bridgesampling")
    set.seed(333)
    stan_bf_0 <- rstanarm::stan_glm(
      Sepal.Length ~ 1,
      data = iris,
      refresh = 0,
      iter = 500,
      diagnostic_file = file.path(tempdir(), "df0.csv")
    )
    stan_bf_1 <- rstanarm::stan_glm(
      Sepal.Length ~ Species,
      data = iris,
      refresh = 0,
      iter = 500,
      diagnostic_file = file.path(tempdir(), "df1.csv")
    )


    set.seed(333) # compare against bridgesampling
    bridge_BF <- bridgesampling::bayes_factor(
      bridgesampling::bridge_sampler(stan_bf_1),
      bridgesampling::bridge_sampler(stan_bf_0)
    )

    set.seed(333)
    expect_warning(stan_models <- bayesfactor_models(stan_bf_0, stan_bf_1))
    expect_is(stan_models, "bayesfactor_models")
    expect_equal(length(log(stan_models$BF)), 2)
    expect_equal(log(stan_models$BF[2]), log(bridge_BF$bf), tol = 0.1)
  })


  # bayesfactor_inclusion ---------------------------------------------------
  test_that("bayesfactor_inclusion | BayesFactor", {
    set.seed(444)
    skip_if_not_installed("BayesFactor")
    # BayesFactor
    ToothGrowth$dose <- as.factor(ToothGrowth$dose)
    BF_ToothGrowth <- BayesFactor::anovaBF(len ~ dose * supp, ToothGrowth)
    expect_equal(
      bayesfactor_inclusion(BF_ToothGrowth),
      bayesfactor_inclusion(bayesfactor_models(BF_ToothGrowth))
    )
  })

  test_that("bayesfactor_inclusion | LMM", {
    # with random effects in all models:
    skip_if_not_installed("lme4")

    expect_true(is.nan(bayesfactor_inclusion(BFM1)["1:Species", "BF"]))

    bfinc_all <- bayesfactor_inclusion(BFM4, match_models = FALSE)
    expect_equal(bfinc_all$p_prior, c(1, 0.8, 0.6, 0.4, 0.2), tolerance = 0.1)
    expect_equal(bfinc_all$p_posterior, c(1, 1, 0.06, 0.01, 0), tolerance = 0.1)
    expect_equal(log(bfinc_all$BF), c(NaN, 56.04, -3.22, -5.9, -8.21), tolerance = 0.1)

    # + match_models
    bfinc_matched <- bayesfactor_inclusion(BFM4, match_models = TRUE)
    expect_equal(bfinc_matched$p_prior, c(1, 0.2, 0.6, 0.2, 0.2), tolerance = 0.1)
    expect_equal(bfinc_matched$p_posterior, c(1, 0.94, 0.06, 0.01, 0), tolerance = 0.1)
    expect_equal(log(bfinc_matched$BF), c(NaN, 57.37, -3.92, -5.25, -3.25), tolerance = 0.1)
  })
}