test_that("two-sample: basic functionality", {

  ### test fixed omega -- non-local prior f-test (two-sample)
  fit <- f_test_BFF(
    f_stat = 1.75,
    n = 25,
    df1 = 50,
    df2 = 50,
    omega = 0.5)

  # check that the BF and omega is consistent
  testthat::expect_equal(fit$log_bf_h1, -2.89426, tolerance = 1e-5)
  testthat::expect_equal(fit$omega_h1,  0.5)

  # test S3 methods
  testthat::expect_equal(
    testthat::capture_output_lines(fit, print = TRUE, width = 100),
    c(
      "",
      "\tBayes Factor Test With Non-Local Priors For A F-Test",
      "",
      "Log BF = -2.89 at standardized effect size 0.5",
      ""
    )
  )
  testthat::expect_error(plot(fit), "Bayes factor function can be plotted only if a specific omega/tau2 is not user set")

  # TODO: fix posterior plots
  # - I fixed the arguments not being properly passed
  # - however, the posterior distribution does not integrate to 1
  # (I remember that I raised this issue when I was in US, and Saptati was working on fixing it)

  # # this is how the functions should work
  # posterior_plot(fit)
  # posterior_plot(fit, prior = TRUE)
  #
  # # this highlights the issue (run `devtools::load_all()` first)
  # tau2 <- get_two_sample_tau2(n1 = fit$input$n1, n2 = fit$input$n2, w = fit$omega, r = fit$r)
  #
  # # does not integrate to 1
  # integrate(
  #   f = function(x) .t_test.posterior(
  #     t_stat = fit$input$t_stat, tau2 = tau2, r = fit$r, effect_size = x,
  #     n = fit$input$n, n1 = fit$input$n1, n2 = fit$input$n2, one_sample = fit$one_sample, one_sided = fit$alternative != "two.sided"),
  #   lower = -Inf,
  #   upper = Inf
  # )
  #
  # # prior seems to work just fine (i.e., integrates to one)
  # integrate(
  #   f = function(x) .t_test.prior(
  #     tau2 = tau2, r = fit$r, effect_size = x,
  #     n = fit$input$n, n1 = fit$input$n1, n2 = fit$input$n2, one_sample = fit$one_sample, one_sided = fit$alternative != "two.sided"),
  #   lower = -Inf,
  #   upper = Inf
  # )
  # # <\TODO> Adjust for F-test later
  #
  # # vdiffr::expect_doppelganger("t_test-two_sample-two_sided-posterior",           posterior_plot(fit))
  # # vdiffr::expect_doppelganger("t_test-two_sample-two_sided-posterior_and_prior", posterior_plot(fit, prior = TRUE))
  #


  ### test unspecified omega -- BFF (two-sample; also change n1/n2)
  fit <- f_test_BFF(
    f_stat = 1.5,
    n = 50,
    df1 = 25,
    df2 = 75)

  # check that the BF and omega is consistent
  testthat::expect_equal(fit$log_bf_h1, 0.82228, tolerance = 1e-5)
  testthat::expect_equal(fit$omega_h1,  0.14)

  # test S3 methods
  testthat::expect_equal(
    testthat::capture_output_lines(fit, print = TRUE, width = 100),
    c(
      "",
      "\tBayes Factor Test With Non-Local Priors For A F-Test"  ,
      ""                                        ,
      "The log BF is maximized in favor of the alternative hypothesis at the standardized effect size of 0.14 with value 0.82. Standardized effect size should be chosen for individual hypotheses based on scientific intent and plausibilty.",
      "",
      "The BF switches from providing evidence for the alternative hypothesis to evidence for the null hypothesis at the standardized effect size of 0.23",
      ""
    )
  )
  #Modify for F test
  # vdiffr::expect_doppelganger("f_test_BFF-two_sample-two_sided-BFF",                 plot(fit))
  # vdiffr::expect_doppelganger("f_test_BFF-two_sample-two_sided-posterior",           posterior_plot(fit))
  # vdiffr::expect_doppelganger("f_test_BFF-two_sample-two_sided-posterior_and_prior", posterior_plot(fit, prior = TRUE, color = c("red", "blue"), linetype = c(3,5),
  #                                                                                                   linewidth = c(2, 1), x_limit = c(-2, 2)))


  # check that the data.frame plot output also works
  # no_plot_plot <- posterior_plot(fit, plot = FALSE, prior = TRUE)
  # testthat::expect_true(is.data.frame(no_plot_plot))
  # testthat::expect_equal(colnames(no_plot_plot), c("x", "prior", "posterior"))


  ### test different r -- non-local prior f-test (two-sample)
  fit <- f_test_BFF(
    f_stat = 1.75,
    n = 25,
    df1 = 50,
    df2 = 50,
    r = 3)

  # check that the BF and omega is consistent
  testthat::expect_equal(fit$log_bf_h1, 1.93714, tolerance = 1e-5)
  testthat::expect_equal(fit$omega_h1,  0.24)

  # test S3 methods
  testthat::expect_equal(
    testthat::capture_output_lines(fit, print = TRUE, width = 100),
    c(
      "",
      "\tBayes Factor Test With Non-Local Priors For A F-Test"  ,
      ""                                        ,
      "The log BF is maximized in favor of the alternative hypothesis at the standardized effect size of 0.24 with value 1.94. Standardized effect size should be chosen for individual hypotheses based on scientific intent and plausibilty.",
      "",
      "The BF switches from providing evidence for the alternative hypothesis to evidence for the null hypothesis at the standardized effect size of 0.4",
      ""
    )
  )

})