if (require("testthat") && suppressPackageStartupMessages(require("bayestestR", quietly = TRUE)) && require("rstanarm") && require("brms") && require("httr") && require("insight") && require("BayesFactor", quietly = TRUE)) {
  test_that("describe_posterior", {
    set.seed(333)

    # numeric -------------------------------------------------

    x <- distribution_normal(1000)

    expect_warning(describe_posterior(
      x,
      centrality = "all",
      dispersion = TRUE,
      test = "all",
      ci = 0.89
    ))

    rez <- as.data.frame(suppressWarnings(describe_posterior(
      x,
      centrality = "all",
      dispersion = TRUE,
      test = "all",
      ci = 0.89
    )))

    expect_equal(dim(rez), c(1, 19))
    expect_equal(colnames(rez), c(
      "Parameter", "Median", "MAD", "Mean", "SD", "MAP", "CI", "CI_low",
      "CI_high", "p_map", "pd", "p_ROPE", "ps", "ROPE_CI", "ROPE_low",
      "ROPE_high", "ROPE_Percentage", "ROPE_Equivalence", "BF"
    ))

    rez <- expect_warning(describe_posterior(
      x,
      centrality = "all",
      dispersion = TRUE,
      test = "all",
      ci = c(0.8, 0.9)
    ))
    expect_equal(dim(rez), c(2, 19))

    rez <- describe_posterior(
      x,
      centrality = NULL,
      dispersion = TRUE,
      test = NULL,
      ci_method = "quantile"
    )
    expect_equal(dim(rez), c(1, 4))

    # dataframes -------------------------------------------------

    x <- data.frame(replicate(4, rnorm(100)))
    rez <- expect_warning(describe_posterior(x, centrality = "all", dispersion = TRUE, test = "all"))
    expect_equal(dim(rez), c(4, 19))
    rez <- expect_warning(describe_posterior(x, centrality = "all", dispersion = TRUE, test = "all", ci = c(0.8, 0.9)))
    expect_equal(dim(rez), c(8, 19))
    rez <- describe_posterior(x, centrality = NULL, dispersion = TRUE, test = NULL, ci_method = "quantile")
    expect_equal(dim(rez), c(4, 4))
  })


  .runThisTest <- Sys.getenv("RunAllbayestestRTests") == "yes"
  if (.runThisTest && Sys.info()["sysname"] != "Darwin") {
    test_that("describe_posterior", {
      set.seed(333)
      # Rstanarm
      x <- rstanarm::stan_glm(mpg ~ wt, data = mtcars, refresh = 0, iter = 500)
      rez <- describe_posterior(x, centrality = "all", dispersion = TRUE, test = "all")
      expect_equal(dim(rez), c(2, 21))
      expect_equal(colnames(rez), c(
        "Parameter", "Median", "MAD", "Mean", "SD", "MAP", "CI", "CI_low",
        "CI_high", "p_MAP", "pd", "p_ROPE", "ps", "ROPE_CI", "ROPE_low",
        "ROPE_high", "ROPE_Percentage", "ROPE_Equivalence", "BF", "Rhat",
        "ESS"
      ))
      rez <- describe_posterior(
        x,
        centrality = "all",
        dispersion = TRUE,
        test = "all",
        ci = c(0.8, 0.9)
      )
      expect_equal(dim(rez), c(4, 21))

      rez <- describe_posterior(
        x,
        centrality = NULL,
        dispersion = TRUE,
        test = NULL,
        ci_method = "quantile",
        diagnostic = NULL,
        priors = FALSE
      )
      expect_equal(dim(rez), c(2, 4))

      # brms -------------------------------------------------

      x <- brms::brm(mpg ~ wt + (1 | cyl) + (1 + wt | gear), data = mtcars, refresh = 0)
      rez <- describe_posterior(x, centrality = "all", dispersion = TRUE, ci = c(0.8, 0.9))

      expect_equal(dim(rez), c(4, 16))
      expect_equal(colnames(rez), c(
        "Parameter", "Median", "MAD", "Mean", "SD", "MAP", "CI", "CI_low",
        "CI_high", "pd", "ROPE_CI", "ROPE_low", "ROPE_high", "ROPE_Percentage",
        "Rhat", "ESS"
      ))

      rez <- describe_posterior(
        x,
        centrality = NULL,
        dispersion = TRUE,
        test = NULL,
        ci_method = "quantile",
        diagnostic = NULL
      )

      expect_equal(dim(rez), c(2, 4))

      model <- brms::brm(
        mpg ~ drat,
        data = mtcars,
        chains = 2,
        algorithm = "meanfield",
        refresh = 0
      )

      expect_equal(nrow(describe_posterior(model)), 2)

      # rstanarm -------------------------------------------------

      model <- rstanarm::stan_glm(mpg ~ drat,
        data = mtcars,
        algorithm = "meanfield",
        refresh = 0
      )

      expect_equal(nrow(describe_posterior(model)), 2)

      model <- rstanarm::stan_glm(mpg ~ drat,
        data = mtcars,
        algorithm = "optimizing",
        refresh = 0
      )

      expect_equal(nrow(describe_posterior(model)), 2)

      model <- rstanarm::stan_glm(mpg ~ drat,
        data = mtcars,
        algorithm = "fullrank",
        refresh = 0
      )

      expect_equal(nrow(describe_posterior(model)), 2)
      # model <- brms::brm(mpg ~ drat, data = mtcars, chains=2, algorithm="fullrank", refresh=0)
      # expect_equal(nrow(describe_posterior(model)), 2)

      # BayesFactor
      # library(BayesFactor)
      # x <- BayesFactor::ttestBF(x = rnorm(100, 1, 1))
      # rez <- describe_posterior(x, centrality = "all", dispersion = TRUE, test = "all")
      # expect_equal(dim(rez), c(4, 16))
      # rez <- describe_posterior(x, centrality = "all", dispersion = TRUE, test = "all", ci = c(0.8, 0.9))
      # expect_equal(dim(rez), c(8, 16))
      # rez <- describe_posterior(x, centrality = NULL, dispersion = TRUE, test = NULL, ci_method="quantile")
      # expect_equal(dim(rez), c(4, 4))
    })

    if (require("insight")) {
      m <- insight::download_model("stanreg_merMod_5")
      p <- insight::get_parameters(m, effects = "all")

      test_that("describe_posterior", {
        expect_equal(
          describe_posterior(m, effects = "all")$Median,
          describe_posterior(p)$Median,
          tolerance = 1e-3
        )
      })

      m <- insight::download_model("brms_zi_3")
      p <- insight::get_parameters(m, effects = "all", component = "all")

      test_that("describe_posterior", {
        expect_equal(
          describe_posterior(m, effects = "all", component = "all")$Median,
          describe_posterior(p)$Median,
          tolerance = 1e-3
        )
      })
    }


    test_that("describe_posterior w/ BF+SI", {
      skip_on_cran()

      x <- insight::download_model("stanreg_lm_1")
      set.seed(555)
      rez <- describe_posterior(x, ci_method = "SI", test = "bf")


      # test si
      set.seed(555)
      rez_si <- si(x)
      expect_equal(rez$CI_low, rez_si$CI_low, tolerance = 0.1)
      expect_equal(rez$CI_high, rez_si$CI_high, tolerance = 0.1)

      # test BF
      set.seed(555)
      rez_bf <- bayesfactor_parameters(x)
      expect_equal(rez$BF, rez_bf$BF, tolerance = 0.1)
    })

    # BayesFactor -------------------------------------------------

    if (getRversion() >= "4.0") {
      set.seed(123)
      expect_equal(
        describe_posterior(correlationBF(mtcars$wt, mtcars$mpg, rscale = 0.5)),
        structure(list(
          Parameter = "rho", Median = -0.832958463649399,
          CI = 0.89, CI_low = -0.903528140372971, CI_high = -0.734146316854132,
          pd = 1, ROPE_CI = 0.89, ROPE_low = -0.1, ROPE_high = 0.1,
          ROPE_Percentage = 0, BF = 33555274.5519413, Prior_Distribution = "beta",
          Prior_Location = 2, Prior_Scale = 2
        ), row.names = 1L, class = c(
          "describe_posterior",
          "see_describe_posterior", "data.frame"
        ), ci_method = "hdi"),
        tolerance = 0.1,
        ignore_attr = TRUE
      )

      set.seed(123)
      expect_equal(
        describe_posterior(ttestBF(mtcars$wt, mu = 3), ci = 0.95),
        structure(list(
          Parameter = "Difference", Median = -0.192596120441321,
          CI = 0.95, CI_low = -0.53739385387061, CI_high = 0.159711264781174,
          pd = 0.8615, ROPE_CI = 0.95, ROPE_low = -0.0978457442989697,
          ROPE_high = 0.0978457442989697, ROPE_Percentage = 0.255985267034991,
          BF = 0.386851835160946, Prior_Distribution = "cauchy", Prior_Location = 0,
          Prior_Scale = 0.707106781186548
        ), row.names = 1L, class = c(
          "describe_posterior",
          "see_describe_posterior", "data.frame"
        ), ci_method = "hdi"),
        tolerance = 0.1,
        ignore_attr = TRUE
      )

      set.seed(123)
      expect_equal(
        describe_posterior(contingencyTableBF(
          x = table(mtcars$am, mtcars$cyl),
          sampleType = "poisson"
        ), ci = 0.95),
        structure(list(
          Parameter = c(
            "cell[1,1]", "cell[2,1]", "cell[1,2]",
            "cell[2,2]", "cell[1,3]", "cell[2,3]", "Ratio"
          ), Median = c(
            3.14460516924512,
            7.31770781415545, 3.90882513071182, 3.10298483676201, 10.7291854218268,
            2.28796135536168, NA
          ), CI = c(
            0.95, 0.95, 0.95, 0.95, 0.95, 0.95,
            NA
          ), CI_low = c(
            0.730049832773011, 2.85868301180857, 0.966013626507231,
            0.613493744479196, 5.50968258473677, 0.215624786285077, NA
          ),
          CI_high = c(
            6.88611720053741, 12.3559628274278, 7.9413271603729,
            6.71829769450508, 17.0745832964079, 5.42058517407967, NA
          ),
          pd = c(1, 1, 1, 1, 1, 1, NA), ROPE_CI = c(
            0.95, 0.95, 0.95,
            0.95, 0.95, 0.95, NA
          ), ROPE_low = c(
            -0.1, -0.1, -0.1, -0.1,
            -0.1, -0.1, NA
          ), ROPE_high = c(
            0.1, 0.1, 0.1, 0.1, 0.1, 0.1,
            NA
          ), ROPE_Percentage = c(0, 0, 0, 0, 0, 0, NA), BF = c(
            46.6128745808996,
            46.6128745808996, 46.6128745808996, 46.6128745808996, 46.6128745808996,
            46.6128745808996, NA
          ), Prior_Distribution = c(
            NA, NA, NA,
            NA, NA, NA, "poisson"
          ), Prior_Location = c(
            NA, NA, NA, NA,
            NA, NA, 0
          ), Prior_Scale = c(NA, NA, NA, NA, NA, NA, 1)
        ), row.names = c(
          1L,
          4L, 2L, 5L, 3L, 6L, 7L
        ), class = c(
          "describe_posterior", "see_describe_posterior",
          "data.frame"
        ), ci_method = "hdi"),
        tolerance = 0.1,
        ignore_attr = TRUE
      )

      set.seed(123)
      expect_equal(
        describe_posterior(contingencyTableBF(
          x = table(mtcars$am, mtcars$cyl),
          sampleType = "indepMulti",
          fixedMargin = "cols",
          priorConcentration = 1.6
        ), ci = 0.95),
        structure(list(
          Parameter = c(
            "cell[1,1]", "cell[2,1]", "cell[1,2]",
            "cell[2,2]", "cell[1,3]", "cell[2,3]", "Ratio"
          ), Median = c(
            3.32424349674923,
            7.28516053335046, 4.14229471859295, 3.3391102912759, 10.3656561909252,
            2.59632695760662, NA
          ), CI = c(
            0.95, 0.95, 0.95, 0.95, 0.95, 0.95,
            NA
          ), CI_low = c(
            0.779242126479362, 3.68155765129431, 1.48888237414841,
            0.950552863618845, 6.27921864506856, 0.442202731672178, NA
          ),
          CI_high = c(
            6.45022241402301, 11.5250588748997, 7.60274937010908,
            6.50585663003352, 15.0650800734588, 5.32715733658863, NA
          ),
          pd = c(1, 1, 1, 1, 1, 1, NA), ROPE_CI = c(
            0.95, 0.95, 0.95,
            0.95, 0.95, 0.95, NA
          ), ROPE_low = c(
            -0.1, -0.1, -0.1, -0.1,
            -0.1, -0.1, NA
          ), ROPE_high = c(
            0.1, 0.1, 0.1, 0.1, 0.1, 0.1,
            NA
          ), ROPE_Percentage = c(0, 0, 0, 0, 0, 0, NA), BF = c(
            12.1022066941064,
            12.1022066941064, 12.1022066941064, 12.1022066941064, 12.1022066941064,
            12.1022066941064, NA
          ), Prior_Distribution = c(
            NA, NA, NA,
            NA, NA, NA, "independent multinomial"
          ), Prior_Location = c(
            NA,
            NA, NA, NA, NA, NA, 0
          ), Prior_Scale = c(
            NA, NA, NA, NA, NA,
            NA, 1.6
          )
        ), row.names = c(1L, 4L, 2L, 5L, 3L, 6L, 7L), class = c(
          "describe_posterior",
          "see_describe_posterior", "data.frame"
        ), ci_method = "hdi"),
        tolerance = 0.1,
        ignore_attr = TRUE
      )

      set.seed(123)
      expect_equal(
        describe_posterior(anovaBF(extra ~ group, data = sleep, progress = FALSE), ci = 0.95),
        structure(list(
          Parameter = c(
            "mu", "group-1", "group-2", "sig2",
            "g_group"
          ), Median = c(
            1.53667371296145, -0.571674439385088,
            0.571674439385088, 3.69268743002151, 0.349038661644431
          ), CI = c(
            0.95,
            0.95, 0.95, 0.95, 0.95
          ), CI_low = c(
            0.691696017646264, -1.31604531656452,
            -0.229408603643392, 1.75779899540302, 0.0192738130412634
          ), CI_high = c(
            2.43317955922589,
            0.229408603643392, 1.31604531656452, 6.88471056133351, 5.30402785651874
          ), pd = c(0.99975, 0.927, 0.927, 1, 1), ROPE_CI = c(
            0.95, 0.95,
            0.95, 0.95, 0.95
          ), ROPE_low = c(
            -0.201791972090071, -0.201791972090071,
            -0.201791972090071, -0.201791972090071, -0.201791972090071
          ),
          ROPE_high = c(
            0.201791972090071, 0.201791972090071, 0.201791972090071,
            0.201791972090071, 0.201791972090071
          ), ROPE_Percentage = c(
            0,
            0.162325703762168, 0.162325703762168, 0, 0.346487766377269
          ), BF = c(
            1.26592514964916, 1.26592514964916, 1.26592514964916,
            1.26592514964916, 1.26592514964916
          ), Prior_Distribution = c(
            NA,
            "cauchy", "cauchy", NA, NA
          ), Prior_Location = c(
            NA, 0, 0,
            NA, NA
          ), Prior_Scale = c(NA, 0.5, 0.5, NA, NA)
        ), row.names = c(
          4L,
          2L, 3L, 5L, 1L
        ), class = c(
          "describe_posterior", "see_describe_posterior",
          "data.frame"
        ), ci_method = "hdi"),
        tolerance = 0.1,
        ignore_attr = TRUE
      )
    }
  }
}