if (!.Call(`_rxode2_isIntel`)) {
  test_that("test meanProb()", {

    x <- rnorm(10)
    m <- mean(x)
    s <- sd(x)
    v <- var(x)
    mn <- min(x)
    mx <- max(x)
    t1 <- c("0%"=mn, "25%"=m+(s/sqrt(10))*qt(0.25, 9), "50%"=m, "75%"=m+(s/sqrt(10))*qt(0.75, 9), "100%"=mx)
    expect_equal(meanProbs(x), t1)

    t1.100 <- c("0%"=mn, "25%"=m+(s/sqrt(100))*qt(0.25, 99), "50%"=m, "75%"=m+(s/sqrt(100))*qt(0.75, 99), "100%"=mx)
    expect_equal(meanProbs(x, n=100), t1.100)

    t1.p100 <- c("0%"=mn, "25%"=m+s*sqrt(1.0+(1.0/100))*qt(0.25, 99), "50%"=m, "75%"=m+s*sqrt(1.0+(1.0/100))*qt(0.75, 99), "100%"=mx)
    expect_equal(meanProbs(x, n=100, pred=TRUE), t1.p100)

    t2 <- c(c("mean"=m, "var"=v, "sd"=s, "min"=mn, "max"=mx, "n"=10), t1)
    expect_equal(meanProbs(x, onlyProbs=FALSE), t2)
    x2 <- c(x, NA_real_)
    setNames(rep(NA_real_, length(t1)),names(t1))

    expect_equal(meanProbs(x2), setNames(rep(NA_real_, length(t1)),names(t1)))

    expect_equal(meanProbs(x2, onlyProbs=FALSE),
                 setNames(rep(NA_real_, length(t2)),names(t2)))

    expect_equal(meanProbs(x2, na.rm=TRUE), t1)

    expect_equal(meanProbs(x2, onlyProbs=FALSE, na.rm=TRUE), t2)

    mod <- rxode2({
      d/dt(intestine) <- -a * intestine
      d/dt(blood) <- a * intestine - b * blood
    })

    et <- eventTable()
    et$add.sampling(seq(0, 10, length.out = 50))
    et$add.dosing(
      dose = 2/24, rate = 2, strt.time = 0,
      nbr.doses = 10, dosing.interval = 1
    )

    p <- data.frame(a = 6, b = seq(0.4, 0.9, length.out = 4))

    pk1 <- suppressWarnings(rxSolve(mod, p, et, cores = 1))

    ci1 <- confint(pk1, "blood", mean=TRUE)

    # use dplyr
    ci2 <- pk1 |>
      dplyr::group_by(time) |>
      dplyr::reframe(eff=meanProbs(blood, c(0.025, 0.5, 0.975), na.rm=TRUE, names=FALSE),
                     p1=c(0.025, 0.5, 0.975), Percentile=c("2.5%", "50%", "97.5%")) |>
      dplyr::arrange(time, p1)

    expect_equal(ci1$eff, ci2$eff)

    ci1 <- confint(pk1, "blood", mean=TRUE, pred=TRUE)

    ci2 <- pk1 |>
      dplyr::group_by(time) |>
      dplyr::reframe(eff=meanProbs(blood, c(0.025, 0.5, 0.975), na.rm=TRUE, names=FALSE, pred=TRUE),
                     p1=c(0.025, 0.5, 0.975), Percentile=c("2.5%", "50%", "97.5%")) |>
      dplyr::arrange(time, p1)

    expect_equal(ci1$eff, ci2$eff)

    ci1 <- confint(pk1, "blood", mean=TRUE, n=100)

    ci2 <- pk1 |>
      dplyr::group_by(time) |>
      dplyr::reframe(eff=meanProbs(blood, c(0.025, 0.5, 0.975), na.rm=TRUE, names=FALSE,
                                   n=100),
                     p1=c(0.025, 0.5, 0.975), Percentile=c("2.5%", "50%", "97.5%")) |>
      dplyr::arrange(time, p1)

    expect_equal(ci1$eff, ci2$eff)


    ci1 <- confint(pk1, "blood", mean=TRUE, pred=TRUE, n=100)

    ci2 <- pk1 |>
      dplyr::group_by(time) |>
      dplyr::reframe(eff=meanProbs(blood, c(0.025, 0.5, 0.975), na.rm=TRUE, names=FALSE,
                                   pred=TRUE, n=100),
                     p1=c(0.025, 0.5, 0.975), Percentile=c("2.5%", "50%", "97.5%")) |>
      dplyr::arrange(time, p1)

    expect_equal(ci1$eff, ci2$eff)


    ci1 <- confint(pk1, "blood", mean=TRUE, useT=FALSE)

    # use dplyr
    ci2 <- pk1 |>
      dplyr::group_by(time) |>
      dplyr::reframe(eff=meanProbs(blood, c(0.025, 0.5, 0.975), na.rm=TRUE, names=FALSE, useT=FALSE),
                     p1=c(0.025, 0.5, 0.975), Percentile=c("2.5%", "50%", "97.5%")) |>
      dplyr::arrange(time, p1)

    expect_equal(ci1$eff, ci2$eff)

    ci1 <- confint(pk1, "blood", mean=TRUE, useT=FALSE, pred=TRUE)

    # use dplyr
    ci2 <- pk1 |>
      dplyr::group_by(time) |>
      dplyr::reframe(eff=meanProbs(blood, c(0.025, 0.5, 0.975), na.rm=TRUE, names=FALSE, useT=FALSE,
                                   pred=TRUE),
                     p1=c(0.025, 0.5, 0.975), Percentile=c("2.5%", "50%", "97.5%")) |>
      dplyr::arrange(time, p1)

    expect_equal(ci1$eff, ci2$eff)

  })
}