test_that("illegal initializations are rejected", {
  expect_silent(NormalDistribution$new(0.0, 1.0))
  expect_error(NormalDistribution$new("0", 1.0), class = "mu_not_numeric")
  expect_error(NormalDistribution$new(0.0, "1"), class = "sigma_not_numeric")
})

test_that("distribution name is correct", {
  sn <- NormalDistribution$new(0.0, 1.0)
  expect_identical(sn$distribution(), "N(0,1)")
  n <- NormalDistribution$new(42.0, 1.0)
  expect_identical(n$distribution(), "N(42,1)")
})

test_that("quantile function checks inputs", {
  x <- NormalDistribution$new(0.0, 1.0)
  probs <- c(0.1, 0.2, 0.5)
  expect_silent(x$quantile(probs))
  probs <- c(0.1, NA, 0.5)
  expect_error(x$quantile(probs), class = "probs_not_defined")
  probs <- c(0.1, "boo", 0.5)
  expect_error(x$quantile(probs), class = "probs_not_numeric")
  probs <- c(0.1, 0.4, 1.5)
  expect_error(x$quantile(probs), class = "probs_out_of_range")
  probs <- c(0.1, 0.2, 0.5)
  expect_length(x$quantile(probs), 3L)
})

test_that("pe, mean, sd and quantiles are returned correctly", {
  sn <- NormalDistribution$new(0.0, 1.0)
  expect_identical(sn$mean(), 0.0)
  expect_identical(sn$SD(), 1.0)
  probs <- c(0.025, 0.975)
  q <- sn$quantile(probs)
  expect_identical(round(q[[1L]], 2L), -1.96)
  expect_identical(round(q[[2L]], 2L),  1.96)
})

test_that("random sampling is from a Normal distribution", {
  mu <- 0.0
  sigma <- 1.0
  sn <- NormalDistribution$new(mu, sigma)
  # mean
  sn$sample(TRUE)
  expect_identical(sn$r(), 0.0)
  # sample
  n <- 1000L
  samp <- vapply(seq_len(n), FUN.VALUE = 1.0, FUN = function(i) {
    sn$sample()
    rv <- sn$r()
    return(rv)
  })
  expect_length(samp, n)
  # check sample mean and sd are within 99.9% CI based on CLT; this is exact
  # for a normal, and is expected to fail for 0.1% of tests; skip for CRAN
  skip_on_cran()
  ht <- ks.test(samp, rnorm(n, mean = mu, sd = sigma))
  expect_gt(ht$p.value, 0.001)
})