tests/testthat/test_distributions.R

In IGPFrailty: Inverse Gaussian Process Degradation Models with Frailty

test_that("Inverse Gaussian distribution functions work accurately", {
  # Density
  d <- d_ig(c(1, 2), mu = 1.5, lambda = 3)
  expect_equal(length(d), 2)
  expect_true(all(d > 0))
  
  # Log density
  d_log <- d_ig(c(1, 2), mu = 1.5, lambda = 3, log = TRUE)
  expect_equal(log(d), d_log)
  
  # CDF
  p <- p_ig(c(1, 2), mu = 1.5, lambda = 3)
  expect_true(all(p >= 0 & p <= 1))
  expect_true(p[1] < p[2])
  
  # Quantiles
  q <- q_ig(p, mu = 1.5, lambda = 3)
  expect_equal(q, c(1, 2), tolerance = 1e-4)
  
  # Random generation
  set.seed(42)
  r <- r_ig(100, mu = 2, lambda = 5)
  expect_equal(length(r), 100)
  expect_true(all(r > 0))
  expect_equal(mean(r), 2, tolerance = 0.5)
  
  # Edge cases
  expect_equal(d_ig(-1, mu = 1, lambda = 1), 0)
  expect_equal(p_ig(-1, mu = 1, lambda = 1), 0)
  expect_equal(q_ig(0, mu = 1, lambda = 1), 0)
  expect_equal(q_ig(1, mu = 1, lambda = 1), Inf)
})