test_that("GLMM supports Poisson", {
  
  skip_if_not_installed("glmmTMB")
  
  set.seed(123)
  
  dat <- data.frame(
    id = factor(rep(1:10, each = 5)),
    x = rnorm(50)
  )
  
  dat$y <- rpois(
    50,
    lambda = exp(1 + 0.3 * dat$x)
  )
  
  m <- fit_glmm(
    y ~ x + (1 | id),
    data = dat,
    family = "poisson"
  )
  
  expect_s3_class(m, "biomix_glmm")
  expect_equal(m$family, "poisson")
  expect_false(m$zero_inflated)
})


test_that("GLMM supports negative binomial 1", {
  
  skip_if_not_installed("glmmTMB")
  
  set.seed(123)
  
  dat <- data.frame(
    id = factor(rep(1:20, each = 10)),
    x = rnorm(200)
  )
  
  ## Deliberately overdispersed count data
  mu <- exp(1 + 0.2 * dat$x)
  dat$y <- rpois(
    200,
    lambda = mu * rgamma(200, shape = 2, rate = 2)
  )
  
  m <- fit_glmm(
    y ~ x + (1 | id),
    data = dat,
    family = "nbinom1"
  )
  
  expect_s3_class(m, "biomix_glmm")
  expect_equal(m$family, "nbinom1")
  expect_false(m$zero_inflated)
})


test_that("GLMM supports negative binomial 2", {
  
  skip_if_not_installed("glmmTMB")
  
  set.seed(123)
  
  dat <- data.frame(
    id = factor(rep(1:20, each = 10)),
    x = rnorm(200)
  )
  
  ## Overdispersed count data
  mu <- exp(1 + 0.2 * dat$x)
  dat$y <- rpois(
    200,
    lambda = mu * rgamma(200, shape = 1.5, rate = 1.5)
  )
  
  m <- fit_glmm(
    y ~ x + (1 | id),
    data = dat,
    family = "nbinom2"
  )
  
  expect_s3_class(m, "biomix_glmm")
  expect_equal(m$family, "nbinom2")
  expect_false(m$zero_inflated)
})


test_that("GLMM rejects unsupported family", {
  
  skip_if_not_installed("glmmTMB")
  
  dat <- data.frame(
    y = rpois(20, 5),
    x = rnorm(20)
  )
  
  expect_error(
    fit_glmm(
      y ~ x,
      data = dat,
      family = "unsupported"
    ),
    "arg.*should be one of"
  )
})