test_that("slim handles vector y with missing values", {
  set.seed(1)
  x <- matrix(rnorm(80), nrow = 20, ncol = 4)
  y <- rnorm(20)
  y[c(3, 7)] <- NA_real_

  fit <- suppressWarnings(
    slim(x, y, nlambda = 2, method = "lasso", verbose = FALSE, max.ite = 2000)
  )

  expect_s3_class(fit, "slim")
  expect_equal(nrow(fit$X), 18L)
  expect_equal(nrow(fit$Y), 18L)
  expect_equal(ncol(fit$beta), fit$nlambda)
  expect_false(anyNA(fit$X))
  expect_false(anyNA(fit$Y))

  # Verify warning is emitted
  expect_warning(
    slim(x, y, nlambda = 2, method = "lasso", verbose = FALSE, max.ite = 2000),
    "Removed 2 rows"
  )
})

test_that("predict.slim validates input shape and returns prediction matrix", {
  set.seed(2)
  x <- matrix(rnorm(120), nrow = 30, ncol = 4)
  y <- rnorm(30)

  fit <- slim(x, y, nlambda = 3, method = "lasso", verbose = FALSE, max.ite = 2000)

  bad_newdata <- matrix(rnorm(30), nrow = 10, ncol = 3)
  expect_error(predict.slim(fit, bad_newdata, lambda.idx = 1, Y.pred.idx = 1),
               "incompatible")

  good_pred <- predict.slim(fit, x, lambda.idx = 1:2, Y.pred.idx = 1:2)
  expect_type(good_pred, "list")
  expect_equal(dim(good_pred$Y.pred), c(nrow(x), 2L))
})

test_that("slim rejects non-positive or non-finite lambda values", {
  set.seed(3)
  x <- matrix(rnorm(60), nrow = 15, ncol = 4)
  y <- rnorm(15)

  expect_error(
    slim(x, y, lambda = c(0.2, 0, 0.1), method = "lasso", verbose = FALSE),
    "positive finite"
  )
})

test_that("coef.slim returns a matrix", {
  set.seed(4)
  x <- matrix(rnorm(120), nrow = 30, ncol = 4)
  y <- rnorm(30)

  fit <- slim(x, y, nlambda = 3, method = "lasso", verbose = FALSE, max.ite = 2000)
  cm <- coef.slim(fit, lambda.idx = 1:3, beta.idx = 1:4)
  expect_true(is.matrix(cm))
  expect_equal(nrow(cm), 5L)  # 1 intercept + 4 betas
  expect_equal(ncol(cm), 3L)  # 3 lambdas
})

test_that("slim.cv selects a lambda and returns expected structure", {
  set.seed(5)
  x <- matrix(rnorm(200), nrow = 50, ncol = 4)
  y <- x[, 1] * 2 + rnorm(50) * 0.5

  cv <- slim.cv(x, y, nlambda = 5, method = "lasso", fold = 3, verbose = FALSE)
  expect_s3_class(cv, "slim.cv")
  expect_length(cv$loss.mean, 5L)
  expect_length(cv$loss.sd, 5L)
  expect_true(cv$opt.idx >= 1 && cv$opt.idx <= 5)
  expect_equal(cv$lambda.opt, cv$lambda[cv$opt.idx])
  expect_s3_class(cv$fit, "slim")
})