# D <- 3
# N <- 10
# dataset <- huge::huge.generator(n = N, d = D, verbose = FALSE)
# res <- cor_w_se(dataset$data)
#
# test_that("make_weights_works", {
# weights <- make_weights(res$SE_S)
# expect_equal(dim(weights), dim(res$SE_S))
# })
#
# test_that("make_weights_max_works", {
# weights <- make_weights(res$SE_S, max_med_ratio = 1.5)
# expect_equal(max(weights)/stats::median(weights), 1.5)
# })
#
# test_that("make_weights_na_works", {
# SE <- res$SE_S
# SE[1, 2]<- NA
#
# weights <- make_weights(SE)
# expect_equal(sum(is.na(weights)), 0)
#
# weights <- make_weights(SE, max_med_ratio = 1.5)
# expect_equal(sum(is.na(weights)), 0)
# expect_equal(max(weights)/stats::median(weights[weights > 0]), 1.5)
# })
#
# test_that("inspre_worker_works", {
# X <- res$S_hat
# SE <- res$SE_S
# X[1, 2] <- NA
# SE[1, 2] <- NA
# W <- make_weights(SE)
# res <- inspre_worker(X, W, its = 10)
# expect_equal(sum(is.na(res$V)), 0)
# expect_equal(dim(res$V), dim(X))
# expect_equal(dim(res$U), dim(X))
# })
#
# test_that("inspre_works", {
# X <- res$S_hat
# SE <- res$SE_S
# X[1, 2] <- NA
# SE[1, 2] <- NA
# W <- make_weights(SE)
# res <- inspre(X, W, its = 10, nlambda = 10, verbose = 0)
# expect_equal(sum(is.na(res$V)), 0)
# expect_equal(dim(res$V), c(dim(X), 10))
# expect_equal(length(res$lambda), 10)
# expect_equal(length(res$rho), 10)
# expect_equal(length(res$L), 10)
# })
#
# test_that("inspre_cv_works", {
# X <- res$S_hat
# SE <- res$SE_S
# X[1, 2] <- NA
# SE[1, 2] <- NA
# W <- make_weights(SE)
# res <- inspre(X, W, its = 10, nlambda = 10, verbose = 0, cv_folds = 3)
# expect_equal(length(res$D_hat), 10)
# expect_equal(length(res$D_hat_se), 10)
# })
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