Running ‘testthat.R’ [22s/36s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
  > library(testthat)
  > library(scutr)
  > 
  > test_check("scutr")
  [ FAIL 4 | WARN 1 | SKIP 0 | PASS 164 ]
  
  ══ Failed tests ════════════════════════════════════════════════════════════════
  ── Error ('test-oversampling.R:6:5'): (code run outside of `test_that()`) ──────
  Error in `get.knnx(data, query, k, algorithm)`: DLL requires the use of native symbols
  Backtrace:
      ▆
   1. └─scutr (local) func(wine, w_cls, "type", w_m) at test-oversampling.R:6:4
   2.   └─smotefamily::SMOTE(data[, -col_ind], data[, col_ind], dup_size = dup_size)
   3.     └─smotefamily::knearest(P_set, P_set, K)
   4.       └─FNN::knnx.index(D, P, k = (n_clust + 1), algo = "kd_tree")
   5.         └─FNN::get.knnx(data, query, k, algorithm)
  ── Error ('test-scut.R:21:5'): oversample_smote undersample_hclust have equal class distribution ──
  Error in `get.knnx(data, query, k, algorithm)`: DLL requires the use of native symbols
  Backtrace:
      ▆
   1. └─scutr::SCUT(wine, "type", oversample = get(osamp), undersample = get(usamp)) at test-scut.R:21:4
   2.   ├─base::do.call(...)
   3.   └─scutr (local) `<fn>`(data = `<df[,14]>`, cls_col = "type", cls = 3L, m = 59)
   4.     └─smotefamily::SMOTE(data[, -col_ind], data[, col_ind], dup_size = dup_size)
   5.       └─smotefamily::knearest(P_set, P_set, K)
   6.         └─FNN::knnx.index(D, P, k = (n_clust + 1), algo = "kd_tree")
   7.           └─FNN::get.knnx(data, query, k, algorithm)
  ── Failure ('test-scut.R:44:15'): oversample_smote undersample_hclust (parallel) have equal class distribution ──
  strictly_balanced(scutted$type) is not TRUE
  
  `actual`:   FALSE
  `expected`: TRUE 
  ── Error ('test-scut.R:62:1'): (code run outside of `test_that()`) ─────────────
  Error in `get.knnx(data, query, k, algorithm)`: DLL requires the use of native symbols
  Backtrace:
      ▆
   1. └─scutr::SCUT(wine, "type", undersample = foo) at test-scut.R:62:0
   2.   ├─base::do.call(...)
   3.   └─scutr (local) `<fn>`(data = `<df[,14]>`, cls_col = "type", cls = 3L, m = 59)
   4.     └─smotefamily::SMOTE(data[, -col_ind], data[, col_ind], dup_size = dup_size)
   5.       └─smotefamily::knearest(P_set, P_set, K)
   6.         └─FNN::knnx.index(D, P, k = (n_clust + 1), algo = "kd_tree")
   7.           └─FNN::get.knnx(data, query, k, algorithm)
  
  [ FAIL 4 | WARN 1 | SKIP 0 | PASS 164 ]
  Error: Test failures
  Execution halted