Running ‘testthat.R’ [7s/13s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
  > library(testthat)
  > library(hdImpute)
  > 
  > test_check("hdImpute")
  ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
  ✔ dplyr     1.1.0     ✔ readr     2.1.4
  ✔ forcats   1.0.0     ✔ stringr   1.5.0
  ✔ ggplot2   3.4.1     ✔ tibble    3.2.0
  ✔ lubridate 1.9.2     ✔ tidyr     1.3.0
  ✔ purrr     1.0.1     
  ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
  ✖ readr::edition_get()   masks testthat::edition_get()
  ✖ dplyr::filter()        masks stats::filter()
  ✖ purrr::is_null()       masks testthat::is_null()
  ✖ dplyr::lag()           masks stats::lag()
  ✖ readr::local_edition() masks testthat::local_edition()
  ✖ dplyr::matches()       masks tidyr::matches(), testthat::matches()
  ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
  v Correlation matrix built
  v Correlation matrix built
  v Correlation matrix built
  v Correlation matrix built
  v Flattened and ranked
  v Correlation matrix built
  v Flattened and ranked
  v Correlation matrix built
  v Flattened and ranked
  
  -- Initializing hdImpute process. ----------------------------------------------
  
  -- Initializing hdImpute process. ----------------------------------------------
  
  -- Initializing hdImpute process. ----------------------------------------------
  
  -- Initializing hdImpute process. ----------------------------------------------
  
  -- Initializing hdImpute process. ----------------------------------------------
  v Correlation matrix built
  v Flattened and ranked
  
  Missing value imputation by random forests
  
    Variables to impute:		X1
    Variables used to impute:	X1
  iter 1:	.
  
  Missing value imputation by random forests
  
    Variables to impute:		X3, X2
    Variables used to impute:	X3, X2
  iter 1:	.
  -- Initializing hdImpute process. ----------------------------------------------
  
  -- Initializing hdImpute process. ----------------------------------------------
  
  -- Initializing hdImpute process. ----------------------------------------------
  
  -- Initializing hdImpute process. ----------------------------------------------
  
  -- Initializing hdImpute process. ----------------------------------------------
  v Correlation matrix built
  v Flattened and ranked
  
  -- Initializing hdImpute process. ----------------------------------------------
  v Correlation matrix built
  v Flattened and ranked
  
  -- Initializing hdImpute process. ----------------------------------------------
  v Correlation matrix built
  v Flattened and ranked
  
  -- Initializing hdImpute process. ----------------------------------------------
  v Correlation matrix built
  v Flattened and ranked
  
  Missing value imputation by random forests
  
    Variables to impute:		X1
    Variables used to impute:	X1
  iter 1:	.
  
  Missing value imputation by random forests
  
    Variables to impute:		X3, X2
    Variables used to impute:	X3, X2
  iter 1:	.
  -- Initializing hdImpute process. ----------------------------------------------
  v Correlation matrix built
  v Flattened and ranked
  
  -- Initializing hdImpute process. ----------------------------------------------
  v Correlation matrix built
  v Flattened and ranked
  v Correlation matrix built
  v Flattened and ranked
  v Correlation matrix built
  v Flattened and ranked
  
  Missing value imputation by random forests
  
    Variables to impute:		X1
    Variables used to impute:	X1
  iter 1:	.
  
  Missing value imputation by random forests
  
    Variables to impute:		X3, X2
    Variables used to impute:	X3, X2
  iter 1:	.Timing stopped at: 0.033 0.002 0.154
  v Correlation matrix built
  v Flattened and ranked
  v Correlation matrix built
  v Flattened and ranked
  v Correlation matrix built
  v Flattened and ranked
  
  Missing value imputation by random forests
  
    Variables to impute:		X1
    Variables used to impute:	X1
  iter 1:	.
  
  Missing value imputation by random forests
  
    Variables to impute:		X3, X2
    Variables used to impute:	X3, X2
  iter 1:	.Timing stopped at: 0.038 0.008 0.213
  v Correlation matrix built
  v Flattened and ranked
  [ FAIL 4 | WARN 0 | SKIP 0 | PASS 39 ]
  
  ══ Failed tests ════════════════════════════════════════════════════════════════
  ── Error ('test_hdImpute.R:32:3'): testing output data class ───────────────────
  Error in `get.knnx(data, query, k, algorithm)`: DLL requires the use of native symbols
  Backtrace:
      ▆
   1. └─hdImpute::hdImpute(data = data, batch = 2) at test_hdImpute.R:32:2
   2.   ├─base::suppressWarnings(...)
   3.   │ └─base::withCallingHandlers(...)
   4.   └─base::lapply(...)
   5.     └─missRanger (local) FUN(X[[i]], ...)
   6.       └─missRanger::pmm(...)
   7.         └─FNN::knnx.index(xtrain, xtest, k)
   8.           └─FNN::get.knnx(data, query, k, algorithm)
  ── Error ('test_hdImpute.R:54:3'): testing visible printing ────────────────────
  Error in `get.knnx(data, query, k, algorithm)`: DLL requires the use of native symbols
  Backtrace:
      ▆
   1. └─hdImpute::hdImpute(data = data, batch = 2) at test_hdImpute.R:54:2
   2.   ├─base::suppressWarnings(...)
   3.   │ └─base::withCallingHandlers(...)
   4.   └─base::lapply(...)
   5.     └─missRanger (local) FUN(X[[i]], ...)
   6.       └─missRanger::pmm(...)
   7.         └─FNN::knnx.index(xtrain, xtest, k)
   8.           └─FNN::get.knnx(data, query, k, algorithm)
  ── Error ('test_impute_batches.R:40:3'): testing output data class ─────────────
  Error in `get.knnx(data, query, k, algorithm)`: DLL requires the use of native symbols
  Backtrace:
      ▆
   1. └─hdImpute::impute_batches(data = data, features = f, batch = 2) at test_impute_batches.R:40:2
   2.   ├─base::system.time(...)
   3.   ├─base::suppressWarnings(...)
   4.   │ └─base::withCallingHandlers(...)
   5.   └─base::lapply(...)
   6.     └─missRanger (local) FUN(X[[i]], ...)
   7.       └─missRanger::pmm(...)
   8.         └─FNN::knnx.index(xtrain, xtest, k)
   9.           └─FNN::get.knnx(data, query, k, algorithm)
  ── Error ('test_impute_batches.R:68:3'): testing visible printing ──────────────
  Error in `get.knnx(data, query, k, algorithm)`: DLL requires the use of native symbols
  Backtrace:
      ▆
   1. └─hdImpute::impute_batches(data = data, features = f, batch = 2) at test_impute_batches.R:68:2
   2.   ├─base::system.time(...)
   3.   ├─base::suppressWarnings(...)
   4.   │ └─base::withCallingHandlers(...)
   5.   └─base::lapply(...)
   6.     └─missRanger (local) FUN(X[[i]], ...)
   7.       └─missRanger::pmm(...)
   8.         └─FNN::knnx.index(xtrain, xtest, k)
   9.           └─FNN::get.knnx(data, query, k, algorithm)
  
  [ FAIL 4 | WARN 0 | SKIP 0 | PASS 39 ]
  Error: Test failures
  Execution halted