Running ‘testthat.R’ [60s/70s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
  > # This file is part of the standard setup for testthat.
  > # It is recommended that you do not modify it.
  > #
  > # Where should you do additional test configuration?
  > # Learn more about the roles of various files in:
  > # * https://r-pkgs.org/tests.html
  > # * https://testthat.r-lib.org/reference/test_package.html#special-files
  > 
  > library(testthat)
  > library(squat)
  
  Attaching package: 'squat'
  
  The following objects are masked from 'package:stats':
  
      dist, hclust, kmeans, smooth
  
  The following objects are masked from 'package:base':
  
      append, scale
  
  > 
  > test_check("squat")
  
  Attaching package: 'purrr'
  
  The following object is masked from 'package:testthat':
  
      is_null
  
  i Computing initial centroids using kmeans++ strategy...
  Information about the data set:
   - Number of observations: 10
   - Number of dimensions: 3
   - Number of points: 101
  
  Information about cluster initialization:
   - Number of clusters: 2
   - Initial seeds for cluster centers:         10         7
  
  Information about the methods used within the algorithm:
   - Warping method: affine
   - Center method: mean
   - Dissimilarity method: l2
   - Optimization method: bobyqa
  
  Information about warping parameter bounds:
   - Warping options:    0.1500   0.1500
  
  Information about convergence criteria:
   - Maximum number of iterations: 100
   - Distance relative tolerance: 0.001
  
  Information about parallelization setup:
   - Number of threads: 1
   - Parallel method: 0
  
  Other information:
   - Use fence to robustify: 0
   - Check total dissimilarity: 1
   - Compute overall center: 0
  
  Running k-centroid algorithm:
   - Iteration #1
     * Size of cluster #0: 6
     * Size of cluster #1: 4
   - Iteration #2
     * Size of cluster #0: 6
     * Size of cluster #1: 4
  
  Active stopping criteria:
   - Memberships did not change.
  i Computing initial centroids using kmeans++ strategy...
  Information about the data set:
   - Number of observations: 10
   - Number of dimensions: 3
   - Number of points: 101
  
  Information about cluster initialization:
   - Number of clusters: 2
   - Initial seeds for cluster centers:         10         7
  
  Information about the methods used within the algorithm:
   - Warping method: affine
   - Center method: mean
   - Dissimilarity method: l2
   - Optimization method: bobyqa
  
  Information about warping parameter bounds:
   - Warping options:    0.1500   0.1500
  
  Information about convergence criteria:
   - Maximum number of iterations: 100
   - Distance relative tolerance: 0.001
  
  Information about parallelization setup:
   - Number of threads: 1
   - Parallel method: 0
  
  Other information:
   - Use fence to robustify: 0
   - Check total dissimilarity: 1
   - Compute overall center: 0
  
  Running k-centroid algorithm:
   - Iteration #1
     * Size of cluster #0: 6
     * Size of cluster #1: 4
   - Iteration #2
     * Size of cluster #0: 6
     * Size of cluster #1: 4
  
  Active stopping criteria:
   - Memberships did not change.
  i Computing initial centroids using kmeans++ strategy...
  Information about the data set:
   - Number of observations: 10
   - Number of dimensions: 3
   - Number of points: 101
  
  Information about cluster initialization:
   - Number of clusters: 2
   - Initial seeds for cluster centers:         10         7
  
  Information about the methods used within the algorithm:
   - Warping method: affine
   - Center method: mean
   - Dissimilarity method: l2
   - Optimization method: bobyqa
  
  Information about warping parameter bounds:
   - Warping options:    0.1500   0.1500
  
  Information about convergence criteria:
   - Maximum number of iterations: 100
   - Distance relative tolerance: 0.001
  
  Information about parallelization setup:
   - Number of threads: 1
   - Parallel method: 0
  
  Other information:
   - Use fence to robustify: 0
   - Check total dissimilarity: 1
   - Compute overall center: 0
  
  Running k-centroid algorithm:
   - Iteration #1
     * Size of cluster #0: 6
     * Size of cluster #1: 4
   - Iteration #2
     * Size of cluster #0: 6
     * Size of cluster #1: 4
  
  Active stopping criteria:
   - Memberships did not change.
  The `original_space` boolean argument is not specified. Defaulting to TRUE.
  The `plane` length-2 integer vector argument is not specified. Defaulting to 1:2.
  The `original_space` boolean argument is not specified. Defaulting to TRUE.
  
   *** caught segfault ***
  address 0x55c300000004, cause 'memory not mapped'
  Fatal error: *** recursive gc invocation
  
  Fatal error: *** recursive gc invocation