R Under development (unstable) (2025-03-01 r87860 ucrt) -- "Unsuffered Consequences"
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> # 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/testing-design.html#sec-tests-files-overview
> # * https://testthat.r-lib.org/articles/special-files.html
> 
> library(testthat)
> library(samesies)
> 
> test_check("samesies")
i Skipping 'order' method because levels are not explicitly ordered. Set ordered = TRUE to compute the order method.
v Computed exact scores for "fruits1_fruits2" [mean: 1]
v Computed exact scores for "fruits1_fruits3" [mean: 0.333]
v Computed exact scores for "fruits2_fruits3" [mean: 0.333]
i Skipping 'order' method because levels are not explicitly ordered. Set ordered = TRUE to compute the order method.
v Computed exact scores for "fruits1_fruits2" [mean: 1]
<samesies::similar_factor>
 @ scores    :List of 1
 .. $ exact:List of 1
 ..  ..$ fruits1_fruits2: Named num [1:3] 1 1 1
 ..  .. ..- attr(*, "names")= chr [1:3] "apple" "orange" "unknown"
 @ summary   :List of 1
 .. $ exact:List of 1
 ..  ..$ fruits1_fruits2:List of 8
 ..  .. ..$ mean  : num 1
 ..  .. ..$ median: num 1
 ..  .. ..$ sd    : num 0
 ..  .. ..$ min   : num 1
 ..  .. ..$ max   : num 1
 ..  .. ..$ q1    : Named num 1
 ..  .. .. ..- attr(*, "names")= chr "25%"
 ..  .. ..$ q3    : Named num 1
 ..  .. .. ..- attr(*, "names")= chr "75%"
 ..  .. ..$ iqr   : num 0
 @ methods   : chr "exact"
 @ list_names: chr [1:2] "fruits1" "fruits2"
 @ digits    : num 3
 @ levels    : chr [1:3] "apple" "orange" "banana"
i Skipping 'order' method because levels are not explicitly ordered. Set ordered = TRUE to compute the order method.
v Computed exact scores for "fruits1_fruits2" [mean: 1]
v Computed exact scores for "fruits1_fruits2" [mean: 1]
v Computed order scores for "fruits1_fruits2" [mean: 1]
i Skipping 'order' method because levels are not explicitly ordered. Set ordered = TRUE to compute the order method.
v Computed exact scores for "fruits" in "nested_cats1"-"nested_cats2" [mean: 0.667]
v Computed exact scores for "colors" in "nested_cats1"-"nested_cats2" [mean: 0.667]
i Skipping 'order' method because levels are not explicitly ordered. Set ordered = TRUE to compute the order method.
v Computed exact scores for "fruits1_fruits2" [mean: 1]
i Skipping 'order' method because levels are not explicitly ordered. Set ordered = TRUE to compute the order method.
v Computed exact scores for "fruits1_fruits2" [mean: 1]
i Using auto-calculated max_diff: 0.45
v Computed exact scores for "nums1_nums2" [mean: 0.333]
v Computed exact scores for "nums1_nums3" [mean: 0.333]
v Computed exact scores for "nums2_nums3" [mean: 0.333]
v Computed pct_diff scores for "nums1_nums2" [mean: 0.958]
v Computed pct_diff scores for "nums1_nums3" [mean: 0.955]
v Computed pct_diff scores for "nums2_nums3" [mean: 0.915]
v Computed normalized scores for "nums1_nums2" [mean: 0.926]
v Computed normalized scores for "nums1_nums3" [mean: 0.926]
v Computed normalized scores for "nums2_nums3" [mean: 0.852]
v Computed fuzzy scores for "nums1_nums2" [mean: 1]
v Computed fuzzy scores for "nums1_nums3" [mean: 1]
v Computed fuzzy scores for "nums2_nums3" [mean: 0.958]
i Using auto-calculated max_diff: 0.4
v Computed exact scores for "nums1_nums2" [mean: 0.333]
v Computed pct_diff scores for "nums1_nums2" [mean: 0.958]
v Computed normalized scores for "nums1_nums2" [mean: 0.917]
v Computed fuzzy scores for "nums1_nums2" [mean: 1]
<samesies::similar_number>
 @ scores    :List of 4
 .. $ exact     :List of 1
 ..  ..$ nums1_nums2: num [1:3] 0 1 0
 .. $ pct_diff  :List of 1
 ..  ..$ nums1_nums2: num [1:3] 0.95 1 0.923
 .. $ normalized:List of 1
 ..  ..$ nums1_nums2: num [1:3] 0.875 1 0.875
 .. $ fuzzy     :List of 1
 ..  ..$ nums1_nums2: num [1:3] 1 1 1
 @ summary   :List of 4
 .. $ exact     :List of 1
 ..  ..$ nums1_nums2:List of 8
 ..  .. ..$ mean  : num 0.333
 ..  .. ..$ median: num 0
 ..  .. ..$ sd    : num 0.577
 ..  .. ..$ min   : num 0
 ..  .. ..$ max   : num 1
 ..  .. ..$ q1    : Named num 0
 ..  .. .. ..- attr(*, "names")= chr "25%"
 ..  .. ..$ q3    : Named num 0.5
 ..  .. .. ..- attr(*, "names")= chr "75%"
 ..  .. ..$ iqr   : num 0.5
 .. $ pct_diff  :List of 1
 ..  ..$ nums1_nums2:List of 8
 ..  .. ..$ mean  : num 0.958
 ..  .. ..$ median: num 0.95
 ..  .. ..$ sd    : num 0.039
 ..  .. ..$ min   : num 0.923
 ..  .. ..$ max   : num 1
 ..  .. ..$ q1    : Named num 0.937
 ..  .. .. ..- attr(*, "names")= chr "25%"
 ..  .. ..$ q3    : Named num 0.975
 ..  .. .. ..- attr(*, "names")= chr "75%"
 ..  .. ..$ iqr   : num 0.0385
 .. $ normalized:List of 1
 ..  ..$ nums1_nums2:List of 8
 ..  .. ..$ mean  : num 0.917
 ..  .. ..$ median: num 0.875
 ..  .. ..$ sd    : num 0.0722
 ..  .. ..$ min   : num 0.875
 ..  .. ..$ max   : num 1
 ..  .. ..$ q1    : Named num 0.875
 ..  .. .. ..- attr(*, "names")= chr "25%"
 ..  .. ..$ q3    : Named num 0.938
 ..  .. .. ..- attr(*, "names")= chr "75%"
 ..  .. ..$ iqr   : num 0.0625
 .. $ fuzzy     :List of 1
 ..  ..$ nums1_nums2:List of 8
 ..  .. ..$ mean  : num 1
 ..  .. ..$ median: num 1
 ..  .. ..$ sd    : num 7.85e-17
 ..  .. ..$ min   : num 1
 ..  .. ..$ max   : num 1
 ..  .. ..$ q1    : Named num 1
 ..  .. .. ..- attr(*, "names")= chr "25%"
 ..  .. ..$ q3    : Named num 1
 ..  .. .. ..- attr(*, "names")= chr "75%"
 ..  .. ..$ iqr   : num 0
 @ methods   : chr [1:4] "exact" "pct_diff" "normalized" "fuzzy"
 @ list_names: chr [1:2] "nums1" "nums2"
 @ digits    : num 3
 @ raw_values:List of 2
 .. $ : num [1:3] 0.95 1 0.6
 .. $ : num [1:3] 1 1 0.65
i Using auto-calculated max_diff: 0.4
v Computed exact scores for "nums1_nums2" [mean: 0.333]
v Computed pct_diff scores for "nums1_nums2" [mean: 0.958]
v Computed normalized scores for "nums1_nums2" [mean: 0.917]
v Computed fuzzy scores for "nums1_nums2" [mean: 1]
i Using auto-calculated max_diff: 0.4
v Computed normalized scores for "nums1_nums2" [mean: 0.917]
v Computed fuzzy scores for "nums1_nums2" [mean: 1]
v Computed pct_diff scores for "nums1_nums2" [mean: 0.958]
i Using auto-calculated max_diff for "weights": 2.5
i Using auto-calculated max_diff for "heights": 20
v Computed exact scores for "weights_nested_nums1_nested_nums2" [mean: 0]
v Computed pct_diff scores for "weights_nested_nums1_nested_nums2" [mean: 0.975]
v Computed normalized scores for "weights_nested_nums1_nested_nums2" [mean: 0.98]
v Computed fuzzy scores for "weights_nested_nums1_nested_nums2" [mean: 1]
v Computed exact scores for "heights_nested_nums1_nested_nums2" [mean: 0]
v Computed pct_diff scores for "heights_nested_nums1_nested_nums2" [mean: 0.988]
v Computed normalized scores for "heights_nested_nums1_nested_nums2" [mean: 0.9]
v Computed fuzzy scores for "heights_nested_nums1_nested_nums2" [mean: 1]
i Using auto-calculated max_diff: 0.45
v Computed exact scores for "nums1_nums2" [mean: 0.333]
v Computed exact scores for "nums1_nums3" [mean: 0.333]
v Computed exact scores for "nums2_nums3" [mean: 0.333]
v Computed pct_diff scores for "nums1_nums2" [mean: 0.958]
v Computed pct_diff scores for "nums1_nums3" [mean: 0.955]
v Computed pct_diff scores for "nums2_nums3" [mean: 0.915]
v Computed normalized scores for "nums1_nums2" [mean: 0.926]
v Computed normalized scores for "nums1_nums3" [mean: 0.926]
v Computed normalized scores for "nums2_nums3" [mean: 0.852]
v Computed fuzzy scores for "nums1_nums2" [mean: 1]
v Computed fuzzy scores for "nums1_nums3" [mean: 1]
v Computed fuzzy scores for "nums2_nums3" [mean: 0.958]
v Computed jw scores for "fruits1_fruits2" [mean: 0.984]
v Computed jw scores for "fruits1_fruits3" [mean: 0.952]
v Computed jw scores for "fruits2_fruits3" [mean: 0.97]
v Computed lv scores for "fruits1_fruits2" [mean: 0.867]
v Computed lv scores for "fruits1_fruits3" [mean: 0.794]
v Computed lv scores for "fruits2_fruits3" [mean: 0.849]
v Computed osa scores for "fruits1_fruits2" [mean: 0.933]
v Computed lv scores for "fruits1_fruits2" [mean: 0.867]
v Computed dl scores for "fruits1_fruits2" [mean: 0.933]
v Computed hamming scores for "fruits1_fruits2" [mean: 0.867]
v Computed lcs scores for "fruits1_fruits2" [mean: 0.867]
v Computed qgram scores for "fruits1_fruits2" [mean: 1]
v Computed cosine scores for "fruits1_fruits2" [mean: 1]
v Computed jaccard scores for "fruits1_fruits2" [mean: 1]
v Computed jw scores for "fruits1_fruits2" [mean: 0.984]
v Computed soundex scores for "fruits1_fruits2" [mean: 1]
v Computed jw scores for "fruits1_fruits2" [mean: 0.984]
v Computed lv scores for "fruits1_fruits2" [mean: 0.867]
<samesies::similar_text>
 @ scores    :List of 2
 .. $ jw:List of 1
 ..  ..$ fruits1_fruits2: Named num [1:3] 0.953 1 1
 ..  .. ..- attr(*, "names")= chr [1:3] "apple" "banana" "orange"
 .. $ lv:List of 1
 ..  ..$ fruits1_fruits2: Named num [1:3] 0.6 1 1
 ..  .. ..- attr(*, "names")= chr [1:3] "apple" "banana" "orange"
 @ summary   :List of 2
 .. $ jw:List of 1
 ..  ..$ fruits1_fruits2:List of 8
 ..  .. ..$ mean  : num 0.984
 ..  .. ..$ median: num 1
 ..  .. ..$ sd    : num 0.0269
 ..  .. ..$ min   : num 0.953
 ..  .. ..$ max   : num 1
 ..  .. ..$ q1    : Named num 0.977
 ..  .. .. ..- attr(*, "names")= chr "25%"
 ..  .. ..$ q3    : Named num 1
 ..  .. .. ..- attr(*, "names")= chr "75%"
 ..  .. ..$ iqr   : num 0.0233
 .. $ lv:List of 1
 ..  ..$ fruits1_fruits2:List of 8
 ..  .. ..$ mean  : num 0.867
 ..  .. ..$ median: num 1
 ..  .. ..$ sd    : num 0.231
 ..  .. ..$ min   : num 0.6
 ..  .. ..$ max   : num 1
 ..  .. ..$ q1    : Named num 0.8
 ..  .. .. ..- attr(*, "names")= chr "25%"
 ..  .. ..$ q3    : Named num 1
 ..  .. .. ..- attr(*, "names")= chr "75%"
 ..  .. ..$ iqr   : num 0.2
 @ methods   : chr [1:2] "jw" "lv"
 @ list_names: chr [1:2] "fruits1" "fruits2"
 @ digits    : num 3
v Computed jw scores for "fruits1_fruits2" [mean: 0.984]
v Computed lv scores for "fruits1_fruits2" [mean: 0.867]
v Computed jw scores for "nested_fruits1_nested_fruits2" [mean: 0.972]
v Computed jw scores for "nested_fruits1_nested_fruits2" [mean: 0.972]
v Computed jw scores for "nested_fruits1_nested_fruits3" [mean: 0.946]
v Computed jw scores for "nested_fruits2_nested_fruits3" [mean: 0.902]
v Computed lv scores for "nested_fruits1_nested_fruits2" [mean: 0.909]
v Computed lv scores for "nested_fruits1_nested_fruits3" [mean: 0.783]
v Computed lv scores for "nested_fruits2_nested_fruits3" [mean: 0.702]
v Computed jw scores for "fruits1_fruits2" [mean: 0.984]
v Computed jw scores for "fruits1_fruits3" [mean: 0.952]
v Computed jw scores for "fruits2_fruits3" [mean: 0.97]
v Computed lv scores for "fruits1_fruits2" [mean: 0.867]
v Computed lv scores for "fruits1_fruits3" [mean: 0.794]
v Computed lv scores for "fruits2_fruits3" [mean: 0.849]
v Computed jw scores for "fruits1_fruits2" [mean: 0.984]
v Computed jw scores for "fruits1_fruits3" [mean: 0.952]
v Computed jw scores for "fruits2_fruits3" [mean: 0.97]
v Computed lv scores for "fruits1_fruits2" [mean: 0.867]
v Computed lv scores for "fruits1_fruits3" [mean: 0.794]
v Computed lv scores for "fruits2_fruits3" [mean: 0.849]
v Computed jw scores for "fruits1_fruits2" [mean: 0.984]
v Computed jw scores for "fruits1_fruits3" [mean: 0.952]
v Computed jw scores for "fruits2_fruits3" [mean: 0.97]
v Computed lv scores for "fruits1_fruits2" [mean: 0.867]
v Computed lv scores for "fruits1_fruits3" [mean: 0.794]
v Computed lv scores for "fruits2_fruits3" [mean: 0.849]
i Skipping 'order' method because levels are not explicitly ordered. Set ordered = TRUE to compute the order method.
v Computed exact scores for "fruits1_fruits2" [mean: 1]
v Computed exact scores for "fruits1_fruits3" [mean: 0.333]
v Computed exact scores for "fruits2_fruits3" [mean: 0.333]
i Using auto-calculated max_diff: 0.45
v Computed exact scores for "nums1_nums2" [mean: 0.333]
v Computed exact scores for "nums1_nums3" [mean: 0.333]
v Computed exact scores for "nums2_nums3" [mean: 0.333]
v Computed pct_diff scores for "nums1_nums2" [mean: 0.958]
v Computed pct_diff scores for "nums1_nums3" [mean: 0.955]
v Computed pct_diff scores for "nums2_nums3" [mean: 0.915]
v Computed normalized scores for "nums1_nums2" [mean: 0.926]
v Computed normalized scores for "nums1_nums3" [mean: 0.926]
v Computed normalized scores for "nums2_nums3" [mean: 0.852]
v Computed fuzzy scores for "nums1_nums2" [mean: 1]
v Computed fuzzy scores for "nums1_nums3" [mean: 1]
v Computed fuzzy scores for "nums2_nums3" [mean: 0.958]
v Computed jw scores for "fruits1_fruits2" [mean: 0.984]
v Computed jw scores for "fruits1_fruits3" [mean: 0.952]
v Computed jw scores for "fruits2_fruits3" [mean: 0.97]
v Computed lv scores for "fruits1_fruits2" [mean: 0.867]
v Computed lv scores for "fruits1_fruits3" [mean: 0.794]
v Computed lv scores for "fruits2_fruits3" [mean: 0.849]
i Skipping 'order' method because levels are not explicitly ordered. Set ordered = TRUE to compute the order method.
v Computed exact scores for "fruits1_fruits2" [mean: 1]
v Computed exact scores for "fruits1_fruits3" [mean: 0.333]
v Computed exact scores for "fruits2_fruits3" [mean: 0.333]
i Using auto-calculated max_diff: 0.45
v Computed exact scores for "nums1_nums2" [mean: 0.333]
v Computed exact scores for "nums1_nums3" [mean: 0.333]
v Computed exact scores for "nums2_nums3" [mean: 0.333]
v Computed pct_diff scores for "nums1_nums2" [mean: 0.958]
v Computed pct_diff scores for "nums1_nums3" [mean: 0.955]
v Computed pct_diff scores for "nums2_nums3" [mean: 0.915]
v Computed normalized scores for "nums1_nums2" [mean: 0.926]
v Computed normalized scores for "nums1_nums3" [mean: 0.926]
v Computed normalized scores for "nums2_nums3" [mean: 0.852]
v Computed fuzzy scores for "nums1_nums2" [mean: 1]
v Computed fuzzy scores for "nums1_nums3" [mean: 1]
v Computed fuzzy scores for "nums2_nums3" [mean: 0.958]
[ FAIL 0 | WARN 0 | SKIP 0 | PASS 83 ]
> 
> proc.time()
   user  system elapsed 
   5.45    0.34    5.87