R Under development (unstable) (2025-03-05 r87885 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]

-- Factor Similarity Analysis --------------------------------------------------
Methods used: "exact"
Lists compared: "fruits1, fruits2"
Levels used: "apple, orange, banana"

-- Overall Method Averages --

* exact: "1"

-- Method: exact --

-- Comparison: "fruits1_fruits2" 
* Mean: 1
* Median: 1
* SD: 0
* Exact Matches: 3 of 3
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]

-- Numeric Similarity Analysis -------------------------------------------------
Methods used: "exact, pct_diff, normalized, fuzzy"
Lists compared: "nums1, nums2"

-- Overall Method Averages --

* exact: "0.333"
* pct_diff: "0.958"
* normalized: "0.917"
* fuzzy: "1"

-- Method: exact --

-- Comparison: "nums1_nums2" 
* Mean: 0.333
* Median: 0
* SD: 0.577
* Range: [0 - 1]
* Exact Matches: 1 of 3

-- Method: pct_diff --

-- Comparison: "nums1_nums2" 
* Mean: 0.958
* Median: 0.95
* SD: 0.039
* Range: [0.923 - 1]
* Exact Matches: 1 of 3

-- Method: normalized --

-- Comparison: "nums1_nums2" 
* Mean: 0.917
* Median: 0.875
* SD: 0.072
* Range: [0.875 - 1]
* Exact Matches: 1 of 3

-- Method: fuzzy --

-- Comparison: "nums1_nums2" 
* Mean: 1
* Median: 1
* SD: 0
* Range: [1 - 1]
* Exact Matches: 2 of 3
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]

-- Text Similarity Analysis ----------------------------------------------------
Methods used: "jw, lv"
Lists compared: "fruits1, fruits2"

-- Overall Method Averages --

* jw: "0.984"
* lv: "0.867"

-- Method: jw --

-- Comparison: "fruits1_fruits2" 
* Mean: 0.984
* Median: 1
* SD: 0.027
* IQR: 0.023
* Range: [0.953 - 1]

-- Method: lv --

-- Comparison: "fruits1_fruits2" 
* Mean: 0.867
* Median: 1
* SD: 0.231
* IQR: 0.2
* Range: [0.6 - 1]
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 
   3.28    0.18    3.45