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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(tlda)
> 
> test_check("tlda")
         Rrel             D            D2             S            DP 
 1.000000e-01 -2.220446e-16  0.000000e+00  1.000000e-01  1.000000e-01 
           DA           DKL 
 0.000000e+00  2.313782e-01 

Scores follow conventional scaling:
  0 = maximally uneven/bursty/concentrated distribution (pessimum)
  1 = maximally even/dispersed/balanced distribution (optimum)

For Gries's DP, the function uses the modified version suggested by
  Egbert et al. (2020)

For DKL, standardization to the unit interval [0,1] is based on the
  odds-to-probability transformation, see Gries (2024: 90)
Rrel    D   D2    S   DP   DA  DKL 
   1    1    1    1    1    1    1 

Scores follow conventional scaling:
  0 = maximally uneven/bursty/concentrated distribution (pessimum)
  1 = maximally even/dispersed/balanced distribution (optimum)

For Gries's DP, the function uses the modified version suggested by
  Egbert et al. (2020)

For DKL, standardization to the unit interval [0,1] is based on the
  odds-to-probability transformation, see Gries (2024: 90)
Rrel    D   D2    S   DP   DA  DKL 
   1    1    1    1    1    1    1 

Scores follow conventional scaling:
  0 = maximally uneven/bursty/concentrated distribution (pessimum)
  1 = maximally even/dispersed/balanced distribution (optimum)

For Gries's DP, the function uses the modified version suggested by
  Egbert et al. (2020)

For DKL, standardization to the unit interval [0,1] is based on the
  odds-to-probability transformation, see Gries (2024: 90)
DA 
 0 

Scores follow conventional scaling:
  0 = maximally uneven/bursty/concentrated distribution (pessimum)
  1 = maximally even/dispersed/balanced distribution (optimum)

Computed using the basic formula for DA, see:
  Wilcox (1967: 343, 'MDA', column 2), Burch et al. (2017: 194-196)
DA 
 1 

Scores follow conventional scaling:
  0 = maximally uneven/bursty/concentrated distribution (pessimum)
  1 = maximally even/dispersed/balanced distribution (optimum)

Computed using the basic formula for DA, see:
  Wilcox (1967: 343, 'MDA', column 2), Burch et al. (2017: 194-196)
DA 
 1 

Scores follow conventional scaling:
  0 = maximally uneven/bursty/concentrated distribution (pessimum)
  1 = maximally even/dispersed/balanced distribution (optimum)

Computed using the basic formula for DA, see:
  Wilcox (1967: 343, 'MDA', column 2), Burch et al. (2017: 194-196)
 DA 
0.1 

Scores follow conventional scaling:
  0 = maximally uneven/bursty/concentrated distribution (pessimum)
  1 = maximally even/dispersed/balanced distribution (optimum)

Computed using the computational shortcut suggested by
  Wilcox (1967: 343, 'MDA', column 4) with a minor
  correction to ensure DA does not exceed 1 (conventional)
DA 
 1 

Scores follow conventional scaling:
  0 = maximally uneven/bursty/concentrated distribution (pessimum)
  1 = maximally even/dispersed/balanced distribution (optimum)

Computed using the computational shortcut suggested by
  Wilcox (1967: 343, 'MDA', column 4) with a minor
  correction to ensure DA does not exceed 1 (conventional)
DA 
 1 

Scores follow conventional scaling:
  0 = maximally uneven/bursty/concentrated distribution (pessimum)
  1 = maximally even/dispersed/balanced distribution (optimum)

Computed using the computational shortcut suggested by
  Wilcox (1967: 343, 'MDA', column 4) with a minor
  correction to ensure DA does not exceed 1 (conventional)
      DKL 
0.2313782 

Scores follow conventional scaling:
  0 = maximally uneven/bursty/concentrated distribution (pessimum)
  1 = maximally even/dispersed/balanced distribution (optimum)


Standardization to the unit interval [0,1] using the odds-to-probability
  transformation, see Gries (2024: 90)
DKL 
  1 

Scores follow conventional scaling:
  0 = maximally uneven/bursty/concentrated distribution (pessimum)
  1 = maximally even/dispersed/balanced distribution (optimum)


Standardization to the unit interval [0,1] using the odds-to-probability
  transformation, see Gries (2024: 90)
       DKL 
0.03608319 

Scores follow conventional scaling:
  0 = maximally uneven/bursty/concentrated distribution (pessimum)
  1 = maximally even/dispersed/balanced distribution (optimum)


Standardization to the unit interval [0,1] using base e,
  see Gries (2021: 20)
DKL 
  1 

Scores follow conventional scaling:
  0 = maximally uneven/bursty/concentrated distribution (pessimum)
  1 = maximally even/dispersed/balanced distribution (optimum)


Standardization to the unit interval [0,1] using base e,
  see Gries (2021: 20)
DKL 
0.1 

Scores follow conventional scaling:
  0 = maximally uneven/bursty/concentrated distribution (pessimum)
  1 = maximally even/dispersed/balanced distribution (optimum)


Standardization to the unit interval [0,1] using base 2,
  see Gries (2024: 90)
DKL 
  1 

Scores follow conventional scaling:
  0 = maximally uneven/bursty/concentrated distribution (pessimum)
  1 = maximally even/dispersed/balanced distribution (optimum)


Standardization to the unit interval [0,1] using base 2,
  see Gries (2024: 90)
DP_nofreq 
        0 

The dispersion score is adjusted for frequency using the min-max
  transformation (see Gries 2024: 196-208); please note that the
  method implemented here does not work well if corpus parts differ
  considerably in size; see vignette('frequency-adjustment')

Scores follow conventional scaling:
  0 = maximally uneven/bursty/concentrated distribution (pessimum)
  1 = maximally even/dispersed/balanced distribution (optimum)

Computed using the original version proposed by Gries (2008)
DP_nofreq 
        1 

The dispersion score is adjusted for frequency using the min-max
  transformation (see Gries 2024: 196-208); please note that the
  method implemented here does not work well if corpus parts differ
  considerably in size; see vignette('frequency-adjustment')

Scores follow conventional scaling:
  0 = maximally uneven/bursty/concentrated distribution (pessimum)
  1 = maximally even/dispersed/balanced distribution (optimum)

Computed using the original version proposed by Gries (2008)
DP_nofreq 
        1 

The dispersion score is adjusted for frequency using the min-max
  transformation (see Gries 2024: 196-208); please note that the
  method implemented here does not work well if corpus parts differ
  considerably in size; see vignette('frequency-adjustment')

Scores follow conventional scaling:
  0 = maximally uneven/bursty/concentrated distribution (pessimum)
  1 = maximally even/dispersed/balanced distribution (optimum)

Computed using the original version proposed by Gries (2008)
DP_nofreq 
        0 

The dispersion score is adjusted for frequency using the min-max
  transformation (see Gries 2024: 196-208); please note that the
  method implemented here does not work well if corpus parts differ
  considerably in size; see vignette('frequency-adjustment')

Scores follow conventional scaling:
  0 = maximally uneven/bursty/concentrated distribution (pessimum)
  1 = maximally even/dispersed/balanced distribution (optimum)

Computed using the modification suggested by Egbert et al. (2020)
DP_nofreq 
        1 

The dispersion score is adjusted for frequency using the min-max
  transformation (see Gries 2024: 196-208); please note that the
  method implemented here does not work well if corpus parts differ
  considerably in size; see vignette('frequency-adjustment')

Scores follow conventional scaling:
  0 = maximally uneven/bursty/concentrated distribution (pessimum)
  1 = maximally even/dispersed/balanced distribution (optimum)

Computed using the modification suggested by Egbert et al. (2020)
DP_nofreq 
        1 

The dispersion score is adjusted for frequency using the min-max
  transformation (see Gries 2024: 196-208); please note that the
  method implemented here does not work well if corpus parts differ
  considerably in size; see vignette('frequency-adjustment')

Scores follow conventional scaling:
  0 = maximally uneven/bursty/concentrated distribution (pessimum)
  1 = maximally even/dispersed/balanced distribution (optimum)

Computed using the modification suggested by Egbert et al. (2020)
DP_nofreq 
        0 

The dispersion score is adjusted for frequency using the min-max
  transformation (see Gries 2024: 196-208); please note that the
  method implemented here does not work well if corpus parts differ
  considerably in size; see vignette('frequency-adjustment')

Scores follow conventional scaling:
  0 = maximally uneven/bursty/concentrated distribution (pessimum)
  1 = maximally even/dispersed/balanced distribution (optimum)

Computed using the modification suggested by Egbert et al. (2020)
DP_nofreq 
        1 

The dispersion score is adjusted for frequency using the min-max
  transformation (see Gries 2024: 196-208); please note that the
  method implemented here does not work well if corpus parts differ
  considerably in size; see vignette('frequency-adjustment')

Scores follow conventional scaling:
  0 = maximally uneven/bursty/concentrated distribution (pessimum)
  1 = maximally even/dispersed/balanced distribution (optimum)

Computed using the modification suggested by Egbert et al. (2020)
DP_nofreq 
        1 

The dispersion score is adjusted for frequency using the min-max
  transformation (see Gries 2024: 196-208); please note that the
  method implemented here does not work well if corpus parts differ
  considerably in size; see vignette('frequency-adjustment')

Scores follow conventional scaling:
  0 = maximally uneven/bursty/concentrated distribution (pessimum)
  1 = maximally even/dispersed/balanced distribution (optimum)

Computed using the modification suggested by Egbert et al. (2020)
Rrel 
 0.1 

Scores represent relative range, i.e. the proportion of corpus parts
  containing at least one occurrence of the item. The size of the
  corpus parts is not taken into account.

Rrel_withsize 
          0.1 

Scores represent relative range, i.e. the proportion of corpus parts
  containing at least one occurrence of the item. The size of the
  corpus parts is taken into account, see Gries (2022: 179-180),
  Gries (2024: 27-28)

Rrel 
   1 

Scores represent relative range, i.e. the proportion of corpus parts
  containing at least one occurrence of the item. The size of the
  corpus parts is not taken into account.

Rrel_withsize 
            1 

Scores represent relative range, i.e. the proportion of corpus parts
  containing at least one occurrence of the item. The size of the
  corpus parts is taken into account, see Gries (2022: 179-180),
  Gries (2024: 27-28)

Rrel_withsize 
            1 

Scores represent relative range, i.e. the proportion of corpus parts
  containing at least one occurrence of the item. The size of the
  corpus parts is taken into account, see Gries (2022: 179-180),
  Gries (2024: 27-28)

Rrel 
   1 

Scores represent relative range, i.e. the proportion of corpus parts
  containing at least one occurrence of the item. The size of the
  corpus parts is not taken into account.

  S 
0.1 

Scores follow conventional scaling:
  0 = maximally uneven/bursty/concentrated distribution (pessimum)
  1 = maximally even/dispersed/balanced distribution (optimum)

S 
1 

Scores follow conventional scaling:
  0 = maximally uneven/bursty/concentrated distribution (pessimum)
  1 = maximally even/dispersed/balanced distribution (optimum)

[ FAIL 0 | WARN 0 | SKIP 0 | PASS 32 ]
> 
> proc.time()
   user  system elapsed 
   0.71    0.17    0.89