R version 4.6.0 RC (2026-04-17 r89914 ucrt) -- "Because it was There"
Copyright (C) 2026 The R Foundation for Statistical Computing
Platform: x86_64-w64-mingw32/x64

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> # This file is part of the statuser package
> # 
> # This file is run automatically by R CMD check
> library(testthat)
> library(statuser)
> 
> test_check("statuser")

desc_var() says:
Multiple grouping variables should not overlap perfectly: 'x1' and 'x2' overlap perfectly
desc_var() says:
'y_char' must be numeric; currently character
desc_var() says:
'y_char' must be numeric; currently character
desc_var() says:
'y_factor' must be numeric; currently factor
desc_var() says:
'nonexistent' not found in data
desc_var() says:
'nonexistent' not found in data
desc_var() says:
'x' must be numeric; currently character
Robin Hood calculations (see Simonsohn, 2018)
Explaining why x = 0.05 is used as the breakpoint

Most extreme value of fitted 'y' with GAM obtained at 'x' = 0.01
Local range of values considered for breakpoint 'x': [-0.33, 0.36]
t-values for two lines at 'x' = 0.01:
   t1 = 6.5
   t2 = 9.35
We compute t2/(t1+t2) = 0.59
We then lookup the value of 'x' at that quantile, so we run
quantile(x, 0.59) = 0.05 and set that value, 0.05, as the breakpoint.

Note: you may turn off this message by setting `quiet=TRUE`
Robin Hood calculations (see Simonsohn, 2018)
Explaining why x = 0.05 is used as the breakpoint

Most extreme value of fitted 'y' with GAM obtained at 'x' = 0.01
Local range of values considered for breakpoint 'x': [-0.33, 0.36]
t-values for two lines at 'x' = 0.01:
   t1 = 6.5
   t2 = 9.35
We compute t2/(t1+t2) = 0.59
We then lookup the value of 'x' at that quantile, so we run
quantile(x, 0.59) = 0.05 and set that value, 0.05, as the breakpoint.

Note: you may turn off this message by setting `quiet=TRUE`
Robin Hood calculations (see Simonsohn, 2018)
Explaining why x = 0.22 is used as the breakpoint

Most extreme value of fitted 'y' with GAM obtained at 'x' = 0.12
Local range of values considered for breakpoint 'x': [-0.22, 0.49]
t-values for two lines at 'x' = 0.12:
   t1 = 2.92
   t2 = 10.2
We compute t2/(t1+t2) = 0.78
We then lookup the value of 'x' at that quantile, so we run
quantile(x, 0.78) = 0.22 and set that value, 0.22, as the breakpoint.

Note: you may turn off this message by setting `quiet=TRUE`
Robin Hood calculations (see Simonsohn, 2018)
Explaining why x = 0.22 is used as the breakpoint

Most extreme value of fitted 'y' with GAM obtained at 'x' = 0.12
Local range of values considered for breakpoint 'x': [-0.22, 0.49]
t-values for two lines at 'x' = 0.12:
   t1 = 2.92
   t2 = 10.2
We compute t2/(t1+t2) = 0.78
We then lookup the value of 'x' at that quantile, so we run
quantile(x, 0.78) = 0.22 and set that value, 0.22, as the breakpoint.

Note: you may turn off this message by setting `quiet=TRUE`
Robin Hood calculations (see Simonsohn, 2018)
Explaining why x = 0.07 is used as the breakpoint

Most extreme value of fitted 'y' with GAM obtained at 'x' = 0.01
Local range of values considered for breakpoint 'x': [-0.41, 0.38]
t-values for two lines at 'x' = 0.01:
   t1 = 5.51
   t2 = 7.83
We compute t2/(t1+t2) = 0.59
We then lookup the value of 'x' at that quantile, so we run
quantile(x, 0.59) = 0.07 and set that value, 0.07, as the breakpoint.

Note: you may turn off this message by setting `quiet=TRUE`
Robin Hood calculations (see Simonsohn, 2018)
Explaining why x = 0.1 is used as the breakpoint

Most extreme value of fitted 'y' with GAM obtained at 'x' = 0.16
Local range of values considered for breakpoint 'x': [-0.21, 0.42]
t-values for two lines at 'x' = 0.16:
   t1 = 7.8
   t2 = 7.95
We compute t2/(t1+t2) = 0.5
We then lookup the value of 'x' at that quantile, so we run
quantile(x, 0.5) = 0.1 and set that value, 0.1, as the breakpoint.

Note: you may turn off this message by setting `quiet=TRUE`
Robin Hood calculations (see Simonsohn, 2018)
Explaining why x = 0.05 is used as the breakpoint

Most extreme value of fitted 'y' with GAM obtained at 'x' = 0
Local range of values considered for breakpoint 'x': [-0.32, 0.41]
t-values for two lines at 'x' = 0:
   t1 = 8.25
   t2 = 7.01
We compute t2/(t1+t2) = 0.46
We then lookup the value of 'x' at that quantile, so we run
quantile(x, 0.46) = 0.05 and set that value, 0.05, as the breakpoint.

Note: you may turn off this message by setting `quiet=TRUE`
Robin Hood calculations (see Simonsohn, 2018)
Explaining why x = 0.08 is used as the breakpoint

Most extreme value of fitted 'y' with GAM obtained at 'x' = 0.01
Local range of values considered for breakpoint 'x': [-0.17, 0.2]
t-values for two lines at 'x' = 0.01:
   t1 = 8.45
   t2 = 13.54
We compute t2/(t1+t2) = 0.62
We then lookup the value of 'x' at that quantile, so we run
quantile(x, 0.62) = 0.08 and set that value, 0.08, as the breakpoint.

Note: you may turn off this message by setting `quiet=TRUE`
Robin Hood calculations (see Simonsohn, 2018)
Explaining why x = 0.11 is used as the breakpoint

Most extreme value of fitted 'y' with GAM obtained at 'x' = 0.18
Local range of values considered for breakpoint 'x': [0, 0.32]
t-values for two lines at 'x' = 0.18:
   t1 = 11.61
   t2 = 8.11
We compute t2/(t1+t2) = 0.41
We then lookup the value of 'x' at that quantile, so we run
quantile(x, 0.41) = 0.11 and set that value, 0.11, as the breakpoint.

Note: you may turn off this message by setting `quiet=TRUE`
print() and summary() show the same information for lm2()
print() and summary() show the same information for lm2()
print() and summary() show the same information for lm2()
print() and summary() show the same information for lm2()
print() and summary() show the same information for lm2()
Test message
Testmessagewithmultipleparts
Red message
Blue message
Cyan message
Green message
Custom color
Plain text
Bold text
This stops
This doesn't stop
Part1andPart2
Value:42
Count:5items
Test
The p-values are based on quantile regressions that assume all observations are independent
plot_cdf() says: dropped 10 observations with missing 'y' values
plot_cdf() says: dropped 10 observations with missing 'y' values
plot_cdf() says: dropped 10 observations with missing 'y' values
plot_density() says: dropped 10 observations with missing 'y' values
plot_density() says: dropped 10 observations with missing 'y' values
plot_freq() says: because there are more than 30 unique values, frequency was printed only for the mode. Set  `value.labels` to modify this behavior
plot_freq() says: dropped 1 observations with missing 'x' values
plot_freq() says: dropped 1 observations with missing 'x' values
plot_freq() says: dropped 2 observations with missing 'value' values
plot_freq() says: dropped 2 observations with missing 'value' values
plot_freq() says: because there are more than 30 unique values, frequency was printed only for the mode. Set  `value.labels` to modify this behavior
plot_freq() says: because there are more than 30 unique values, frequency was printed only for the mode. Set  `value.labels` to modify this behavior
tests:
1) A vs B: t(97.5)=-0.03, p=0.979
You can specify which statistical tests to report with the `tests` argument.

Set `quiet=TRUE` to suppress this output.
tests:
1) X_A vs X_B: t(97.6)=-1.70, p=0.092
2) Y_A vs Y_B: t(97.7)=-0.32, p=0.753
3) interaction(x1:x2): regression interaction: t(196)=-0.89, p=0.375
You can specify which statistical tests to report with the `tests` argument.

Set `quiet=TRUE` to suppress this output.
tests:
1) A vs B: t(96.9)=-1.10, p=0.274
You can specify which statistical tests to report with the `tests` argument.

Set `quiet=TRUE` to suppress this output.
tests:
1) B vs A: t(96.9)=-1.10, p=0.274
You can specify which statistical tests to report with the `tests` argument.

Set `quiet=TRUE` to suppress this output.
tests:
1) B vs A: t(96.9)=-1.10, p=0.274
You can specify which statistical tests to report with the `tests` argument.

Set `quiet=TRUE` to suppress this output.
desc_var() says:
Some possible group combinations are not observed:
x1=B, x2=Y, x3=N
tests:
1) A vs B: t(57.5)=2.14, p=0.037
You can specify which statistical tests to report with the `tests` argument.

Set `quiet=TRUE` to suppress this output.
tests:
1) X_A vs X_B: t(53.3)=0.44, p=0.661
2) Y_A vs Y_B: t(57.8)=-0.93, p=0.355
3) interaction(x1:x2): regression interaction: t(116)=0.94, p=0.348
You can specify which statistical tests to report with the `tests` argument.

Set `quiet=TRUE` to suppress this output.
tests:
1) X_A vs X_B: t(56.0)=0.44, p=0.662
2) Y_A vs Y_B: t(57.6)=-1.33, p=0.189
3) Z_A vs Z_B: t(58.0)=-0.45, p=0.655
You can specify which statistical tests to report with the `tests` argument.

Set `quiet=TRUE` to suppress this output.     status
group X Y
    A 2 1
    B 1 1
     status
group X Y
    A 2 1
    B 1 1
   y
x   X Y
  A 2 1
  B 1 1

Row proportions:
   y
x   X     Y     Total
  A 0.667 0.333 1.000
  B 0.500 0.500 1.000
, , z = high

   y
x   X Y
  A 1 0
  B 1 0

, , z = low

   y
x   X Y
  A 0 1
  B 0 1

   y
x   X Y
  A 1 1
  B 1 1
x
A B 
3 2 
[ FAIL 0 | WARN 0 | SKIP 14 | PASS 910 ]

══ Skipped tests (14) ══════════════════════════════════════════════════════════
• On CRAN (14): 'test-lm2.R:651:1', 'test-lm2.R:661:1', 'test-lm2.R:670:1',
  'test-t.test2.R:360:1', 'test-t.test2.R:372:1', 'test-t.test2.R:385:1',
  'test-t.test2.R:396:1', 'test-t.test2.R:406:1', 'test-t.test2.R:418:1',
  'test-table2.R:362:1', 'test-table2.R:373:1', 'test-table2.R:384:1',
  'test-table2.R:395:1', 'test-table2.R:406:1'

[ FAIL 0 | WARN 0 | SKIP 14 | PASS 910 ]
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> proc.time()
   user  system elapsed 
  37.81    3.70   41.71