R Under development (unstable) (2025-09-14 r88831 ucrt) -- "Unsuffered Consequences"
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> # Generated by ChatGPT — testthat entrypoint
> library(testthat)
> library(OneTwoSamples)
> 
> test_check("OneTwoSamples")
Interval estimation and test of hypothesis

	Shapiro-Wilk normality test

data:  x
W = 0.96359, p-value = 0.7545


	Shapiro-Wilk normality test

data:  y
W = 0.96036, p-value = 0.6986


x and y are both from the normal populations.

x: descriptive statistics, plot, interval estimation and test of hypothesis
quantile of x
  0%  25%  50%  75% 100% 
58.0 61.5 65.0 68.5 72.0 
data_outline of x
   N Mean Var  std_dev Median std_mean       CV CSS   USS  R R1 Skewness
1 15   65  20 4.472136     65 1.154701 6.880209 280 63655 14  7        0
  Kurtosis
1     -1.2

	Shapiro-Wilk normality test

data:  x
W = 0.96359, p-value = 0.7545


The data is from the normal population.
dev.new(): using pdf(file="Rplots132.pdf")
dev.new(): using pdf(file="Rplots133.pdf")
dev.new(): using pdf(file="Rplots134.pdf")

The data is from the normal population.

Interval estimation and test of hypothesis of mu
Interval estimation and test of hypothesis: t.test()
H0: mu = 0     H1: mu != 0 

	One Sample t-test

data:  x
t = 56.292, df = 14, p-value < 2.2e-16
alternative hypothesis: true mean is not equal to 0
95 percent confidence interval:
 62.52341 67.47659
sample estimates:
mean of x 
       65 


Interval estimation and test of hypothesis of sigma
Interval estimation: interval_var3()
  var df        a        b
1  20 14 10.72019 49.74483
Test of hypothesis: var_test1()
H0: sigma2 = 1     H1: sigma2 != 1 
  var df chisq2 P_value
1  20 14    280       0

y: descriptive statistics, plot, interval estimation and test of hypothesis
quantile of y
   0%   25%   50%   75%  100% 
115.0 124.5 135.0 148.0 164.0 
data_outline of y
   N     Mean      Var  std_dev Median std_mean       CV      CSS    USS  R
1 15 136.7333 240.2095 15.49869    135 4.001746 11.33498 3362.933 283803 49
    R1  Skewness  Kurtosis
1 23.5 0.2814297 -1.040715

	Shapiro-Wilk normality test

data:  y
W = 0.96036, p-value = 0.6986


The data is from the normal population.
dev.new(): using pdf(file="Rplots135.pdf")
dev.new(): using pdf(file="Rplots136.pdf")
dev.new(): using pdf(file="Rplots137.pdf")

The data is from the normal population.

Interval estimation and test of hypothesis of mu
Interval estimation and test of hypothesis: t.test()
H0: mu = 0     H1: mu != 0 

	One Sample t-test

data:  y
t = 34.168, df = 14, p-value = 6.907e-15
alternative hypothesis: true mean is not equal to 0
95 percent confidence interval:
 128.1504 145.3162
sample estimates:
mean of x 
 136.7333 


Interval estimation and test of hypothesis of sigma
Interval estimation: interval_var3()
       var df        a       b
1 240.2095 14 128.7545 597.459
Test of hypothesis: var_test1()
H0: sigma2 = 1     H1: sigma2 != 1 
       var df   chisq2 P_value
1 240.2095 14 3362.933       0

Interval estimation and test of hypothesis of mu1-mu2

Interval estimation and test of hypothesis: t.test()

	Welch Two Sample t-test

data:  x and y
t = -17.223, df = 16.315, p-value = 6.826e-12
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -80.54891 -62.91775
sample estimates:
mean of x mean of y 
  65.0000  136.7333 


Interval estimation and test of hypothesis of sigma1^2/sigma2^2
Interval estimation and test of hypothesis: var.test()

	F test to compare two variances

data:  x and y
F = 0.083261, num df = 14, denom df = 14, p-value = 3.586e-05
alternative hypothesis: true ratio of variances is not equal to 1
95 percent confidence interval:
 0.02795306 0.24799912
sample estimates:
ratio of variances 
        0.08326065 

n1 == n2

Test whether x and y are from the same population
H0: x and y are from the same population (without significant difference)
ks.test(x,y)

	Exact two-sample Kolmogorov-Smirnov test

data:  x and y
D = 1, p-value = 1.289e-08
alternative hypothesis: two-sided

binom.test(sum(x<y), length(x))

	Exact binomial test

data:  sum(x < y) and length(x)
number of successes = 15, number of trials = 15, p-value = 6.104e-05
alternative hypothesis: true probability of success is not equal to 0.5
95 percent confidence interval:
 0.7819806 1.0000000
sample estimates:
probability of success 
                     1 

wilcox.test(x, y, alternative = alternative, paired = TRUE)

	Wilcoxon signed rank exact test

data:  x and y
V = 0, p-value = 6.104e-05
alternative hypothesis: true location shift is not equal to 0


Find the correlation coefficient of x and y
H0: rho = 0 (x, y uncorrelated)

	Pearson's product-moment correlation

data:  x and y
t = 37.855, df = 13, p-value = 1.091e-14
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 0.9860970 0.9985447
sample estimates:
      cor 
0.9954948 


	Kendall's rank correlation tau

data:  x and y
T = 105, p-value = 1.529e-12
alternative hypothesis: true tau is not equal to 0
sample estimates:
tau 
  1 


	Spearman's rank correlation rho

data:  x and y
S = 1.2434e-13, p-value < 2.2e-16
alternative hypothesis: true rho is not equal to 0
sample estimates:
rho 
  1 

[ FAIL 0 | WARN 0 | SKIP 3 | PASS 87 ]

══ Skipped tests (3) ═══════════════════════════════════════════════════════════
• On CRAN (2): 'test-input-validation.R:1:1', 'test-smoke-exported-calls.R:1:1'
• Skipping (1): 'test-examples.R:1:1'

[ FAIL 0 | WARN 0 | SKIP 3 | PASS 87 ]
> 
> proc.time()
   user  system elapsed 
   1.21    0.25    1.45