I updated to v.0.2.8 today, and I noticed my code to be much slower than before. This seem to be related to the inclusion of the lqrt test in the results.

• test 1: virtual env with python 3.7.5 pandas 0.24.0 dabest 0.2.7

import numpy as np
import pandas as pd
import dabest

np.random.seed(1234)
df = pd.DataFrame({'Group1':np.random.normal(loc=0, size=(1000,)),
                   'Group2':np.random.normal(loc=1, size=(1000,))})
test = dabest.load(df, idx=['Group1','Group2'])
%time print(test.mean_diff)

DABEST v0.2.7

Good morning!
The current time is Tue Dec 31 11:46:00 2019.

The unpaired mean difference between Group1 and Group2 is 1.03 [95%CI 0.941, 1.11].
The two-sided p-value of the Mann-Whitney test is 2.63e-97.

5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.
The p-value(s) reported are the likelihood(s) of observing the effect size(s),
if the null hypothesis of zero difference is true.

To get the results of all valid statistical tests, use .mean_diff.statistical_tests

CPU times: user 558 ms, sys: 5.83 ms, total: 564 ms
Wall time: 564 ms

• test 2: virtual env with python 3.7.5 pandas 0.25.3 dabest 0.2.8

import numpy as np
import pandas as pd
import dabest

np.random.seed(1234)
df = pd.DataFrame({'Group1':np.random.normal(loc=0, size=(1000,)),
                   'Group2':np.random.normal(loc=1, size=(1000,))})
test = dabest.load(df, idx=['Group1','Group2'])
%time print(test.mean_diff)

DABEST v0.2.8

Good morning!
The current time is Tue Dec 31 11:47:09 2019.

The unpaired mean difference between Group1 and Group2 is 1.03 [95%CI 0.941, 1.11].
The two-sided p-value of the Mann-Whitney test is 2.63e-97.

5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.
The p-value(s) reported are the likelihood(s) of observing the effect size(s),
if the null hypothesis of zero difference is true.

To get the results of all valid statistical tests, use .mean_diff.statistical_tests

CPU times: user 2.46 s, sys: 8.69 ms, total: 2.47 s
Wall time: 2.47 s

Would it be possible to delay doing the statistical tests to when effect_size.statistical_tests is called instead of calculating all the tests a priori?