stats/proportion.py's binom_test_reject_interval function's error

[wolyosung]

Describe the bug

from version 0.13.5 to 0.14.x, there is change in stats/proportion.py 's binom_test_reject_interval's return value.
now it is set to be an integer mandatorily,whereas it did not specify any type before.
However, scipy's stats.binom.ppf and isf function do not guarantee the value to be integer.

especially, ppf and isf function's output is np.full(shape(cond), fill_value=self.badvalue, dtype='d')
as badvalue is set to be np.nan as default, so if output.ndim == 0:, then output[()] can be float(nan).

therefore, x_low and x_upp can receive the nan value.
is it an intended behavior? could you please check the issue?

Code Sample, a copy-pastable example if possible

# Your code here that produces the bug
# This example should be self-contained, and so not rely on external data.
# It should run in a fresh ipython session, and so include all relevant imports.

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Expected Output

A clear and concise description of what you expected to happen.

Output of import statsmodels.api as sm; sm.show_versions()

[paste the output of import statsmodels.api as sm; sm.show_versions() here below this line]

[josef-pkt]

Do you have an example that returned nan?

Right now I don't remember and don't understand how boundary cases are supposed to be handled.

[josef-pkt]

misleading docstring

binom_test_reject_interval is not for the current (scipy) binom_test in the two-sided case.
scipy binom test is minlike, while binom_test_reject_interval is for equal-tail binomtest
e.g #2735 #8233

I don't see any internal use of binom_test_reject_interval
The tost rejection interval is used in computing exact power in power_binom_tost, AFAICS.
But there is no version for the test case.

Aside: I guess the int casting also breaks the usage for vectorized calls.
But for the minlike intervals, we don't have vectorized solution anyway.