Added sample size estimator for a case of binomial proportion

[pkaf]

Description

Related issues or pull requests

Pull Request Checklist

• [X ] Added a note about the modification or contribution to the ./docs/sources/CHANGELOG.md file (if applicable)
• [ X] Added appropriate unit test functions in the ./mlxtend/*/tests directories (if applicable)
• Modify documentation in the corresponding Jupyter Notebook under mlxtend/docs/sources/ (if applicable)
• [ X] Ran PYTHONPATH='.' pytest ./mlxtend -sv and make sure that all unit tests pass (for small modifications, it might be sufficient to only run the specific test file, e.g., PYTHONPATH='.' pytest ./mlxtend/classifier/tests/test_stacking_cv_classifier.py -sv)
• [X ] Checked for style issues by running flake8 ./mlxtend

[coveralls]

Coverage increased (+0.002%) to 90.662% when pulling 4f07177 on pkaf:sample_size_estimator into 276fdd3 on rasbt:master.

[rasbt]

Thanks a lot! Btw. is there a way you can add some unit tests to make sure the results are as intended? Maybe comparing it with an equivalent implementation in R or sth along these lines?

[pkaf]

Numbers in the LHS of assert statements within test_one_sided_binomial_proportion_estimates() and test_two_sided_binomial_proportion_estimates() are taken from there. Are you suggesting something more/different?

Happy to add.

[rasbt]

Oh I am sorry, I may have overlooked the unit test file before. Sorry, it's been a hectic week due to teaching. Will go over it more carefully soon :)

[pkaf]

All good. Anything please let me know.

[rasbt]

Coming back to this, the context of this function is to compute the sample size for normal approximation intervals / hypothesis tests based on normal approximations? Just asking because we probably should draft a documentation for this. Maybe we could embed this in the context of normal approximation-based confidence intervals and t-tests like I described here on pg. 10: https://arxiv.org/pdf/1811.12808.pdf