https://github.com/nolanbconaway/binoculars

Confidence intervals for binomial proportions.
https://github.com/nolanbconaway/binoculars

binomial-distribution confidence-intervals python statistics

Last synced: 6 days ago
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Confidence intervals for binomial proportions.

• Host: GitHub
• URL: https://github.com/nolanbconaway/binoculars
• Owner: nolanbconaway
• License: mit
• Created: 2020-12-22T19:50:01.000Z (about 4 years ago)
• Default Branch: main
• Last Pushed: 2021-10-08T00:16:55.000Z (about 3 years ago)
• Last Synced: 2024-12-15T03:24:04.051Z (13 days ago)
• Topics: binomial-distribution, confidence-intervals, python, statistics
• Language: Python
• Homepage:
• Size: 82 KB
• Stars: 4
• Watchers: 3
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: readme.md
  • License: LICENSE

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README

        
# Binoculars: Binomial Confidence Intervals

![https://deepnote.com/project/e17fa473-51c6-45aa-8de0-980be7d2dc5f](demo.png)

![Unit Tests](https://github.com/nolanbconaway/binoculars/workflows/Unit%20Tests/badge.svg)

[![codecov](https://codecov.io/gh/nolanbconaway/binoculars/branch/main/graph/badge.svg?token=EOH4M3PIYL)](https://codecov.io/gh/nolanbconaway/binoculars)

[![PyPI](https://img.shields.io/pypi/v/binoculars)](https://pypi.org/project/binoculars/)

This is a small package that provides functions to compute the confidence interval for a binomial proportion. I made it because I spend altogether too much time staring at the Binomial proportion confidence interval [wiki page](https://en.wikipedia.org/wiki/Binomial_proportion_confidence_interval).

Presently, the package implements:

- [The Normal Approximation](https://en.wikipedia.org/wiki/Binomial_proportion_confidence_interval#Normal_approximation_interval)

- [The Wilson Interval](https://en.wikipedia.org/wiki/Binomial_proportion_confidence_interval#Wilson_score_interval) (no continuity correction)

- [Jeffrey's interval](https://en.wikipedia.org/wiki/Binomial_proportion_confidence_interval#Jeffreys_interval) (via scipy.stats.beta)

- [Clopper-Pearson interval](https://en.wikipedia.org/wiki/Binomial_proportion_confidence_interval#Clopper%E2%80%93Pearson_interval) (also via scipy.stats.beta)

If you haven't spent a lot of time thinking about which interval _you_ should use (and why would you want to?), I suggest using the Wilson interval or Jeffrey's interval. Jeffrey's interval is returned by default by the `binomial_confidence` function in this package. 

> You _oughtn't_ use the normal approximation if you don't have to. It produces patently inaccurate values with low/high probabilities at low Ns. The plot at the top of this readme shows the normal approximation producing lower lower bounds of _less than 0_ in these cases.

## Install

```sh

pip install binoculars

```

## Usage

```py

from binoculars import binomial_confidence

N, p = 100, 0.2

binomial_confidence(p, N)  # default to jeffrey's interval

# (0.1307892803998113, 0.28628125447599173)

binomial_confidence(p, N, tail='lower') # grab one tail

# 0.1307892803998113

# set Z value

binomial_confidence(p, N, tail='lower', z=2.58)

# 0.11212431621448567

# choose your method

binomial_confidence(p, N, method='normal')

# (0.12160000000000001, 0.2784)

binomial_confidence(p, N, method='wilson')

# (0.1333659225590988, 0.28883096192650237)

binomial_confidence(p, N, method='clopper-pearson')

# (0.1266544343411484, 0.291844378634278

```

## Development

I honestly do not imagine touching this a lot. But maybe you want to add one of the other interval methods?

1. Make a python 3.6+ venv

3. `pip install -e .[test]`

4. `black lib --check`

5. `pytest`

## Later (?)

 - [ ] Add confidence intervals for odds ratios, differences

 - [ ] Add the unimplemented intervals

 - [x] Add plots comparing the intervals to readme.