Welcome to DBCI’s documentation!#
A package for computing confidence intervals for binomial proportions, and confidence intervals for the difference of binomial proportions.
Installation instructions#
The package can be installed via PyPI using
python -m pip install diff-binom-confint
or one can install the latest version on GitHub using
python -m pip install git+https://github.com/DeepPSP/DBCI.git
or clone this repository and install locally via
git clone https://github.com/DeepPSP/DBCI.git
cd DBCI
python -m pip install .
Numba accelerated version#
One can install the Numba accelerated version of the package using
python -m pip install diff-binom-confint[acc]
Streamlit app#
One can also use the Streamlit app to compute confidence intervals for binomial proportions, and even upload a categorized table of data to obtain a report of risk report for binomial confidence intervals.
Usage examples#
The following example shows how to compute the confidence interval for the difference of two binomial proportions using the Wilson method.
from diff_binom_confint import compute_difference_confidence_interval
n_positive, n_total = 84, 101
ref_positive, ref_total = 89, 105
confint = compute_difference_confidence_interval(
n_positive,
n_total,
ref_positive,
ref_total,
conf_level=0.95,
method="wilson",
)
Implemented methods#
Confidence intervals for binomial proportions#
We list the implemented methods for confidence intervals for binomial proportions in the following table.
Method (type) |
Implemented |
|---|---|
wilson |
✔️ |
wilson-cc |
✔️ |
wald |
✔️ |
wald-cc |
✔️ |
agresti-coull |
✔️ |
jeffreys |
✔️ |
clopper-pearson |
✔️ |
arcsine |
✔️ |
logit |
✔️ |
pratt |
✔️ |
witting |
✔️ |
mid-p |
✔️ |
lik |
✔️ |
blaker |
✔️ |
modified-wilson |
✔️ |
modified-jeffreys |
✔️ |
Confidence intervals for difference of binomial proportions#
The following is the table of implemented methods for computing confidence intervals for the difference of binomial proportions.
Method (type) |
Implemented |
|---|---|
wilson |
✔️ |
wilson-cc |
✔️ |
wald |
✔️ |
wald-cc |
✔️ |
haldane |
✔️ |
jeffreys-perks |
✔️ |
mee |
✔️ |
miettinen-nurminen |
✔️ |
true-profile |
✔️ |
hauck-anderson |
✔️ |
agresti-caffo |
✔️ |
carlin-louis |
✔️ |
brown-li |
✔️ |
brown-li-jeffrey |
✔️ |
miettinen-nurminen-brown-li |
✔️ |
exact |
❌ |
mid-p |
❌ |
santner-snell |
❌ |
chan-zhang |
❌ |
agresti-min |
❌ |
wang |
❌ |
pradhan-banerjee |
❌ |
API reference