Resampling-based inference in Python
Project description
Resampling-based inference in Python based on data resampling and permutation.
This package was created by Daniel Saxton and is now maintained by Hans Dembinski.
Features
Bootstrap resampling: ordinary or balanced with optional stratification
Extended bootstrap resampling: also varies sample size
Parametric resampling: Gaussian, Poisson, gamma, etc.)
Jackknife estimates of bias and variance of any estimator
Compute bootstrap confidence intervals (percentile or BCa) for any estimator
Permutation-based variants of traditional statistical tests (USP test of independence and others)
Tools for working with empirical distributions (CDF, quantile, etc.)
Example
We bootstrap the uncertainty of the arithmetic mean, an estimator for the expectation. In this case, we know the formula to compute this uncertainty and can compare it to the bootstrap result.
from resample.bootstrap import variance
import numpy as np
d = [1, 2, 6, 3, 5]
stdev_of_mean = variance(np.mean, d) ** 0.5
print(f"bootstrap {stdev_of_mean:.2f} exact {np.std(d) / len(d) ** 0.5:.2f}")
# bootstrap 0.82 exact 0.83The amazing thing is that the bootstrap works as well for arbitrarily complex estimators.
Installation
You can install with pip.
pip install resampleRelease history Release notifications | RSS feed
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