Resampling-based inference in Python
Project description
Randomisation-based inference in Python based on data resampling and permutation.
Features
Bootstrap samples (ordinary or balanced with optional stratification)
Support for parametric (Gaussian, Poisson, gamma, etc.) and extended bootstrapping (also varies sample size)
Compute bootstrap confidence intervals (percentile or BCa) for any estimator
Jackknife estimates of bias and variance of any estimator
Permutation-based variants of traditional statistical tests (USP test of independence and others)
Tools for working with empirical distributions (CDF, quantile, etc.)
Optional code acceleration with numba
Example
# bootstrap uncertainty of arithmetic mean
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 0.83Installation
You can install with pip, but you need a C compiler on the target machine.
pip install resampleRelease history Release notifications | RSS feed
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