resample 1.6.0rc3

Resampling-based inference in Python

Link to full documentation

Resampling-based inference in Python based on data resampling and permutation.

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.)

• Depends only on numpy and scipy

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 0.83

Installation

You can install with pip.

pip install resample