resample 1.5.0b0

Randomisation-based inference in Python

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

Features

• Bootstrap samples (ordinary or balanced with optional stratification) from N-D arrays

• Apply parametric bootstrap (Gaussian, Poisson, gamma, etc.) on samples

• 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 (t-test, K-S test, etc.)

• 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]

print(f"bootstrap {variance(np.mean, d) ** 0.5:.2f} exact {(np.var(d) / len(d)) ** 0.5:.2f}")
# bootstrap 0.82 exact 0.83