Randomisation-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) 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.)
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 0.83Release history Release notifications | RSS feed
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