Resampling-based inference in Python
Project description
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.)
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.
pip install resampleRelease history Release notifications | RSS feed
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