resample provides a set of tools for performing randomization-based inference in Python, primarily through the use of bootstrapping methods and Monte Carlo permutation tests. Documentation can be found on Read the Docs.
- Bootstrap samples (ordinary or balanced, both with optional stratification) of arrays with arbitrary dimension
- Parametric bootstrap samples (Gaussian, Poisson, gamma, etc.) of one-dimensional arrays
- Bootstrap confidence intervals (percentile or BCa) for any well-defined parameter
- Jackknife estimates of bias and variance
- Randomization-based variants of traditional statistical tests (t-test, ANOVA F-test, K-S test, etc.)
- Tools for working with empirical distributions (cumulative distribution, quantile, and influence functions)
Installation requires numpy and scipy.
The latest release can be installed from PyPI:
pip install resample
or using conda:
conda install resample -c conda-forge