Randomization-based inference in Python
resample
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.)
Dependencies
Installation requires only numpy and scipy.
Installation
The latest release can be installed from PyPI:
pip install resample
or using conda:
conda install resample -c conda-forge
GitHub