resample 0.21

Tools for randomization-based inference in Python

resample

Description

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. See the example notebook for a brief tutorial.

Features

• Bootstrap samples (ordinary or balanced, both with optional stratification and smoothing) of arrays with arbitrary dimension
• Parametric bootstrap samples (Gaussian, Poisson, gamma, etc.) of one-dimensional arrays
• Bootstrap confidence intervals (percentile, BCA and Studentized) 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 (empirical cumulative distribution and quantile functions, empirical influence functions, distance metrics for comparing distributions)

Dependencies

Installation requires numpy and scipy.

Installation

The latest release can be installed from PyPI:

pip install resample