Tools for randomization-based inference in Python

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  • License: BSD-3-Clause
  • Author: Daniel Saxton

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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. Documentation can be found on Read the Docs.

Features

  • 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)

Dependencies

Installation requires numpy and scipy.

Installation

The latest release can be installed from PyPI:

pip install resample

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Avatar for dsaxton from gravatar.com dsaxton Avatar for hdembins from gravatar.com hdembins Avatar for scikit-hep from gravatar.com scikit-hep

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Project links
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  • License: BSD-3-Clause
  • Author: Daniel Saxton

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