Metadata-Version: 2.1
Name: resample
Version: 1.6.0
Summary: Resampling-based inference in Python
Author-email: Daniel Saxton <dsaxton@pm.me>, Hans Dembinski <hans.dembinski@gmail.com>
License: BSD-3-Clause
Project-URL: repository, http://github.com/resample-project/resample
Project-URL: documentation, https://resample.readthedocs.io/en/stable/
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: BSD License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: Implementation :: CPython
Classifier: Programming Language :: Python :: Implementation :: PyPy
Requires-Python: >=3.8
Description-Content-Type: text/x-rst
Provides-Extra: test
Provides-Extra: doc
License-File: LICENSE

.. |resample| image:: doc/_static/logo.svg
   :alt: resample
   :target: http://resample.readthedocs.io

|resample|
==========

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   :target: https://pypi.org/project/resample
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   :target: https://github.com/conda-forge/resample-feedstock
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`Link to full documentation`_

.. _Link to full documentation: http://resample.readthedocs.io

.. skip-marker-do-not-remove

Resampling-based inference in Python based on data resampling and permutation.

This package was created by Daniel Saxton and is now maintained by Hans Dembinski.

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.)
- Depends only on `numpy`_ and `scipy`_

Example
-------

We bootstrap the uncertainty of the arithmetic mean, an estimator for the expectation. In this case, we know the formula to compute this uncertainty and can compare it to the bootstrap result.

.. code-block:: python

      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 0.83

The amazing thing is that the bootstrap works as well for arbitrarily complex estimators.

.. _numpy: http://www.numpy.org
.. _scipy: https://www.scipy.org

Installation
------------
You can install with pip.

.. code-block:: shell

      pip install resample
