Metadata-Version: 2.1
Name: resample
Version: 1.5.3
Summary: Randomisation-based inference in Python
Home-page: http://github.com/resample-project/resample
Author: Daniel Saxton and Hans Dembinski
License: BSD-3-Clause
Project-URL: Bug Tracker, https://github.com/resample-project/resample/issues
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: BSD License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.7
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|
==========

.. image:: https://img.shields.io/pypi/v/resample.svg
   :target: https://pypi.org/project/resample
.. image:: https://github.com/resample-project/resample/actions/workflows/test.yml/badge.svg
   :target: https://github.com/resample-project/resample/actions/workflows/tests.yml
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   :target: https://coveralls.io/github/resample-project/resample
.. image:: https://readthedocs.org/projects/resample/badge/?version=stable
   :target: https://resample.readthedocs.io/en/stable
.. image:: https://img.shields.io/pypi/l/resample
   :target: https://pypi.org/project/resample

`Link to full documentation`_

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

.. skip-marker-do-not-remove

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

Features
--------

- Bootstrap samples (ordinary or balanced with optional stratification)
- Support for parametric (Gaussian, Poisson, gamma, etc.) and extended
  bootstrapping (also varies sample size)
- 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 (**USP test of independence** and others)
- Tools for working with empirical distributions (CDF, quantile, etc.)
- Depends only on `numpy`_ and `scipy`_
- Optional code acceleration with `numba`_

Example
-------

.. code-block:: python

      # bootstrap uncertainty of arithmetic mean
      from resample.bootstrap import variance
      import numpy as np

      d = [1, 2, 6, 3, 5]

      print(f"bootstrap {variance(np.mean, d) ** 0.5:.2f} exact {(np.var(d) / len(d)) ** 0.5:.2f}")
      # bootstrap 0.82 exact 0.83

.. _numpy: http://www.numpy.org
.. _scipy: https://www.scipy.org
.. _numba: https://numba.pydata.org

Installation
------------
You can install with pip, but you need a C compiler on the target machine.

.. code-block:: shell

      pip install resample


