Metadata-Version: 2.1
Name: resample
Version: 1.5.0b0
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
Description: .. |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
        .. image:: https://coveralls.io/repos/github/resample-project/resample/badge.svg
           :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
        
        
        .. 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) 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.)
        - Depends only on `numpy`_ and `scipy`_
        
        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
        
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
