Tools for randomization-based inference in Python
Project description
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
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
resample-0.2.tar.gz
(6.5 kB
view details)
File details
Details for the file resample-0.2.tar.gz.
File metadata
- Download URL: resample-0.2.tar.gz
- Upload date:
- Size: 6.5 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/1.11.0 pkginfo/1.4.2 requests/2.18.4 setuptools/40.2.0 requests-toolbelt/0.8.0 tqdm/4.25.0 CPython/3.6.5
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | d1fc997c2fef667e8bce26a8a8bc222af252a6d87a701d3018ef52ceeeea315d |
|
| MD5 | 4bce44e965c7e7d10f4f29c126b490f6 |
|
| BLAKE2b-256 | bc1837ea2c2c6285b70e8aa79a5c7b1946491e840c3f8b65ab41bfe0d6aab85f |
