xicor
xi correlation method adapted for python
What is xicor?
xicor is an implementation of the "xi" correlation metric described in Chatterjee, S. (2019, September 22). A new coefficient of correlation. arxiv.org/abs/1909.10140. It is based off the R code mentioned in the paper: https://statweb.stanford.edu/~souravc/xi.R
- Free software: MIT license
- Documentation: https://czbiohub.github.io/xicor
Installation
The package can be installed from PyPI using pip here:
pip install xicor
Developmental install
To install this code and play around with the code locally, clone this github repository and use pip to install:
git clone https://github.com/czbiohub/xicor.git
cd xicor
# The "." means "install *this*, the folder where I am now"
pip install .
# or you could install using
python setup.py install
Usage
from xicor.xicor import Xi
xi_obj = Xi([1, 2, 3], [1, 2, 3])
correlation = xi_obj.correlation
pvals = xi_obj.pval_asymptotic(ties=False, nperm=1000)
Metadata
Release files for xicor 1.0.1
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Source distribution (sdist)
| File | Size | Uploaded | Provenance | Actions |
|---|---|---|---|---|
| xicor-1.0.1.tar.gz | 139.0 kB | Without provenance | Details for xicor-1.0.1.tar.gz |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Actions Reset |
|---|---|---|---|---|
| xicor-1.0.1-py3-none-any.whl | Python 3 | none | any | Details for xicor-1.0.1-py3-none-any.whl |
Total release size: 153.0 kB
Release files / xicor-1.0.1.tar.gz
| Download URL | xicor-1.0.1.tar.gz |
|---|---|
| Size | 139.0 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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No |
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twine/3.4.2 importlib_metadata/4.6.1 pkginfo/1.7.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.61.2 CPython/3.9.6
|
Release files / xicor-1.0.1-py3-none-any.whl
| Download URL | xicor-1.0.1-py3-none-any.whl |
|---|---|
| Size | 14.0 kB |
| Tags | Python 3 |
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SHA-256 checksum How to use checksums |
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| Uploaded via |
twine/3.4.2 importlib_metadata/4.6.1 pkginfo/1.7.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.61.2 CPython/3.9.6
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Security for xicor 1.0.1
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