# pyKrack
<!-- WARNING: THIS FILE WAS AUTOGENERATED! DO NOT EDIT! -->
This file will become your README and also the index of your
documentation.
THIS IS CLEARLY STILL A WORK IN PROGRESS PROJECT
## Install
Due to the comparisons with the r package sna We recommend using conda.
Create an environment using the environment.yml file, load it and
install/run the package.
Alternatively pyKrack can also be isntalled using pip via the following
command
``` sh
pip install pyKrack
```
Then install the R dependencies listed in the conda environmnet.yml
manually.
## How to use
Please see the core and hierarchy notebooks for more detailed
explanations.
**pyKrack** consists of one main function,
[`compute_hierarchy`](https://FerranC96.github.io/pykrack/hierarchy.html#compute_hierarchy).
------------------------------------------------------------------------
<a
href="https://github.com/FerranC96/pykrack/blob/main/pykrack/hierarchy.py#LNone"
target="_blank" style="float:right; font-size:smaller">source</a>
### compute_hierarchy
> compute_hierarchy (G, metric='pykrack')
Compute one of the possible hierarchy scores
| | **Type** | **Default** | **Details** |
|-------------|-----------|-------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| G | | | Directed NetworkX graph |
| metric | str | pykrack | Type of hierarchy metric to compute. Accepted types are:<br>‘pykrack’ for this module’s implementation of the Krackhardt score.<br>‘rsnakrack’ for the sna implementation in R.<br>‘hierarchy_flow’ for the Luo and Magee 2011 as implemented in the NetworkX package. |
| **Returns** | **float** | | **One of the possible hierarchy scores** |
Raw data
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"description": "# pyKrack\n\n<!-- WARNING: THIS FILE WAS AUTOGENERATED! DO NOT EDIT! -->\n\nThis file will become your README and also the index of your\ndocumentation.\n\nTHIS IS CLEARLY STILL A WORK IN PROGRESS PROJECT\n\n## Install\n\nDue to the comparisons with the r package sna We recommend using conda.\nCreate an environment using the environment.yml file, load it and\ninstall/run the package.\n\nAlternatively pyKrack can also be isntalled using pip via the following\ncommand\n\n``` sh\npip install pyKrack\n```\n\nThen install the R dependencies listed in the conda environmnet.yml\nmanually.\n\n## How to use\n\nPlease see the core and hierarchy notebooks for more detailed\nexplanations.\n\n**pyKrack** consists of one main function,\n[`compute_hierarchy`](https://FerranC96.github.io/pykrack/hierarchy.html#compute_hierarchy).\n\n------------------------------------------------------------------------\n\n<a\nhref=\"https://github.com/FerranC96/pykrack/blob/main/pykrack/hierarchy.py#LNone\"\ntarget=\"_blank\" style=\"float:right; font-size:smaller\">source</a>\n\n### compute_hierarchy\n\n> compute_hierarchy (G, metric='pykrack')\n\nCompute one of the possible hierarchy scores\n\n| | **Type** | **Default** | **Details** |\n|-------------|-----------|-------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| G | | | Directed NetworkX graph |\n| metric | str | pykrack | Type of hierarchy metric to compute. Accepted types are:<br>\u2018pykrack\u2019 for this module\u2019s implementation of the Krackhardt score.<br>\u2018rsnakrack\u2019 for the sna implementation in R.<br>\u2018hierarchy_flow\u2019 for the Luo and Magee 2011 as implemented in the NetworkX package. |\n| **Returns** | **float** | | **One of the possible hierarchy scores** |\n",
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