Metadata-Version: 2.1
Name: preliz
Version: 0.0.3
Summary: The place for all your prior elicitation needs.
Author-email: ArviZ team <arviz.devs@gmail.com>
Description-Content-Type: text/markdown
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Education
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Requires-Dist: arviz
Requires-Dist: numpy>=1.22
Requires-Dist: scipy>=1.9.1
Requires-Dist: matplotlib>=3.5
Requires-Dist: nbclient<0.6,>=0.2
Requires-Dist: ipywidgets
Project-URL: source, https://github.com/arviz-devs/preliz
Project-URL: tracker, https://github.com/arviz-devs/preliz/issues

# PreliZ
Tools to help you pick a prior.


**This package is very new and all of its features are experimental, not yet well tested, and subject to change without notice.**


## Documentation

The PreliZ documentation can be found in the [official docs](https://preliz.readthedocs.io/en/latest/).

## Installation

### Last release
PreliZ is available for installation from [PyPI](https://pypi.org/project/preliz/).
The latest  version can be installed using pip:

```
pip install preliz
```

### Development
The latest development version can be installed from the main branch using pip:

```
pip install git+git://github.com/arviz-devs/preliz.git
```


## The Zen of PreliZ
* Being open source, community-driven, diverse and inclusive.
* Avoid fully-automated solutions, keep the human in the loop
* Separate tasks between humans and computers, so users can retain control of important decisions while numerically demanding, error-prone or tedious tasks are automatized.
* Prevent users to become overconfidence in their own opinions.
* Easily integrate with other tools.
* Allow predictive elicitation
* Having a simple and intuitive interface suitable for non-specialists in order to minimize cognitive biases and heuristics.
* Switching between different types of visualization such as kernel density estimates plots, quantile dotplots, histograms, etc. 
* Being agnostic of the underlying probabilistic programming language
* Being modular


## Contributions
PreliZ is a community project and welcomes contributions.
Additional information can be found in the [Contributing Readme](https://github.com/arviz-devs/preliz/blob/main/CONTRIBUTING.md)


## Code of Conduct
PreliZ wishes to maintain a positive community. Additional details
can be found in the [Code of Conduct](https://github.com/arviz-devs/preliz/blob/main/CODE_OF_CONDUCT.md)

## Donations
PreliZ, as other ArviZ-devs projects, is a non-profit project under the NumFOCUS umbrella. If you want to support PreliZ financially, you can donate [here](https://numfocus.org/donate-to-arviz).

## Sponsors
[![NumFOCUS](https://www.numfocus.org/wp-content/uploads/2017/07/NumFocus_LRG.png)](https://numfocus.org)

