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.
Installation
Last release
PreliZ is available for installation from PyPI. 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 overconfident 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
Code of Conduct
PreliZ wishes to maintain a positive community. Additional details can be found in the Code of Conduct
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.
Sponsors
2 maintainers
Metadata
2 maintainers
Release files for preliz 0.1.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| preliz-0.1.1.tar.gz | 35.1 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| preliz-0.1.1-py2.py3-none-any.whl | Python 2, Python 3 | none | any | Details |
Total release size: 81.7 kB
Release files / preliz-0.1.1.tar.gz
| Download URL | preliz-0.1.1.tar.gz |
|---|---|
| Size | 35.1 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
7036275e415c1fa281438aa4f2a46590067d444a179c719d98801bc0d2a99328
|
|
BLAKE2b-256 checksum How to use checksums |
80e8733b42441538148a253cd3ae7f35253b547ed2341b8d17e58302dd2eef06
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/4.0.1 CPython/3.11.1
|
Release files / preliz-0.1.1-py2.py3-none-any.whl
| Download URL | preliz-0.1.1-py2.py3-none-any.whl |
|---|---|
| Size | 46.6 kB |
| Tags | Python 2 Python 3 |
|
SHA-256 checksum How to use checksums |
0e3cd1a4f2f824d73e66fbf2e4ab85980f5b70d89e98e5d050dc5cc88d096e3a
|
|
BLAKE2b-256 checksum How to use checksums |
4bd1574dab6ec9302c6bf366040969bb887f3b42ee1cbe1410c18353a52cf863
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/4.0.1 CPython/3.11.1
|
