Bayesian Monte Carlo Markov Chain Regression
baypy is a python package for solving bayesian regression models through a Monte Carlo Markov chain sampling.
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Contributing
The baypy project welcomes your expertise and enthusiasm! All contributions, bug reports, bug fixes, documentation improvements, enhancements and ideas are welcome. Writing code isn’t the only way to contribute to baypy. You can also:
develop tutorials, presentations and other educational materials
maintain and improve the documentation,
help with outreach and onboard new contributors
Have a look at the contributing guide. If you are new to contributing to open source, this guide helps explain why, what, and how to successfully get involved.
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1 maintainer
Release files for baypy 1.5.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| baypy-1.5.1.tar.gz | 58.3 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| baypy-1.5.1-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 111.2 kB
Release files / baypy-1.5.1.tar.gz
| Download URL | baypy-1.5.1.tar.gz |
|---|---|
| Size | 58.3 kB |
| Tags | Source |
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SHA-256 checksum How to use checksums |
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twine/6.2.0 CPython/3.12.0
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Release files / baypy-1.5.1-py3-none-any.whl
| Download URL | baypy-1.5.1-py3-none-any.whl |
|---|---|
| Size | 52.9 kB |
| Tags | Python 3 |
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SHA-256 checksum How to use checksums |
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BLAKE2b-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.2.0 CPython/3.12.0
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