PBSA : Proximity Based Survival Analysis
Due to some uncertain cause we had to retract the package, this will bw made avaialble after December,2024.
Proximity Based Survival Analysis are algorithms, designed for survival prediction using proximity information. The k-NN survival, Random Survival Forest, Kernel Survival are some examples of proximity based survival analysis. While this package tends to provide those algorithms later, currently the package provides the following algorithms:
- COBRA Survival
For now other algorithms are taken from scikit-survival and np_survival to provide as a base learner for the ensemble algorithms.
installation
pip install proxsurv
The documentation is available at https://pbsa.readthedocs.io/en/latest/
Metadata
Release files for proxsurv 0.0.3
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| proxsurv-0.0.3.tar.gz | 15.1 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| proxsurv-0.0.3-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 31.1 kB
Release files / proxsurv-0.0.3.tar.gz
| Download URL | proxsurv-0.0.3.tar.gz |
|---|---|
| Size | 15.1 kB |
| Tags | Source |
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SHA-256 checksum How to use checksums |
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No |
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twine/4.0.2 CPython/3.9.18
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Release files / proxsurv-0.0.3-py3-none-any.whl
| Download URL | proxsurv-0.0.3-py3-none-any.whl |
|---|---|
| Size | 16.0 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/4.0.2 CPython/3.9.18
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