pycox is a python package for survival analysis and time-to-event prediction with PyTorch. It is built on the torchtuples package for training PyTorch models.
Read the documentation at: https://github.com/havakv/pycox
The package contains
- survival models: (Logistic-Hazard, DeepHit, DeepSurv, Cox-Time, MTLR, etc.)
- evaluation criteria (concordance, Brier score, Binomial log-likelihood, etc.)
- event-time datasets (SUPPORT, METABRIC, KKBox, etc)
- simulation studies
- illustrative examples
1 maintainer
Release files for pycox 0.2.3
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| pycox-0.2.3.tar.gz | 57.8 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| pycox-0.2.3-py3-none-any.whl | Python 3 | none | any | Details |
Total release size:131.4 kB
Release files / pycox-0.2.3.tar.gz
| Download URL | pycox-0.2.3.tar.gz |
|---|---|
| Size | 57.8 kB |
| Tags | Source |
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SHA-256 checksum How to use checksums |
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twine/3.7.1 importlib_metadata/4.10.0 pkginfo/1.8.2 requests/2.27.1 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.9.9
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Release files / pycox-0.2.3-py3-none-any.whl
| Download URL | pycox-0.2.3-py3-none-any.whl |
|---|---|
| Size | 73.7 kB |
| Tags | Python 3 |
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SHA-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/3.7.1 importlib_metadata/4.10.0 pkginfo/1.8.2 requests/2.27.1 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.9.9
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