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 0.3.0
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-0.3.0.tar.gz | 59.3 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| pycox-0.3.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 133.0 kB
Release files / pycox-0.3.0.tar.gz
| Download URL | pycox-0.3.0.tar.gz |
|---|---|
| Size | 59.3 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
745859ecfa4097cf8e8aef06f08048a41b7b2254fcffb9fc9f8926b1f38b6b1b
|
|
BLAKE2b-256 checksum How to use checksums |
ee839b8437ebeba230a5311d9409afe792094d25aa9d8c83269d2da147d068be
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/5.1.1 CPython/3.9.19
|
Release files / pycox-0.3.0-py3-none-any.whl
| Download URL | pycox-0.3.0-py3-none-any.whl |
|---|---|
| Size | 73.6 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
764c988da8579bd2aedea5cee1d425009a09bf048ad56450b02a0a24122a1eb2
|
|
BLAKE2b-256 checksum How to use checksums |
97d5467cf21524e03b41daa1e5c66ab53e226d1e84cf2b17eeaf831aadb0b72a
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/5.1.1 CPython/3.9.19
|
