Survival analysis with PyTorch
Project description
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
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
pycox-0.3.0.tar.gz
(59.3 kB
view details)
Built Distribution
pycox-0.3.0-py3-none-any.whl
(73.6 kB
view details)
File details
Details for the file pycox-0.3.0.tar.gz.
File metadata
- Download URL: pycox-0.3.0.tar.gz
- Upload date:
- Size: 59.3 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/5.1.1 CPython/3.9.19
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | 745859ecfa4097cf8e8aef06f08048a41b7b2254fcffb9fc9f8926b1f38b6b1b |
|
| MD5 | d7143fe05f55282310f447916c22e54a |
|
| BLAKE2b-256 | ee839b8437ebeba230a5311d9409afe792094d25aa9d8c83269d2da147d068be |
File details
Details for the file pycox-0.3.0-py3-none-any.whl.
File metadata
- Download URL: pycox-0.3.0-py3-none-any.whl
- Upload date:
- Size: 73.6 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/5.1.1 CPython/3.9.19
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | 764c988da8579bd2aedea5cee1d425009a09bf048ad56450b02a0a24122a1eb2 |
|
| MD5 | abd859a94ef312e0354c93d0f19cbc0d |
|
| BLAKE2b-256 | 97d5467cf21524e03b41daa1e5c66ab53e226d1e84cf2b17eeaf831aadb0b72a |
