[](https://opensource.org/licenses/BSD-3-Clause)
[](https://boevalab.github.io/sparsesurv/)
# sparsesurv
`sparsesurv` [1] is a toolbox for high-dimensional survival analysis. Currently, the package is focused exclusively on knowledge distillation for sparse survival analysis, sometimes also called preconditoning [2, 3]. In the future, we plan to also extend `sparsesurv` to other techniques useful for (high-dimensional) survival analysis that are not commonly available in Python.
## Installation
The easiest way to install `sparsesurv` is currently via PyPi:
```
pip install sparsesurv
```
If you want to install directly from Github, you can also install by cloning the repo, or directly piping the repo to pip:
```
git clone https://github.com/BoevaLab/sparsesurv/
cd sparsesurv
pip install .
```
```
pip install git+https://github.com/BoevaLab/sparsesurv.git
```
If there is sufficient interest, we may also provide a conda package in the future.
## Bug reports and feature requests
If you have a bug report to make or a feature request for something you would like included in `sparsesurv` in the future, please open a [Github issue](https://github.com/BoevaLab/sparsesurv/issues).
## General questions
If you have general __questions__, meaning you are unsure about the usage of `sparsesurv`, or have other questions about the package that do not seem like a bug or feature request, please use [Github discussions](https://github.com/BoevaLab/sparsesurv/discussions/).
## Documentation and user guides
Documentation and user guides are available on [Github pages](https://boevalab.github.io/sparsesurv/index.html).
## Contributing
We always welcome new contributors to `sparsesurv`. If you're interested in contributing, get in touch with us (see Contact) or have a look at the open issues.
## Contact
[Nikita Janakarajan](jnikita@ethz.ch)
[David Wissel](dwissel@ethz.ch)
## Citation
If you use any or part of this package, please cite our work.
[TODO - add bibtext]
## References
[1] David Wissel, Nikita Janakarajan, Daniel Rowson, Julius Schulte, Xintian Yuan, Valentina Boeva. "sparsesurv: Sparse survival models via knowledge distillation." (2023, under review).
[2] Paul, Debashis, et al. "“Preconditioning” for feature selection and regression in high-dimensional problems." (2008): 1595-1618.
[3] Pavone, Federico, et al. "Using reference models in variable selection." Computational Statistics 38.1 (2023): 349-371.
Raw data
{
"_id": null,
"home_page": "https://github.com/BoevaLab/sparsesurv",
"name": "sparsesurv",
"maintainer": "",
"docs_url": null,
"requires_python": "",
"maintainer_email": "",
"keywords": "Survival,Knowledge Distillation,Sparse Survival,High Dimensional Data",
"author": "",
"author_email": "dwissel@inf.ethz.ch, jnikita@inf.ethz.ch",
"download_url": "https://files.pythonhosted.org/packages/88/45/e2eaf40b78e0006f0cca22dc6d4c36f73cad85a1e70166b3222e708fda82/sparsesurv-0.1.tar.gz",
"platform": null,
"description": "[](https://opensource.org/licenses/BSD-3-Clause)\n[](https://boevalab.github.io/sparsesurv/)\n\n\n# sparsesurv\n`sparsesurv` [1] is a toolbox for high-dimensional survival analysis. Currently, the package is focused exclusively on knowledge distillation for sparse survival analysis, sometimes also called preconditoning [2, 3]. In the future, we plan to also extend `sparsesurv` to other techniques useful for (high-dimensional) survival analysis that are not commonly available in Python.\n\n## Installation\nThe easiest way to install `sparsesurv` is currently via PyPi:\n\n```\npip install sparsesurv\n```\n\nIf you want to install directly from Github, you can also install by cloning the repo, or directly piping the repo to pip:\n\n```\ngit clone https://github.com/BoevaLab/sparsesurv/\ncd sparsesurv\npip install .\n```\n\n```\npip install git+https://github.com/BoevaLab/sparsesurv.git\n```\n\nIf there is sufficient interest, we may also provide a conda package in the future.\n\n## Bug reports and feature requests\nIf you have a bug report to make or a feature request for something you would like included in `sparsesurv` in the future, please open a [Github issue](https://github.com/BoevaLab/sparsesurv/issues).\n\n## General questions\nIf you have general __questions__, meaning you are unsure about the usage of `sparsesurv`, or have other questions about the package that do not seem like a bug or feature request, please use [Github discussions](https://github.com/BoevaLab/sparsesurv/discussions/).\n\n## Documentation and user guides\nDocumentation and user guides are available on [Github pages](https://boevalab.github.io/sparsesurv/index.html).\n\n## Contributing\nWe always welcome new contributors to `sparsesurv`. If you're interested in contributing, get in touch with us (see Contact) or have a look at the open issues.\n\n## Contact\n[Nikita Janakarajan](jnikita@ethz.ch)\n\n[David Wissel](dwissel@ethz.ch)\n\n## Citation\nIf you use any or part of this package, please cite our work.\n[TODO - add bibtext]\n\n\n## References\n[1] David Wissel, Nikita Janakarajan, Daniel Rowson, Julius Schulte, Xintian Yuan, Valentina Boeva. \"sparsesurv: Sparse survival models via knowledge distillation.\" (2023, under review).\n\n[2] Paul, Debashis, et al. \"\u201cPreconditioning\u201d for feature selection and regression in high-dimensional problems.\" (2008): 1595-1618.\n\n[3] Pavone, Federico, et al. \"Using reference models in variable selection.\" Computational Statistics 38.1 (2023): 349-371.\n",
"bugtrack_url": null,
"license": "BSD-3-Clause",
"summary": "Installable sparsesurv package via pip or source. Modify as needed.",
"version": "0.1",
"project_urls": {
"Homepage": "https://github.com/BoevaLab/sparsesurv"
},
"split_keywords": [
"survival",
"knowledge distillation",
"sparse survival",
"high dimensional data"
],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "1b2c4ad8976f03111356c2fdf7b2c22976d8e2d1d64581be9c01329eab70b020",
"md5": "068f4fef997e62b245844df298d4f9d6",
"sha256": "0c7b682a7011afca86896aeb572d66295d90b64d941ec04667c8d695dfb9d8df"
},
"downloads": -1,
"filename": "sparsesurv-0.1-py3-none-any.whl",
"has_sig": false,
"md5_digest": "068f4fef997e62b245844df298d4f9d6",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": null,
"size": 32138,
"upload_time": "2023-10-26T18:45:10",
"upload_time_iso_8601": "2023-10-26T18:45:10.489144Z",
"url": "https://files.pythonhosted.org/packages/1b/2c/4ad8976f03111356c2fdf7b2c22976d8e2d1d64581be9c01329eab70b020/sparsesurv-0.1-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "8845e2eaf40b78e0006f0cca22dc6d4c36f73cad85a1e70166b3222e708fda82",
"md5": "3cb518a83559277a8fd1d6732eec5a54",
"sha256": "70c5f1e1b55eb5cc5aa0c57d6cc14a7ca26a03e276964e4406a5955cc36f66a3"
},
"downloads": -1,
"filename": "sparsesurv-0.1.tar.gz",
"has_sig": false,
"md5_digest": "3cb518a83559277a8fd1d6732eec5a54",
"packagetype": "sdist",
"python_version": "source",
"requires_python": null,
"size": 30013,
"upload_time": "2023-10-26T18:45:12",
"upload_time_iso_8601": "2023-10-26T18:45:12.950012Z",
"url": "https://files.pythonhosted.org/packages/88/45/e2eaf40b78e0006f0cca22dc6d4c36f73cad85a1e70166b3222e708fda82/sparsesurv-0.1.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2023-10-26 18:45:12",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "BoevaLab",
"github_project": "sparsesurv",
"travis_ci": false,
"coveralls": false,
"github_actions": true,
"requirements": [],
"lcname": "sparsesurv"
}
JSON
