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fix: ensure doc links and tag workflow (#69)
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‎.github/workflows/ci.yml‎

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push:
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branches:
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- main
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tags:
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- 'v*'
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pull_request:
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branches:
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- main
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- name: Run pre-commit checks
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run: poetry run pre-commit run --all-files
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publish:
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name: Publish to PyPI
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needs: [test, lint]
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runs-on: ubuntu-latest
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if: startsWith(github.ref, 'refs/tags/')
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steps:
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- name: Checkout code
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uses: actions/checkout@v4
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with:
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fetch-depth: 0
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- name: Set up Python
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uses: actions/setup-python@v5
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with:
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python-version: "3.11"
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- name: Verify version matches tag
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run: python scripts/check_version_match.py
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- name: Install Poetry
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run: |
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curl -sSL https://install.python-poetry.org | python3 -
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echo "$HOME/.local/bin" >> $GITHUB_PATH
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- name: Publish
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env:
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POETRY_PYPI_TOKEN_PYPI: ${{ secrets.PYPI_TOKEN }}
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run: poetry publish --build --no-interaction

‎README.md‎

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# gen_surv
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[![Coverage](https://codecov.io/gh/DiogoRibeiro7/genSurvPy/branch/main/graph/badge.svg)](https://app.codecov.io/gh/DiogoRibeiro7/genSurvPy)
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[![Docs](https://readthedocs.org/projects/gensurvpy/badge/?version=latest)](https://gensurvpy.readthedocs.io/en/latest/)
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[![PyPI](https://img.shields.io/pypi/v/gen_surv)](https://pypi.org/project/gen-surv/)
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[![Tests](https://github.com/DiogoRibeiro7/genSurvPy/actions/workflows/ci.yml/badge.svg)](https://github.com/DiogoRibeiro7/genSurvPy/actions/workflows/ci.yml)
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[![Python](https://img.shields.io/pypi/pyversions/gen_surv)](https://pypi.org/project/gen-surv/)
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**gen_surv** is a Python package for simulating survival data under a variety of statistical models. It is inspired by the R package [genSurv](https://cran.r-project.org/package=genSurv) and provides a unified interface for generating realistic survival datasets.
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[![Coverage][cov-badge]][cov-link]
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[![Docs][docs-badge]][docs-link]
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[![PyPI][pypi-badge]][pypi-link]
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[![Tests][ci-badge]][ci-link]
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[![Python][py-badge]][pypi-link]
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[cov-badge]: https://codecov.io/gh/DiogoRibeiro7/genSurvPy/branch/main/graph/badge.svg
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[cov-link]: https://app.codecov.io/gh/DiogoRibeiro7/genSurvPy
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[docs-badge]: https://readthedocs.org/projects/gensurvpy/badge/?version=latest
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[docs-link]: https://gensurvpy.readthedocs.io/en/latest/
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[pypi-badge]: https://img.shields.io/pypi/v/gen_surv
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[pypi-link]: https://pypi.org/project/gen-surv/
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[ci-badge]: https://github.com/DiogoRibeiro7/genSurvPy/actions/workflows/ci.yml/badge.svg
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[ci-link]: https://github.com/DiogoRibeiro7/genSurvPy/actions/workflows/ci.yml
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[py-badge]: https://img.shields.io/pypi/pyversions/gen_surv
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**gen_surv** is a Python library for simulating survival data under a wide range of statistical models. Inspired by the R package [genSurv](https://cran.r-project.org/package=genSurv), it offers a unified interface for generating realistic datasets for research, teaching and benchmarking.
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---
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## Features
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- Cox proportional hazards model (CPHM)
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- Accelerated failure time models (log-normal, log-logistic)
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- Accelerated failure time models (log-normal, log-logistic, Weibull)
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- Continuous-time multi-state Markov model (CMM)
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- Time-dependent covariate model (TDCM)
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- Time-homogeneous hidden Markov model (THMM)
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- Mixture cure and piecewise exponential models
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- Competing risks generators (constant and Weibull hazards)
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- Command-line interface and export utilities
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- Visualization utilities for simulated datasets
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- Scikit-learn compatible data generator
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- Conversion helper for scikit-survival and lifelines
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- Conversion helpers for scikit-survival and lifelines
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- Command-line interface and export utilities
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## Installation
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poetry install --with dev
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```
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Integration tests that rely on scikit-survival are automatically skipped if the
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package is not installed.
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Integration tests that rely on scikit-survival are automatically skipped if the package is not installed.
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## Development Setup
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```
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## Quick Example
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## Usage
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### Python API
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```python
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from gen_surv import export_dataset, generate
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from gen_surv import generate, export_dataset, to_sksurv
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from gen_surv.visualization import plot_survival_curve
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# basic Cox proportional hazards data
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sim = generate(
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# save to an RDS file
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plot_survival_curve(sim)
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export_dataset(sim, "survival_data.rds")
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```
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You can also convert the resulting DataFrame for use with
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[scikit-survival](https://scikit-survival.readthedocs.io) or
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[lifelines](https://lifelines.readthedocs.io):
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```python
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from gen_surv import to_sksurv
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# convert for scikit-survival
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sks_dataset = to_sksurv(sim)
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```
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See the [usage guide](docs/source/getting_started.md) for more examples.
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See the [usage guide](https://gensurvpy.readthedocs.io/en/latest/getting_started.html) for more examples.
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## Supported Models
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| Model | Description |
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|----------------------|-----------------------------------------|
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| **CPHM** | Cox proportional hazards |
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| **AFT** | Accelerated failure time (log-normal, log-logistic) |
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| **CMM** | Continuous-time multi-state Markov |
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| **TDCM** | Time-dependent covariates |
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| **THMM** | Time-homogeneous hidden Markov |
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| **Competing Risks** | Multiple event types with cause-specific hazards |
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| **Mixture Cure** | Models long-term survivors |
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| **Piecewise Exponential** | Flexible baseline hazard via intervals |
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More details on each algorithm are available in the [Algorithms](docs/source/algorithms.md) page and the [theory guide](docs/source/theory.md).
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## Command-Line Usage
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### Command Line
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Datasets can be generated without writing Python code:
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```bash
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python -m gen_surv dataset cphm --n 1000 -o survival.csv
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```
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## Supported Models
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| Model | Description |
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|-------|-------------|
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| **CPHM** | Cox proportional hazards |
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| **AFT** | Accelerated failure time (log-normal, log-logistic, Weibull) |
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| **CMM** | Continuous-time multi-state Markov |
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| **TDCM** | Time-dependent covariates |
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| **THMM** | Time-homogeneous hidden Markov |
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| **Competing Risks** | Multiple event types with cause-specific hazards |
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| **Mixture Cure** | Models long-term survivors |
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| **Piecewise Exponential** | Flexible baseline hazard via intervals |
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More details on each algorithm are available in the [Algorithms](https://gensurvpy.readthedocs.io/en/latest/algorithms.html) page. For additional background, see the [theory guide](https://gensurvpy.readthedocs.io/en/latest/theory.html).
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## Documentation
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Full documentation is hosted on [Read the Docs](https://gensurvpy.readthedocs.io/en/latest/). It includes installation instructions, tutorials, API references and a bibliography.
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- ORCID: <https://orcid.org/0009-0001-2022-7072>
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- Professional email: <[email protected]>
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- Personal email: <[email protected]>
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‎pyproject.toml‎

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[tool.poetry]
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name = "gen_surv"
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version = "1.0.9"
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version = "1.0.8"
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description = "A Python package for simulating survival data, inspired by the R package genSurv"
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authors = ["Diogo Ribeiro <[email protected]>"]
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license = "MIT"
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readme = "README.md"
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packages = [{ include = "gen_surv" }]
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homepage = "https://github.com/DiogoRibeiro7/genSurvPy"
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repository = "https://github.com/DiogoRibeiro7/genSurvPy"
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documentation = "https://gensurvpy.readthedocs.io/en/stable/"
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documentation = "https://gensurvpy.readthedocs.io/en/latest/"
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keywords = ["survival-analysis", "simulation", "cox-model", "markov-model", "time-dependent", "statistics"]
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classifiers = [
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"Development Status :: 5 - Production/Stable",

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