gen_surv

Coverage PyPI Tests Python

gen_surv is a Python package for simulating survival data under a variety of models, inspired by the R package genSurv. It supports data generation for:

Cox Proportional Hazards Models (CPHM)
Continuous-Time Markov Models (CMM)
Time-Dependent Covariate Models (TDCM)
Time-Homogeneous Hidden Markov Models (THMM)

📦 Installation

poetry install

✨ Features

Consistent interface across models
Censoring support (uniform or exponential)
Easy integration with pandas and NumPy
Suitable for benchmarking survival algorithms and teaching

🧪 Example

from gen_surv.cphm import gen_cphm

df = gen_cphm(
    n=100,
    model_cens="uniform",
    cens_par=1.0,
    beta=0.5,
    covar=2.0
)
print(df.head())

from gen_surv import generate

df = generate(
    model="cphm",
    n=100,
    model_cens="uniform",
    cens_par=1.0,
    beta=0.5,
    covar=2.0
)

print(df.head())

🔧 Available Generators

Function	Description
`gen_cphm()`	Cox Proportional Hazards Model
`gen_cmm()`	Continuous-Time Multi-State Markov Model
`gen_tdcm()`	Time-Dependent Covariate Model
`gen_thmm()`	Time-Homogeneous Markov Model

genSurvPy/
├── gen_surv/             # Pacote principal
│   ├── __main__.py       # Interface CLI via python -m
│   ├── cphm.py
│   ├── cmm.py
│   ├── tdcm.py
│   ├── thmm.py
│   ├── censoring.py
│   ├── bivariate.py
│   ├── validate.py
│
├── tests/                # Testes automatizados
│   ├── test_cphm.py
│   ├── test_cmm.py
│   ├── test_tdcm.py
│   ├── test_thmm.py
│
├── examples/             # Exemplos de uso
│   ├── run_cphm.py
│   ├── ...
│
├── pyproject.toml        # Configurado com Poetry
├── README.md
├── LICENSE
├── .gitignore

🧠 License

MIT License. See LICENSE for details.

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