Metadata-Version: 2.4
Name: pygformula
Version: 1.1.7
Summary: A python implementation of the parametric g-formula
Maintainer: Jing Li
Maintainer-email: jing_li@hsph.harvard.edu
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: joblib>=1.2
Requires-Dist: lifelines>=0.27
Requires-Dist: matplotlib>=3.5
Requires-Dist: numpy>=1.22
Requires-Dist: pandas>=1.5
Requires-Dist: prettytable>=3.10
Requires-Dist: pytruncreg>=0.1
Requires-Dist: scipy>=1.10
Requires-Dist: seaborn>=0.11
Requires-Dist: statsmodels>=0.14
Requires-Dist: tqdm>=4.64
Requires-Dist: PyQt5>=5.15
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# pygformula: a python implementation of the parametric g-formula

[![PyPI version](https://badge.fury.io/py/pygformula.svg)](https://pypi.org/project/pygformula)
[![Documentation Status](https://readthedocs.org/projects/pygformula/badge/?version=latest)](https://pygformula.readthedocs.io)
[![Downloads](https://static.pepy.tech/badge/pygformula)](https://pepy.tech/project/pygformula)

**Authors: Jing Li, Sophia Rein, Sean McGrath, Roger Logan, Ryan O’Dea, Miguel Hernán**


## Overview
The pygformula package implements the non-iterative conditional expectation (NICE) estimator of the g-formula algorithm
(Robins, 1986). The g-formula can estimate an outcome’s counterfactual mean or risk under hypothetical treatment strategies
(interventions) when there is sufficient information on time-varying treatments and confounders.


### Features

* Treatments: discrete or continuous time-varying treatments.
* Outcomes: failure time outcomes or continuous/binary end of follow-up outcomes.
* Interventions: interventions on a single treatment or joint interventions on multiple treatments.
* Random measurement/visit process.
* Incorporation of a priori knowledge of the data structure.
* Censoring events.
* Competing events.


## Requirements

The package requires python 3.8+ and these necessary dependencies:

- cmprsk
- joblib
- lifelines
- matplotlib
- numpy
- pandas
- prettytable
- pytruncreg
- scipy
- seaborn
- statsmodels
- tqdm


## Documentation

The online documentation is available at [pygformula documentation](https://pygformula.readthedocs.io).

## Issues

If you have any issues, please open an [issue](https://github.com/CausalInference/pygformula/issues) on github, we will 
regularly check the questions. For any additional questions or comments, please email jing_li@hsph.harvard.edu.
