Metadata-Version: 2.1
Name: python-iArt
Version: 0.1.3
Summary: iArt: A Generalized Framework for Imputation-Assisted Randomization Tests
Home-page: https://github.com/Imputation-Assisted-Randomization-Tests/iArt-py
Author: Siyu Heng, Jiawei Zhang, and Yang Feng
Author-email: siyuheng@nyu.edu,jz4721@nyu.edu,yang.feng@nyu.edu
License: UNKNOWN
Description: # iArt: Imputation-Assisted Randomization Tests
        
        ## Authors
        
        Jiawei Zhang*, Siyu Heng*, and Yang Feng (* indicates equal contribution)
        
        ## Maintainers
        
        Jiawei Zhang (Email: jz4721@nyu.edu), Siyu Heng (Email: siyuheng@nyu.edu), and Yang Feng (Email: yang.feng@nyu.edu)
        
        ## Description
        
        iArt (Imputation-Assisted Randomization Tests) is a Python package designed for conducting finite-population-exact randomization tests in design-based causal studies with missing outcomes. It offers a robust solution to handle missing data in causal inference, leveraging the potential outcomes framework and integrating various outcome imputation algorithms.
        
        ## Installation
        
        To install [iArt](https://pypi.org/project/python-iArt/), run the following command:
        
        ```bash
        pip install python-iArt
        ```
        
        ## Usage
        
        Here is a basic example of how to use iArt:
        
        ```python
        import numpy as np
        import iArt
        
        Z = [1, 1, 1, 1, 0, 0, 0, 0]
        X = [[5.1, 3.5], [4.9, np.nan], [4.7, 3.2], [4.5, np.nan], [7.2, 2.3], [8.6, 3.1], [6.0, 3.6], [8.4, 3.9]]
        Y = [[4.4, 0.5], [4.3, 0.7], [4.1, np.nan], [5.0, 0.4], [1.7, 0.1], [np.nan, 0.2], [1.4, np.nan], [1.7, 0.4]]
        result = iArt.test(Z=Z, X=X, Y=Y, L=1000, verbose=True)
        print(result)
        ```
        Detailed usage can be found here [ReadDoc](https://iart.readthedocs.io/en/latest/)
        
        ## Features
        
        - Conducts finite-population-exact randomization tests.
        - Handles missing data in causal inference studies.
        - Supports various outcome imputation algorithms.
        - Offers covariate adjustment in exact randomization tests.
        
        
        ## Contributing
        
        Your contributions to iArt are highly appreciated! If you're looking to contribute, we encourage you to open issues for any bugs or feature suggestions, or submit pull requests with your proposed changes. 
        
        ### Setting Up a Development Environment
        
        To set up a development environment for contributing to iArt, follow these steps:
        
        ```bash
        python -m venv venv
        source venv/bin/activate 
        pip install -r requirements.txt
        python setup.py install
        ```
        This creates a virtual environment (`venv`) for Python and activates it, allowing you to work on the package without affecting your global Python environment.
        
        ## License
        This project is licensed under the MIT License
        
        ## Citation
        If you use iArt in your research, please consider citing it:
        
        ```code
        @misc{heng2023designbased,
              title={Design-Based Causal Inference with Missing Outcomes: Missingness Mechanisms, Imputation-Assisted Randomization Tests, and Covariate Adjustment}, 
              author={Siyu Heng and Jiawei Zhang and Yang Feng},
              year={2023},
              eprint={2310.18556},
              archivePrefix={arXiv},
              primaryClass={stat.ME}
        }
        ```
        
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.8
Requires-Python: >=3.8
Description-Content-Type: text/markdown
