Metadata-Version: 2.4
Name: microimpute
Version: 0.2.0
Summary: Benchmarking imputation methods for microdata
Requires-Python: >=3.11
Description-Content-Type: text/markdown
Requires-Dist: numpy<2.0.0,>=1.26.0
Requires-Dist: pandas<3.0.0,>=2.2.0
Requires-Dist: plotly<6.0.0,>=5.24.0
Requires-Dist: kaleido<0.3.0,>=0.2.1
Requires-Dist: scikit-learn<2.0.0,>=1.6.1
Requires-Dist: scipy<2.0.0,>=1.11.0
Requires-Dist: requests<3.0.0,>=2.32.0
Requires-Dist: tqdm<5.0.0,>=4.65.0
Requires-Dist: statsmodels<0.15.0,>=0.14.0
Requires-Dist: quantile-forest<1.5.0,>=1.4.0
Requires-Dist: pydantic<3.0.0,>=2.8.0
Requires-Dist: optuna==4.3.0
Requires-Dist: joblib<2.0.0,>=1.2.0
Provides-Extra: dev
Requires-Dist: pytest<9.0.0,>=8.0.0; extra == "dev"
Requires-Dist: pytest-cov<7.0.0,>=6.0.0; extra == "dev"
Requires-Dist: flake8<7.0.0,>=6.0.0; extra == "dev"
Requires-Dist: black>=23.0.0; extra == "dev"
Requires-Dist: isort<6.0.0,>=5.9.0; extra == "dev"
Requires-Dist: mypy<2.0.0,>=1.0.0; extra == "dev"
Requires-Dist: build<2.0.0,>=1.0.0; extra == "dev"
Requires-Dist: linecheck<0.2.0,>=0.1.0; extra == "dev"
Provides-Extra: matching
Requires-Dist: rpy2<4.0.0,>=3.5.0; extra == "matching"
Provides-Extra: docs
Requires-Dist: sphinx<6.0.0,>=5.0.0; extra == "docs"
Requires-Dist: docutils<0.18.0,>=0.17.0; extra == "docs"
Requires-Dist: jupyter-book>=0.15.0; extra == "docs"
Requires-Dist: sphinx-book-theme>=1.0.0; extra == "docs"
Requires-Dist: sphinx-copybutton>=0.5.0; extra == "docs"
Requires-Dist: sphinx-design>=0.3.0; extra == "docs"
Requires-Dist: ipywidgets<8.0.0,>=7.8.0; extra == "docs"
Requires-Dist: plotly<6.0.0,>=5.24.0; extra == "docs"
Requires-Dist: sphinx-argparse>=0.4.0; extra == "docs"
Requires-Dist: sphinx-math-dollar>=1.2.1; extra == "docs"
Requires-Dist: myst-parser==0.18.1; extra == "docs"
Requires-Dist: myst-nb==0.17.2; extra == "docs"
Requires-Dist: pyyaml; extra == "docs"
Requires-Dist: furo==2022.12.7; extra == "docs"
Requires-Dist: h5py<4.0.0,>=3.1.0; extra == "docs"

# MicroImpute

MicroImpute enables variable imputation through different statistical methods. It facilitates comparison and benchmarking across methods through quantile loss calculations.

To install, run pip install microimpute.
 
