semvec/pypi-packages huggingface dataset:
zt-features-lib 0.1.6
A Python library with FastAPI endpoints, ready for PyPI.
License: MIT
Requires Python: <3.13,>=3.12

# zt-features-lib

A Python library with a FastAPI app, designed for PyPI publishing. It provides a simple endpoint returning fake data, and is installable via Poetry. The FastAPI app exposes endpoints with Swagger UI, organized under a custom tag for easy identification.

## Features
- FastAPI app with a sample endpoint
- Poetry for dependency management
- Ready for PyPI publishing
- Swagger UI with custom tag for endpoints

## Usage
After installing, you can run the FastAPI app and access the endpoints via Swagger UI.

## Installation
```bash
poetry add zt-features-lib
```

## Running the API
```bash
poetry run zt-features-lib
```

## License
MIT
mawii-surv 0.1.0
Semiparametric Causal Inference for Right-Censored Outcomes with Many Weak Invalid Instruments
License: MIT
Requires Python: >=3.8
Keywords: Censored outcomes, Deep neural networks, instrumental variable, generalized empirical likelihood, Mendelian randomization, Over-identification test, Semiparametric theory, Weak and invalid instruments

# mawii_surv

`mawii_surv` implements G‐estimation methods for treatment effects under endogeneity, both with and without right‐censoring, using a variety of machine‐learning and classical estimators.

---

## Features

- **Complete‐data G‐estimator** (`mawii_noncensor`)  
- **Right‐censoring G‐estimator** (`gmawii_censor`)  
- Multiple model backends:
  - Neural networks
  - Linear regression
  - Random forests
  - XGBoost  
- Choice of Generalized Empirical Likelihood (GEL) functions:
  - Empirical Tilting (ET)
  - Empirical Likelihood (EL)
  - Continuous Updating Estimator (CUE)

---

## Installation

Install from PyPI:

```bash
pip install mawii_surv



Dependencies
Make sure you have the following installed (the minimal compatible versions shown):
numpy>=1.19
torch>=1.8
scipy>=1.5
scikit-learn>=0.24
xgboost>=1.3
numba>=0.53