semvec/pypi-packages huggingface dataset:
discopula 0.6.1
This package is for discrete copula modeling and implementation of new scoring methods pertaining to ordinal and categorical discrete data.
License: MIT
Requires Python: >=3.8
Keywords: discrete-copula

# discopula

> Discrete checkerboard copula modeling and implementation of new scoring methods pertaining to ordinal and categorical discrete data.

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## Installation

This package (discopula) is hosted on PyPi, so for installation follow the following workflow ...

```console
$ pip install discopula
```

Now, you should be all set to use it in a Jupyter Notebook!

Alternatively, if you would like to use it in a project, we recommend you to have a virtual environment for your use of this package, then follow the following workflow. For best practices, it's recommended to use a virtual environment:

1. First, create and activate a virtual environment (Python 3.8+ recommended):

```bash
# Create virtual environment
$ python -m venv discopula-env

# Activate virtual environment (Mac/Linux)
$ source discopula-env/bin/activate

# Verify you're in the virtual environment
$ which python
```

2. Install package

```bash
$ pip install discopula
```

3. To deactivate the virtual environment, when done:

```bash
$ deactivate
```

## Documentation

Visit [Read the Docs](https://discopula.readthedocs.org) for the full documentation, including overviews and several examples.

## Examples

For detailed examples in Jupyter Notebooks and beyond (organized by functionality) please refer to our [GitHub repository's examples folder](https://github.com/dmavani25/discopula/tree/master/examples).

## Features

- Construction of checkerboard copulas from contingency tables and/or list of cases
- Calculation of marginal distributions and CDFs
- Computation of Checkerboard Copula Regression (CCR) and Prediction based on CCR
- Implementation of Checkerboard Copula Regression Association Measure (CCRAM) and the Scaled CCRAM (SCCRAM)
- Bootstrap functionality for CCR-based prediction, CCRAM and SCCRAM
- Permutation testing functionality for CCRAM & SCCRAM
- Vectorized implementations for improved performance
- Rigorous Edge-case Handling & Unit Testing with Pytest 

## Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

## License

This project is licensed under the MIT License - see the LICENSE file for details.
exfil-kit 0.1.0
A stealthy data extraction toolkit
License: MIT
Requires Python: <4.0,>=3.11
Keywords: scraping, data-extraction, stealth, automation

# exfil-kit

A stealthy data extraction toolkit built with Botright and Botasaurus.

## Installation

```bash
pip install exfil-kit
```

## Quick Start

```python
from exfil_kit import StealthExtractor

extractor = StealthExtractor()
data = extractor.extract("https://example.com")
print(data)
```

## Features

- Built on Botright and Botasaurus
- Stealth-focused extraction
- Clean, reliable data output
- Simple, focused API

## License

MIT