semvec/pypi-packages huggingface dataset:
pval-corrections 0.1.0
A Python package for statistical p-value corrections
License: MIT
Requires Python: >=3.8

# P-value Corrections

A Python package for statistical p-value corrections including Bonferroni and Benjamini-Hochberg corrections.

## Features

- **Bonferroni Correction**: Controls family-wise error rate (FWER)
- **Benjamini-Hochberg Correction**: Controls false discovery rate (FDR)
- **Python API**: Simple programmatic interface

## Installation

### From Source

```bash
git clone https://github.com/Chris-R030307/Pvalue_correction.git
cd pval-corrections
pip install -e .
```

### Development Installation

```bash
git clone https://github.com/Chris-R030307/Pvalue_correction.git
cd pval-corrections
pip install -e ".[dev]"
```

## Usage

### Python API

```python
from pval_corrections import correction

# Example p-values
p_values = [0.01, 0.02, 0.03, 0.04, 0.05]

# Bonferroni correction
bonferroni_result = correction(p_values, 'bonferroni')
print("Bonferroni adjusted p-values:", bonferroni_result['padj'])

# Benjamini-Hochberg correction
bh_result = correction(p_values, 'benjamini_hochberg')
print("BH adjusted p-values:", bh_result['padj'])
```


## Methods

### Bonferroni Correction

The Bonferroni correction controls the family-wise error rate (FWER) by multiplying each p-value by the number of tests:

```
p_adj = p * m
```

Where `m` is the number of tests.

### Benjamini-Hochberg Correction

The Benjamini-Hochberg correction controls the false discovery rate (FDR) using a step-up procedure:

```
p_adj = p * m / i
```

Where `m` is the number of tests and `i` is the rank of the p-value.

## Requirements

- Python >= 3.8
- pandas >= 2.1.3
- numpy >= 2.1.3
- matplotlib >= 3.10.0
- seaborn >= 0.13.2
- scipy >= 1.13.0
- scikit-learn >= 1.3.0
- statsmodels >= 0.14.0

## Development

### Running Tests

```bash
pytest
```

### Code Formatting

```bash
black pval_corrections/
```

### Type Checking

```bash
mypy pval_corrections/
```

## License

MIT License - see LICENSE file for details.

## Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests
5. Submit a pull request

## Citation

If you use this package in your research, please cite:

```bibtex
@software{pval_corrections,
  title={P-value Corrections},
  author={Chris-R030307},
  year={2024},
  url={https://github.com/Chris-R030307/Pvalue_correction}
}
```
aviro 0.1.5
thin sdk for agents to learn through experience
License: MIT
Requires Python: >=3.8

# Aviro

A Python package for [describe your package's purpose here].

## Installation

You can install aviro using pip:

```bash
pip install aviro
```

## Usage

```python
import aviro

# Your package usage examples here
```

## Development

To set up the development environment:

```bash
# Clone the repository
git clone https://github.com/yourusername/aviro.git
cd aviro

# Install in development mode
pip install -e .

# Install development dependencies
pip install -e ".[dev]"

# Run tests
pytest

# Format code
black aviro tests

# Lint code
flake8 aviro tests
```

## Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests for your changes
5. Run the test suite
6. Submit a pull request

## License

This project is licensed under the MIT License - see the LICENSE file for details.