https://github.com/r1cheu/quangpy

quangpy: a python package for quantitative genetics analysis
https://github.com/r1cheu/quangpy

python quantitative-genetics statistics

Last synced: 3 months ago
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quangpy: a python package for quantitative genetics analysis

• Host: GitHub
• URL: https://github.com/r1cheu/quangpy
• Owner: r1cheu
• License: mit
• Created: 2024-10-09T02:54:20.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2025-02-13T09:25:07.000Z (over 1 year ago)
• Last Synced: 2025-09-19T03:56:19.212Z (9 months ago)
• Topics: python, quantitative-genetics, statistics
• Language: Python
• Homepage:
• Size: 43.9 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# quangpy: a python package for **q**uantitative **g**enetics analysis

[![](https://img.shields.io/pypi/v/quangpy.svg?color=brightgreen)](https://pypi.org/pypi/quangpy/)

[![](https://img.shields.io/github/issues/r1cheu/quangpy?color=green)](https://github.com/r1cheu/quangpy/issues/new)

[![](https://img.shields.io/github/license/r1cheu/quangpy)]()

`quangpy` mainly provides Joint Scale Analysis and Variance Decomposition Analysis.

## Installation

```bash

pip install quangpy

```

## Getting Started

### input data format

The input file should be separated by tab and provide the following columns in the same order:

- population: population name

- mean: mean of the population phenotype

- var: variance of the population phenotype

The following is an example of the input data format:

|     | mean  | var      |

| --- | ----- | -------- |

| b1l | 7.5   | 0.010000 |

| b1s | 5.575 | 0.004096 |

| f1  | 5.5   | 0.007396 |

| f2  | 6.595 | 0.013924 |

| p1  | 9.125 | 0.008281 |

| p2  | 5.475 | 0.003249 |

### Joint Scale Analysis

```python

from quangpy import JointScale

input_data = "/path/to/input/data"

js = JointScale(input_data)

js.fit(("m", "a"))

```

### Variance Decomposition Analysis

```python

from quangpy import VarianceDecomposition

input_data = "/path/to/input/data"

vd = VarianceDecomposition(input_data)

vd.fit(("m", "a", "d", "aa", "dd"))

```

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.