scikit-longitudinal 0.0.1
Scikit-longitudinal, an open-source Python lib for longitudinal data analysis, builds on Scikit-learn's foundation. It offers specialized tools to tackle challenges of repeated measures data, ideal for researchers, data scientists, & analysts.
Requires Python: >=3.10,<4.0

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      Scikit-longitudinal
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   <h4 align="center">A specialised Python library for longitudinal data analysis built on Scikit-learn</h4>
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<!-- TABLE OF CONTENTS -->
<details open="open">
   <summary style="font-size: 1.5em; font-weight: bold;">
      📚 Table of Contents
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   <ol>
      <li><a href="#about-the-project">😊 About The Project</a></li>
      <li><a href="#installation">🛠️ Installation</a></li>
      <li><a href="#key-features">🌟 Key Features</a></li>
      <li><a href="#how-to-use">🚀 Getting Started</a></li>
      <li><a href="#documentation">📖 Documentation</a></li>
      <li>
         <a href="#contributing">🤝 Contributing</a>
         <ul>
            <li><a href="#code-convention">Code convention</a></li>
            <li><a href="#pull-request-process">Pull Request Process</a></li>
         </ul>
      </li>
      <li><a href="#examples">📚 Examples</a></li>
      <li><a href="#faq">❓ FAQ</a></li>
      <li><a href="#citation">📝 How to Cite?</a></li>
      <li><a href="#related">🔗 Related</a></li>
      <li><a href="#license">🔐 License</a></li>
      <li><a href="#contact">📞 Contact</a></li>
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</details>

## 💡 About The Project

Scikit-longitudinal is a machine learning library specifically designed for longitudinal data analysis. It provides a collection of tools and models to process, analyze, and make predictions on longitudinal data, with a simple and user-friendly interface compatible with the Scikit-learn ecosystem.

## 🛠️ Installation

_TODO: Describe how to install the package, including any dependencies._

## 🌟 Key Features
_TODO: Describe key features._

## 🚀 Getting Started

_TODO: Provide a simple example of using the framework, including importing the package, loading data, and training a model._

````python
import scikit_longitudinal as skl

# Load your data
data = ...

# Initialize the Scikit-longitudinal model
model = skl.<your_desired_estiamator>()

# Train the model
model.fit(data)

# Make predictions
predictions = model.predict(new_data)
````

## 📖 Documentation

_TODO: Describe where to find the documentation for the framework, including tutorials and API reference._

For detailed documentation, including tutorials and API reference, please visit our [official documentation](https://scikit-longitudinal.github.io/docs).

## 🤝 Contributing

### Coding Conventions
We follow the [Karma Git Commit Convention](http://karma-runner.github.io/6.4/dev/git-commit-msg.html) and a modified version of the Google Python Style Guide with fewer restrictions. Please familiarize yourself with these conventions before contributing.


### Pull Request Process

To submit a pull request, please follow these steps:

* Fork the repository: Click the "Fork" button at the top right corner of the repository page to create a copy of the project in your GitHub account.
* Create a new branch: In your forked repository, create a new branch named after the feature or fix you are working on (e.g., feature/new-feature-name or fix/bug-fix-name).
* Make your changes: Implement the desired feature or fix in your new branch, and make sure to adhere to the project's coding conventions. 
* Commit your changes: Use clear and concise commit messages, following the Karma Git Commit Convention. Make sure to include any necessary tests, documentation updates, or code style adjustments. 
* Submit a pull request: Click the "New Pull Request" button in your forked repository and select your newly created branch as the source. Then, target the main branch of the original repository as the destination. Provide a detailed description of the changes you've made, and submit the pull request.

* Once your pull request is submitted, maintainers will review your changes and provide feedback. Be prepared to make any necessary adjustments, and collaborate with the maintainers to get your contribution merged.

## 📚 Examples

_TODO: Provide links to example notebooks or scripts that demonstrate how to use the framework._

Please visit our [examples folder](https://github.com/Scikit-longitudinal/tree/main/examples) for sample notebooks and scripts demonstrating how to use Scikit-Longitudinal.

## ❓ FAQ

Explore the properties of Longitudinal Data and Time-Series Data in this comprehensive FAQ.

**Q: What is Longitudinal Data?**

A: Longitudinal Data refers to observations made on multiple variables of interest for the same subject over an extended period of time. This type of data is particularly valuable for studying changes and trends, as well as making predictions about future outcomes. For example, in a medical study, a patient's health measurements such as blood pressure, heart rate, and weight might be recorded over several years to analyze the effectiveness of a treatment.

**Q: What are the differences between Time-Series Data and Longitudinal Data?**

A: Time-Series Data and Longitudinal Data both involve observations made over time, but they differ in several aspects:

Focus: Time-Series Data focuses on a single variable measured at regular intervals, while Longitudinal Data involves multiple variables observed over time for each subject.
Nature: Time-Series Data is usually employed for continuous data, whereas Longitudinal Data can handle both continuous and categorical data.
Time gap: Time-Series Data typically deals with shorter time periods (e.g., seconds, minutes, or hours), while Longitudinal Data often spans longer durations (e.g., months or years).
In summary, the main differences between Time-Series and Longitudinal Data lie in the focus, nature, and the length of the time intervals considered.

## 📝 How to Cite?

If you use Scikit-Longitudinal in your research, please cite our paper:

_TODO: Add citation information for the Scikit-Longitudinal papers._

## 🔗 Related

- Auto-prognosis: [GitHub](https://github.com/vanderschaarlab/autoprognosis)
- Clairvoyance: [GitHub](https://github.com/vanderschaarlab/clairvoyance)

## 🔐 License

MIT License