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SMdRQA: Implementing Sliding Window MdRQA to get Summary Statistics Estimate of MdRQA measures from the Data

Description:
SMdRQA is a powerful Python package designed for conducting Sliding Window Multidimensional Recurrent Quantification Analysis (SMdRQA). This specialized analysis technique allows researchers, data scientists, and analysts to explore temporal patterns and dependencies in multidimensional data sequences using a sliding window approach.

Key Features:

Perform Sliding Window Multidimensional Recurrent Quantification Analysis (SMdRQA) on time series data.
Analyze temporal relationships and recurrent patterns across multiple dimensions using a sliding window mechanism.
Customizable parameters for fine-tuning analysis settings such as window size, recurrence thresholds, and measures.
Visualize and interpret recurrent patterns using interactive plots and visualizations.
Gain insights into system dynamics, evolution, and behavior patterns over time with sliding window analysis.
Conduct parameter exploration to find optimal values for time delay and embedding dimension.

How SMdRQA Benefits You:
SMdRQA with sliding window functionality empowers users to:

Uncover hidden temporal patterns and structures in complex data sequences.
Identify recurrent events, transitions, and dependencies across multiple dimensions using a dynamic sliding window approach.
Gain insights into evolving system dynamics and behavior patterns with continuous monitoring and analysis.
Make informed decisions based on comprehensive temporal analysis results.

Who Might be Using the Package:

Researchers and academics studying temporal dynamics and patterns in multidimensional data using a sliding window approach.
Data scientists and analysts working with time series data from diverse domains requiring dynamic pattern detection.
Professionals seeking advanced tools for temporal analysis, pattern recognition, and dynamic modeling with sliding window analysis.
Anyone interested in exploring complex temporal relationships and dependencies over time using a dynamic sliding window approach.

Get Started with SMdRQA:
Start leveraging the capabilities of Sliding Window Multidimensional Recurrent Quantification Analysis with SMdRQA. Dive into your time series data, uncover recurrent patterns dynamically, and gain valuable insights into temporal dynamics. Install SMdRQA now and embark on a journey of advanced temporal analysis and discovery with sliding window functionality.

Documentation

For detailed documentation and usage instructions, visit the SMdRQA Documentation.

Obtain SMdRQA

You can obtain SMdRQA by following these steps:

Clone the repository from GitHub using Git and install(cuttig edge version)

Cloning

git clone https://github.com/SwaragThaikkandi/SMdRQA.git

Installing

pip install -e path/to/cloned/repository

Install directly from GitHub

pip install git+https://github.com/SwaragThaikkandi/SMdRQA.git

Install using PyPI
```
pip install SMdRQA
```
Install using Docker
```
docker pull tsk365/smdrqa
```

Provide Feedback

Your feedback is valuable for improving SMdRQA. You can provide feedback in the following ways:

Report bugs or issues: Submit bug reports and issues on the GitHub Issue Tracker.
Suggest enhancements or new features: Share your ideas and suggestions for enhancements on the Issue Tracker.

Contribute to SMdRQA

Requirements for Acceptable Contributions

Ensure that your contributions align with the project's goals and objectives.
Follow the coding standards and guidelines outlined in our Coding Standards document.
Provide clear and detailed descriptions of your contributions, including any changes or enhancements made.
Test your code thoroughly to ensure it functions as intended and does not introduce any regressions.
Adhere to the project's licensing terms and copyright policies.

You can contribute to the development of SMdRQA by:

Fork the repository on GitHub.
Create a new branch for your contributions (git checkout -b feature/your-feature-name).
Make your changes and commit them with clear and descriptive commit messages.
Push your changes to your forked repository (git push origin feature/your-feature-name).
Submit a pull request (PR) to the main repository for review and integration.

We welcome contributions from the community to improve and enhance SMdRQA!

For more detailed information on how to use SMdRQA, refer to the SMdRQA Documentation.