Hypex

This release marks a significant update to HypEx, introducing new features, enhancements to existing functionalities, and various optimizations and bug fixes. Below is a detailed breakdown of the updates and improvements included in this release.

New Features
- **MDE Calculation Function**: Added a function for calculating the Minimum Detectable Effect (MDE).
- **Sample Size Calculation with MDE**: Introduced a function to calculate the sample size required for a given MDE.
- **Test Power Calculation Function**: Implemented a function for calculating the power of the test.

Enhancements to AA Tests
- **Simplified Pipeline**: The classical pipeline can now be invoked with a single `process` function.
- **Stratification Optimization**: The process now allows for optimization in stratification.
- **Enhanced Test Interpretation**:
- Built a table with test statistics for better analysis.
- Created a summary table for AA test outcomes.
- Developed visualizations (graphs) for AA test results.
- **Improved Split Analysis**:
- Added graphs for distribution analysis to ensure group homogeneity.
- Conducted statistical tests for homogeneity.
- Calculated and presented group statistics.
- Receiving a brief summary of the test


General Improvements
- **Bug Fixes**: Addressed and resolved known issues.
- **Code Refactoring**: Improved code structure for better maintainability and readability.
- **Process Optimization**: Enhanced overall process efficiency.

Documentation and Community Engagement
- **Updated README**: A new and improved README for better project understanding.
- **Issue Templates**: Introduced templates for streamlined issue reporting.
- **Pull Request Templates**: Added templates to facilitate consistent and structured pull requests.
- **Contributing Guidelines**: Updated `CONTRIBUTING.md` with new guidelines and templates.
- **New Tutorials**: Added tutorials to guide users through the new features and enhancements.

Fixed issues related to OutLiers Filters. It returned set, which made further Matching work impossible. Now it immediately deletes rows and returns DataFrame without these rows

First release

- Faiss KNN Matching: Utilizes Faiss for efficient and precise nearest neighbor searches, aligning with RCM for optimal pair matching.
- Data Filters: Built-in outlier and Spearman filters ensure data quality for matching.
- Result Validation: Offers multiple validation methods, including random treatment, feature, and subset validations.
- Data Tests: Incorporates SMD, KS, PSI, and Repeats tests to affirm the robustness of effect estimations.
- Automated Feature Selection: Employs LGBM feature selection to pinpoint the most impactful features for causal analysis.
- AB Testing Suite: Features a suite of AB testing tools for comprehensive hypothesis evaluation.