Riemannian STATS Modules¶
The Riemannian STATS package is a comprehensive collection of tools designed for data analysis and dimensionality reduction, specifically focusing on techniques like UMAP and PCA, along with methods for visualizing and processing high-dimensional data. It includes several modules that provide easy-to-use functionalities for tackling common tasks in data science and statistical analysis.
This package is organized into the following key modules:
RiemannianAnalysis: A module for performing UMAP-based dimensionality reduction combined with Riemannian geometry, allowing for the analysis and exploration of high-dimensional data.
DataProcessing: A module for handling the loading and preparation of data, especially for CSV files, making it easier to import data into a format ready for analysis.
Visualization: A module for generating a variety of visualizations, including 2D and 3D scatter plots, correlation circles, and principal component analysis (PCA) plots, to facilitate the interpretation of data.
Utilities: A utility module that provides a collection of common mathematical and statistical functions, such as PCA inertia calculations, to streamline data analysis workflows.
Each module is designed to be highly flexible, enabling users to apply the appropriate tools in their specific data analysis tasks. Whether you’re working on dimensionality reduction, preparing data for analysis, or creating insightful visualizations, the ´Riemannian STATS´ package offers the necessary tools to enhance your data science workflows.
Explore the following modules for detailed information on their functionalities: