Search Results for "data visualization"

PyMC is a Python library for probabilistic programming focused on Bayesian statistical modeling and machine learning. Built on top of computational tools like Aesara and NumPy, PyMC allows users to define models using intuitive syntax and perform inference using MCMC, variational inference, and other advanced algorithms. It’s widely used in scientific research, data science, and decision modeling.

LabPlot is a FREE, open source and cross-platform Data Visualization and Analysis software accessible to everyone.

TAVIA is a desktop application for structured data analysis, visualization, reporting, and reproducible research workflows. It combines spreadsheet-style data handling with statistical tools, publication-ready graphs, confidence intervals, project files, encrypted storage, audit trails, workflow automation, and extensible plugins. The application includes CSV, TSV, XLSX, and SQLite import and export, customizable chart styles, confidence bands, error bars, report generation, generated analysis scripts, parameter sweeps, and a documented plugin API with a starter template. ...

Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics. Seaborn helps you explore and understand your data. Its plotting functions operate on dataframes and arrays containing whole datasets and internally perform the necessary semantic mapping and statistical aggregation to produce informative plots.

Charting tools, backtesting tools, and data visualization tools for the most popular cryptocurrencies.

This is Slycat - a web-based data science analysis and visualization platform, created at Sandia National Laboratories. The goal of the Slycat project is to develop processes, tools and techniques to support data science, particularly analysis of large, high-dimensional data.

This project examines techniques to model three-dimensional rigid body motion using the geometric algebra of Dual Quaternions and how such models compare to more traditional models when used in underconstrained filtering applications.

PGAF provides a framework tuned, user-specific genetic algorithms by handling I/O, UI, and parallelism. It is designed for optimizing functions that take a "very long time" to evaluate.