GAUSS Platform is an easy-to-use mathematical and statistical data analysis environment based on the powerful, fast and efficient GAUSS matrix programming language.
GAUSS Platform is aneasy-to-use mathematical and statistical data analysis environment based on the powerful, fast and efficient GAUSS matrix programming language.
What is GAUSS?
GAUSS Platform is an interactive environment designed for mathematics and statistics, which has been used by leaders in data-dependent fields in major institutions for over 30 years.
Comprehensive environment for modeling and analysis
The basic GAUSS Platform platform is a complete analysis environment with the built-in tools needed for estimation, forecasting, simulation, visualization, and more.
Fully customizable
GAUSS Platform allows you to stay ahead of the curve, providing the tools you need to modify the algorithms or create the latest and best from scratch.
Interactive and Fast
GAUSS Platform provides a fully interactive environment for exploring data, performing calculations, and analyzing results. These interactive features speed up your workflow, while the exceptionally fast GAUSS analysis engine speeds up your calculations.
The GAUSS Platform analytical engine is designed to be extremely light and efficient while making maximum use of your hardware. From a small laptop to a large cluster, nothing is scalable, just like GAUSS.
Viewing and Presentation
Intuitive and powerful interactive and programmatic methods, they make it easy to create beautiful and professional 2D and 3D graphics to analyze your data and present your findings. Its high-resolution GAUSS graphics can be exported to various popular formats such as: PNG, PDF, SVG, TIFF and more.
System requirements:
Windows 32-bit: Windows Vista / 7 / 8.0 + / 10 +
Windows 64-bit: Windows Vista / 7 / 8.0 + / 10 +
64-bit Windows Server: 2008 and higher
64-bit Mac: Mac OSX 10.7 and above
Linux 64 bits: Red Hat 6.X + / CentOS / Ubuntu
Hardware Requirements
Hard drive: minimum 400 MB
RAM: minimum of 1 GB; 4 GB or more recommended
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