GAUSS (software)

GAUSS is a matrix programming language for mathematics and statistics, developed and marketed by Aptech Systems. Its primary purpose is the solution of numerical problems in statistics, econometrics, time-series, optimization and 2D- and 3D-visualization. It was first published in 1984 for MS-DOS and is available for Linux, macOS and Windows.

Examples

GAUSS has several Application Modules as well as functions in its Run-Time Library (i.e., functions that come with GAUSS without extra cost) Qprog – Quadratic programming SqpSolvemt – Sequential quadratic programming QNewton - Quasi-Newton unconstrained optimization EQsolve - Nonlinear equations solver

A range of toolboxes are available for GAUSS at additional cost.

Toolboxes
Toolbox	Description
	A program for generating GAUSS procedures for computing algorithmic derivatives.
	Solves the general maximum likelihood problem subject to general constraints on the parameters.
	Solves the nonlinear programming problem subject to general constraints on the parameters.
	Nonlinear curve fitting.
	Basic sample statistics including means, frequencies and crosstabs. This application is backwards compatible with programs written with Descriptive Statistics 3.1.
	Basic sample statistics including means, frequencies and crosstabs. This application is thread-safe and takes advantage of structures.
	A statistical package for estimating discrete choice and other models in which the dependent variable is qualitative in some way.
	Comprehensive suite of GARCH (Generalized AutoRegressive Conditional Heteroskedastic) models for estimating volatility.
	Solves small and large scale linear programming problems.
	Least squares estimation.
	Analysis of categorical data using log-linear analysis.
	Maximum likelihood estimation of the parameters of statistical models.
	Solves systems of nonlinear equations having as many equations as unknowns.
	Unconstrained optimization.
	Exact ML estimation of VARMAX, VARMA, ARIMAX, ARIMA, and ECM models subject to general constraints on the parameters. Panel data estimation. Cointegration and unit root tests.