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 currently also available for Linux, macOS and Windows.
Gauss programs are not required to have any particular file extension, although is recommended.
Examples of Functions Included in Run-Time Library
- GAUSS has several Application Modules as well as functions in its Run-Time Library
- * Qprog – Quadratic programming
- * SqpSolvemt – Sequential quadratic programming
- * QNewton - Quasi-Newton unconstrained optimization
- * EQsolve - Nonlinear equations solver
GAUSS Applications
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 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. |