GAUSS

The GAUSS Mathematical and Statistical System is a fast matrix programming language widely used by scientists, engineers, statisticians, biometricians, econometricians, and financial analysts. Designed for computationally intensive tasks, the GAUSS system is ideally suited for the researcher who does not have the time required to develop programs in C/C++ or FORTRAN but finds that most statistical or mathematical “packages” are not flexible or powerful enough to perform complicated analysis or to work on large problems. Whatever mathematical tool or language you are now using, you’ll find that GAUSS can greatly increase your productivity!

This software is also referenced in ORMS.


References in zbMATH (referenced in 119 articles )

Showing results 1 to 20 of 119.
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  1. Phillips, Peter C. B.; Han, Chirok: Dynamic panel GMM using R (2019)
  2. Chow, Sy-Miin; Ou, Lu; Ciptadi, Arridhana; Prince, Emily B.; You, Dongjun; Hunter, Michael D.; Rehg, James M.; Rozga, Agata; Messinger, Daniel S.: Representing sudden shifts in intensive dyadic interaction data using differential equation models with regime switching (2018)
  3. Giovanni Millo: Robust Standard Error Estimators for Panel Models: A Unifying Approach (2017) not zbMATH
  4. Marek Hlavac: ExtremeBounds: Extreme Bounds Analysis in R (2016) not zbMATH
  5. Pek, Jolynn; Wu, Hao: Profile likelihood-based confidence intervals and regions for structural equation models (2015)
  6. Roger Bivand; Gianfranco Piras: Comparing Implementations of Estimation Methods for Spatial Econometrics (2015) not zbMATH
  7. Victor Gómez: SSMMATLAB: A Set of MATLAB Programs for the Statistical Analysis of State Space Models (2015) not zbMATH
  8. Brenton Kenkel; Curtis Signorino: Estimating Extensive Form Games in R (2014) not zbMATH
  9. Haines, Linda M.; Clark, Allan E.: The construction of optimal designs for dose-escalation studies (2014)
  10. Peter Ruckdeschel; Matthias Kohl: General Purpose Convolution Algorithm in S4 Classes by Means of FFT (2014) not zbMATH
  11. Ringle, Christian M.; Sarstedt, Marko; Schlittgen, Rainer: Genetic algorithm segmentation in partial least squares structural equation modeling (2014)
  12. Wang, Fu-Kwun; Lee, Chih-Wen: M-estimator for estimating the Burr type III parameters with outliers (2014)
  13. Chow, Sy-Miin; Zhang, Guangjian: Nonlinear regime-switching state-space (RSSS) models (2013)
  14. Martin, Vance; Hurn, Stan; Harris, David: Econometric modelling with time series. Specification, estimation and testing (2013)
  15. A. Talha Yalta, Sven Schreiber: Random Number Generation in gretl (2012) not zbMATH
  16. Holden, Darryl: Testing for heteroskedasticity in the tobit and probit models (2011)
  17. Yu, Jun-Wu; Tian, Guo-Liang: Efficient algorithms for generating truncated multivariate normal distributions (2011)
  18. Cai, Li: High-dimensional exploratory item factor analysis by a Metropolis-Hastings Robbins-Monro algorithm (2010)
  19. de Grange, Louis; Fernández, Enrique; de Cea, Joaquín; Irrazábal, Magdalena: Combined model calibration and spatial aggregation (2010)
  20. Marcelo Almiron; Bruno Lopes; Alyson Oliveira; Antonio Medeiros; Alejandro Frery: On the Numerical Accuracy of Spreadsheets (2010) not zbMATH

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