Gauss 14

I'm currently using Gauss 11. What's new in Gauss 14 compared to 11?



2 Answers


There have been numerous improvements and additions to GAUSS between version 11 and 14. Below is a partial list of the new functions and features by version.

  1. New in GAUSS 12
    • Improved graphics for bar, box, histogram, log, polar, scatter, surface and xy that are easier to use and nicer looking.
    • Support for integrated program input/output window added as opposed to an input window that is separate from the output window
    • Greatly improved speed of output printed to the screen and decreased memory usage.
    • New random number generators for the following distributions: Bernoulli, binomial, Bernoulli, Cauchy, exponential, generalized Pareto, geometric, Gumbel, Laplace, log-normal, multi-variate normal, multi-variate student-t, negative binomial, Rayleigh, Weibull, and Wishart.
    • Option to select one of the following underlying random number generators (for any supported distribution), Mersenne-Twister 19937, Mersenne-Twister 2203, MRG32K3A, or Wichmann-Hill.
    • Support for parallel random number generation through block-skipping and the ability to create multiple random number streams.
  2. New in GAUSS 13
    • This link illustrates some of the many improvements in version 13.
    • This link illustrates some of the improvements to GAUSS 13.1.
  3. New in GAUSS 14
    • Full suite of database functions with support for MySql, Oracle, ODBC, PostGRE, MS-SQL, SQLite and more.
    • New Project View Window lets you add any number of folders. Simple GUI controls allow you to run or edit any file in the Project View Window.
    • Smart editor finds and highlights errors in your program file before you run the file.
    • Improved error report formatting provides error reports that are easier to read and understand as well as looking great and allowing one-click navigation to the source of the error.

Throughout these versions there have also been many smaller enhancements, new functions and bug fixes.


Thanks a lot for your reply.

We estimate model use Markov chain Monte Carlo. We also heavily use constraint optimization and comt. Is there any important aspect of the upgrade for our problem?


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