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GAUSS 24

GAUSS is an easy-to-use data analysis, mathematical and statistical environment

GAUSS is an easy-to-use data analysis, mathematical and statistical environment based on the powerful, fast and efficient GAUSS Matrix Programming Language. GAUSS has been used by leaders in data-dependent fields at major institutions for more than 30 years. Get a complete analysis environment with the built-in tools you need for estimation, forecasting, simulation, visualization and more

  • Fully customizable
    GAUSS allows you to stay ahead of the curve by giving you the tools you need to modify our algorithms or create the latest and greatest from the ground up.
  • Interactive and fast
    A fully interactive environment for exploring data, performing calculations and analyzing results. The exceptionally fast GAUSS analytics engine will speed up your computations.
  • Visualization and presentation
    Intuitive and powerful interactive and programmatic methods make it easy to create beautiful and professional 2D and 3D graphics to analyze your data and present your findings.

The world needs your research. Discover how GAUSS can help you make your mark!

Info

What is GAUSS?

An Introduction to GAUSS

The GAUSS Platform is an interactive environment designed for math and statistics, which has been used by leaders in data-dependent fields at major institutions for more than 30 years.

Comprehensive Environment for Modeling and Analysis

The GAUSS base platform is a complete analysis environment with the built-in tools you need for estimation, forecasting, simulation, visualization and more.

It allows you to stay ahead of the curve by giving you the tools you need to modify our algorithms or create the latest and greatest from the ground up.

The 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 analytics engine will speed up your computations.

GAUSS - Time series

The GAUSS analytics engine has been designed to be extremely lightweight and efficient while making the most use of your hardware. From a small laptop to a large cluster, nothing scales as well as GAUSS.

Intuitive and powerful interactive and programmatic methods make it easy to create beautiful and professional 2D and 3D graphics to analyze your data and present your findings. Your high-resolution GAUSS graphics can be exported to many popular formats such as PNG, PDF, SVG, TIFF and more.

GAUSS - Visualisation

Please click here to learn more about the features!

News

New features in GAUSS 24

  • Pivot between long and wide forms with new dfLonger and dfWider functions:
    – Easy and intuitive to use but flexible enough to tackle even the most complicated cases.
  • Expanded by keyword functionality for:
    – Exploring group-level descriptive statistics.
    – Estimating group-specific linear models.
  • Compute feasible GLS coefficients and associated standard errors, t-statistics, p-values, and confidence intervals.
  • Provides model evaluation statistics, including R-squared, F-stat, and Durbin-Watson.
  • Choose from 7 built-in covariance estimation methods or provide your own covariance estimate.
  • New procedure tabulate for computing two-way tables with advanced options for excluding categories and formatting reports.
  • Option to compute two-way tables using frequency.
  • Added ability to sort frequency reports and charts.
  • 12 new procedures for extracting date and time components from dates.
  • Extract components ranging from seconds to years.
  • dropCategories – Drops observations of specific categories from a data frame and updates the associated labels and key values
  • getCategories – Returns the category labels for a categorical variable.
  • isString – Verify if an input is a string or string array.
  • startsWith – Locates elements that start with a specified string.
  • insertCols – Inserts one or more new columns into a matrix or data frame at a specified location.
  • Expanded functionality of strindx allows for the searching of unique substrings across multiple variables.
  • The upmat function now has the option to specify an offset from the main diagonal, the option to return only the upper triangular elements as a vector, and is faster for medium and large matrices.
  • Significant speed improvements when using combinate with large values of n.
  • Remove missing values from large vectors more efficiently with speed increases in packr.

New features in GAUSS 23

GAUSS 23 is the latest and most user-friendly version of GAUSS, designed to streamline your research process by making it easier to find, import, and model data.

Data at Your Fingertips

GAUSS 23 - Data at your fingertip
  • Access millions of global economic and financial data series with FRED and DBnomics integration.
  • Aggregate, filter, sort, and transform FRED data series during import.
  • Search FRED series from GAUSS.

Load Data from Anywhere on the Internet

// Load an Excel file from the aptech website
file_url = “http://www.aptech.com/wp-content/uploads/2019/03/skincancer2.xlsx”;
skin_cancer = loadd(file_url);

// Print the first 5 rows of the dataframe
head(skin_cancer);

GAUSS 23 - Table data from the internet

Simplified Data Loading with…

Automatic Type Detection

Previous versions required formula strings with keywords to specify date, string, and categorical variables from some file types.

GAUSS 23 - Load variables 1

Smart data type detection in GAUSS 23 figures out the variable type so you do not have to specify it manually. Automatically detects nearly 40 popular date formats.

GAUSS 23 - Load variables 2

Automatic Header and Delimiter Detection

Replace old code like this:

load X[127,4] = mydata.txt;


with

X = loadd(“mydata.txt”);

Automatically handles:

  • Present or absent header row.
  • Delimiter (tab, comma, semi-colon or space).
  • Number of rows and columns.
  • Variable types.

First-Class Dataframe Storage

GAUSS 23 - Dataframe storage

No new code to learn, use the .gdat file extension with loadd and saved to load and store your dataframes.

Expanded Quantile Regressions

GAUSS 23 - Quantile regressions

hitters = loadd(“islr_hitters.xlsx”);
tau = 0.90;
call quantileFit(hitters, “ln(salary) ~ AtBat + Hits + HmRun”, tau);

GAUSS 23 - Quantile regressions table
  • New kernel estimated variance-covariance matrix.
  • Up to 4x speed improvement.
  • Expanded model diagnostics including pseudo R-squared, coefficient t-statistics and p-values, and degrees of freedom.

Kernel Density Estimations

GAUSS 23 - Kernel denisty
  • Estimate unknown probability functions with 13 available kernels.
  • Automatic or user-specified bandwidth.
  • Kernel density plots with easy-to-use options for customization.

Improved Covariance Computations

// Load data
fname = getGAUSShome(“examples/auto2.dta”);
auto = loadd(fname);

// Declare control structure
struct olsmtControl ctl;
ctl = olsmtControlCreate();

// Turn on residuals
ctl.res = 1;

// Turn on HAC errors
ctl.cov = “hac”;
call olsmt(auto, “mpg ~ weight + foreign”, ctl);

GAUSS 23 - Improved covariance table
  • New procedure for computing Newey-West HAC robust standard errors.
  • All robust covariance procedures now include the option to turn off small sample corrections.
  • Expanded dataframe and formula string compatibility.

New Functions for Data Cleaning and Exploration

between

Returns a binary vector indicating which observations fall in a specified range. It can be used with selif to select rows. Dates and ordinal categorical columns are supported.

// Return a 1 if the observation is between the listed dates
match = between(unemp[.,”DATE”], “2020-03”, “2020-08”);

// Select the matching observations
unemp = selif(unemp, match);

GAUSS 23 - between function

where

Provides a convenient and intuitive way to combine or modify data. It returns elements from either a or b depending upon condition.

// Daily hotel room price
hotel_price = { 238, 405, 405, 329, 238 };

// Daily temperature forecast
temperature = { 89, 94, 110, 103, 97 };

// Decrease the price by 10% if the
// temperature will be more than 100 degrees
new_price = where(temperature .> 100,
hotel_price .* 0.9,
hotel_price);

new_price = 238 405 364.50 296.10 238

  • Explore sample symmetry and tails with skewness and kurtosis functions.
  • Test for normality using the new JarqueBera function.

Speed-ups and Efficiency Improvements

  • Up to 10x speed-up and 50% decrease in memory usage for lag creation with shiftc and lagn.
  • Up to 2x speed-up (or more for large data) and 50% decrease in memory usage for miss, missrv.
  • Up to 2x speed-up (or more for large data) and 50% decrease in memory usage for element-by-element mathematical (+, -, .*, ./), relational (.>, .<, .>=, .<=, .==, .!=) and logical (.and, .not, .or, .xor) operators.
  • Up to 100x speed-up for some cases with indsav.
  • Up to 40% speed-up for reclassify.
  • Up to 3x speed-up for loading Excel® files with loadd and the Data Import Window.

For a complete list of all GAUSS 23 offers please see the complete changelog.


New features in GAUSS 22

GAUSS 22 brings many substantial new features that will save you hours of time and frustration with everyday tasks like:

  • Data exploration
  • Data cleaning and management
  • Graphics

See some of the ways that will help you make the most of your limited research time below!

Intelligent Graph Attributes

  • Specify variables to plot by name with convenient formula strings.
  • Automatic, intelligent use of variable names and category labels.
  • Split data using the new ‘by’ keyword.
  • Never overwrites your settings.

GAUSS highlight’s Important Parts of Your Data

Gauss 22 - Highlight Important Parts of Your Data
  • Specify locations with simple date strings, category labels, numbers, or popular datasets.
Gauss 22 - Specify locations with simple date strings, category labels, numbers, or popular datasets.
  • Add spanning bars and lines with one line of code.

Simple Data Filtering with Strings and Dates

Gauss 22 - Simple Data Filtering with Strings and Dates
  • Reference dates, category labels, and variable names.
  • Use greater than and less than comparisons with ordinal data.

Easily Generate New Variables

Gauss 22 - Easily Generate New Variables
  • Create categorical variables from string arrays.
  • Automatically convert simple date strings to real date variables.
  • Simple new functions and interactive controls to rename and change types.
  • Function for combining, splitting, and manipulating strings now support string, categorical and date variables.

Clean Messy Data with GAUSS

Clean Messy Data
  • Fix typos and misentries.
  • Consolidate categories.
  • Identify and remove outliers.
  • Identify and remove duplicate observations.

Aggregate by String, Category or Date

Aggregate by String, Category or Date
  • Options for how to handle missing values.

Simple Data Merge

Simple data merge
  • Load and combine data from CSV, Excel, SAS, Stata, SPSS.

Changelog

GAUSS 22 has more new features and bug fixes than any previous version. For a complete list of what’s new, see the complete changelog.

System requirements

System requirements for GAUSS

  • Windows 7, 8, 10 and 11 (32-bit and 64-bit)
  • Windows Server 2008 or newer (64-bit)
  • Linux Red Hat 6.X+/CentOS/Ubuntu (64-bit)
  • Mac OSX 10.9 or newer (64-bit)
  • Harddisk: 1 GB
  • RAM: 4 GB (8 GB recommended)
  • A graphic card with at least 1 GB RAM

Licensing

License Options

  • Single user (Academic and Commercial pricing)
  • Volume/Multi-user
  • Concurrent/Floating Network (Simus)
  • Site or Department Wide licenses

Please contact us for an offer today!

Support

Support

For technical support, please contact Software Support!

Please include your serial number with all email correspondence.

Please describe your problem as detailed as possible when contacting our support. Remember to always inform about your serial/license number, product version, and your operating system.

We also recommend these online support pages and resources: