New in GAUSS 24
The new version GAUSS 24 offers new features for everything from everyday data management to refined statistical modeling:
- New Panel Data Management Tools
- Feasible GLS Estimation
- Expanded Tabulation Capabilities
- New Time and Date Extraction Tools
- New Convenience Functions for Data Management and Exploration
- Improved Performance and Speed-ups
New Panel Data Management Tools
GAUSS 24 makes working with panel data easier than ever. Effortlessly load, clean, and explore panel data without ever leaving GAUSS, making it the smoothest experience yet!
- Easily and intuitively pivot between long and wide form data with new
- Explore group-level descriptive statistics and estimate group-level linear models with expanded
Feasible GLS Estimation
- 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 the Durbin-Watson statistic.
- Choose from 7 built-in covariance estimation methods or provide your own covariance matrix.
Expanded Tabulation Capabilities
New tools for two-way tabulation provides a structured and systematic approach to understanding and drawing insights from categorical variables.
- New procedure tabulate for computing two-way tables with advanced options for excluding categories and formatting reports.
- Expanded functionality for the frequency function:
- New two-way tables.
- Sorted frequency reports and charts.
New Time and Date Extraction Tools
- 12 new procedures for extracting date and time components from dataframe dates.
- Extract date and time components ranging from seconds to years.
New Convenience Functions for Data Management and Exploration
dropCategories- Drops observations of specific categories from a dataframe 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 dataframe at a specified location.
Improved Performance and Speed-ups
- Expanded functionality of
strindxallows for searching of unique substrings across multiple variables.
upmatfunction 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
combinatewith large values of n.
- Remove missing values from large vectors more efficiently with speed increases in