Introducing GAUSS 24

Introduction

We're happy to announce the release of GAUSS 24, with new features for everything from everyday data management to refined statistical modeling.

GAUSS 24 features a robust suite of tools designed to elevate your research. With these advancements, GAUSS 24 continues our commitment to helping you conduct insightful analysis and achieve your goals.

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 dfLonger and dfWider functions.
• Explore group-level descriptive statistics and estimate group-level linear models with expanded `by` keyword functionality.
``````// Load data

// Print statistics table
call dstatmt(auto2, "mpg + by(foreign)");``````
```=======================================================================
foreign: Domestic
-----------------------------------------------------------------------
Variable        Mean     Std Dev      Variance     Minimum     Maximum
-----------------------------------------------------------------------
mpg            19.83       4.743          22.5          12          34
=======================================================================
foreign: Foreign
-----------------------------------------------------------------------
Variable        Mean     Std Dev      Variance     Minimum     Maximum
-----------------------------------------------------------------------
mpg            24.77       6.611         43.71          14          41 ```

Feasible GLS Estimation

``````// Load data

// Run FGLS with defaults AR(1) Innovations
fgls(df_returns, "rcoe ~ rcpi";``````
```Valid cases:                    248          Dependent variable:            rcpi
Total SS:                     0.027          Degrees of freedom:             246
R-squared:                    0.110          Rbar-squared:                 0.107
Residual SS:                  0.024          Std error of est:             0.010
F(1,246)                     30.453          Probability of F:             0.000
Durbin-Watson                 0.757
--------------------------------------------------------------------------------
Standard                    Prob
Variable   Estimates       Error     t-value        >|t|  [95% Conf.   Interval]
--------------------------------------------------------------------------------

Constant      0.0148     0.00122        12.1       0.000      0.0124      0.0172
rcoe       0.196      0.0685        2.86       0.005      0.0619        0.33 ```
• 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

``````// Load data

// Two-way table
call tabulate(df, "sex ~ smoker");``````
```============================================================
sex                   smoker               Total
============================================================
No            Yes

Female             55             33             88
Male             99             60            159

Total            154             93            247
============================================================```

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.
``````// Print sorted frequency table
// of 'rep78' in 'auto2' dataframe
frequency(auto2, "rep78", 1)``````
```    Label      Count   Total %    Cum. %
Average         30     43.48     43.48
Good         18     26.09     69.57
Excellent         11     15.94     85.51
Fair          8     11.59      97.1
Poor          2     2.899       100
Total         69       100```

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.
• insertCols - Inserts one or more new columns into a matrix or dataframe at a specified location.

Improved Performance and Speed-ups

• Expanded functionality of strindx allows for 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.

Conclusion

For a complete list of all GAUSS 24 offers please see the complete changelog.