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kvr2 0.2.0

New Features

• Added model_info() function to extract metadata used for calculations, such as regression type (linear/power), sample size (\(n\)), and degrees of freedom (\(k\), \(df_{res}\)).
• The print() methods for r2_kvr2 and comp_kvr2 objects now display model information at the end of the output by default.
• Added a new argument model_info (default is TRUE) to print() methods, allowing users to toggle the display of model metadata.
• Added comp_model() to contrast intercept and no-intercept versions of the same model using QR-decomposition for robust re-calculation.
• Added a set of plot functions that visually display the difference between the actual and predicted values of the dependent variable in the model and the coefficient of determination.

Improvements

• Improved the auto-detection logic for power regression models. It now correctly distinguishes between a variable named “log” and the log() function call (e.g., lm(log(y) ~ x) is correctly identified while lm(log ~ x) is treated as linear).
• Internal calculations now explicitly store model attributes to ensure consistency between r2() and model_info().

Bug Fixes

• Fixed several typographical errors in the output and documentation. Notably, corrected “RMES” to “RMSE” (Root Mean Square Error) in the output of comp_fit().
• Fixed a misclassification issue where models with a dependent variable named “log” were incorrectly identified as power regression when using type = "auto".
• Corrected “liner” to “linear” in various internal labels and documentation to ensure consistency with standard statistical terminology.

kvr2 0.1.0

• First releases on CRAN.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.