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