easyViz 1.2.0

• Renamed re.form to re_form for consistency with other arguments. Argument re.form is deprecated. Please use re_form instead.
• Added ci_level argument to control the confidence interval level (default is 0.95). The confidence level used for the interval is included in the returned prediction data frame.
• Documentation improvements.

easyViz 1.1.0

• Added support for the new pred_resolution argument, which controls the number of prediction points used for numeric predictors. The default is 101 (matching visreg), but higher values can be helpful for predictors with a wide range or when visualizing nonlinear relationships.
• Added support for ci_type = NULL to suppress confidence intervals for numeric predictors.
• Added support for custom legend titles using the new legend_title argument.
• Added support for adjusting legend title text size using the new legend_title_size argument.
• Added support for horizontal legends using the new legend_horiz argument.
• Added support for fine-tuned legend customization via the new legend_args argument, which allows passing any additional parameters to legend() (e.g., pt.cex, bg, box.lwd, inset).
• Renamed legend_text_size to legend_label_size for consistency with legend_title_size and legend_labels. The argument legend_text_size is deprecated. Please use legend_label_size instead.
• Added support for categorical predictors: users can now customize x-axis text size, color, and font using plot_args (e.g., cex.axis, col.axis, font.axis) when the predictor is a factor.
• Added finite-sample adjusted t-based confidence intervals for lm(), rlm(), gls(), and nls() models. For models with asymptotic inference (e.g., glm(), mgcv::gam(), glmer(), glmmTMB()), confidence intervals are based on standard normal approximations.
• Improved visual clarity: when by is specified for interactions, all prediction lines are now drawn on top of their associated confidence intervals, ensuring they remain visible and are not obscured by overlapping CI polygons.
• Improved detection of binomial-style responses.
• Improved internal handling of link functions: the logit inverse now uses plogis() for greater numerical stability, and the cloglog inverse is now more robust to extreme values.
• Added a diagnostic warning when user-supplied aesthetic vectors (e.g., point_col) have a length mismatch with the cleaned data used for predictions, helping users avoid misaligned plot aesthetics when the original data contains missing values.
• Added an example demonstrating how to visualize 3-way interactions using the by and fix_values arguments in a multi-panel layout.
• Documentation improvements.