easyViz 2.0.0

• Major internal redesign: the package has been fully modularized to improve clarity, maintainability, and extensibility. Core functionality is now organized into dedicated components for data preparation, newdata construction, prediction, plotting, and output assembly.
• Added support for betareg models: confidence intervals for the mean submodel are computed using a delta-method approximation.
• Added support for survival models fitted with survival::coxph(). Conditional effects are plotted on the linear predictor (log-hazard) scale. Raw survival data (event times and censoring) are not plotted, and confidence intervals are based on standard errors of the linear predictor.
• New by_breaks argument: allows users to manually specify conditioning values when by is numeric, replacing the previous fixed default behavior. If not provided, numeric by variables with more than 6 unique values default to their 10th, 50th, and 90th percentiles.
• New rlm_vcov argument: robust standard errors for MASS::rlm() models can now be computed using any sandwich estimator type (e.g., "HC3", "HC5", "const", etc.), a user-supplied covariance matrix, or a custom function.
• New ci_polygon_alpha argument: allows users to control the transparency of polygon-style confidence interval bands when ci_type = "polygon". Defaults to 0.5 and accepts values between 0 and 1.
• New show_conditioning argument: allows users to print a concise summary in the console describing how predictions are conditioned. When show_conditioning = TRUE, easyViz() reports which variables are held fixed and which vary in the prediction grid, whether predictions from mixed-effects or GAM models are conditional on random effects or marginal, and, for numeric by variables, the values at which predictions are evaluated (e.g., quantiles or user-specified by_breaks). This option does not affect plots or returned values and is intended as a diagnostic aid.
• New plot argument: allows users to suppress graphical output. When plot = FALSE, easyViz() returns only the predictions (as an invisible easyviz.pred.df object) without drawing a plot. Useful when storing or further processing predicted values.
• Improved offset handling: offsets specified either in the model formula or via offset = are now automatically detected. For log-link count models (e.g., Poisson, negative binomial, COM-Poisson, generalized or truncated count models), observed responses are automatically scaled by the offset for plotting, and the prediction grid sets the offset to 1 by default, ensuring predictions and raw data are displayed on a common rate scale.
• Improved handling of numeric by variables: numeric conditioning variables with a small number of distinct values (currently ≤ 6) are now treated as discrete groups and plotted with one line per value (like a factor), instead of always using quantile cross-sections. For truly continuous numeric by variables (more than 6 unique values), easyViz() still uses default cross-sections (10th, 50th, and 90th percentiles) or the user-supplied values from by_breaks. This makes the behavior more intuitive for common cases such as 0/1 or small ordinal scales.
• Improved stability and consistency in handling numeric conditioning variables, interactions, binomial data, and GAM models with random-effect smooths.
• Improved legend behavior: when a by variable is specified and users do not explicitly set add_legend, easyViz() now automatically enables the legend. This ensures that multi-line or multi-group plots always display a clear legend by default, while preserving user control when add_legend is manually set.
• New legend placement preset "out" for legend_position: setting legend_position = "out" applies a predefined layout that places the legend outside the plotting region above the panel.
• Documentation and diagnostic messages significantly expanded and clarified.

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.