check_model() linearity & variance for categorical predictors

[strengejacke]

Fixes #873

[gemini-code-assist]

Summary of Changes

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This pull request enhances the check_model() function by introducing a new show_ci argument across its various implementations. This addition provides users with fine-grained control over the display of confidence intervals within diagnostic plots, which is especially beneficial for improving the clarity and interpretability of visualizations when models include categorical predictors that might otherwise lead to disproportionately large interval scales. The change aims to address and fix issue #873.

Highlights

• New Parameter for Confidence Intervals: A new logical parameter, show_ci, has been introduced to the check_model() function and its various methods (.default, .stanreg, .model_fit, .performance_simres).
• Control over Plot Visualization: This parameter allows users to explicitly control whether confidence intervals are displayed in the diagnostic plots generated by check_model(). By default, show_ci is set to TRUE.
• Improved Handling of Categorical Predictors: The show_ci parameter is particularly useful for models involving categorical predictors, where confidence intervals can sometimes appear on very large scales, making it more appropriate to disable them for clearer visualization.
• Package Version Update: The package version has been incremented from 0.15.3.4 to 0.15.3.5.

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[gemini-code-assist]

Code Review

This pull request introduces a new show_ci parameter to the check_model() function and its associated methods. This parameter allows users to control the visibility of confidence intervals in the diagnostic plots, which is a helpful feature, especially for models with categorical predictors where confidence intervals can sometimes be very large. The changes have been applied consistently across the check_model() default method and its wrappers for stanreg, model_fit, and performance_simres objects. The documentation in both the R script and the Rd file has been updated accordingly. The implementation is clean, correct, and aligns well with the existing codebase. I have no further recommendations.