Add vdiffr snapshot tests for diagnostic plots (#236)

[ANAMASGARD]

I've added 18 vdiffr tests that cover the main check_model() plots and their variations. These should catch any visual regressions in future updates.

Tests cover check_model(), check_normality(), check_outliers(), and check_homogeneity() with various themes, colors, and options. Also validates that the large dataset performance fix produces visually consistent output.

I followed the tutorial from https://indrajeetpatil.github.io/intro-to-snapshot-testing/ to learn how vdiffr works.

Contributes to #236

[gemini-code-assist[bot]]

Summary of Changes

Hello @ANAMASGARD, I'm Gemini Code Assist[^1]! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request significantly enhances the robustness of the plotting functions by adding a suite of vdiffr snapshot tests. The primary goal is to catch any unintended visual changes in diagnostic plots early in the development cycle, ensuring a consistent user experience and reliable output. These tests cover a wide range of plot types and configurations, including how they render with different themes and custom color palettes, and specifically validates the visual output for large datasets.

Highlights

• New Snapshot Tests: Introduced 18 new vdiffr snapshot tests to prevent visual regressions in diagnostic plots.
• Comprehensive Plot Coverage: Tests cover check_model(), check_normality(), check_outliers(), and check_homogeneity() plots, including variations in themes, colors, and options.
• Large Dataset Validation: Ensures visual consistency of plots when handling large datasets, validating performance fixes.

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[ANAMASGARD]

Code Quality Improvements

Implemented Gemini AI code review suggestions:

• Added expect_doppelganger_with_seed() helper function

[strengejacke]

Thanks a lot, let me review this PR!