add outlier method as additional class for custom plotting methods

[rempsyc]

Minor class change for outliers for new see outlier plotting methods (easystats/see#407)

[gemini-code-assist]

Summary of Changes

Hello @rempsyc, I'm Gemini Code Assist¹! 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 primarily focuses on improving the extensibility of outlier detection results for plotting purposes by introducing a method-specific class to the outlier objects. This change enables more granular control and differentiation when visualizing outlier analyses, particularly in conjunction with external plotting packages. Additionally, it includes minor code quality enhancements and updates to internal maintenance files.

Highlights

• Enhanced Outlier Plotting: I've added a new, dynamic class to the outlier object within the check_outliers.default and check_outliers.data.frame functions. This new class, paste0("outlier_", method), allows for more specific plotting methods in the see package by differentiating outlier results based on the detection method used. This directly supports the new plotting methods referenced in the PR description (easystats/see#407).
• Code Robustness Improvement: In the check_outliers.item_omega function, I've updated the for loops from 1:n to seq_len(n). This is a best practice in R to ensure loops behave correctly when n is zero, preventing potential errors and improving code robustness.
• Internal Wordlist Updates: I've made several minor adjustments to the inst/WORDLIST file. These changes involve adding, removing, and reordering various terms, likely to refine the package's internal spell-checking or linting dictionary.

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[Copilot]

Pull Request Overview

This PR adds an additional class identifier to outlier detection results to support custom plotting methods in the easystats/see package. The change enables method-specific plotting by adding paste0("outlier_", method) to the class hierarchy.

Key changes:

• Added method-specific class to outlier objects for enhanced plotting capabilities
• Improved code style by replacing 1:n loops with seq_len(n) for better practices
• Updated WORDLIST alphabetical ordering

Reviewed Changes

Copilot reviewed 2 out of 2 changed files in this pull request and generated 1 comment.

File	Description
R/check_outliers.R	Added method-specific class identifier and improved loop iteration patterns
inst/WORDLIST	Reordered entries alphabetically with no functional changes

[Copilot]

The # nolint comment suggests a linting issue that's being suppressed. Consider addressing the underlying linting concern rather than suppressing it, as this can hide legitimate code quality issues.

[rempsyc]

The lint is:

 Line 888[paste_linter] Construct file paths with file.path(...) instead of paste(..., sep = "/"). If you are using paste(sep = "/") to construct a date, consider using format() or lubridate helpers instead. Note that paste() converts empty inputs to "", whereas file.path() leaves it empty.

However, this is not a file path, we're creating label strings like "Var1/Var2" to name correlation pairs. Use of #nolint seems justified.

[gemini-code-assist]

Code Review

This pull request introduces a new class, outlier_<method>, to the results of check_outliers(). This will enable custom plotting methods based on the specific outlier detection technique used. The changes are applied consistently across different function implementations.

My review focuses on an opportunity to improve performance in R/check_outliers.R. I've suggested refactoring a nested loop that builds a character vector, which can be inefficient in R, with a more idiomatic and performant vectorized approach.

The changes to inst/WORDLIST appear to be maintenance-related and have not been reviewed for correctness.

[gemini-code-assist]

For improved performance and code clarity, this nested loop can be refactored. Growing a vector with c() inside a loop is often inefficient in R because it can require reallocating memory in each iteration.

A more idiomatic and performant approach is to first find the indices of the non-NA values and then construct the names in a single, vectorized operation. This avoids the nested loop and repeated memory allocation, which will be significantly faster for larger matrices. Using colnames() for the column index is also more explicit and robust than using row.names().

indices <- which(!is.na(model_resid), arr.ind = TRUE)
pair_names <- paste(
  row.names(model_resid)[indices[, "row"]],
  colnames(model_resid)[indices[, "col"]],
  sep = "/"
)

[rempsyc]

Suggestion integrated

[codecov]

Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 62.13%. Comparing base (98f2f6a) to head (1410efe).
⚠️ Report is 61 commits behind head on main.

Additional details and impacted files

@@            Coverage Diff             @@
##             main     #846      +/-   ##
==========================================
+ Coverage   61.90%   62.13%   +0.22%     
==========================================
  Files          89       93       +4     
  Lines        6641     7220     +579     
==========================================
+ Hits         4111     4486     +375     
- Misses       2530     2734     +204     

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