Open
Conversation
Codecov Report✅ All modified and coverable lines are covered by tests. Additional details and impacted files@@ Coverage Diff @@
## main #482 +/- ##
=======================================
Coverage 98.54% 98.54%
=======================================
Files 19 19
Lines 3362 3363 +1
Branches 493 493
=======================================
+ Hits 3313 3314 +1
Misses 26 26
Partials 23 23 ☔ View full report in Codecov by Sentry. 🚀 New features to boost your workflow:
|
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.This suggestion is invalid because no changes were made to the code.Suggestions cannot be applied while the pull request is closed.Suggestions cannot be applied while viewing a subset of changes.Only one suggestion per line can be applied in a batch.Add this suggestion to a batch that can be applied as a single commit.Applying suggestions on deleted lines is not supported.You must change the existing code in this line in order to create a valid suggestion.Outdated suggestions cannot be applied.This suggestion has been applied or marked resolved.Suggestions cannot be applied from pending reviews.Suggestions cannot be applied on multi-line comments.Suggestions cannot be applied while the pull request is queued to merge.Suggestion cannot be applied right now. Please check back later.
Fixes #478
To merge after #480
The previous implementation removed eigenvalues using a fixed absolute threshold (> 0.001), which is scale-dependent and can discard valid, non-zero eigenvalues when the covariance matrix has small but meaningful variance. This breaks can lead to incorrect W and p-values in Mauchly's test. The new implementation uses a relative tolerance based on machine precision and matrix scale to ensure that only numerically zero eigenvalues are discarded.