February 2026 — easystats/performance: Delivered a critical bug fix to ensure accurate extraction of p-values from Levene's test results. Implemented corrected parsing logic and added a regression test to validate correctness, reducing the risk of incorrect statistics being consumed by downstream analytics. This work improves reliability of statistical reporting and supports future feature work in robust test result handling.

November 2025 monthly summary for easystats/insight focusing on feature delivery and rollback related to get_modelmatrix.fixest, with emphasis on business value and stability.

May 2025 (2025-05) monthly summary for easystats/performance: Delivered targeted feature enhancements to improve check_group_variation usage and ensure consistent downstream analytics. Key changes include enhanced logic for variable types (nested vs non-nested), standardized outputs with title-cased columns (Variable, Group, Variation, Design), clarified input argument naming, and improved heterogeneity bias handling in summaries. Documentation and tests were updated to reflect the new design, and formatting was standardized (styler). The check_heterogeneity_bias function was deprecated by updating its title/docs to steer users toward removal. No major bugs fixed this month; the focus was on reliability, maintainability, and a clearer API surface to support dashboards and analytics pipelines. These changes reduce user confusion, improve data quality, and position the project for smoother migrations and future enhancements.

February 2025: Delivered a key feature in easystats/modelbased by clarifying predictions on a regular grid and refining the notation for estimates and standard errors in Paper.Rmd. No critical bugs fixed this month. This work improves interpretability of predictions, consistency of statistical notation, and readiness of manuscript materials for review/publication, supporting clear communication of methods and results.

January 2025 (2025-01) monthly summary for easystats/modelbased. Focused on delivering user-facing documentation improvements for ModelBased Predictions and Marginal Effects, with clear explanations of prediction types, intervals, and backend components; reduced ambiguity for end users; prepared groundwork for broader adoption.