fixes 0.1.0 (March 17, 2025)

Major Changes

• fe_var argument now supports additive notation (firm_id + year) instead of character vectors.
• Improved plot_es() efficiency and documentation.

Minor Improvements

• Fixed cluster variable handling to correctly reference column names.
• Updated test cases to support new fe notation.
• Improved package documentation.

fixes 0.0.2 (Enhancements & Fixes)

This version introduced several enhancements and refinements to improve usability and maintainability.

Improvements

• Refactored variable name handling:
  • outcome_var, treated_var, and time_var are now processed using rlang::ensym() for better robustness.
  • fe_var and cluster_var handling improved for more reliable column referencing.
• More informative error messages when variables are missing in the dataset.
• Enhanced baseline term handling in regression models to prevent incorrect factor levels.
• Improved plot_es() function:
  • Added validation checks to ensure required columns (relative_time, estimate, etc.) are present.
  • Adjusted confidence interval calculations to avoid missing values in error bars.

Fixes

• Addressed an issue where baseline handling could lead to incorrect sorting of lead/lag terms.
• Resolved a minor inconsistency in fixed effects variable name parsing.

fixes 0.0.1 (Initial Release)

This is the first release of the fixes package, providing tools for estimating and visualizing event study models with fixed effects.

Features

• run_es(): A function to estimate event study models using fixest::feols(), generating lead and lag variables automatically.
  • Supports fixed effects (fe_var as character vector).
  • Allows clustered standard errors via cluster_var.
  • Handles time scaling through the interval argument.
• plot_es(): A function to visualize event study results with ggplot2.
  • Supports ribbon-style confidence intervals (type = "ribbon", default).
  • Allows error bar visualization (type = "errorbar").
  • Customizable plot elements including colors, line styles, and reference lines.

Initial Implementation

• Fixed effects regression model using fixest::feols().
• Automated creation of lead/lag dummy variables based on treatment timing.
• Baseline period exclusion to avoid multicollinearity.
• Support for custom time intervals (interval argument).

Limitations in 0.0.1

• Fixed effects must be specified as a character vector (c("firm_id", "year")).
• Clustered standard errors require variable names as character strings ("state_id").
• No direct support for additive notation in fe_var.