fixes 0.4.0 (May 25, 2025)

New Features

• Added support for staggered treatment timing:
  • New staggered = TRUE option allows timing to vary by unit (e.g., treatment year column).
  • Units with NA in timing are safely retained as untreated.
• Added support for observation weights:
  • Use the weights argument (e.g., ~ popwt) to run weighted regressions.
• Automatic lead/lag range detection:
  • If lead_range or lag_range is NULL, the function computes the maximum feasible range from the data.

Improvements

• Safer handling of collinearity and untreated units.
• Warning is issued when unit is specified without time_transform = TRUE.
• Input validation and internal structure cleaned up for better robustness.

Compatibility

• Backward compatible with previous versions.

fixes 0.3.1 (May 18, 2025)

Improvements

• Improved default x-axis scaling in plot_es():
  • The x-axis now uses ggplot2::scale_x_continuous() with integer breaks spaced by 1, aligned to the relative_time range.
  • This provides more readable and consistent event time labeling in plots.

fixes 0.3.0 (May 18, 2025)

New Features

• Added support for irregular and non-numeric time variables such as Date:
  • Introduced time_transform = TRUE to automatically convert the time variable into a unit-level sequential index (1, 2, 3, …) for event study estimation.
  • Added unit argument to specify the panel unit identifier required when time_transform = TRUE.
  • This enables seamless analysis of panel data with monthly, quarterly, or irregular time formats.

Improvements

• Updated input validation:
  • Now accepts Date class in the time variable and converts it automatically to numeric if time_transform = FALSE.
  • Improved error messages and warnings for better user feedback when unit is missing or time is of unsupported type.
• Enhanced documentation:
  • Updated @examples in the function documentation to include Date-based examples.
  • Extended vignette to illustrate time_transform usage.
  • Expanded README.md to describe irregular time handling and demonstrate new use cases.
• Strengthened test coverage:
  • Added new unit tests for time_transform, unit handling, and Date conversion edge cases.

Compatibility

• No breaking changes.
• This is a backward-compatible minor release introducing significant new functionality for irregular time formats.

fixes 0.2.1 (May 11, 2025)

Minor Improvements

• Added a warning when lead/lag dummy variable names (e.g., lead1, lag0) already exist in the dataset to prevent accidental overwriting.
• Added a warning when filtered data (based on lead_range, lag_range, and interval) has fewer than 10 rows, helping users identify overly narrow estimation windows.
• Improved handling of the treatment variable: it is now coerced to logical using as.logical() to support both binary numeric (0/1) and logical (TRUE/FALSE) formats.
• Fixed internal bug in model formula construction:
  • Previously, fixed effects specified via the fe argument (e.g., ~ id + year) were combined using model_formula | fe_text, which caused evaluation errors during tests.
  • Now, the full model formula is safely constructed as a string and parsed with as.formula() to ensure compatibility with fixest::feols().

Compatibility

• No breaking changes. This is a backward-compatible patch release with internal robustness improvements and enhanced error handling.

fixes 0.2.0 (March 29, 2025)

Major Features

• Support for covariates in run_es():
  • Covariates must now be specified as a one-sided formula (e.g., ~ x1 + x2).
• Fixed Effects and Clustering Interface Updated:
  • fe and cluster arguments must now be specified using a one-sided formula (e.g., ~ id + year).
  • Character vector input for cluster is still accepted.
  • Improved internal handling and validation of fixed effects and clustering variables.
• Improved error messages for invalid or missing variable names.

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.