The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.

fixes 0.7.2 (2026-05-02)

Bug Fixes

• Clustered standard errors no longer silently overridden by HC1:
  • Fixed a bug where specifying cluster in run_es() did not produce clustered standard errors
  • The default vcov = "HC1" was being applied after model estimation, overriding the clustered SE from feols()
  • Now, when cluster is specified and vcov is left at its default ("HC1"), the model’s clustered standard errors are used
  • To explicitly request HC1 SEs even when cluster is set, pass vcov = "HC1" together with cluster = NULL
  • Applies to both classic and sunab methods
  • This resolves discrepancies between run_es() results and equivalent direct fixest::feols() calls

fixes 0.7.1 (2025-11-02)

Bug Fixes

• Lead/lag range filtering now enforced:
  • Fixed bug where lead_range and lag_range parameters were ignored, causing all estimated coefficients to be returned regardless of specified ranges
  • Results now correctly filtered to only include coefficients within [-lead_range, lag_range]
  • Applies to both classic and sunab methods
• sunab() namespace error resolved:
  • Fixed 'sunab' is not an exported object from 'namespace:fixest' error when using method = "sunab"
  • Formula environment now correctly configured to access sunab function from fixest package
  • sunab() calls now work identically to direct fixest::feols() usage

Enhancements

• Baseline row now included in sunab results:
  • The baseline parameter (default: -1) now applies to both classic and sunab methods
  • Baseline period added to results with estimate = 0, std.error = 0, and is_baseline = TRUE
  • Provides consistent behavior across both estimation methods
  • Updated documentation to clarify that baseline is used for both methods
• Improved term column formatting:
  • Term values now display as clean numeric strings (e.g., "-9", "0", "3")
  • Removed complex fixest-specific notation (e.g., "year::-9") for better readability
  • Consistent formatting across both classic and sunab methods
• Modernized code with native pipe operator:
  • Replaced tidyverse pipe (%>%) with base R pipe (|>) throughout the package
  • Requires R >= 4.1.0 (already enforced in DESCRIPTION)
  • Cleaner code with no functional changes

Testing

• Comprehensive test suite overhaul:
  • Added 25+ test cases covering all bug fixes and enhancements
  • Separate test suites for classic, staggered, and sunab methods
  • Tests for baseline row inclusion, term formatting, and range filtering
  • Integration tests with fixest built-in datasets (base_did, base_stagg)
  • Tests use native pipe operator (|>)

Compatibility

• Fully backward compatible with previous versions
• All changes maintain existing API and functionality
• Enhanced robustness and user experience

fixes 0.7.0 (2025-11-01)

Bug Fixes

• Critical fix for relative_time calculation in non-staggered designs:
  • Fixed bug where relative_time was NA for all coefficients except baseline when using non-staggered treatment timing
  • The reference period in fixest::i() now correctly corresponds to the baseline parameter
  • For example, with timing = 5 and baseline = -1, period 4 (not period 5) is now used as the reference
  • This ensures proper event study estimation with the intended baseline period

New Features

• Interactive plotting with plotly:
  • New function plot_es_interactive() for creating interactive event study plots
  • Hover tooltips display: relative time, point estimate, confidence intervals, standard error, and p-value
  • Supports customizable confidence levels, colors, and styling options
  • Optional ribbon or point-only display modes
  • Fully compatible with es_result objects from run_es()

Performance Improvements

• Optimized relative_time extraction:
  • Vectorized term parsing replaces previous sapply() approach for significant performance gains
  • Faster processing of large event study results
• Enhanced error reporting:
  • More informative warnings when term parsing fails
  • Better debugging information for malformed coefficient names

Compatibility

• Fully backward compatible with previous versions
• Requires R >= 4.1.0
• Interactive plotting requires plotly package (optional, suggested dependency)

fixes 0.5.0 (2025-07-06)

New Features

• Multiple Confidence Intervals:
  run_es() and plot_es() now support returning and visualizing multiple confidence levels (e.g., 90%, 95%, 99%) in a single analysis.
• Enhanced Plotting Options:
  plot_es() adds ci_level selection, more theme options (theme_style), and improved ribbon/error bar display.
• Expanded Error Handling:
  More informative error reporting for all argument validation and model mis-specification.
• Documentation Overhaul:
  Updated README, vignette, and roxygen docs to reflect all new features and usage patterns.

Improvements

• Input Validation:
  Stricter checks for all arguments and clearer, friendlier error messages.
• Staggered Timing:
  More robust handling of partial or missing treatment timing.
• Code Refactoring:
  Internal codebase cleaned for maintainability and reliability.

Bug Fixes

• Fixed: Confidence intervals always correctly labeled and included in output.
• Fixed: Dummy variables not created for untreated/never-treated units in staggered settings.
• Fixed: CRAN warnings by declaring all global variables used in tidy evaluation.

Compatibility

• Fully backward compatible with previous versions.
• Minimum required R version remains 4.1.0.

fixes 0.4.1 (2025-06-30)

New Features

• Support for untreated units (NA in timing):
  • Units with missing timing are now retained as never-treated controls (staggered only).
• Flexible weights input:
  • Accepts formulas (~ popwt), bare names (popwt), or character strings ("popwt").
• Improved cluster input handling:
  • Now supports formula, character vector, and bare names consistently.

Improvements

• Better validation and error messages across all inputs.
• Warning added if unit is supplied without time_transform = TRUE.
• Documentation switched to Markdown-style lists for better readability.

Bug Fixes

• Ensured untreated units don’t receive event dummies.
• Improved internal dummy creation and baseline handling.

Compatibility

• Fully backward compatible with previous versions.

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.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.