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fixes 0.12.0 (2026-07-05)

Bug fixes

  • run_did() reported nrow(data) instead of the estimation sample size. attr(., "N") and N_treated (and the printed "N = … obs") counted all rows of data, including those fixest::feols() drops for missing values, disagreeing with nobs(model), broom::glance(), and run_es() (which already report the fitted sample). N, N_units, and N_treated now describe the estimation sample, recovered from the model's obs_selection. On data without missing values the numbers are unchanged.

New features

  • calc_att() results can now be tabulated. att_result gains broom::tidy() and broom::glance() methods, so aggregated ATTs (overall, by-cohort, or by-time; CS or BJS) can be passed to modelsummary::modelsummary() and rendered with tinytable. Several estimators — e.g. run_did() TWFE, CS, and BJS — can be stacked side by side in one table. The term column is derived from the aggregation type ("ATT", "Cohort <g>", "Time <t>") so rows align across estimators.

  • run_es(..., rel_time = <col>): pass a pre-built event-time column (time relative to treatment) — e.g. the Time_to_Treatment from paneltools/fect's get.cohort() — and run_es() uses it verbatim as the event-study factor (i(rel_time, treatment, ref = baseline)) instead of computing relative time from time/timing. This normalises the previous time = event_time, timing = 0 idiom and, unlike that idiom, keeps never-treated controls (NA rel_time) in the estimation sample. Requires estimator = "twfe" and method = "classic"; the default calendar-difference behaviour is unchanged. See the new Details section of ?run_es for the three relative-time conventions (calendar difference, within-unit rank, pre-built) and when they diverge in sparse/unbalanced panels.

Diagnostics

  • CS now names cohorts that are silently dropped because their base period g-1 is not observed (a real hazard in sparse panels, where a cohort can vanish from the by-cohort and event-study aggregates), and exposes them as attr(result, "dropped_cohorts") on both run_es() and calc_att().
  • SA warns when the saturated design is degenerate (a large share of cohort×period interactions dropped as collinear, or implausibly large CATT standard errors), which is easy to miss with sparse/irregular time.
  • BJS now reports attr(result, "n_unimputed") (and n_treated_obs): the number of treated observations whose counterfactual could not be imputed and were excluded from aggregation, on both run_es() and calc_att().

Internals — reduced fixest dependency

The long-term plan of trimming the fixest dependency (see CLAUDE.md) begins in this release. Two internal engines now replace fixest::feols() in most estimators; all user-facing APIs, output columns, and numerics are unchanged — every change here is verified against the previous fixest-based results to numerical agreement (tolerance = 1e-6, typically much closer).

  • BJS Step 1 (fixed-effects nuisance regression) is now estimated by an internal alternating-projections solver (src/fe_solver.cpp) instead of fixest::feols(y ~ 0 | unit + time) + fixest::fixef(). It reproduces fixest's estimation sample exactly, including the iterative removal of singleton fixed-effect levels (dropping one singleton can create another).
  • SA, TWM, FLEX, and compute_contamination_weights() now run their saturated cohort × period regressions through a new internal FE-OLS engine (R/fe_ols.R, src/demean_kway.cpp, src/fe_ols_kernels.cpp): k-way fixed-effects demeaning, keep-first collinearity dropping, and iid / heteroskedasticity-robust (HC1) / one-way cluster-robust standard errors that reproduce fixest's default small-sample corrections exactly (ssc(adj = TRUE, cluster.adj = TRUE), nested-fixed-effect accounting). Requests the internal engine cannot reproduce exactly — multi-way clustering, custom vcov_args, or fixed-effect specifications that are not plain columns — are transparently delegated to fixest, so every code path keeps returning identical results.
  • The regressions are parallelised with OpenMP (falls back to sequential execution where OpenMP is unavailable) and are within a few percent of fixest's multi-threaded speed on saturated designs with hundreds of cohort × period columns. Thread count defaults to half the detected physical cores and can be set with options(fixes.nthreads = <n>).
  • fixest remains the estimation engine for classic TWFE / method = "sunab" in run_es(), for run_did(), and as the fallback described above; it is still a required (Imports) dependency. parallel (base R) is now also imported, for core-count detection only.

fixes 0.11.2 (2026-06-11)

Bug fixes (silent-corruption class)

  • CS estimator: character / factor unit IDs produced silently wrong results (run_es(estimator = "cs") and calc_att(estimator = "cs")): unit IDs were coerced with as.integer(), which turns every character ID into NA — all units collapsed into one inside the Rcpp kernel and the ATT(g,t) table was meaningless, with no error raised. Unit IDs are now densely re-encoded with match(); character, factor, and arbitrary numeric IDs give results identical to integer IDs.

  • CS estimator: a cohort with timing == 0 leaked into the control group: 0L was the internal never-treated sentinel, so a genuine cohort first treated at time 0 (e.g., event-time-centred data) was silently used as never-treated controls. time/timing are now shifted by a common internal offset so the sentinel can never collide; results are invariant to common shifts of the time axis.

  • Classic staggered TWFE dropped the entire never-treated control group (estimator = "twfe", staggered = TRUE): the relative-time variable is NA for never-treated units and fixest::feols() removes rows with NA regressors, so every control row was excluded from the estimation sample (visible only as a fixest NOTE). Those rows are now assigned the (excluded) baseline level — their event dummies are identically zero because the i() interaction is multiplied by treatment = 0 — so the control group is correctly retained. N_treated / N_nevertreated metadata still reflect the original NA pattern.

  • method = "sunab" dropped the entire never-treated control group: fixest::sunab() has no NA-cohort convention and drops those rows. Never-treated units are now recoded internally to a cohort far beyond the sample period (the fixest::base_stagg convention) and retained as controls. Verified numerically identical to a direct fixest::feols(y ~ sunab(...)) call using the same recoding.

  • Rcpp (unit, time) lookup key: replaced the multiplicative hash (which could collide for time values above 1,000,003, e.g. Unix timestamps) with a collision-free 64-bit (unit << 32) | time key in src/compute_att_gt.cpp.

Improved input validation

  • CS: clear, actionable error when the time grid does not advance in steps of 1 (the g - 1 base-period assumption), instead of the generic "No ATT(g,t) estimates were computed".
  • CS: warning that names cohorts skipped because their base period (g - 1 - anticipation) is not observed.
  • CS / SA / TWM / FLEX: fractional time / timing values now raise an error instead of being silently truncated by as.integer() (new shared helper .assert_integerish()).
  • SA / TWM / FLEX: warning when the requested baseline reference period is not feasible for any cohort, in which case no period would be excluded and the regression is unnormalised.

Performance

  • Verified on a 400,000-row staggered panel (20,000 units × 20 periods): every estimator completes in roughly 1–3 s (CS bootstrap with B = 999 ≈ 3 s), peak memory ≈ 1.3 GB.
  • BJS: singleton-unit FE recovery vectorised (was O(#singletons × N), which could take minutes on large unbalanced panels).
  • CS: event-study aggregation and bootstrap influence-function bookkeeping vectorised (match() on keys instead of per-cohort scans).

Tests

  • New tests/testthat/test-robustness.R (19 assertions) covering all of the above: ID-type invariance, time-shift invariance, control-group retention (classic TWFE and sunab vs. direct fixest reference), grid-spacing errors, fractional-time rejection, and BJS singleton imputation.

fixes 0.11.1 (2026-05-30)

Improvements

  • run_es() — diagnostic warning for ambiguous timing = NA (classic TWFE, staggered = TRUE only): a warning() is now emitted when any unit has treatment = 1 in at least one row but timing = NA. Such units are silently absorbed as never-treated controls — correct when NA is intentional, but dangerous when NA represents genuinely missing treatment timing. The warning names up to 5 affected unit IDs (when unit is supplied) for quick diagnosis.

  • @param timing documentation clarified: all staggered estimators (cs, sa, bjs, twm, flex, classic with staggered = TRUE) share the same NA = never treated convention as did::att_gt() and fixest::sunab(). The roxygen documentation now states this explicitly and notes the risk of silent misclassification when NA represents missing data rather than "never treated".

fixes 0.11.0 (2026-05-24)

New Features

  • honest_sensitivity() — honest robust inference (Rambachan & Roth 2023) (R/honest_did.R): sensitivity analysis for event-study / DiD designs when parallel trends may be violated. Instead of assuming parallel trends holds exactly, it reports confidence sets for a post-treatment effect under progressively weaker restrictions on the difference in trends, and a breakdown value (the largest restriction at which the effect is still significant):

    res <- run_es(df, outcome = y, treatment = treat, time = year, timing = 6,
              fe = ~ id + year)
h <- honest_sensitivity(res, type = "relative_magnitude",
                        Mvec = c(0, 0.5, 1, 1.5, 2))
plot_honest(h)
    • Restriction families: "relative_magnitude" (\eqn{\Delta^{RM}(\bar M)}) and "smoothness" (\eqn{\Delta^{SD}(M)}).
    • Inference via the Andrews-Roth-Pakes (ARP) conditional moment-inequality test — a pure-R reimplementation (no dependency on the HonestDiD package).
    • Works directly from a run_es() es_result (estimator "twfe": classic or method = "sunab", which now carry the event-study coefficient covariance via the new es_vcov attribute), or from a raw betahat / sigma pair for any estimator.
    • plot_honest() / autoplot() draw the "top-down" sensitivity plot; print.honest_result() reports the breakdown value.
    • Validated against the HonestDiD reference package (method "Conditional"): \eqn{\Delta^{SD}} (single post period) matches to machine precision; \eqn{\Delta^{RM}} matches to grid resolution. Heavy numeric helpers (lpSolveAPI, Rglpk, TruncatedNormal, Matrix, pracma) are in Suggests and gated with requireNamespace().

Internal / Refactor

  • Estimator-core consolidation (behaviour-preserving): factored the boilerplate duplicated across run_es(), calc_att(), run_did(), and the five estimator backends into shared helpers in R/utils-internal.R — .resolve_col() (NSE column resolution), .add_ci_columns() (CI construction), .validate_panel_cols(), .compute_cohort_sizes(), .model_vcov_full(), and .aggregate_iw(). The calc_att() aggregation loops were vectorised. All existing tests pass unchanged.

  • run_did() — basic TWFE DiD estimator (R/run_did.R): New function for classic two-way fixed-effects difference-in-differences. Accepts a pre-built binary treatment indicator D_it and returns a did_result S3 object with full modelsummary / tinytable compatibility:

    df$D <- as.integer(df$treated & df$year >= 2006)
result <- run_did(df, outcome = y, treatment = D, fe = ~ id + year)
print(result)
modelsummary::modelsummary(result)   # tinytable output (modelsummary >= 2.0)
    • NSE API consistent with run_es() and calc_att() — specify variables by name, not by formula construction.
    • fe auto-inferred as ~ unit + time when both unit and time are supplied and fe = NULL.
    • tidy.did_result() delegates to broom::tidy.fixest() for full regression table (all regressors); glance.did_result() delegates to broom::glance.fixest() for model-level stats (within.r.squared, nobs, AIC, etc.).
    • print.did_result() shows a clean header: estimator, sample sizes, FE spec, VCOV type.
    • Outcome expressions like log(y) supported.
    • modelsummary and tinytable added to Suggests.

  • calc_att() — ATT aggregation function (R/calc_att.R): New function that separates "calculating ATT" from "running an event study". While run_es() produces a full dynamic event-study curve (effects by relative time), calc_att() yields aggregated ATT estimates with a clear, minimal interface:

    calc_att(data, outcome = y, time = year, timing = g, unit = id,
         estimator = "cs",
         aggregation = "simple")     # overall ATT
calc_att(..., aggregation = "by_cohort")  # ATT per cohort
calc_att(..., aggregation = "by_time")    # ATT per calendar period
    • Estimators: "cs" (Callaway-Sant'Anna 2021) and "bjs" (Borusyak et al. 2024). SA, TWM, FLEX are event-study-only estimators and are not supported (use run_es()).
    • Aggregation formulas (CS) match did::aggte() for point estimates:
      • "simple": exposure-weighted average over all post-treatment (g,t) pairs.
      • "by_cohort": mean ATT(g,t) over post-treatment periods per cohort.
      • "by_time": cohort-size-weighted ATT per calendar period.
    • SEs: independence assumption (diagonal delta-method). Systematically smaller than did::aggte SEs, which use influence-function variance accounting for within-cohort time correlation. Cluster-robust SE for BJS aggregations planned for v0.12.
    • Returns an att_result S3 object (data.frame subclass) with columns group, estimate, std.error, statistic, p.value, conf_low_XX/conf_high_XX. Attributes: aggregation, estimator, N, N_units, N_treated, control_group (CS only), att_gt (raw CS ATT(g,t) table), tau_it (BJS unit-time effects table).
    • print.att_result() S3 method provides a concise summary.

Testing

  • 48 new tests in tests/testthat/test-att.R:
    • Return type and column contract for all aggregation types
    • CS point estimates match did::aggte() at tolerance 1e-4
    • BJS estimates match manual mean(tau_it$tau_hat) at tolerance 1e-12
    • Multiple conf.level support verified

fixes 0.10.1 (2026-05-14)

Internal

  • run_es.R refactoring: Extracted .make_tidy() and .es_finalize() private helpers that centralise the repeated tidy-construction / baseline-row / range-filter / attr-stamping pattern that was previously duplicated across the cs, sa, bjs, twm, and flex branches (~360 lines removed). No API change; all 593 tests pass.
  • profvis benchmark harness: Added inst/profile/benchmark.R with profile_estimator() and benchmark_all() utilities. Run interactively to generate flamegraph HTML and wall-clock summary across estimators before starting Phase 3 Rcpp work.
  • CLAUDE.md documentation: Added src/compute_att_gt.cpp (shipped in v0.8.1 but missing from the Rcpp Acceleration Roadmap), and updated Package Structure to list all 13 R source files, 5 Rcpp files, and 16 test files that now exist.
  • Edge-case tests (19 new, 593 total):
    • test-twm.R Test 16 — trends=TRUE with exactly 2 pre-treatment periods succeeds without warning (boundary condition of the ≥ 2 requirement).
    • test-twm.R Test 17 — explicit lead_range/lag_range correctly trims TWM output and preserves estimates in the shared window (exercises .es_finalize() filter).
    • test-flex.R Test 12 — explicit lead_range/lag_range trims FLEX output correctly.
    • test-flex.R Test 13 — FLEX succeeds with an all-treated panel (no never-treated groups; N_nevertreated == 0).
    • test-sa.R — explicit lead_range/lag_range trims SA output and preserves estimates within the shared window.

fixes 0.10.0 (2026-05-14)

New Features

  • run_es(estimator = "twm", trends = TRUE): Cohort-specific linear trend detrending (Wooldridge 2025, Section 8). Adds d_g * t regressors so each cohort's counterfactual trend can deviate linearly from the common time trend. Implemented as post-treatment-only cells + trend columns; requires

    = 2 pre-treatment periods per cohort. Output shows relative_time >= 0 only (pre-trend testing is deferred to the no-trend model).

  • run_es(estimator = "twm", covariates = ~ x1 + x2): Full Wooldridge (2025) Procedure 5.1 covariate interactions. Adds treatment-cell × cohort-demeaned covariate terms (ẋ_{ig} = x_i - x̄_g) and i(time, x_j) conditional parallel-trends controls (Eqs. 5.2-5.3).
  • run_es(estimator = "flex", covariates = ~ x1 + x2): Full Deb et al. (2024) Eq. 3.1 covariate interactions for RCS data. Cell-level centering X_{i,t} - X̄_{g,t} (Eq. 2.11) with i(time, x_j) and i(group, x_j) conditional PT controls.

Performance (Rcpp Phase 2)

  • src/indicator_matrix.cpp — build_indicator_matrix_cpp(): Replaces the pure-R nested for-loop that fills the 0/1 indicator matrix in the SA, TWM, and FLEX estimators. Single shared Rcpp utility, O(N×K) integer fill with no R allocation overhead per column.
  • src/iw_aggregation.cpp — aggregate_iw_cpp(): Replaces the R event-time aggregation loop and the t(w) %*% V_sub %*% w quadratic-form VCOV step in SA, TWM, and FLEX. Uses RcppArmadillo arma::mat::submat() for O(1) VCOV block extraction and arma::as_scalar(w.t() * V_sub * w) for the quadratic form.
  • src/cov_demeaning.cpp — build_cov_interactions_cpp(): Replaces the pure-R covariate demeaning loops in TWM (cohort-level centering) and FLEX (group×time cell-level centering) with a single std::unordered_map-based O(N) grouping pass followed by column-wise mean subtraction, then builds the N × (K×p) treatment-cell × centred-covariate interaction matrix in C++.
  • src/bootstrap_cs.cpp — bootstrap_cs_cpp(): Replaces the R-level apply/sweep calls in the CS multiplier bootstrap with RcppArmadillo DGEMM for both the pilot and main stages. Uses R::runif() for Mammen weight generation (column-major fill order matches R's matrix()) so results are numerically identical to the pure-R path under the same set.seed().
  • Added src/Makevars and src/Makevars.win linking $(LAPACK_LIBS) $(BLAS_LIBS) $(FLIBS) for cross-platform RcppArmadillo support.
  • RcppArmadillo added to LinkingTo in DESCRIPTION.

Internal

  • 574 tests passing (47 new over v0.9.0: trends 4, TWM-covariates 3, FLEX-covariates 3, Rcpp-indicator 4, Rcpp-IW 3, Rcpp-covdemeaning 6, Rcpp-bootstrap 12, plus sub-assertions).

fixes 0.9.0 (2026-05-14)

New Features

  • run_es(estimator = "twm"): Wooldridge (2025) Two-Way Mundlak (TWM) estimator for panel data. Implements Procedure 5.1 via POLS on cohort x calendar-time treatment cells with two-way FE. Algebraically identical to estimator = "sa" in the no-covariate base case (verified numerically at tolerance 1e-10). The trends = TRUE extension (cohort-specific linear trend detrending, Wooldridge 2025 Section 8) is reserved for v0.9.1.
  • run_es(estimator = "flex", group = <group_id>): Deb, Norton, Wooldridge & Zabel (2024) FLEX estimator for repeated cross-section (RCS) data. Uses group x calendar-time OLS with group+time FE (no unit FE). Algebraically equivalent to the multi-step imputation estimator (Proposition 2.1). New group argument identifies the treatment group (R_{ig}) each individual belongs to. Default clustering is by group. Covariate interactions deferred to v0.9.1.
  • Reference PDFs converted to TXT: papers/Deb et al. (2024).txt, papers/Woodridge (2025).txt.

Internal

  • 517 tests passing (41 new: TWM 21, FLEX 20).
  • Both new estimators: pre-allocated integer indicator matrix construction (same strategy as SA optimisation)
    • feols() + quadratic-form VCOV. No new dependencies.

fixes 0.8.1 (2026-05-10)

New Features

  • run_es(estimator = "cs"): Callaway-Sant'Anna (2021) estimator. Computes group-time ATT(g,t) via the unconditional DiD estimand (eq. 2.8) with never-treated or not-yet-treated control groups. Supports control_group = "nevertreated" (default) or "notyettreated". The full ATT(g,t) matrix is stored as the att_gt attribute of the result.
  • run_es(estimator = "sa"): Sun-Abraham (2021) interaction-weighted estimator. Aggregates cohort x relative-time interactions by cohort share weights. Numerically identical to fixest::sunab() to machine precision.
  • run_es(estimator = "bjs"): Borusyak, Jaravel & Spiess (2024) imputation estimator. Fits TWFE on untreated observations only, imputes counterfactuals for treated observations, and averages treatment effects by horizon. Handles singleton unit fixed effects via closed-form recovery.
  • run_es(bootstrap = TRUE): Multiplier bootstrap for simultaneous confidence bands (Algorithm 1, Callaway & Sant'Anna 2021). Adds conf_low_sim / conf_high_sim columns to the result and stores the (g,t)-level bootstrap object as attr(result, "bootstrap"). Controlled by B (draws, default 999) and boot_seed arguments. Available only when estimator = "cs".
  • plot_att_gt(): Visualize the full ATT(g,t) matrix from CS results as a heatmap (type = "heatmap") or cohort-faceted time series (type = "facet"). Requires estimator = "cs" result as input. When bootstrap = TRUE was used, the heatmap adds a subtitle with the simultaneous critical value and open-diamond markers for simultaneously significant cells; the facet adds a lighter simultaneous CI ribbon.
  • plot_es(show_simultaneous = TRUE): Overlays the simultaneous bootstrap CI (lighter band, alpha 0.15) alongside the pointwise CI (alpha 0.3) with a two-entry legend. Errors informatively if bootstrap was not run.
  • plot_es_interactive(show_simultaneous = TRUE): Adds a second lighter ribbon trace for the simultaneous CI and extends the hover tooltip with simultaneous CI bounds.

Bug Fixes

  • run_es() bootstrap path: base::merge() silently dropped all custom attributes (e.g. att_gt, N_units, lead_range) from the es_result object. Attributes are now saved and restored around the merge, so plot_att_gt() works correctly after a bootstrap = TRUE run.

Internal

  • Added did and didimputation to Suggests for numerical agreement tests against reference implementations.
  • 195 tests passing across all estimators, visualization functions, and bootstrap routines.

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.