The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
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[@]metanet.ch.

fixes

Overview

fixes is an R package for event study analysis in panel data — the workhorse tool for visualizing parallel trends and dynamic treatment effects in difference-in-differences research.

Version 0.8.0 adds three modern estimators designed for staggered adoption (where different units adopt treatment at different times), all accessible through the same run_es() interface.

Key functions:

Estimators (selected via the estimator argument in run_es()):

Installation

# From CRAN
install.packages("fixes")

# Development version
pak::pak("yo5uke/fixes")

Quick start

library(fixes)

All four estimators share the same interface:

# Classic TWFE (single treatment date)
res_twfe <- run_es(
  data = df,
  outcome = y,
  treatment = treat,
  time = period,
  timing = 5,
  fe = ~ id + period
)

# Sun & Abraham (2021) — staggered adoption
res_sa <- run_es(
  data = df_stagg,
  outcome = y,
  time = year,
  timing = timing,
  unit = id,
  fe = ~ id + year,
  staggered = TRUE,
  estimator = "sa"
)

# Callaway & Sant'Anna (2021) — staggered adoption
res_cs <- run_es(
  data = df_stagg,
  outcome = y,
  time = year,
  timing = timing,
  unit = id,
  staggered = TRUE,
  estimator = "cs",
  control_group = "nevertreated"
)

# Borusyak, Jaravel & Spiess (2024) — staggered adoption
res_bjs <- run_es(
  data = df_stagg,
  outcome = y,
  time = year,
  timing = timing,
  unit = id,
  staggered = TRUE,
  estimator = "bjs"
)

Classic event study (single treatment date)

Use run_es() with a fixed event date. Here we use fixest::base_did, a simulated balanced panel where all units are treated at period 5.

df <- fixest::base_did

es <- run_es(
  data = df,
  outcome = y, # outcome variable (unquoted)
  treatment = treat, # 0/1 treatment indicator
  time = period, # time variable
  timing = 5, # treatment occurs at period 5
  fe = ~ id + period,
  cluster = ~id,
  baseline = -1 # period -1 is the reference (default)
)

print(es)

## Event Study Result (fixes)
##   N: 1080  | Units: NA  | Treated units: 1080  | Never-treated: NA 
##   FE: id + period
##   VCOV: HC1  | Cluster: id 
##   Method: classic  | lead_range: 4  lag_range: 5  baseline: -1

plot_es(es)

Staggered adoption

When units adopt treatment at different times, the classic TWFE estimator can be biased. fixes provides three modern alternatives.

Setup: create a shared dataset from fixest::base_stagg. Never-treated units use NA for their timing column — this is the convention for all three staggered estimators.

df_stagg <- fixest::base_stagg
df_stagg$timing <- df_stagg$year_treated
df_stagg$timing[df_stagg$year_treated == 10000] <- NA # mark never-treated

Callaway & Sant’Anna (2021) — estimator = "cs"

Estimates a separate ATT(g,t) for every combination of cohort g and calendar time t, then aggregates to the event-study curve. Comparison group can be never-treated units (default) or not-yet-treated units.

cs <- run_es(
  data = df_stagg,
  outcome = y,
  time = year,
  timing = timing, # first treatment period; NA = never treated
  unit = id,
  staggered = TRUE,
  estimator = "cs",
  control_group = "nevertreated" # or "notyettreated"
)

print(cs)

## Event Study Result (fixes)
##   N: 950  | Units: 95  | Treated units: 45  | Never-treated: 50 
##   FE: 
##   VCOV: analytic  | Cluster: - 
##   Method: classic  | lead_range: 9  lag_range: 8  baseline: -1

plot_es(cs)

Visualise the full ATT(g,t) matrix

plot_att_gt() shows every cohort × calendar-time cell, making it easy to spot anticipation effects or heterogeneous dynamics.

plot_att_gt(cs, type = "heatmap")

plot_att_gt(cs, type = "facet")

Sun & Abraham (2021) — estimator = "sa"

Builds cohort × horizon interaction terms, then aggregates with cohort-share weights. Gives the same result as fixest::sunab() but through the unified run_es() interface.

sa <- run_es(
  data = df_stagg,
  outcome = y,
  treatment = treated,
  time = year,
  timing = timing,
  unit = id,
  fe = ~ id + year,
  staggered = TRUE,
  estimator = "sa",
  cluster = ~id
)

print(sa)

## Event Study Result (fixes)
##   N: 950  | Units: 95  | Treated units: 45  | Never-treated: 50 
##   FE: id + year
##   VCOV: HC1  | Cluster: id 
##   Method: classic  | lead_range: 9  lag_range: 8  baseline: -1

plot_es(sa)

Borusyak, Jaravel & Spiess (2024) — estimator = "bjs"

A three-step imputation approach:

1. Fit a TWFE model on untreated observations only (never-treated + not-yet-treated).
2. Impute each treated unit’s counterfactual outcome.
3. Average the imputed treatment effects by event-study horizon.

bjs <- run_es(
  data = df_stagg,
  outcome = y,
  time = year,
  timing = timing,
  unit = id,
  staggered = TRUE,
  estimator = "bjs"
)

print(bjs)

## Event Study Result (fixes)
##   N: 950  | Units: 95  | Treated units: 45  | Never-treated: 50 
##   FE: id + year
##   VCOV: bjs_conservative  | Cluster: - 
##   Method: classic  | lead_range: 1  lag_range: 8  baseline: -1

plot_es(bjs)

Bootstrap simultaneous confidence bands

Pointwise confidence intervals control error rates one period at a time. When you display 15 pre- and post-treatment estimates on a single plot, the chance that at least one interval incorrectly excludes zero can be much higher than the nominal 5%.

Simultaneous bands (Callaway & Sant’Anna 2021, Corollary 1) provide joint coverage: with probability ≥ 1 − α, the entire event-study curve is contained within the band.

Use bootstrap = TRUE in run_es() and show_simultaneous = TRUE in plot_es():

cs_boot <- run_es(
  data = df_stagg,
  outcome = y,
  time = year,
  timing = timing,
  unit = id,
  staggered = TRUE,
  estimator = "cs",
  control_group = "nevertreated",
  bootstrap = TRUE, # run multiplier bootstrap (Algorithm 1, CS 2021)
  B = 999, # number of bootstrap draws; 999 recommended in practice
  boot_seed = 42 # for reproducibility
)

# Lighter outer band = simultaneous CI; darker inner band = pointwise CI
plot_es(cs_boot, show_simultaneous = TRUE)

The simultaneous band is always at least as wide as the pointwise band. The interactive plot also gains a second ribbon trace:

plot_es_interactive(cs_boot, show_simultaneous = TRUE)

Plotting options

plot_es() works with results from any estimator.

# Error bars instead of ribbon
plot_es(es, type = "errorbar")

# Multiple CI levels at once
es_multi <- run_es(
  data = df,
  outcome = y,
  treatment = treat,
  time = period,
  timing = 5,
  fe = ~ id + period,
  cluster = ~id,
  conf.level = c(0.90, 0.95, 0.99)
)
plot_es(es_multi, ci_level = 0.90, theme_style = "minimal")

Interactive plots

plot_es_interactive() produces a plotly chart with hover tooltips (requires the plotly package).

plot_es_interactive(es)

Key run_es() arguments

Contributing

Found a bug or have a feature request? Open an issue on GitHub.

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.