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This is a large release that consolidates all development since 2.3.0. Headline changes: a substantially faster engine (group-time ATTs are roughly 2.5-3x faster in common settings, and the conditional pre-test is several times faster and far lighter on memory), first-class clustered and unbalanced-panel inference, support for transformation and factor covariates, a point-estimates-only mode, and a long list of correctness fixes. Numerical results are unchanged up to floating-point precision except where a bug fix is explicitly noted.
DRDID (>= 1.3.0), which provides faster and
more robust 2x2 DiD estimators (used internally for every group-time
ATT) and guards against silently-incorrect standard errors on
ill-conditioned (near-singular) designs.New compute_inffunc argument in
att_gt() (default TRUE). Set
compute_inffunc = FALSE for a
point-estimates-only run: it returns the group-time ATT
point estimates (identical to a full run) without influence functions,
standard errors, uniform bands, or the pre-test. This is faster and uses
much less memory (no influence-function matrix is ever formed or
bootstrapped), which helps for quick exploration or very large datasets.
The result cannot be passed to aggte() (which now errors
with a clear message); bstrap and cband are
set to FALSE automatically.
Covariate formulas (xformla) may now use
transformations and other model-matrix terms,
e.g. ~ I(X^2), ~ log(X),
~ poly(X, 2), ~ X1 * X2. These previously
errored because pre-processing stored the evaluated model frame (losing
the raw variable and creating matrix-valued columns) instead of the raw
covariates. Pre-processing now keeps the raw covariates so the design
matrix can be rebuilt, and drops rows whose evaluated design is
non-finite (e.g. log() of a non-positive value).
Factor covariates now produce exactly the same estimates,
standard errors, and warning messages as adding their dummy columns by
hand. Previously the faster_mode = FALSE path applied
droplevels() within each 2x2 comparison, so a factor level
absent from a comparison changed the design (and could error with
“contrasts can be applied only to factors with 2 or more levels”). The
design matrix is now built once over the full sample, with global factor
levels, and row-subset per cell.
New fix_weights argument in att_gt()
for explicit control over how time-varying sampling weights are resolved
in each 2x2 comparison: NULL (default, prior behavior),
"varying" (per-observation weights via the RC estimators),
"base_period" (fix at g-1), or "first_period".
See ?att_gt. A runtime message points users to it when
time-varying weights are detected in balanced panel data.
att_gt() accepts ... to forward
additional arguments to a custom est_method
function.
Added nobs() S3 methods for MP and
AGGTEobj objects (number of unique cross-sectional units),
and statistic (t-statistic) and p.value
(pointwise, two-sided) columns to tidy() output for both
classes, following broom conventions.
The influence-function matrix returned in MP$inffunc
now carries the unit ids as rownames (the idname values; an
internal observation index for repeated cross sections), and its
row-order contract is documented in ?att_gt and
?MP. The row ORDER is mode-specific –
faster_mode = FALSE sorts units by id while
faster_mode = TRUE uses an internal (period, cohort, id)
ordering – so external consumers of the influence functions
(e.g. sensitivity analyses or custom cluster aggregation) must align
rows by rowname, never by position. Values are unchanged; only the
labels are new.
att_gt(faster_mode = TRUE) (the default) is about
2.5-3x faster on common problems, with identical results. Each (g,t)
cell previously built and extracted a data.table even
though the cohort vectors are already materialized in the pre-computed
tensors; the per-cell cohort is now a plain list of vectors passed
straight to the DRDID estimators (panel, repeated cross-section, and
unbalanced-panel paths), and the unbalanced-panel influence-function
aggregation applies a sparse unit-aggregation operator built once per
call (bit-identical to the previous per-cell
rowsum()/data.table group-by, but without
re-hashing the row-to-unit map for every (g,t) cell — about 2x faster
end-to-end on large unbalanced panels). Earlier (g,t) work also feeds
in: cumulative cohort sizes, cached pre-treatment periods, pre-computed
cohort and period-membership masks on the repeated-cross-section /
unbalanced-panel path, and sparse-triplet influence-matrix construction.
One intentional edge-case change in the unbalanced-panel aggregation:
NA/NaN entries in a 2x2 influence function
(only reachable with a custom est_method that returns a
finite ATT alongside a partially-NA influence function) now
propagate to that unit’s aggregated influence value, so the affected
(g,t) standard error is NA, matching
faster_mode = FALSE; previously the fast path silently
zeroed them, understating the standard error.
The per-(g,t) overlap-check propensity logit (fit for every
dr/ipw cell to detect propensity-score overlap
violations) now uses fastglm’s low-level entry point
(fastglmPure) instead of the fastglm()
wrapper, skipping its per-call input coercion and family/deviance
bookkeeping. The fitted values – and therefore the overlap decision –
are bit-identical.
Two further per-cell guard speedups, in both
faster_mode paths, with bit-identical estimates, standard
errors, influence functions, and warnings. (1) For intercept-only
designs (xformla = NULL/~1, the default) the
overlap and regression-feasibility guards use their closed forms – an
unweighted intercept-only logit fits every unit at mean(D),
and the control-unit Gram matrix is the scalar control count – skipping
the per-cell logit fit entirely; within 1e-6 of the
0.999 cutoff the real fit is still used, so knife-edge
decisions are unchanged. (2) For panel data with
control_group = "nevertreated" and non-varying weights, the
guard booleans are computed once per (group, covariate-period,
weight-period) instead of being refit for every post-treatment cell of a
group (the guards’ inputs are bit-identical across those cells); failed
cells still warn once per cell, and
options(did.disable_check_cache = TRUE) restores the
per-cell checks. Together these make a default no-covariate
att_gt() run roughly 10-15% faster, with similar gains on
covariate runs using never-treated controls.
att_gt(faster_mode = FALSE) builds the covariate
design matrix once and assembles each 2x2 cell directly from precomputed
per-period blocks (outcomes, weights, design) indexed by position,
instead of rebuilding model.matrix() and reshaping
(get_wide_data()) the long data for every cell.
Bit-identical, with about half the transient allocation;
options(did.disable_precompute = TRUE) restores the
original per-cell assembly (the once-built design matrix is used either
way, so the option is a debugging escape hatch for the cell assembly
only, with identical results). The repeated-cross-section /
unbalanced-panel slow path gets the same treatment: each (g,t) cell is
assembled positionally from per-period row indices and plain column
vectors precomputed once per call, replacing two full
data.table subsets, a full-data %in%, and a
droplevels() per cell (about 1.7x faster end-to-end on both
the repeated-cross-section and unbalanced-panel paths, bit-identical,
behind the same escape hatch); the
fix_weights = "base_period"/"first_period"
weight lookup likewise uses per-period vectors instead of a per-cell
full-table subset. The repeated-cross-section / unbalanced
influence-function aggregation now uses rowsum() instead of
stats::aggregate() (about 40x faster on that step).
faster_mode = TRUE and faster_mode = FALSE
remain identical to numerical precision for every supported
option.
Pre-processing for faster_mode = TRUE is leaner,
with identical outputs: only the columns the call references
(id/time/group/outcome/weights/cluster plus the raw xformla
variables) are copied out of the input data, so wide data frames no
longer pay a full-table copy (cutting the transient memory peak by
roughly the size of the unused columns); the balanced-panel checks use
an arithmetic row-count test instead of full by-unit groupings (the
grouping now only runs when unbalanced units actually need to be
identified); guaranteed no-op complete-case passes are short-circuited
behind anyNA(); the temporary
asif_never_treated/treated_first_period
columns are replaced by local vectors; and the period/crosstable count
tables are derived without re-grouping the unit-level table.
The conditional pre-test (conditional_did_pretest())
is several times faster end-to-end and far lighter on memory. Its
multiplier bootstrap (test.mboot()) previously looped over
biters draws, each multiplying the full
n x k x nX influence array by fresh weights –
O(n^2 k) work and an O(n^2 k)
transient allocation per draw (over 1 GB per draw at a few thousand
units). It is now a single tiled matrix contraction (100x+ faster on
that step, with the per-draw gigabyte allocations eliminated),
numerically identical to the old loop up to floating-point summation
order; the indicator() weighting function is also
vectorized.
Internal speedups with identical results: vectorized the
multiplier-bootstrap post-processing (mboot()), removed a
duplicated n x k matrix construction in the aggregation
estimated-weight influence term (wif()), preallocated the
sparse influence-function assembly, and removed redundant work in
pre-processing and simulation.
Clustered standard errors are now available
without the bootstrap. With clustervars
set and bstrap = FALSE, att_gt() and
aggte() report cluster-robust standard errors computed
analytically from the cluster sums of the influence function, at every
aggregation level (group-time, simple, group, dynamic, calendar), and
the pre-test Wald statistic is reported under clustering.
The cluster-robust multiplier bootstrap (mboot) now
follows Callaway & Sant’Anna (2021), Remark 10: it draws one
multiplier per cluster and aggregates the influence function to cluster
sums. Identical to before for equal-sized clusters; correct cluster-sum
aggregation for unbalanced clusters and repeated cross
sections.
Clustered inference (bootstrap and analytical) is supported for
panel data, unbalanced panels, and repeated cross sections, and is
identical under faster_mode = TRUE and
faster_mode = FALSE. For repeated cross sections without an
idname, the internal observation id is used to align the
cluster identifiers with the influence function (idname
itself is required whenever panel = TRUE; see
below).
aggte() no longer silently ignores a
clustervars request it cannot honor (the aggregation can
only use the cluster information att_gt() retained). It now
warns – including when an override names a different variable than
att_gt() clustered on – and falls back to non-clustered
standard errors, instead of silently returning the i.i.d. error or
crashing in mboot().
Fixed two faster_mode = TRUE
clustered-standard-error bugs on unbalanced panels so
the fast path reproduces the slow path: (1) the analytical clustered SE
silently fell back to the i.i.d. error because the stored per-unit
cluster vector was observation-length and no longer aligned with the
influence function; and (2) in aggte(), the
estimated-weight influence term was added in id-sorted order while the
influence function is in first-appearance order, misattributing it and
giving a wrong aggregated SE (point estimates were unaffected). Balanced
panels and repeated cross sections were unaffected.
Fixed the conditional parallel-trends pre-test
(conditional_did_pretest()), which had silently broken
under R >= 4.0 and spuriously rejected almost always whenever there
was more than one pre-treatment ATT(g,t) cell. The observed Cramér-von
Mises statistic was left in (n_gt x nX) orientation while
its bootstrap null distribution is (nX x n_gt), scaling the
observed statistic by n / n_gt and driving the p-value to
~0. The root cause was ifelse(class(J) == "matrix", ...):
class() of a matrix became length-2
(c("matrix","array")) in R 4.0, so ifelse()
evaluated both branches and the no-transpose branch always won. The
orientation is now selected with is.matrix().
aggte(type = "group", na.rm = TRUE) with a finite
max_e no longer errors (“No valid att_gt() estimates found
…”) when a group’s only non-missing ATT(g,t) lies past
max_e; the group filter now applies the same
max_e window. The default max_e = Inf is
unchanged.
Duplicated (idname, tname) rows (the same unit
observed more than once in a period, a common long-format data-prep
mistake) are now rejected with a clear error in both
code paths. Previously only faster_mode = TRUE caught this;
faster_mode = FALSE silently produced incorrect
estimates.
A weightsname column with negative values or a
non-positive mean is now rejected with a clear, identical error in both
code paths, instead of silently producing
NA/NaN estimates.
Fixed faster_mode = TRUE vs FALSE ATT
disagreement when sampling weights (weightsname) vary
across time: the fast path was always using first-period weights and now
uses the same period’s weights as the slow path.
Fixed influence-function aggregation for
fix_weights = "varying" on balanced panels (now aggregates
by unit id with rowsum() rather than assuming stacked
order), and a length mismatch for
fix_weights = "base_period"/"first_period" on
unbalanced panels after weight-based unit dropping.
Fixed glance.MP() returning NULL for
ngroup/ntime under
faster_mode = TRUE.
Fixed an aggte() crash (“Error in get(gname):
invalid first argument”) when the group column is literally named
gname and dreamerr >= 1.5.0 is installed
(data.table’s get() was intercepted; replaced with
set()).
aggte() no longer modifies the data stored inside
the MP object by reference: under
faster_mode = TRUE it previously recoded the never-treated
gname value Inf to 0 in
MP$DIDparams$data as a side effect. The input object is now
left untouched; all results are unchanged.
Fixed groups treated after the last observed period but within
the anticipation window being coerced to never-treated (contaminating
the control group with anticipation effects), and a data-filter
inconsistency for always-treated units when
anticipation > 0. Added an informative message
clarifying that never-treated units are unaffected by
anticipation.
When internal 2x2 estimation fails for a specific (g,t) cell
(e.g. a singular design), att_gt() now warns and sets that
cell’s ATT to NA instead of crashing, in both
faster_mode = TRUE and FALSE (#185,
#190).
pl = TRUE on Windows now warns and falls back to
sequential processing instead of crashing (#176).
Misspelled yname, idname,
tname, gname, weightsname, or
clustervars now produce a clear message listing the missing
columns (#203).
Column names reserved for internal use by did
(.w, .rowid, .G, .C,
post, asif_never_treated,
treated_first_period) are now rejected with a clear error
when used as yname, tname,
idname, gname, weightsname, or
clustervars, or referenced in xformla.
Previously they could silently collide with the columns did
creates internally; rename such columns before calling
att_gt().
control_group and base_period must now
exactly match one of their documented values, in both modes. Previously
faster_mode = TRUE accepted partial and case-insensitive
abbreviations (e.g. control_group = "never"), and
faster_mode = FALSE silently treated any unrecognized
base_period value as "varying".
An invalid est_method (an unrecognized string or an
unquoted variable) now errors clearly instead of silently defaulting to
"dr" (#194).
fix_weights = "base_period"/"first_period"
are blocked for repeated cross sections (panel = FALSE);
fix_weights = "varying" is blocked with a custom
est_method function (whose signature differs from the
internal RC path). Both with clear messages.
anticipation must now be a non-negative number in
both modes. Previously only faster_mode = TRUE enforced
this; the faster_mode = FALSE path silently accepted
negative values (shifting the base period later than the treatment
period), which was undocumented and inconsistent across modes.
panel = TRUE (the default) without
idname now errors with “Must provide idname when panel =
TRUE. Set panel = FALSE for repeated cross sections.” Previously this
failed with a cryptic internal data.table error
(faster_mode = TRUE) or a misleading “All observations
dropped while converting data to balanced panel” message
(faster_mode = FALSE).
A non-numeric outcome variable (yname) is now
rejected up front with a clear message in both code paths (logical 0/1
outcomes remain allowed). Previously a character or factor outcome “ran”
to completion with all-NA ATTs and misleading per-cell
warnings, and a list-column outcome failed with a cryptic
complete.cases() error.
The per-(g,t) regression-feasibility check now reports the real
cause when it fails: “Covariate matrix for control units is singular or
numerically ill-conditioned … consider centering/rescaling covariates or
removing collinear terms” instead of the misleading “Not enough control
units … to run specified regression” (which fired even with thousands of
control units, e.g. for a quadratic in a year-scale covariate). The
check itself is unchanged (and now uses crossprod());
affected cells still return NA with a warning.
alp must now be a single number strictly between 0
and 1 (e.g. alp = 1.5 previously inverted the confidence
bands silently or errored deep inside quantile()), and
biters must be a single positive whole number when
bstrap = TRUE (a negative value previously crashed inside
the bootstrap’s linear-algebra code with no hint about the
cause).
Cleaner failed-cell warnings under
faster_mode = FALSE: each failed (g,t) cell now warns
exactly once with the same text as faster_mode = TRUE
(“overlap condition violated for group g in time period t”). Previously
the slow path warned twice per failed overlap/rank check – the
diagnostic plus a wrapper warning leaking the internal sentinel
("... : overlap. The ATT for this cell will be set to NA.").
Genuine estimator errors are still surfaced by the wrapper warning.
Additionally, when the Wald pre-test is unavailable, the warning now
distinguishes “pre-treatment ATT(g,t) estimates exist but all have
missing/zero variance” from “no pre-treatment cells exist at all” (the
latter previously mis-diagnosed the former as “all groups are first
treated early in the panel”).
The documented clustervars contract – at most one
cluster variable beyond idname, and it must be
time-invariant within unit – is now enforced up front in
both code paths, with one shared, plainly-worded error
message (also used by mboot()). Previously
faster_mode = FALSE with bstrap = FALSE (the
analytical clustered-SE path) accepted extra cluster variables and
silently clustered on the first one only; a
time-varying cluster variable on that path triggered a fallback warning
advising bstrap = TRUE, advice that then errored in
mboot() for the very same input; and the
faster_mode = TRUE error exposed internal argument names
(“args$clustervars must be … a character scalar”), contradicting the
documented vector interface.
Per-cell empty-cell warnings under
faster_mode = FALSE now name the period that is actually
empty. Under base_period = "universal" (and for
post-treatment cells under "varying"), the
repeated-cross-section path warned “No units in group g in time period
t” with the cell’s current period – a period where the group
does have observations – instead of the empty base period;
faster_mode = TRUE already reported the base period
correctly.
Aligned two pre-processing warnings across modes: the
faster_mode = TRUE “no never-treated group” warning now
also discloses that data from periods on/after the last cohort’s
treatment date is filtered out (both modes always dropped those periods;
only the slow path said so), and the faster_mode = FALSE
balanced-panel coercion warning now reports the number of dropped
units (“k units are missing in some periods. Converting to
balanced panel by dropping them.”, same text as
faster_mode = TRUE) instead of mislabeling the unit count
as “observations” (an undercount of the rows actually removed).
Reduced namespace pollution: replaced blanket
import(stats), import(utils), and
import(BMisc) with selective importFrom()
calls. did no longer re-exports
stats::filter/stats::lag (which previously
masked dplyr::filter/dplyr::lag).
R CMD check passes with 0 code-related NOTEs.
Replaced fragile ifelse(cond, x <- a, x <- b)
side-effect idioms (which relied on R’s branch-evaluation order) with
if/else, and get()/:= with
set() inside data.table loops, throughout;
behavior is unchanged.
Substantially expanded the test suite: glance(),
ggdid(), error handling, edge cases, all aggregation types,
systematic faster_mode consistency across dozens of
parameter combinations, and JEL replication tests. The suite now runs
with 0 warnings (previously 66+). Added a GitHub Action to auto-bump the
dev version in DESCRIPTION on PR merge.
Expanded weightsname documentation (how time-varying
weights are handled for balanced panels vs. repeated cross sections /
unbalanced panels); grammar and typo fixes across docs, vignettes, and
error messages; corrected the mpdta data
documentation.
Replaced deprecated BMisc function names
(getListElement, rhs.vars) with their
snake_case equivalents (get_list_element,
rhs_vars).
Code improvements that make the package faster and more memory efficient
Improved automated testing and regression testing
Check if data is balanced if panel = TRUE and
allow_unbalanced_panel = TRUE. If it is, disable
allow_unbalanced_panel and proceed with panel data setup.
This is different from the previous behavior, which would always proceed
as if panel = FALSE.
Significantly reduced the number of recursive package dependencies, enabling faster installation times and a smaller build footprint.
Added wrapper function for HonestDiD package
Fix bug for setups where gname is not contained in
tname (but is in the tname range)
Fix bug for including too many groups with universal base period in pre-treatment periods
Bug fix for anticipation using notyettreated
comparison group
Bug fixes related to unbalanced panel and clustered standard errors
Bug fixes for conditional_did_pretest
Even faster bootstrap code (thanks Kyle Butts)
Updated version requirement for BMisc
package
Bug fix for unbalanced panel and repeated cross sections in pre-treatment periods using universal base period
Code is substantially faster/more memory efficient
Support for universal base period
Major improvements to unit testing
Completely removed mp.spatt and
mp.spatt.test functions (which were the original names for
att_gt)
Simulation/testing code now exported
Removed some slow running checks
Multiplier bootstrap code is now written in C++
Improvements to error handling, added some additional warning messages, removed some unnecessary warning messages
Bug fixes for NA standard errors that occur with very small groups
Improved plots
Maximum event time for event studies
Compute critical value for simultaneous confidence bands even when some standard error is zero (set these to NA)
Improved codes for unbalanced panel data: faster and more memory efficient
Correct estimates of P(G=g|Eventually treated) with unbalanced panel data. This affects aggte objects with unbalanced panel data
Bug fixes for summary aggte objects
Allow clustering for unbalanced panel data
Fixed error in calendar-type aggregation within aggte function (point estimates were not being weighted by group-size; now they are).
Additional error handling
Big improvement on code base / functionality / testing
Deprecated mp.spatt function and replaced it with att_gt function
Calling att_gt is similar to calling
mp.spatt; instead of formula for outcome of the form
y~treat, now just pass the name of the outcome
variable
Deprecated mp.spatt function and replaced it with conditional_did_pretest function
New est_method parameter. Can call any function for 2x2 DID in the DRDID package (default is now doubly robust estimation, but inverse probability weights and regression estimators are also supported) as well as provide custom 2x2 DID estimators
Bug fixes for including groups that are already treated in the first period
Allow for user to select control group – either never treated or not yet treated
Add functionality for uniform confidence bands for all aggregated treatment effect parameters
Introduced dynamic effects in pre-treatment periods. These allow for users to report event study plots that are common that include pre-treatment periods and are common in applied work. The event study plots in the did package are robust to selective treatment timing (unlike standard regression event study plots)
Support for using repeated cross sections data instead of panel data is much improved
Support for using sampling weights is much improved
Big improvement to website, vignettes, and code documentation
Code for dealing with unbalanced panels
Allow for event studies to be computed over subsets of event times
Allow for treatment anticipation via anticipation argument
Improved ways to summarize aggregated treatment effect parameters
Fixed bug related to needing new version of BMisc
Fixed bug related to plotting with no pre-treatment periods
Improved ways to easily plot aggregated treatment effect parameters
Added some error handling for some cases with small group sizes, and fixed some cryptic error messages
Fixes handling for data being in format besides data.frame (e.g. tibble)
Add warnings about small group sizes which are a very common source of trouble
fixed issues between BMisc and formula.tools
added point estimates for repeated cross sections data
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.