triplediff 0.1.0
- Initial release of triplediff in alpha stage, functions for
computing group-time average treatment effects in DDD and combining them
into a smaller number of parameters are available.
triplediff 0.1.1
- Bug fix in
cluster parameter. When user specifies a
cluster variable, the function now correctly uses it for clustering
standard errors performing Multiplier Bootstrap.
triplediff 0.1.2
- Bug fix in
preprocess when checking for small
groups.
triplediff 0.2.0
- Replaced
parglm with fastglm to avoid
issues related to parglm’s scheduled archival on 2026-01-29.
- Added support for unbalanced panel data and repeated cross-sectional
data by properly implementing the
allow_unbalanced_panel
parameter across all functions.
triplediff 0.2.1
- Add asymmetric propensity score trimming for control units with
pscore >= 0.995.
- Add partition-specific collinearity detection with two-stage
checking.
- Add comprehensive test suite including Monte Carlo coverage test
when trimming.
triplediff 0.2.2
- Track BMisc (>= 1.4.9) API rename: replaced
makeBalancedPanel with make_balanced_panel and
rhs.vars with rhs_vars in internal
preprocessing. No user-visible behavior change. (#34)
- Replaced the remaining deprecated
BMisc::getListElement
call with BMisc::get_list_element to silence deprecation
warnings (follow-up to #34).
triplediff 0.2.3
- Added analytical cluster-robust standard errors without the
bootstrap in the multiple-period path. Calling
ddd() with
cluster = <var> and boot = FALSE now
returns analytical cluster-robust standard errors (cluster-sum CRVE on
the influence function) instead of requiring the bootstrap. The
ddd object now carries cluster_vector and
cluster_var. Two-period designs still require
boot = TRUE for clustered inference.
- Added a
cluster argument to agg_ddd().
Aggregated parameters (simple, event-study, group, and calendar) now
report analytical cluster-robust standard errors. If clustering is
requested on a different variable than ddd() used (or on an
object built without clustering), agg_ddd() warns and falls
back to i.i.d. standard errors instead of silently mis-reporting.
- Behavior change: the clustered multiplier bootstrap now follows
Callaway & Sant’Anna (2021, Remark 10), applying one multiplier per
cluster to the influence function aggregated to cluster sums
rather than cluster means. Clustered bootstrap standard errors
change for unbalanced clusters and repeated cross-sections; equal-sized
clusters are unaffected.
triplediff 0.2.4
- Resubmission. Simplified the
ddd() examples by removing
the bootstrap-based clustered standard errors example, so the package
examples stay within CRAN’s limit on the number of cores used during
checks. No changes to package functionality.