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.