Changes in version 0.2.0 (2026-05-20)                  

  - Added support for repeated cross-section staggered DiD designs.
  - Added data_type = "panel" and data_type = "repeated_cross_section"
    options.
  - Made idname optional for repeated cross-section designs.
  - Added support for sampling weights through weightsname.
  - Added support for clustered inference through cluster_var.
  - Added examples for binary repeated cross-section outcomes with
    staggered treatment timing.
  - Preserved all panel-data functionality from version 0.1.0.

Implementation notes

  - Repeated cross-section ATT(g,t) uses the Wooldridge (2023) pooled
    QMLE with a treatment-by-period interaction. The doubly-robust
    variant augments this with inverse probability weighting on the
    estimated propensity score.
  - Sampling weights (when supplied via weightsname) are used
    throughout: the outcome regression, the propensity score model, and
    the pooled QMLE. They are multiplied with the IPW factor in the
    doubly-robust path.
  - Analytical SEs for the RCS path use sandwich::vcovCL when
    cluster_var is supplied, sandwich::vcovHC (HC1) otherwise. Panel SEs
    continue to use the influence-function approach from v0.1.0; set
    boot = TRUE for fully clustered panel inference.
  - The bootstrap automatically resamples whole clusters when
    cluster_var is provided, units when data_type = "panel", or
    individual rows when data_type = "repeated_cross_section" without
    clustering.
  - The compiled C++ helpers from v0.1.0 have been replaced with
    equivalent pure-R implementations, eliminating the Rcpp dependency.
    The package no longer requires compilation.

Bug fixes

  - None.

                 Changes in version 0.1.0 (2026-05-05)                  

  - Initial CRAN release.
  - Panel-data ATT(g,t) estimation under logit, probit, Poisson,
    negative binomial, and linear outcome models.
  - Doubly-robust estimator combining outcome regression and propensity
    score weighting.
  - nonlinear_aggte(): event-study, group, calendar, and simple
    aggregations.
  - nonlinear_pretest(): joint chi-squared and individual pre-trend
    tests.
  - nonlinear_bounds(): Manski and parallel-trends-constrained bounds.
  - binary_did_logit(), binary_did_probit(), binary_did_dr(): 2x2 binary
    DiD estimators.
  - count_did_poisson(): Poisson QMLE DiD.
  - odds_ratio_did(): scale-free odds-ratio DiD.
  - sim_binary_panel(), sim_count_panel(): simulation utilities.