Changes in version 0.1.0 (2026-09-15)                  

Released on CRAN 2026-09-15. First release, implementing Arora and Wagle
(2026), "A Bayesian Approach to Partial Homogeneity in Staggered
Difference-in-Difference" (doi:10.2139/ssrn.7207083).

Estimation

  - ph_data() assembles the inputs from a did::att_gt() fit, a micro
    panel, or any vector of first-stage effects with their joint
    covariance. Lemma 1 of the paper guarantees the three routes give
    identical answers, and the test suite asserts it.
  - bayes_ph() fits the Dirichlet Process mixture by collapsed Gibbs
    sampling (Neal 2000, Algorithm 3), with the exact collapsed marginal
    likelihood in the cluster-assignment moves by default.
  - l0_ph() computes the two-step l0 estimator of Appendix B, supporting
    both BIC selection along the agglomeration path and the fixed-lambda
    stopping rule.
  - ph_fit(), flex_twfe() and pooled_twfe() provide the restricted GLS
    fit under a known partition and the two benchmark corners.

Inference

  - aggregate() applies overall, event-study, by-cohort and by-calendar
    aggregators to each posterior draw, so reported intervals propagate
    uncertainty about the partition itself.
  - coclustering() and point_partition() summarise the grouping
    structure, the latter by Wade-Ghahramani VI or Binder loss.

Specification testing

  - homogeneity_test() reports a model-implied common-effect chi^2 test
    on the pooled GLS deviance, an excess-dispersion heterogeneity share
    computed against the exact covariance, and -- when placebo estimates
    are supplied -- the pre/post noise gauge and within-cell
    randomization test of Section 5.2.3.

Diagnostics

  - enumerate_partitions() computes the exact partition posterior for
    small designs, the Appendix E benchmark.
  - ph_rhat() reports Gelman-Rubin statistics and effective sample sizes
    across dispersed chains.
  - covariance_check() compares exact against diagonal handling of the
    covariance in the cluster-assignment moves.

Regularisation paths

  - alpha_sensitivity() traces the DP estimator across the concentration
    parameter, and lambda_sensitivity() traces the l0 estimator across
    its penalty. Both take type = "cells", reporting the path for every
    cohort-time effect separately rather than only for the aggregate.
    This is usually the more informative view: the overall ATT is robust
    to over-pooling, so a flat aggregate path can hide cells that move a
    great deal.
  - plot_sensitivity() draws either table; a per-cell table gets one
    labelled line per cohort-time effect.
  - lambda_sensitivity() can index the l0 path by the penalty (by =
    "lambda") or by the number of groups (by = "m"). These are different
    paths: the Appendix B stopping rule compares each merge cost against
    lambda * |A| * |B|, and that factor grows as groups absorb one
    another, so the effective per-merge threshold is not monotone in the
    merge order and some group counts are unreachable by any single
    lambda. On the paper's first application the thresholds
    run 0.079, 0.686, 0.397, 1.350, 2.765, 3.571, so nothing selects
    five groups. by = "m" walks the path one merge at a time.
  - l0_ph() returns pair_product in its path, the |A| * |B| of each
    merge.

Simulation

  - ph_design(), ph_truth(), ph_sample() and sim_study() reproduce the
    calibrated Monte Carlo of Section 4.

Notes on choices that move results

  - The BIC penalty on the two-stage route needs a sample size that the
    theory does not fix. The default is K, which reproduces the paper's
    reported group counts; n_bic exposes the choice because it changes
    the selected partition.
  - The default base measure is data-scaled (mu0 = median(tau),
    sigma0_sq = (10 * sd(tau))^2), matching the paper's applications.
    This is mildly empirical-Bayes; supply fixed values for a prior
    independent of the data.
  - bayes_ph(marginal = "diagonal") reproduces the faster shortcut used
    in the original replication scripts for the cluster-assignment
    moves. The default is "exact", matching what the paper reports.