phdid 0.1.0

First release, implementing Arora and Wagle (2026), “Partial Homogeneity in Staggered Difference-in-Differences”.

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.
• alpha_sensitivity() traces the posterior across the concentration parameter, and covariance_check() compares exact against diagonal handling of the covariance.

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.

Unreleased

New

• lambda_sensitivity() traces the l0 estimator across its penalty, the frequentist counterpart of alpha_sensitivity(). Index it by lambda or, to avoid the gaps described below, by the number of groups with by = "m".
• Both sensitivity functions 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, replacing plot_alpha_sensitivity(). A per-cell table gets one labelled line per cohort-time effect.
• l0_ph() now returns pair_product in its path, the |A| * |B| of each merge.

Documented

• Some group counts are unreachable by any single lambda. 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. 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.

Breaking

• plot_alpha_sensitivity() is removed in favour of plot_sensitivity(), which handles both estimators and both views.