BayesianDisaggregation 0.2.1

Robustness fix to the CPI reader (debt D-7.2, settled in the OU-hierarchical Session 10).

• read_cpi() now locates the date and CPI columns with which() instead of which.max(). which.max() on an all-FALSE str_detect() (no column matched) silently returns index 1, a false positive that pointed read_cpi at the first column; it also masked ambiguity by taking only the first of several matches. which() returns every matching index (or integer(0)), so the existing length(col) != 1 guard now errors honestly on both no-match and multiple-match. Behaviour on a well-formed CPI file is unchanged (verified against the bundled extdata/CPI.xlsx: 1960-2023, columns Year/CPI).

BayesianDisaggregation 0.2.0

Complete redesign to a genuinely evidence-based method. The aggregate index now enters the estimation as a real observation density; the sectoral indices come out as a posterior with credible intervals.

Two honest Bayesian engines

• disaggregate_statespace() — canonical engine. A Bayesian state-space model: a random-walk-with-drift transition in log phi (partial pooling on the drift and the innovation scale), an estimable cross-sectional concentration, and a Student-t (or Gaussian) observation cpi_t ~ (nu, sum_k W[t,k] phi[t,k], sigma). Sampled by HMC (cmdstanr or rstan). Returns a [T, K, draws] array of posterior draws of phi — exactly the multiple-imputation input consumed by bayesianOU::fit_ou_nested_mi() — plus credible bands and diagnostics.
• disaggregate_conjugate() — closed-form Bayesian baseline. The exact linear-Gaussian posterior (Kalman filter + RTS smoother), MCMC-free, with joint posterior draws via the Durbin-Koopman simulation smoother. This is the correct realization of the package’s original “MCMC-free posterior” aspiration: it does condition on the aggregate evidence in closed form.

Helpers: simulate_disagg() (the model’s own DGP, for recovery and examples), align_disagg_inputs() and disaggregate_from_files() (read + align CPI and VAB-weight Excel files), disagg_default_priors(), disagg_stan_code().

Removed: the entire deterministic legacy (F1–F6 audit)

The 0.1.2 “deterministic Bayesian” family never conditioned on the aggregate CPI (F1): the posterior was derived from the prior weight matrix alone, several pieces cancelled on renormalization (Dirichlet concentration F2, temporal pattern F3), the “efficiency” term was a fixed constant (F4), there were no recovery tests (F5), and robust_cor opportunistically picked the larger correlation (F6). Because that foundational defect cannot be fixed without turning the method into the new evidence-based engine, the deterministic blend was retained for one design cycle as a baseline and then removed entirely (it added nothing the two Bayesian engines do not do, honestly). Deleted: bayesian_disaggregate(), posterior_weighted/multiplicative/dirichlet/adaptive(), compute_L_from_P(), spread_likelihood(), coherence_score(), numerical_stability_exp(), temporal_stability(), stability_composite(), interpretability_score(), run_grid_search(), save_results(), and the robust_cor/kl_divergence/ total_variation/safe_div utilities.

Validation and documentation

• Recovery on synthetic data from the model’s own DGP (the aggregate is strongly identified; the sectoral split is honestly weakly identified — wide bands).
• Bit-for-bit golden regression of the Stan computation path via generate_quantities on frozen draws (isolating CSV serialization rounding).
• New vignette evidence-based-disaggregation documenting the model, the two engines, the F1–F6 rationale and the coupling to the nested OU.
• DESCRIPTION no longer claims a “novel/original” contribution (anti-overreach).