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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).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.
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().
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.
generate_quantities on frozen draws (isolating CSV
serialization rounding).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).These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.