expect_identical) against fixtures frozen on the
development machine. The underlying computations run through
floating-point linear algebra (BLAS), whose last few significant digits
are not portable across CRAN’s heterogeneous platforms / BLAS builds, so
the bit-exact assertions failed on the CRAN check farm at the ~1e-11
(Leave-Cluster-Out) and ~1e-7 (reweighting) level. The portable
invariant is now numerical: expect_equal() with tolerances
(1e-6 and 1e-5 respectively) that absorb the
non-portable noise while staying two-to-five orders below the scale of
any genuine algorithmic regression. The stronger bit-exact assertion is
retained as a developer-machine guard via skip_on_cran().
Logical outputs (robust) remain compared bit-exactly, as
they are platform-invariant. No package code changed; this is a
test-rigor fix that keeps the three rigor layers (algebraic /
statistical / numerical) strictly separate.compute_wedge(),
wedge_stationarity(),
placebo_values() (file
R/wedge_diagnostics.R). The wedge
W_i = Phi_i - V_i = K_i * G' - p_i isolates the marxian
redistribution of surplus, cancelling the shared cost price
k = c + v, and sums to zero across sectors by construction.
The module builds the wedge (with a per-period zero-sum check), tests
its per-sector and panel mean-reversion / unit-root behaviour (AR(1)
reversion speed and half-life plus an augmented Dickey-Fuller test via
urca), and constructs aggregate-preserving placebo “values”
as negative controls (a uniform-profit- rate scheme, where the wedge
vanishes and value equals the production price, and a seeded derangement
that mis-pairs capital and surplus while preserving the per-period
total). Used to move the evidential weight of the gravitation claim from
the definitional level co-movement to the construction-orthogonal wedge
dynamics, and to provide negative controls for the internal adversarial
validation. Fully tested in
tests/testthat/test-wedge.R.Single canonical disaggregation engine and a cross-sectional leave-out test for coupling robustness under input-output dependence.
run_disaggregation_custom_prior() (and its private helpers
compute_L_from_P, spread_likelihood,
posterior_weighted) never conditioned on the observed CPI
and duplicated the purpose of the dedicated package. They are deleted.
The single source of truth is now the closed-form conjugate engine
BayesianDisaggregation::disaggregate_conjugate() (a
Kalman/RTS smoother, pure R), added to Imports.test_reweighting_robustness() rewired.
Each alternative weighting scheme is a constant-in-time prior replicated
across periods to form the weight matrix; the sectoral levels are the
conjugate posterior median, genuinely conditioned on the CPI, instead of
a deterministic blend.read_cpi() (clean error on an unidentifiable column) and
the internal read_weights_matrix() move to
R/data_io.R; they are generic Excel input/output, not part
of the disaggregation engine, and stay in this package.test_leave_cluster_out() (new,
exported). Generalizes the delete-one-sector jackknife to
delete-one-cluster: each group of sectors is dropped from both
X and Y and the coupling is re-estimated.
Under input-output (“MIP”) dependence a naive leave-one-out is
optimistic; dropping an entire value chain forces the prediction onto
the general gravitation. It is the cross-sectional complement of the
temporal time-shift / block-bootstrap nulls and reuses their coupling
pipeline (it does not reimplement it).build_cluster_map() (new, exported).
Pluggable sector-to-cluster map. A genuine map (the Leontief partition)
is supplied via cluster_map; when it is absent a documented
fallback ("correlation" or "com") is built and
flagged as a stopgap proxy, not a demand-linkage partition.bias/se
are reported with the documented balanced-cluster caveat; the primary
outputs are per-cluster influence and the robustness verdict.test_reweighting_robustness() now propagates its
seed to the data diagnosis and the cross-validated PLS
component selection, closing a gap where the per-scheme couplings
depended on the ambient RNG state (call order). A pre-existing
error-handler bug that dropped the failure marker for a failed scheme
(<- instead of <<-) is fixed.test-disaggregation-import.R,
test-leave-cluster-out.R) and regression goldens
(test-golden-convergence.R): the Leave-Cluster-Out
reproduces a frozen result bit-exactly, and the rewired reweighting
reproduces frozen couplings within numerical tolerance (the layers are
kept separate). Goldens and Excel fixtures are regenerated by
validacion/make_golden_convergence.R.canonical-disaggregation-and-leave-cluster-out documents
both decisions.Robustness-focused overhaul. This release fixes several correctness bugs and methodological flaws that could bias or invalidate convergence/coupling inference, and aligns the documentation with the implemented API.
rotation_null_test() previously generated its null by
random orthogonal rotation of the factor space. The four coupling
statistics (Procrustes, CCA, principal angles, dynamic-beta norm) are
invariant to orthogonal rotation, so the null was degenerate
and the test could never reject. The default null is now a
circular time-shift / moving-block bootstrap that
preserves each series’ marginal dynamics while breaking the cross-series
temporal alignment. The pure-rotation null is kept only as an explicitly
documented invariance diagnostic
(null_method = "rotation").test_permutation_robustness() previously permuted the
columns of the Y factors; the coupling strength is invariant to
that permutation, so the null was degenerate. It now permutes the
time index of Y (circular shift) to break the X->Y
relationship.phi is no longer hard-constrained to
(0, 1); it can express unit-root and mildly explosive
dynamics, so the convergence verdict is now a genuine test of
H0: not stationary rather than a constraint that is always
satisfied. The prior on phi is weakly informative and
centred at a neutral value rather than at high persistence.estimate_factor_OU()
now returns and (optionally) warns on R-hat, bulk/tail effective sample
size and divergent transitions, and the previously dead
adapt2/mtd2 controls now drive an automatic
re-run when divergences are detected.test_jackknife_sectors() now performs a leave-one-sector
-out jackknife with bias and standard-error estimates and influence
ranking, instead of a single leave-top-k drop.deltaR2_ou(),
rescue_short_run_channel()) now report a Clark-West /
Diebold-Mariano style test instead of a bare RMSE delta.seed argument:
rotation_null_test(),
test_permutation_robustness(),
sensitivity_analysis_weights() and the noise injection in
diagnose_data() previously ignored it.zzz.R defined .onAttach() twice; the
Stan-availability message is restored..pkgenv was overwritten with a vector, destroying the
package environment; it is now a real environment created in
.onLoad().read_cpi() used which.max() on a logical,
which silently selected column 1 when no date/CPI column matched; it now
errors cleanly.run_convergence_robustness_tests() used
exists("indices$sensitivity") (always FALSE);
the sensitivity block is now propagated.to_num_commas() now parses both European and
Anglo-Saxon number formats.calculate_hc_manual() implements HC4 (it previously
fell through to HC0) and uses sandwich/lmtest
when available.estimate_DFM() now returns robust t-statistics and
p-values, reports the dominant-root half-life, and no longer floors the
out-of-sample R^2 at -2.visualize_factor_dynamics() no longer fails when
save_plot = TRUE without a file name, and graphics are
opt-in within the pipeline.test_reweighting_robustness() no longer references an
unassigned object when weight reading fails.prepare_marxist_factors() now actually incorporates the
auxiliary series (TMG, COM_matrix,
SPVR_matrix, CA) it accepts.prepare_panel_data, estimate_dfm,
estimate_ou_process, plot_convergence); it now
uses the real API.@return sections of the test functions,
select_optimal_components_safe() and
estimate_DFM() now match the objects actually
returned.DESCRIPTION no longer over-claims
sandwich/lmtest usage; both are now Suggests
and used when present, with a documented HC fallback.
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