feat(psychometric): restore Driver p.16 TIPREDEFFECTstd on main

CHANGELOG.md

@@ -13,6 +13,7 @@ All notable changes to TEPP are documented here. The format follows Keep a Chang
 - **TDT/CHRONOS durable result contract**: canonical typed JSON and deterministic GraphML now export the actual bounded Allen reasoner result, observed/derived status, and conservative accepted-assertion support; canonical payload digest and `tdt_chronos_interval_consistency_v1` type bind the immutable bytes into ADR 0013's append-only `model_artifact` chain.
 - **Analysis engine**: deterministic end-to-end analysis-run execution with cutoff-safe eligibility, immutable evidence binding, and reproducibility manifests (`analysis_engine` crate).
 - **Restore Driver p.16 `MANIFESTVARstd`**: `recover_standardised_manifest_variance` maps `θ / θ = 1` with strictly positive `MANIFESTVAR`, refusing unstandardised manifest-variance, `MANIFESTTRAITVARstd`, and Equation 5 `Var(y)` substitutions (`psychometric_core`).
+- **Restore Driver p.16 `TIPREDEFFECTstd`**: `recover_standardised_time_independent_predictor_effect` maps `B · √v / √p` after strictly positive `asymDIFFUSION` and `TIPREDVAR`, refusing unstandardised `B`, `asymTIPREDEFFECTstd`, finite-interval `A^{-1}[e^{A Δt} − I] B · √v / √p`, and trait-contaminated total-variance substitutions (`psychometric_core`).
 - **Posterior network estimator**: cross-draw Pearson correlations in ILR space, jackknife SE and CI, Benjamini–Hochberg FDR edge admission, nonparametric bootstrap stability, greedy modularity consensus clustering (`network_analysis` crate).
 - **Topic measurement reference estimator**: bounded deterministic CPU `f64` TRSL-TM fitting with ALR/ILR coordinates, Aitchison distance, and lexical-inferential-weight refusal gates (`topic_measurement` crate).
 - **Psychometric core**: Driver et al. (2017) SDE discrete-time recovery suite including drift, diffusion, T0VAR, TIPRED/TDPRED effects, standardised parameters, trait/state variance decomposition, and observed-indicator mapping — 18 K lines of production Rust with true-parameter RMSE tests.
@@ -40,6 +41,7 @@ All notable changes to TEPP are documented here. The format follows Keep a Chang
 
 - `event_core` adds bounded Allen interval-consistency classification, atomic path-consistency closure, contradiction/resource refusals, and an explicit dependency-error fallback without claiming unrestricted global satisfiability.
 
+- `psychometric_core` recovers the Driver, Oud, and Voelkle (2017, p. 16 `TIPREDEFFECTstd`; §7.2, pp. 20–21; Eq. 3, p. 5; Table 2, p. 12; footnote 4; 2017-era ctsem `summary.ctsemFit.R`; JSS PDF re-opened 2026-08-29T14:20Z from https://www.jstatsoft.org/index.php/jss/article/download/v077i05/1104) scalar standardised continuous time-independent predictor effect rebased onto protected `main` `6444a81` after `0ce16e8` dropped the pre-consolidation code while research notes already named the map (register items 65–66). Page 16 prints continuous-time parameters and, when appropriate, standardised matrices with the suffix `std`. The printed example on p. 16 is `discreteDRIFTstd`, not `TIPREDEFFECTstd`. Footnote 4 standardises using only the relevant variance, not the total. Table 2 names `B` `TIPREDEFFECT`. The affecting variance is predictor variance `TIPREDVAR` `v`. The affected variance is within-subject `asymDIFFUSION` `p = −q / (2 a)`, because the process dynamics are individual, or average individual, temporal dynamics. The 2017-era source forms the standardiser `sqrt(diag(TIPREDVAR)) / sqrt(diag(asymDIFFUSION))` (comment: "sqrt of affecting latent variance divided by sqrt of affected") and uses it for `asymTIPREDEFFECTstd` whenever `verbose = TRUE`. That source does not form a `TIPREDEFFECTstd` matrix; the scalar map here is the footnote 4 standardisation of the named Table 2 coefficient: `B · √v / √p`. Form strictly positive `p` first, then strictly positive `v`, then the continuous coefficient, then the SD ratio. A zero coefficient with positive `v` and `p` is exactly zero. Unstandardised `B` is defined for a zero coefficient and for zero predictor variance; standardised `TIPREDEFFECT` is not. Zero `v` has no positive predictor SD and fails closed. Zero `q` has no positive process SD and fails closed. Lasting `p` requires stable `a < 0`. `TIPREDEFFECT` is an event-time process-dynamics quantity, so a non-event clock fails closed. A larger positive `q` yields a smaller `|std|`. The asymptotic standardisation `(-B / a) · √v / √p` is the `Δt → ∞` total-change map and is not this continuous coefficient. The finite-interval standardisation `A^{-1}[e^{A Δt} − I] B · √v / √p` depends on the event interval and is not this continuous map. `B · √v / √(trait + p + added)` uses the total, not `asymDIFFUSION`, and is not `TIPREDEFFECTstd` when `TRAITVAR` is nonzero. `TRAITVAR` is not the standardisation variance. Independent of open `#299` `asymTIPREDEFFECTstd`, `#298` `DRIFTstd`, `#280` `discreteDRIFTstd`, `#296` `discreteDIFFUSIONstd`, `#297` `DIFFUSIONstd`, and `#272` `TIPREDVARstd`. Meredith (1993) remains unread (web search 2026-08-29T14:20Z: Springer/Cambridge Core paywalled; Unpaywall this cycle `is_oa: false`; Springer `content/pdf` is an HTML stub). Mislevy (1991, *Psychometrika, 56*, 177–196) remains unread on the same terms (DOI `10.1007/bf02294457`; Unpaywall this cycle `is_oa: false`). Still not a Kalman filter, not a matrix `expm`, not ESEM estimation, not DSEM, and not ctsem estimation.
 - `psychometric_core` recovers the Driver, Oud, and Voelkle (2017, Table 2, p. 12 `MANIFESTTRAITVAR`; §7.1, p. 19; p. 16 `MANIFESTTRAITVARstd`; footnote 4; 2017-era ctsem `summary.ctsemFit.R`; JSS PDF re-opened 2026-08-27T14:20Z from https://www.jstatsoft.org/index.php/jss/article/download/v077i05/1104) scalar standardised manifest-trait variance on current main after `0ce16e8` dropped the pre-consolidation code while research notes already named the map (register items 83–84). Table 2 names `MANIFESTTRAITVAR` `Ψ_τ` the additional time-invariant variance-covariance on the measurement level and sets it `NULL` when there is no manifest trait. Equation 5 writes `Γ ~ N(τ, Ψ)` and names that covariance the manifest traits. Section 7.1 names manifest traits stable individual differences in indicator levels, distinct from process-level `TRAITVAR` `φ_ξ`. Page 16 prints standardised matrices with the suffix `std` when appropriate. The printed example on p. 16 is `discreteDRIFTstd`, not `MANIFESTTRAITVARstd`. Footnote 4 standardises using only the relevant variance, not the total. The relevant variance for that named indicator-level correlation is `MANIFESTTRAITVAR`, not process-level `TRAITVAR` and not residual `MANIFESTVAR` `θ`. The 2017-era source forms `MANIFESTTRAITVARstd` only when `MANIFESTTRAITVAR != 0`, as `solve(sqrt(diag(MANIFESTTRAITVAR) + ridging)) %&% MANIFESTTRAITVAR` when `verbose = TRUE`. OpenMx `%&%` is `t(A) %*% B %*% A`. Unlike `TRAITVARstd`, that formation adds `diag(c(ridging), n.manifest)`. The default `ridging = FALSE` adds 0, not `0.0001`; that ridge is a numerical hack and is not this exact map. The scalar correlation is `ψ / ψ = 1` after strictly positive `MANIFESTTRAITVAR`. Form strictly positive `ψ` first, then `1 / √ψ`, then `(1 / √ψ) ψ (1 / √ψ)`. Unstandardised `MANIFESTTRAITVAR` is defined for a zero trait; standardised `MANIFESTTRAITVAR` is not. Zero `MANIFESTTRAITVAR` skips forming `MANIFESTTRAITVARstd` in the 2017-era source and fails closed here. Indicator-level trait variance is an event-time structural quantity, so a non-event clock fails closed. `MANIFESTTRAITVAR` does not require stable `a < 0`. Distinct positive `ψ` recover the same 1. `trait / trait = 1` is `TRAITVARstd` and recovers the same number and remains a distinct named quantity. `θ` is `MANIFESTVAR` and is measurement error, not this correlation. Meredith (1993) remains unread (web search 2026-08-27T14:20Z: Springer/Cambridge Core paywalled; Unpaywall historically `is_oa: false`; Springer `content/pdf` is an HTML stub). Mislevy (1991, *Psychometrika, 56*, 177–196) remains unread on the same terms (DOI `10.1007/bf02294457`). Still not a Kalman filter, not a matrix `expm`, not ESEM estimation, not DSEM, and not ctsem estimation.
 
 - `psychometric_core` recovers the Driver, Oud, and Voelkle (2017, Table 2, p. 12 `TRAITVAR`; §7.1, pp. 18–19; p. 16 `TRAITVARstd`; footnote 4; 2017-era ctsem `summary.ctsemFit.R`; JSS PDF re-opened 2026-08-26T17:45Z from https://www.jstatsoft.org/index.php/jss/article/download/v077i05/1104) scalar standardised trait variance on current main after `0ce16e8` dropped the pre-consolidation code while research notes already named the map (register items 81–82). Table 2 names `TRAITVAR` `φ_ξ` the latent trait variance/covariance and sets it `NULL` when there is no trait. Section 7.1 names traits the stable between-subject differences (unit-level unobserved heterogeneity). Page 16 prints standardised matrices with the suffix `std` when appropriate. The printed example on p. 16 is `discreteDRIFTstd`, not `TRAITVARstd`. Footnote 4 standardises using only the relevant variance, not the total. The relevant variance for that named between-subject correlation is `TRAITVAR`, not free first-occasion `T0VAR` and not process-dynamics `asymDIFFUSION`. The 2017-era source forms `TRAITVARstd` only when `TRAITVAR != 0`, as `solve(sqrt(diag(TRAITVAR))) %&% TRAITVAR` when `verbose = TRUE`. OpenMx `%&%` is `t(A) %*% B %*% A`. Unlike `T0VARstd`, that formation uses `diag(diag(TRAITVAR))` and does not add `diag(c(ridging))`. The ridge is a `T0VAR` numerical hack and is not this exact map. The scalar correlation is `trait / trait = 1` after strictly positive `TRAITVAR`. Form strictly positive `trait` first, then `1 / √trait`, then `(1 / √trait) trait (1 / √trait)`. Unstandardised `TRAITVAR` is defined for a zero trait; standardised `TRAITVAR` is not. Zero `TRAITVAR` skips forming `TRAITVARstd` in the 2017-era source and fails closed here. Between-subject variance is an event-time structural quantity, so a non-event clock fails closed. `TRAITVAR` does not require stable `a < 0`. Distinct positive `trait` recover the same 1. `p_0 / p_0 = 1` is `T0VARstd` and recovers the same number and remains a distinct named quantity. `t0_b² v` is `addedT0TIPREDVAR` and is extra first-occasion TI variance, not this correlation. Meredith (1993) remains unread (Unpaywall 2026-08-26T17:20Z: `is_oa: false`; OpenAlex closed; Springer `content/pdf` is an HTML stub). Mislevy (1991, *Psychometrika, 56*, 177–196) remains unread on the same terms (DOI `10.1007/bf02294457`; Unpaywall `is_oa: false`). Still not a Kalman filter, not a matrix `expm`, not ESEM estimation, not DSEM, and not ctsem estimation.

crates/psychometric_core/src/error.rs

@@ -710,6 +710,35 @@ pub enum PsychometricError {
     /// `MANIFESTVARstd`. `λ² Var(η) + θ` is `Var(y)`, not the
     /// correlation form of `Θ`.
     ObservedVarianceIsNotStandardisedManifestVariance,
+    /// Driver p. 16 `TIPREDEFFECTstd` was requested without a
+    /// strictly positive `asymDIFFUSION`. Footnote 4 standardises
+    /// using only the relevant within-subject variance; zero `q`
+    /// has no positive process SD.
+    StandardisedTimeIndependentEffectRequiresPositiveWithinSubjectVariance,
+    /// Driver p. 16 `TIPREDEFFECTstd` was requested without a
+    /// strictly positive `TIPREDVAR`. Footnote 4 standardises using
+    /// the affecting predictor variance; zero `v` has no positive
+    /// predictor SD.
+    StandardisedTimeIndependentEffectRequiresPositivePredictorVariance,
+    /// Driver Table 2 unstandardised `TIPREDEFFECT` `B` was treated
+    /// as `TIPREDEFFECTstd`. Unstandardised `B` is defined for a
+    /// zero coefficient and for zero predictor variance;
+    /// standardised `TIPREDEFFECT` is not.
+    UnstandardisedTimeIndependentEffectIsNotStandardisedTimeIndependentEffect,
+    /// Driver p. 16 `asymTIPREDEFFECTstd` `(-B / a) · √v / √p` was
+    /// treated as `TIPREDEFFECTstd`. The asymptotic map is the
+    /// `Δt → ∞` total change and is not the continuous coefficient.
+    AsymptoticStandardisedTimeIndependentEffectIsNotStandardisedTimeIndependentEffect,
+    /// Driver p. 16 finite-interval
+    /// `A^{-1}[e^{A Δt} − I] B · √v / √p` was treated as
+    /// `TIPREDEFFECTstd`. The finite-interval map depends on `Δt`
+    /// and is not the continuous standardisation.
+    DiscreteStandardisedTimeIndependentEffectIsNotStandardisedTimeIndependentEffect,
+    /// `B · √v / √(trait + p + added)` was treated as
+    /// `TIPREDEFFECTstd`. Footnote 4 uses only `asymDIFFUSION`,
+    /// not total variance. `TRAITVAR` is not the standardisation
+    /// variance.
+    TraitContaminatedTimeIndependentEffectIsNotStandardisedTimeIndependentEffect,
 }
 
 impl fmt::Display for PsychometricError {
@@ -1235,6 +1264,24 @@ impl fmt::Display for PsychometricError {
             Self::ObservedVarianceIsNotStandardisedManifestVariance => {
                 "observed-indicator variance is not standardised measurement-error variance"
             }
+            Self::StandardisedTimeIndependentEffectRequiresPositiveWithinSubjectVariance => {
+                "standardised time-independent predictor effect requires strictly positive stationary within-subject variance"
+            }
+            Self::StandardisedTimeIndependentEffectRequiresPositivePredictorVariance => {
+                "standardised time-independent predictor effect requires strictly positive predictor variance"
+            }
+            Self::UnstandardisedTimeIndependentEffectIsNotStandardisedTimeIndependentEffect => {
+                "unstandardised time-independent predictor effect is not standardised time-independent predictor effect"
+            }
+            Self::AsymptoticStandardisedTimeIndependentEffectIsNotStandardisedTimeIndependentEffect => {
+                "asymptotic standardised time-independent predictor effect is not standardised time-independent predictor effect"
+            }
+            Self::DiscreteStandardisedTimeIndependentEffectIsNotStandardisedTimeIndependentEffect => {
+                "finite-interval standardised time-independent predictor effect is not standardised time-independent predictor effect"
+            }
+            Self::TraitContaminatedTimeIndependentEffectIsNotStandardisedTimeIndependentEffect => {
+                "trait-contaminated time-independent predictor effect is not standardised time-independent predictor effect"
+            }
         };
         formatter.write_str(message)
     }
@@ -2073,4 +2120,38 @@ mod tests {
             "measurement error is not standardised manifest-trait variance"
         );
     }
+
+    #[test]
+    fn standardised_time_independent_effect_boundary_messages_are_stable() {
+        assert_eq!(
+            PsychometricError::StandardisedTimeIndependentEffectRequiresPositiveWithinSubjectVariance
+                .to_string(),
+            "standardised time-independent predictor effect requires strictly positive stationary within-subject variance"
+        );
+        assert_eq!(
+            PsychometricError::StandardisedTimeIndependentEffectRequiresPositivePredictorVariance
+                .to_string(),
+            "standardised time-independent predictor effect requires strictly positive predictor variance"
+        );
+        assert_eq!(
+            PsychometricError::UnstandardisedTimeIndependentEffectIsNotStandardisedTimeIndependentEffect
+                .to_string(),
+            "unstandardised time-independent predictor effect is not standardised time-independent predictor effect"
+        );
+        assert_eq!(
+            PsychometricError::AsymptoticStandardisedTimeIndependentEffectIsNotStandardisedTimeIndependentEffect
+                .to_string(),
+            "asymptotic standardised time-independent predictor effect is not standardised time-independent predictor effect"
+        );
+        assert_eq!(
+            PsychometricError::DiscreteStandardisedTimeIndependentEffectIsNotStandardisedTimeIndependentEffect
+                .to_string(),
+            "finite-interval standardised time-independent predictor effect is not standardised time-independent predictor effect"
+        );
+        assert_eq!(
+            PsychometricError::TraitContaminatedTimeIndependentEffectIsNotStandardisedTimeIndependentEffect
+                .to_string(),
+            "trait-contaminated time-independent predictor effect is not standardised time-independent predictor effect"
+        );
+    }
 }