feat(psychometric): restore Driver p.16 TIPREDVARstd v/v=1 on main

CHANGELOG.md

@@ -40,6 +40,9 @@ 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, Table 2, p. 12 `TIPREDVAR`; p. 16 `TIPREDVARstd`; footnote 4; 2017-era ctsem `summary.ctsemFit.R`; JSS PDF re-opened 2026-08-30T16:50Z from https://www.jstatsoft.org/index.php/jss/article/download/v077i05/1104) scalar standardised time-independent predictor variance on current main after `0ce16e8` dropped the pre-consolidation code while research notes already named the map (register items 87–88). Table 2 names `TIPREDVAR` the `n.TIpred × n.TIpred` Cholesky matrix of variance/covariance between time-independent predictors and defaults it to 1. Page 16 prints standardised matrices with the suffix `std` when appropriate. The printed example on p. 16 is `discreteDRIFTstd`, not `TIPREDVARstd`. Footnote 4 standardises using only the relevant variance, not the total. The relevant variance for that named predictor correlation is `TIPREDVAR` itself, not §7.2 `addedTIPREDVAR` `(B / a)² v` and not residual `MANIFESTVAR` `θ`. The 2017-era source forms `TIPREDVARstd` as `solve(sqrt(diag(TIPREDVAR))) %&% TIPREDVAR` when `verbose = TRUE`. OpenMx `%&%` is `t(A) %*% B %*% A`. The 2017-era source adds ridging; the default ridge is 0; `dimnames` are `TIpredNames`. The scalar correlation is `v / v = 1` after strictly positive `TIPREDVAR`. Form strictly positive `v` first, then `1 / √v`, then `(1 / √v) v (1 / √v)`. Unstandardised `TIPREDVAR` is defined for a zero predictor; standardised `TIPREDVAR` is not. Zero `v` has no positive SD and fails closed. Predictor variance is an event-time structural quantity, so a non-event clock fails closed. `TIPREDVAR` does not require stable `a < 0`. Distinct positive `v` recover the same 1. `θ / θ = 1` is `MANIFESTVARstd` and recovers the same number and remains a distinct named quantity. `(B / a)² v` is `addedTIPREDVAR` and is not this correlation. Meredith (1993) remains unread (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 `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

@@ -633,6 +633,29 @@ pub enum PsychometricError {
     /// the correlation form of between-subject `TRAITVAR`.
     InitialTimeIndependentVarianceIsNotStandardisedTraitVariance,
 
+    /// Driver p. 16 `TIPREDVARstd` was requested with a non-positive
+    /// time-independent predictor variance. The 2017-era source
+    /// forms `TIPREDVARstd` as `solve(sqrt(diag(TIPREDVAR))) %&%
+    /// TIPREDVAR`; footnote 4 standardisation requires strictly
+    /// positive `TIPREDVAR`.
+    StandardisedTimeIndependentPredictorVarianceRequiresPositivePredictorVariance,
+    /// Driver Table 2 unstandardised `TIPREDVAR` `v` was treated as
+    /// p. 16 `TIPREDVARstd`. Unstandardised `v` is defined for a
+    /// zero predictor; standardised `TIPREDVAR` is not.
+    UnstandardisedTimeIndependentPredictorVarianceIsNotStandardisedTimeIndependentPredictorVariance,
+    /// Driver p. 16 `MANIFESTVARstd` was treated as p. 16
+    /// `TIPREDVARstd`. Equal numbers of 1 after a strictly positive
+    /// relevant variance are still distinct named quantities.
+    /// `TIPREDVARstd` is the correlation form of `TIPREDVAR`;
+    /// `MANIFESTVARstd` is the correlation form of residual
+    /// `MANIFESTVAR`.
+    StandardisedManifestVarianceIsNotStandardisedTimeIndependentPredictorVariance,
+    /// Driver §7.2 `addedTIPREDVAR` `(B / a)² v` was treated as
+    /// p. 16 `TIPREDVARstd`. Extra process variance accounted for
+    /// by a time-independent predictor is not the correlation form
+    /// of `TIPREDVAR`.
+    AsymptoticTimeIndependentPredictorVarianceIsNotStandardisedTimeIndependentPredictorVariance,
+
     /// Driver p. 16 `discreteCINTstd` was requested without a strictly
     /// positive `asymDIFFUSION`. Footnote 4 standardises using only the
     /// relevant variance; zero `q` has no positive process SD.
@@ -1183,6 +1206,18 @@ impl fmt::Display for PsychometricError {
             Self::InitialTimeIndependentVarianceIsNotStandardisedTraitVariance => {
                 "initial time-independent predictor variance is not standardised trait variance"
             }
+            Self::StandardisedTimeIndependentPredictorVarianceRequiresPositivePredictorVariance => {
+                "standardised time-independent predictor variance requires strictly positive time-independent predictor variance"
+            }
+            Self::UnstandardisedTimeIndependentPredictorVarianceIsNotStandardisedTimeIndependentPredictorVariance => {
+                "unstandardised time-independent predictor variance is not standardised time-independent predictor variance"
+            }
+            Self::StandardisedManifestVarianceIsNotStandardisedTimeIndependentPredictorVariance => {
+                "standardised manifest variance is not standardised time-independent predictor variance"
+            }
+            Self::AsymptoticTimeIndependentPredictorVarianceIsNotStandardisedTimeIndependentPredictorVariance => {
+                "asymptotic time-independent predictor variance is not standardised time-independent predictor variance"
+            }
 
 
 
@@ -2002,6 +2037,30 @@ mod tests {
         );
     }
 
+    #[test]
+    fn standardised_time_independent_predictor_variance_boundary_messages_are_stable() {
+        assert_eq!(
+            PsychometricError::StandardisedTimeIndependentPredictorVarianceRequiresPositivePredictorVariance
+                .to_string(),
+            "standardised time-independent predictor variance requires strictly positive time-independent predictor variance"
+        );
+        assert_eq!(
+            PsychometricError::UnstandardisedTimeIndependentPredictorVarianceIsNotStandardisedTimeIndependentPredictorVariance
+                .to_string(),
+            "unstandardised time-independent predictor variance is not standardised time-independent predictor variance"
+        );
+        assert_eq!(
+            PsychometricError::StandardisedManifestVarianceIsNotStandardisedTimeIndependentPredictorVariance
+                .to_string(),
+            "standardised manifest variance is not standardised time-independent predictor variance"
+        );
+        assert_eq!(
+            PsychometricError::AsymptoticTimeIndependentPredictorVarianceIsNotStandardisedTimeIndependentPredictorVariance
+                .to_string(),
+            "asymptotic time-independent predictor variance is not standardised time-independent predictor variance"
+        );
+    }
+
     #[test]
 
     fn standardised_discrete_continuous_intercept_boundary_messages_are_stable() {