feat(psychometric): restore Driver p.16 DRIFTstd 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, p. 16 `DRIFTstd`; Eq. 1, p. 4; footnote 4; Table 2, p. 12; §7.1, pp. 18–19; 2017-era ctsem `summary.ctsemFit.R`; JSS PDF re-opened 2026-08-30T16:12Z from https://www.jstatsoft.org/index.php/jss/article/download/v077i05/1104) scalar standardised continuous `DRIFT` on current main after `0ce16e8` dropped the pre-consolidation code while research notes already named the map (register items 61–62). Page 16 prints continuous-time parameters (e.g., `DRIFT`, `DIFFUSION`) and, when appropriate, standardised matrices with the suffix `std`. The printed example on p. 16 is `discreteDRIFTstd`, not `DRIFTstd`. Footnote 4 standardises using only the relevant variance, not the total. For `DRIFT` that relevant variance is within-subject `asymDIFFUSION` `p = −q / (2 a)`, because `DRIFT` is intended to represent individual, or average individual, temporal dynamics. The 2017-era source forms `discreteDRIFTstd` as `discreteDRIFT * standardiser` after the `asymDIFFUSION` SD ratio and does **not** form a `DRIFTstd` matrix. The scalar map here is the footnote 4 standardisation of the named continuous auto-effect: form strictly positive `p` first, then `DRIFT * standardiser`. In the scalar stationary case that SD ratio is 1 after strictly positive `p`, so the standardised auto-effect equals the unstandardised log-rate `a` numerically; those remain distinct named quantities. Unstandardised `a` is defined for growing `a ≥ 0` and for zero diffusion; standardised `DRIFT` is not. Zero `q` has no positive SD and fails closed. Lasting `p` requires stable `a < 0`. A non-event clock fails closed. Distinct positive `q` recover the same `a`. `e^{a Δt}` is `discreteDRIFTstd` and depends on the event interval. This crate does not currently export `discreteDRIFTstd`; the refuse still names that quantity. Section 7.1 warns that omitting trait variance confounds between- and within-person information. `a p / (trait + p + added)` uses the total, not `asymDIFFUSION`, and is not `DRIFTstd` when `TRAITVAR` is nonzero. `TRAITVAR` is not the standardisation variance. 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

@@ -613,6 +613,23 @@ pub enum PsychometricError {
     /// correlation form of `TIPREDVAR`.
     StandardisedTimeIndependentPredictorVarianceIsNotStandardisedAsymptoticDiffusion,
 
+    /// Driver p. 16 `DRIFTstd` was requested with a non-positive
+    /// within-subject variance. Footnote 4 standardises continuous
+    /// `DRIFT` using only strictly positive `asymDIFFUSION`.
+    StandardisedDriftRequiresPositiveWithinSubjectVariance,
+    /// Driver p. 16 unstandardised `DRIFT` `a` was treated as
+    /// `DRIFTstd`. Unstandardised `a` is defined for growing or
+    /// zero-diffusion processes; standardised `DRIFT` is not.
+    UnstandardisedDriftIsNotStandardisedDrift,
+    /// Driver p. 16 `discreteDRIFTstd` `e^{a Δt}` was treated as
+    /// `DRIFTstd`. The discrete auto-effect depends on the event
+    /// interval; the continuous auto-effect does not.
+    StandardisedDiscreteDriftIsNotStandardisedDrift,
+    /// Driver §7.1 trait-contaminated continuous auto-effect
+    /// `a p / (trait + p + added)` was treated as p. 16 `DRIFTstd`.
+    /// Footnote 4 uses only `asymDIFFUSION`, not `TRAITVAR`.
+    TraitContaminatedDriftIsNotStandardisedDrift,
+
     /// Driver p. 16 `TRAITVARstd` was requested with a non-positive
     /// trait variance. The 2017-era source skips forming
     /// `TRAITVARstd` when `TRAITVAR == 0`; footnote 4
@@ -1171,6 +1188,18 @@ impl fmt::Display for PsychometricError {
             Self::StandardisedTimeIndependentPredictorVarianceIsNotStandardisedAsymptoticDiffusion => {
                 "standardised time-independent predictor variance is not standardised asymptotic diffusion"
             }
+            Self::StandardisedDriftRequiresPositiveWithinSubjectVariance => {
+                "standardised drift requires strictly positive within-subject variance"
+            }
+            Self::UnstandardisedDriftIsNotStandardisedDrift => {
+                "unstandardised drift is not standardised drift"
+            }
+            Self::StandardisedDiscreteDriftIsNotStandardisedDrift => {
+                "standardised discrete drift is not standardised drift"
+            }
+            Self::TraitContaminatedDriftIsNotStandardisedDrift => {
+                "trait-contaminated drift is not standardised drift"
+            }
             Self::StandardisedTraitVarianceRequiresPositiveTraitVariance => {
                 "standardised trait variance requires strictly positive trait variance"
             }
@@ -1977,6 +2006,22 @@ mod tests {
                 .to_string(),
             "standardised time-independent predictor variance is not standardised asymptotic diffusion"
         );
+        assert_eq!(
+            PsychometricError::StandardisedDriftRequiresPositiveWithinSubjectVariance.to_string(),
+            "standardised drift requires strictly positive within-subject variance"
+        );
+        assert_eq!(
+            PsychometricError::UnstandardisedDriftIsNotStandardisedDrift.to_string(),
+            "unstandardised drift is not standardised drift"
+        );
+        assert_eq!(
+            PsychometricError::StandardisedDiscreteDriftIsNotStandardisedDrift.to_string(),
+            "standardised discrete drift is not standardised drift"
+        );
+        assert_eq!(
+            PsychometricError::TraitContaminatedDriftIsNotStandardisedDrift.to_string(),
+            "trait-contaminated drift is not standardised drift"
+        );
     }
 
     #[test]