feat(psychometric): restore Driver p.16 discreteDIFFUSIONstd QΔt/p on main

CHANGELOG.md

@@ -40,6 +40,7 @@ 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 `discreteDIFFUSIONstd`; Eq. 3–4, pp. 4–5; Table 2, p. 12; footnote 4; 2017-era ctsem `summary.ctsemFit.R`; JSS PDF re-opened 2026-09-01T19:20Z from https://www.jstatsoft.org/index.php/jss/article/download/v077i05/1104) scalar standardised discrete diffusion on current main after `0ce16e8` dropped the pre-consolidation code while research notes already named the map (register items 57–58). Page 16 prints discrete-time transformations for a chosen event interval (`discreteDRIFT`, `discreteDIFFUSION`) and, when appropriate, standardised matrices with the suffix `std`. The printed example on p. 16 is `discreteDRIFTstd`, not `discreteDIFFUSIONstd`. Footnote 4 standardises using only the relevant variance, not the total. Table 2 names `DIFFUSION` `Q` the lower-triangular `n.latent × n.latent` Cholesky of diffusion variance/covariance. Process noise is within-subject stochastic input, so that relevant variance is within-subject `asymDIFFUSION` `p = −q / (2 a)`. The 2017-era source forms unstandardised `discreteDIFFUSION` whenever `verbose = TRUE`. That source does not form a `discreteDIFFUSIONstd` matrix; the scalar map is the footnote 4 standardisation of that named discrete diffusion: `Q_Δt / p` after strictly positive `p`. Form strictly positive `p` first, then `Q_Δt` from Equation 3–4, then divide `Q_Δt` by `p`. In the scalar stationary case that ratio equals `1 − exp(2 a Δt)`. Unstandardised `Q_Δt` is defined for growing `a ≥ 0` and for a zero process; standardised discrete diffusion is not. Zero `q` has no positive process SD and fails closed. Lasting `p` requires stable `a < 0`. A non-event clock fails closed. A non-positive event interval fails closed. `q / p = −2 a` is `DIFFUSIONstd` and does not depend on `Δt`. `Q_Δt / (trait + p + added)` uses the total and is not this map when `TRAITVAR` is nonzero. 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.
 - `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

@@ -650,6 +650,24 @@ pub enum PsychometricError {
     /// `discreteCINTstd`. The asymptotic map is the total change, not
     /// the finite-interval intercept.
     AsymptoticStandardisedContinuousInterceptIsNotStandardisedDiscreteContinuousIntercept,
+    /// Driver p. 16 `discreteDIFFUSIONstd` was requested without a
+    /// strictly positive `asymDIFFUSION`. Footnote 4 standardises
+    /// using only the relevant variance; zero `q` has no positive
+    /// process SD.
+    StandardisedDiscreteDiffusionRequiresPositiveStationaryVariance,
+    /// Unstandardised `discreteDIFFUSION` `Q_Δt` was treated as
+    /// `discreteDIFFUSIONstd`. Unstandardised process noise is
+    /// defined for growing `a ≥ 0` and for zero diffusion;
+    /// standardised discrete diffusion is not.
+    UnstandardisedDiscreteDiffusionIsNotStandardisedDiscreteDiffusion,
+    /// Driver p. 16 `DIFFUSIONstd` `q / p = −2 a` was treated as
+    /// `discreteDIFFUSIONstd`. The continuous ratio does not depend
+    /// on the event interval.
+    StandardisedContinuousDiffusionIsNotStandardisedDiscreteDiffusion,
+    /// Driver §7.1 total `Q_Δt / (trait + p + added)` was treated as
+    /// p. 16 `discreteDIFFUSIONstd`. Footnote 4 uses only
+    /// within-subject `asymDIFFUSION`, not the total.
+    TotalVarianceScaledDiscreteDiffusionIsNotStandardisedDiscreteDiffusion,
     /// Driver p. 16 `asymCINTstd` was requested without a strictly
     /// positive `asymDIFFUSION`. Footnote 4 standardises using only
     /// the relevant variance; zero `q` has no positive process SD.
@@ -1198,6 +1216,18 @@ impl fmt::Display for PsychometricError {
             Self::AsymptoticStandardisedContinuousInterceptIsNotStandardisedDiscreteContinuousIntercept => {
                 "asymptotic standardised continuous intercept is not standardised discrete continuous intercept"
             }
+            Self::StandardisedDiscreteDiffusionRequiresPositiveStationaryVariance => {
+                "standardised discrete diffusion requires strictly positive stationary within-subject variance"
+            }
+            Self::UnstandardisedDiscreteDiffusionIsNotStandardisedDiscreteDiffusion => {
+                "unstandardised discrete diffusion is not standardised discrete diffusion"
+            }
+            Self::StandardisedContinuousDiffusionIsNotStandardisedDiscreteDiffusion => {
+                "standardised continuous diffusion is not standardised discrete diffusion"
+            }
+            Self::TotalVarianceScaledDiscreteDiffusionIsNotStandardisedDiscreteDiffusion => {
+                "total-variance scaled discrete diffusion is not standardised discrete diffusion"
+            }
             Self::StandardisedAsymptoticContinuousInterceptRequiresPositiveStationaryVariance => {
                 "standardised asymptotic continuous intercept requires strictly positive stationary within-subject variance"
             }
@@ -2027,6 +2057,30 @@ mod tests {
         );
     }
 
+    #[test]
+    fn standardised_discrete_diffusion_boundary_messages_are_stable() {
+        assert_eq!(
+            PsychometricError::StandardisedDiscreteDiffusionRequiresPositiveStationaryVariance
+                .to_string(),
+            "standardised discrete diffusion requires strictly positive stationary within-subject variance"
+        );
+        assert_eq!(
+            PsychometricError::UnstandardisedDiscreteDiffusionIsNotStandardisedDiscreteDiffusion
+                .to_string(),
+            "unstandardised discrete diffusion is not standardised discrete diffusion"
+        );
+        assert_eq!(
+            PsychometricError::StandardisedContinuousDiffusionIsNotStandardisedDiscreteDiffusion
+                .to_string(),
+            "standardised continuous diffusion is not standardised discrete diffusion"
+        );
+        assert_eq!(
+            PsychometricError::TotalVarianceScaledDiscreteDiffusionIsNotStandardisedDiscreteDiffusion
+                .to_string(),
+            "total-variance scaled discrete diffusion is not standardised discrete diffusion"
+        );
+    }
+
     #[test]
     fn standardised_asymptotic_continuous_intercept_boundary_messages_are_stable() {
         assert_eq!(