feat(psychometric): recover Driver p.16 DRIFTstd after positive asymDIFFUSION

CHANGELOG.md

@@ -4,6 +4,7 @@ All notable changes to TEPP are documented here. The format follows Keep a Chang
 
 ## [Unreleased]
 
+- `psychometric_core` recovers the Driver, Oud, and Voelkle (2017, p. 16 `DRIFTstd`; Eq. 1, p. 4; footnote 4; §7.1, pp. 18–19; JSS PDF re-opened 2026-08-23T13:28Z from https://www.jstatsoft.org/index.php/jss/article/download/v077i05/1104) scalar standardised continuous `DRIFT`. Page 16 prints continuous-time parameters (e.g., `DRIFT`) and, when appropriate, standardised matrices with the suffix `std`. Footnote 4: standardisations use only the relevant variance, not the total. For `DRIFT` that relevant variance is within-subject `asymDIFFUSION` `-q / (2 a)`, because `DRIFT` is intended to represent individual, or average individual, temporal dynamics. Form strictly positive `asymDIFFUSION` first. In the scalar stationary case the within-subject SD ratio is 1, so the standardised auto-effect equals the unstandardised log-rate numerically; those remain distinct named quantities. Unstandardised `a` is defined for growing `a ≥ 0` and for zero diffusion; standardised `DRIFT` is not. Zero `asymDIFFUSION` has no positive SD and fails closed. The discrete standardisation `e^{a Δt}` depends on the event interval and is not the continuous map. 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. Still not a Kalman filter, not a matrix `expm`, not ESEM estimation, not DSEM, and not ctsem estimation. Meredith (1993) remains unread (Unpaywall 2026-08-23T13:19Z: `is_oa: false`; title *Measurement Invariance, Factor Analysis and Factorial Invariance*). Mislevy (1991, *Psychometrika, 56*, 177–196) remains unread (Unpaywall 2026-08-23T13:19Z: `is_oa: false`; title *Randomization-Based Inference about Latent Variables from Complex Samples*).
 - `psychometric_core` recovers the Driver, Oud, and Voelkle (2017, p. 16 `DIFFUSIONstd`; Eq. 4, p. 5; footnote 4; §7.1, pp. 18–19; JSS PDF re-opened 2026-08-23T13:20Z from https://www.jstatsoft.org/index.php/jss/article/download/v077i05/1104) scalar standardised continuous `DIFFUSION`. Page 16 prints continuous-time parameters (e.g., `DRIFT`, `DIFFUSION`) and, when appropriate, standardised matrices with the suffix `std`. Footnote 4: standardisations use only the relevant variance, not the total. Process noise is within-subject stochastic input, so that relevant variance is within-subject `asymDIFFUSION` `-q / (2 a)`, the same footnote 4 variance used for `DRIFT`. Form strictly positive `asymDIFFUSION` first, then `q / (−q / (2 a))`. In the scalar stationary case that ratio equals `-2 a` and does not depend on `q` once `q > 0`. Unstandardised `q` is defined for growing `a ≥ 0` and for zero diffusion; standardised `DIFFUSION` is not. Zero `asymDIFFUSION` has no positive SD and fails closed. The discrete standardisation `Q_Δt / (−q / (2 a)) = 1 − exp(2 a Δt)` depends on the event interval and is not the continuous map. Section 7.1 warns that omitting trait variance confounds between- and within-person information. `q / (trait + p + added)` uses the total, not `asymDIFFUSION`, and is not `DIFFUSIONstd` when `TRAITVAR` is nonzero. `TRAITVAR` is not the standardisation variance. Still not a Kalman filter, not a matrix `expm`, not ESEM estimation, not DSEM, and not ctsem estimation. Meredith (1993) remains unread (Unpaywall 2026-08-23T13:19Z: `is_oa: false`; title *Measurement Invariance, Factor Analysis and Factorial Invariance*). Mislevy (1991, *Psychometrika, 56*, 177–196) remains unread (Unpaywall 2026-08-23T13:19Z: `is_oa: false`; title *Randomization-Based Inference about Latent Variables from Complex Samples*).
 - `psychometric_core` recovers the Driver, Oud, and Voelkle (2017, p. 16 `discreteDIFFUSIONstd`; Eq. 3–4, pp. 4–5; footnote 4; §7.1, pp. 18–19; JSS PDF re-opened 2026-08-23T13:06Z from https://www.jstatsoft.org/index.php/jss/article/download/v077i05/1104) scalar standardised discrete `DIFFUSION`. Page 16 prints discrete-time transformations for a chosen event interval (`discreteDRIFT`, `discreteDIFFUSION`) and, when appropriate, standardised matrices with the suffix `std`. Footnote 4: standardisations use only the relevant variance, not the total. Process noise is within-subject stochastic input, so that relevant variance is within-subject `asymDIFFUSION` `-q / (2 a)`, the same footnote 4 variance used for `DRIFT`. Form strictly positive `asymDIFFUSION` first, then `Q_Δt` from Equation 4, then `Q_Δt / (−q / (2 a))`. In the scalar stationary case that ratio equals `1 − exp(2 a Δt)`. Unstandardised `Q_Δt` is defined for growing `a ≥ 0` and for zero diffusion; standardised `DIFFUSION` is not. Zero `asymDIFFUSION` has no positive SD and fails closed. The continuous standardisation `q / (−q / (2 a)) = −2 a` is not the discrete map. Section 7.1 warns that omitting trait variance confounds between- and within-person information. `Q_Δt / (trait + p + added)` uses the total, not `asymDIFFUSION`, and is not `discreteDIFFUSIONstd` when `TRAITVAR` is nonzero. `TRAITVAR` is not the standardisation variance. Still not a Kalman filter, not a matrix `expm`, not ESEM estimation, not DSEM, and not ctsem estimation. Meredith (1993) remains unread (Unpaywall 2026-08-23T13:06Z: `is_oa: false`; title *Measurement Invariance, Factor Analysis and Factorial Invariance*). Mislevy (1991, *Psychometrika, 56*, 177–196) remains unread (Unpaywall 2026-08-23T13:06Z: `is_oa: false`; title *Randomization-Based Inference about Latent Variables from Complex Samples*).
 - `psychometric_core` recovers the Driver, Oud, and Voelkle (2017, p. 16 `discreteDRIFTstd`; Eq. 3, p. 5; footnote 4; §7.1, pp. 18–19; JSS PDF re-opened 2026-08-23T11:40Z from https://www.jstatsoft.org/index.php/jss/article/download/v077i05/1104) scalar standardised discrete `DRIFT`. Page 16 prints `discreteDRIFT` as `expm(DRIFT Δt)` and, when appropriate, `discreteDRIFTstd`. Footnote 4: standardisations use only the relevant variance, not the total. For `DRIFT` that relevant variance is within-subject `asymDIFFUSION` `-q / (2 a)`, because `DRIFT` is intended to represent individual, or average individual, temporal dynamics. Form strictly positive `asymDIFFUSION` first, then `φ = exp(a Δt)`. In the scalar stationary case the within-subject SD ratio is 1, so the standardised auto-effect equals the unstandardised discrete lag numerically; those remain distinct named quantities. Unstandardised `e^{a Δt}` is defined for growing `a ≥ 0` and for zero diffusion; standardised `DRIFT` is not. Zero `asymDIFFUSION` has no positive SD and fails closed. Section 7.1 warns that omitting trait variance confounds between- and within-person information. The trait-plus-state autocorrelation `(trait + e^{a Δt} p + added) / (trait + p + added)` uses the total, not `asymDIFFUSION`, and is not `discreteDRIFTstd` when `TRAITVAR` is nonzero. `TRAITVAR` is not the standardisation variance. Still not a Kalman filter, not a matrix `expm`, not ESEM estimation, not DSEM, and not ctsem estimation. Meredith (1993) remains unread (Unpaywall 2026-08-23T11:40Z: `is_oa: false`; title *Measurement Invariance, Factor Analysis and Factorial Invariance*). Mislevy (1991, *Psychometrika, 56*, 177–196) remains unread (Unpaywall 2026-08-23T11:40Z: `is_oa: false`; title *Randomization-Based Inference about Latent Variables from Complex Samples*).

crates/psychometric_core/src/error.rs

@@ -717,6 +717,22 @@ pub enum PsychometricError {
     /// `q / (trait + p + added)` was treated as p. 16 `DIFFUSIONstd`.
     /// Footnote 4 uses only `asymDIFFUSION`, not `TRAITVAR`.
     TraitContaminatedContinuousDiffusionIsNotStandardisedContinuousDiffusion,
+    /// Driver p. 16 `DRIFTstd` was requested with a non-positive
+    /// within-subject variance. Footnote 4 standardises `DRIFT` using
+    /// only strictly positive `asymDIFFUSION`.
+    StandardisedContinuousDriftRequiresPositiveWithinSubjectVariance,
+    /// Driver p. 16 unstandardised `DRIFT` `a` was treated as
+    /// `DRIFTstd`. Unstandardised `a` is defined for growing or
+    /// zero-diffusion processes; standardised `DRIFT` is not.
+    UnstandardisedContinuousDriftIsNotStandardisedContinuousDrift,
+    /// Driver p. 16 `discreteDRIFTstd` `e^{a Δt}` was treated as
+    /// `DRIFTstd`. The discrete auto-effect is not the continuous
+    /// log-rate.
+    StandardisedDiscreteDriftIsNotStandardisedContinuousDrift,
+    /// Driver §7.1 trait-contaminated continuous drift
+    /// `a p / (trait + p + added)` was treated as p. 16 `DRIFTstd`.
+    /// Footnote 4 uses only `asymDIFFUSION`, not `TRAITVAR`.
+    TraitContaminatedContinuousDriftIsNotStandardisedContinuousDrift,
 }
 
 impl fmt::Display for PsychometricError {
@@ -1269,6 +1285,18 @@ impl fmt::Display for PsychometricError {
             Self::TraitContaminatedContinuousDiffusionIsNotStandardisedContinuousDiffusion => {
                 "trait-contaminated continuous DIFFUSION is not standardised continuous DIFFUSION"
             }
+            Self::StandardisedContinuousDriftRequiresPositiveWithinSubjectVariance => {
+                "standardised continuous DRIFT requires strictly positive within-subject variance"
+            }
+            Self::UnstandardisedContinuousDriftIsNotStandardisedContinuousDrift => {
+                "unstandardised continuous DRIFT is not standardised continuous DRIFT"
+            }
+            Self::StandardisedDiscreteDriftIsNotStandardisedContinuousDrift => {
+                "standardised discrete DRIFT is not standardised continuous DRIFT"
+            }
+            Self::TraitContaminatedContinuousDriftIsNotStandardisedContinuousDrift => {
+                "trait-contaminated continuous DRIFT is not standardised continuous DRIFT"
+            }
         };
         formatter.write_str(message)
     }
@@ -2176,4 +2204,28 @@ mod tests {
             "trait-contaminated continuous DIFFUSION is not standardised continuous DIFFUSION"
         );
     }
+
+    #[test]
+    fn standardised_continuous_drift_boundary_messages_are_stable() {
+        assert_eq!(
+            PsychometricError::StandardisedContinuousDriftRequiresPositiveWithinSubjectVariance
+                .to_string(),
+            "standardised continuous DRIFT requires strictly positive within-subject variance"
+        );
+        assert_eq!(
+            PsychometricError::UnstandardisedContinuousDriftIsNotStandardisedContinuousDrift
+                .to_string(),
+            "unstandardised continuous DRIFT is not standardised continuous DRIFT"
+        );
+        assert_eq!(
+            PsychometricError::StandardisedDiscreteDriftIsNotStandardisedContinuousDrift
+                .to_string(),
+            "standardised discrete DRIFT is not standardised continuous DRIFT"
+        );
+        assert_eq!(
+            PsychometricError::TraitContaminatedContinuousDriftIsNotStandardisedContinuousDrift
+                .to_string(),
+            "trait-contaminated continuous DRIFT is not standardised continuous DRIFT"
+        );
+    }
 }