From 7cb4b68fc2e0cc5b78df087e9e1e66ce262c897c Mon Sep 17 00:00:00 2001 From: Seongho Bae Date: Sun, 23 Aug 2026 22:50:57 +0000 Subject: [PATCH 1/2] =?UTF-8?q?feat(psychometric):=20recover=20Driver=20p.?= =?UTF-8?q?16=20MANIFESTVARstd=20after=20positive=20=CE=98?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Map measurement-error MANIFESTVAR through 2017-era summary.ctsemFit.R as solve(sqrt(diag(MANIFESTVAR))) %&% MANIFESTVAR after strictly positive θ. OpenMx %&% is t(A)%*%B%*%A; unlike TRAITVARstd the 2017-era source adds ridging and the default ridge is 0. The 2017-era dimnames assignment to latentNames is a source bug and is not this map. The scalar correlation is θ/θ = 1. Zero θ makes solve(sqrt(0)) fail and fails closed. Refuse unstandardised MANIFESTVAR, MANIFESTTRAITVARstd even when both equal 1, and Eq. 5 Var(y). MANIFESTVAR does not require a<0. --- ARCHITECTURE.md | 2 +- CHANGELOG.md | 1 + CLAUDE.md | 2 +- crates/psychometric_core/src/error.rs | 55 +++++ crates/psychometric_core/src/event_time.rs | 227 +++++++++++++++++- crates/psychometric_core/src/lib.rs | 25 ++ ...multilevel_event_time_recovery_contract.rs | 77 +++++- .../scientific_claim_boundary_contract.rs | 84 ++++++- docs/TRACEABILITY.md | 2 +- docs/adr/0005-posterior-esem-dsem.md | 4 +- .../multilevel-event-time-recovery.md | 13 +- docs/validation/temporal-event-foundation.md | 2 +- 12 files changed, 458 insertions(+), 36 deletions(-) diff --git a/ARCHITECTURE.md b/ARCHITECTURE.md index 11c4a44a..3b30abbd 100644 --- a/ARCHITECTURE.md +++ b/ARCHITECTURE.md @@ -61,7 +61,7 @@ boundaries above remain the target modular MSA architecture. | `tepp_simulation` | known-truth temporal/event data generation | | `validation_core` | RMSE, bias, coverage, graph, and Monte Carlo metrics | | `tepp_api` | versioned DTO, schema, and export contracts | -| `psychometric_core` | posterior-aware structural input gates, CWC within/between OLS plus the contextual effect, event-time log-rate, unequal-interval discrete-lag remapping, constant-predictor discrete effect, time-varying-predictor discrete effect (Eq. 14), exact scalar discrete process noise (Driver et al., 2017, Eq. 3), lagged latent covariance and unconditional latent variance (Driver et al., 2017, Eq. 3–4), stationary within-subject variance (Driver et al., 2017, Eq. 4 as `Δt → ∞`; `asymDIFFUSION`), trait-plus-state variance (Driver et al., 2017, §4.3 `TRAITVAR`; not process noise), observed-indicator variance and lagged observed covariance (Driver et al., 2017, Eq. 5; Table 2 `MANIFESTVAR` is `Θ`, not `Var(y)`; `MANIFESTTRAITVAR` is not `MANIFESTVAR`; `Θ` does not enter lagged observed covariance; observed-indicator mean is `τ + λ μ`; `MANIFESTMEANS` is not `E(y)`; `CINT` is not `MANIFESTMEANS`; discrete latent mean is `exp(a Δt) μ_0 + (exp(a Δt) − 1)/a κ`; `T0MEANS` is not `μ_t`; `CINT` is not the discrete increment; evolved observed mean is `τ + λ μ_t`; `τ + λ μ_0` is not `E(y_t)`; contemporaneous `TDPREDEFFECT` impulse is `m x`, not `CINT`, not `TIPREDEFFECT`, and not Voelkle Eq. 14; Eq. 5 of that contemporaneous impulse is `τ + λ(μ_t + m x)`, and `τ + λ μ_t` is not that observed mean; time-independent `TIPREDEFFECT` increment is `A^{-1}[e^{A Δt} − I] B z`, not `CINT`, not `M x`, not Voelkle Eq. 14, and not the coefficient `B`; Eq. 5 of that increment is `τ + λ(μ_t + A^{-1}[e^{A Δt} − I] B z)`, and `τ + λ μ_t` is not that observed mean; `τ + λ(μ_t + m x)` is not that observed mean; `τ + λ(μ_t + e^{a(t−u)} m x)` is not that observed mean when `u ≠ t`; within-interval `TDPREDEFFECT` carry is `e^{A(t−u)} M x` for `t0 < u < t`, not the contemporaneous Dirac, not `CINT`, not `TIPREDEFFECT`, and not Voelkle Eq. 14; Eq. 5 of that carry is `τ + λ(μ_t + e^{a(t−u)} m x)`, and `τ + λ μ_t` is not that observed mean; `τ + λ(μ_t + m x)` is not that carried observed mean when `u ≠ t`; first-occasion `T0TIPREDEFFECT` shift is `t0_b z` and Eq. 3 first-summand carry is `e^{A Δt} t0_b z` (`T0TIPREDEFFECT` is not `TIPREDEFFECT` `B`; `t0_b z` is not `A^{-1}[e^{A Δt} − I] B z`; `e^{A Δt} t0_b z` is not `t0_b z`; Eq. 5 of that carry is `τ + λ(μ_t + e^{a Δt} t0_b z)`, and `τ + λ μ_t` is not that observed mean; `τ + λ(μ_t + A^{-1}[e^{A Δt} − I] B z)` is not that observed mean), first-occasion `T0TDPREDEFFECT` shift is `t0_m x0` and Eq. 3 first-summand carry is `e^{A Δt} t0_m x0` (`T0TDPREDEFFECT` is not `TDPREDEFFECT` `M`; `t0_m x0` is not `M x`; `e^{A Δt} t0_m x0` is not `t0_m x0`; `e^{A Δt} t0_m x0` is not `e^{A(t−u)} M x` for `t0 < u < t`; `t0_m x0` is not `t0_b z`; an impulse at `u ≤ t0` that used `M` is already in `η(t0)` as `TDPREDEFFECT`, not as `T0TDPREDEFFECT`; Eq. 5 of that carry is `τ + λ(μ_t + e^{a Δt} t0_m x0)`, and `τ + λ μ_t` is not that observed mean; `τ + λ(μ_t + A^{-1}[e^{A Δt} − I] B z)` is not that observed mean; `τ + λ(μ_t + e^{a Δt} t0_b z)` is not that observed mean; §7.2 level-change `CINT` is `κ = −a m x` with `a < 0` so `−κ / a = m x` (`−a m x` is not the dissipating Dirac, not a free `CINT`, not `TIPREDEFFECT`, and not the extra near-zero-drift latent process also named in §7.2; Eq. 3 of that setting is `(1 − e^{a Δt}) m x`, which is not `m x`, not `κ`, and not `TIPREDEFFECT`; §7.2 extra-process contribution is `a_{ηξ} x (e^{ε Δt} − e^{a Δt}) / (ε − a)` (`ε = a` is `a_{ηξ} x Δt e^{a Δt}`; identification `TDPREDEFFECT` on the extra process is 1; printed extra `DRIFT` is `−0.000001`; not `κ = −a m x`, not `(1 − e^{a Δt}) m x`, and not the dissipating Dirac `m x`; `ε ≥ 0` fails closed; Eq. 5 of that contribution is `τ + λ(μ_t + a_{ηξ} x (e^{ε Δt} − e^{a Δt}) / (ε − a)`; the extra process has `LAMBDA` 0 and is not an observed indicator; `τ + λ μ_t` is not that observed mean; `τ + λ(μ_t + m x)` is not that observed mean; the contribution is not `E(y_t)`; the evolved-plus-contribution latent mean is not `E(y_t)`; after-t0 extra-process `TDPREDEFFECT` is `a_{ηξ} x (e^{ε(t−u)} − e^{a(t−u)}) / (ε − a)` for `t0 < u < t` while `μ_t` uses `Δt`; Eq. 5 of that after-t0 contribution is `τ + λ(μ_t + a_{ηξ} x (e^{ε(t−u)} − e^{a(t−u)}) / (ε − a)`; the first-occasion extra-process observed mean is not that observed mean when `u ≠ t0`; `e^{a(t−u)} m x` is a Dirac on the original process, not this `DRIFT` drive; §7.2 `asymTIPREDEFFECT` is `-B z / a` for `a < 0` (`-B z / a` is not the coefficient `B`, not `A^{-1}[e^{A Δt} − I] B z`, not `CINT`, and not `M x`; §7.2 `addedTIPREDVAR` is `(B / a)² v`, not `TRAITVAR`, not `asymDIFFUSION`, and not `-B z / a`; Table 2 `asymCINT` is `-κ / a` for `a < 0` and is not `κ`, not `A^{-1}[e^{A Δt} − I] κ`, not `T0MEANS`, and not `-B z / a`; p. 16 stationary `T0MEANS` is `-κ / a + −B z / a` and is not free `T0MEANS`, not `asymCINT` alone, not `asymTIPREDEFFECT` alone, and not the finite-interval discrete latent mean; Eq. 5 of that constrained mean is `τ + λ(−κ / a + −B z / a)`; `τ + λ μ_0` is not that observed mean; `τ + λ(−κ / a)` is not that observed mean when `B z ≠ 0`; `τ + λ μ_t` is not that observed mean; `MANIFESTMEANS` is not `E(y_0)`; the constrained latent mean is not `E(y_0)`; stationary `T0VAR` is `trait + −q / (2 a) + (B / a)² v` (not free `T0VAR`, not `asymDIFFUSION` alone, not `TRAITVAR` alone, not `addedTIPREDVAR` alone, and not the finite-interval discrete latent variance. Eq. 5 of that constrained variance is `λ²(trait + −q / (2 a) + (B / a)² v) + θ + ψ` (JSS PDF re-opened 2026-08-22T03:20Z; form the stationary latent variance first, then `λ² p + θ + ψ`; `λ² p_0` is not that observed variance; `λ²(−q / (2 a)) + θ` is not that observed variance when `TRAITVAR` or `addedTIPREDVAR` is nonzero; `MANIFESTVAR` is not `Var(y_0)`; the constrained latent variance is not `Var(y_0)`); lagged stationary `T0VAR` is `trait + e^{a Δt}(−q / (2 a)) + (B / a)² v` (trait and `addedTIPREDVAR` do not decay; contemporaneous `T0VAR` is not that lagged map; decaying the constrained total as if it were all state is not that lagged map; Eq. 5 of that lagged covariance is `λ²(trait + e^{a Δt}(−q / (2 a)) + (B / a)² v) + ψ`; `Θ` does not enter; contemporaneous `Var(y_0)` is not that lagged observed covariance; the lagged latent covariance is not that observed covariance); later-occasion stationary `T0VAR` is `trait + e^{2 a Δt}(−q / (2 a)) + Q_Δt + (B / a)² v` (trait and `addedTIPREDVAR` do not enter `Q_Δt`; under stationarity that composition equals contemporaneous `T0VAR`; evolving the constrained total as if it were all state is not that later map; the lagged covariance omits `Q_Δt`; `Q_Δt` is not that later map; Eq. 5 of that later-occasion variance is `λ²(trait + e^{2 a Δt}(−q / (2 a)) + Q_Δt + (B / a)² v) + θ + ψ`; lagged observed covariance omits `Q_Δt` and `θ`; `MANIFESTVAR` is not `Var(y_t)`; the later-occasion latent variance is not `Var(y_t)`); predetermined later-occasion `T0VAR` is `trait + e^{2 a Δt} p_0 + Q_Δt + (B / a)² v` (free `T0VAR` `p_0` is not that later map; setting `p_0 = −q / (2 a)` recovers the stationary later-occasion map; stationary later variance uses `−q / (2 a)` in place of `p_0` and is not that later map when `p_0` is free; evolving `trait + p_0 + (B / a)² v` as if it were all state is not that later map; Eq. 5 of that predetermined later-occasion variance is `λ²(trait + e^{2 a Δt} p_0 + Q_Δt + (B / a)² v) + θ + ψ`; `MANIFESTVAR` is not `Var(y_t)`; the predetermined later-occasion latent variance is not `Var(y_t)`; stationary later observed variance is not that observed variance when `p_0` is free); predetermined lagged `T0VAR` is `trait + e^{a Δt} p_0 + (B / a)² v` (free `T0VAR` `p_0` is not that lagged map; setting `p_0 = −q / (2 a)` recovers the stationary lagged map; stationary lagged covariance uses `−q / (2 a)` in place of `p_0` and is not that lagged map when `p_0` is free; evolving `trait + p_0 + (B / a)² v` as if it were all state is not that lagged map; later-occasion variance includes `Q_Δt` and is not that lagged map; Eq. 5 of that predetermined lagged covariance is `λ²(trait + e^{a Δt} p_0 + (B / a)² v) + ψ`; `MANIFESTVAR` does not enter; the predetermined lagged latent covariance is not that observed covariance; predetermined later observed variance includes `Q_Δt` and `θ` and is not that lagged observed covariance; stationary lagged observed covariance is not that observed covariance when `p_0` is free; the predetermined first-occasion variance of §4.3 predetermined `T0VAR` is `trait + p_0 + (B / a)² v`; free `p_0` is not that map; stationary first-occasion variance uses `−q / (2 a)` in place of `p_0` and is not that map when `p_0` is free; lagged covariance decays the state and is not that map; later-occasion variance includes `Q_Δt` and is not that map; Eq. 5 of that predetermined first-occasion variance is `λ²(trait + p_0 + (B / a)² v) + θ + ψ`; `MANIFESTVAR` is not that first-occasion observed variance; the predetermined first-occasion latent variance is not that observed variance; stationary first-occasion observed variance is not that observed variance when `p_0` is free; predetermined later observed variance includes `Q_Δt` and is not that first-occasion observed variance; later-start lagged covariance of predetermined `T0VAR` is `trait + e^{a s}(e^{2 a u} p_0 + Q_u) + (B / a)² v` (Driver et al., 2017, §4.3 `startoffset`; Eq. 4; JSS PDF re-opened 2026-08-23T10:27Z; first-occasion lagged omits `e^{a s} Q_u`; later-occasion variance does not lag; stationary lagged uses `−q / (2 a)`; decaying the later total is not that map; Eq. 5 of that later-start lagged covariance is `λ²` of it plus `ψ`; `Θ` does not enter; first-occasion lagged observed omits `e^{a s} Q_u`; later observed variance includes `Q_u` and `θ`; later-start later-occasion variance of predetermined `T0VAR` is `trait + e^{2 a s}(e^{2 a u} p_0 + Q_u) + Q_s + (B / a)² v` (Driver et al., 2017, §4.3 `startoffset`; Eq. 3–4 Chapman–Kolmogorov `Q_{u+s} = e^{2 a s} Q_u + Q_s`; JSS PDF re-opened 2026-08-23T11:05Z; later-occasion variance at `u` omits `Q_s`; later-start lagged covariance omits `Q_s`; stationary later uses `−q / (2 a)`; evolving the later total as if it were all state is not that map; ignoring `startoffset` omits `e^{2 a s} Q_u`; Eq. 5 of that later-start later-occasion variance is `λ²` of it plus `θ + ψ`; `MANIFESTVAR` is not that observed variance; p. 16 `discreteDRIFTstd` is `e^{a Δt}` after strictly positive `asymDIFFUSION` `-q / (2 a)` (footnote 4; unstandardised `e^{a Δt}` is defined for growing `a ≥ 0` and for zero diffusion and is not `discreteDRIFTstd`; the §7.1 trait-plus-state autocorrelation uses `TRAITVAR` and is not `discreteDRIFTstd`; `TRAITVAR` is not the standardisation variance; p. 16 `discreteDIFFUSIONstd` is `Q_Δt / (−q / (2 a))` after strictly positive `asymDIFFUSION` `-q / (2 a)` (footnote 4; unstandardised `Q_Δt` is defined for growing `a ≥ 0` and for zero diffusion and is not `discreteDIFFUSIONstd`; the continuous standardisation `−2 a` is not `discreteDIFFUSIONstd`; `Q_Δt / (trait + p + added)` uses `TRAITVAR` and is not `discreteDIFFUSIONstd`; `TRAITVAR` is not the standardisation variance; p. 16 `DIFFUSIONstd` is `q / (−q / (2 a)) = −2 a` after strictly positive `asymDIFFUSION` `-q / (2 a)` (Driver et al., 2017, p. 16; Eq. 4; footnote 4; JSS PDF re-opened 2026-08-23T13:20Z; unstandardised `q` is defined for growing `a ≥ 0` and for zero diffusion and is not `DIFFUSIONstd`; the discrete standardisation `Q_Δt / (−q / (2 a))` depends on `Δt` and is not `DIFFUSIONstd`; `q / (trait + p + added)` uses `TRAITVAR` and is not `DIFFUSIONstd`; `TRAITVAR` is not the standardisation variance; p. 16 `DRIFTstd` is the continuous auto-effect after strictly positive `asymDIFFUSION` `-q / (2 a)` (Driver et al., 2017, p. 16; Eq. 1; footnote 4; JSS PDF re-opened 2026-08-23T13:28Z); unstandardised `a` is defined for growing `a ≥ 0` and for zero diffusion and is not `DRIFTstd`; the discrete standardisation `e^{a Δt}` depends on the event interval and is not `DRIFTstd`; `a p / (trait + p + added)` uses `TRAITVAR` and is not `DRIFTstd`; `TRAITVAR` is not the standardisation variance); p. 16 `asymTIPREDEFFECTstd` is `(-B / a) · √v / √(-q / (2 a))` after strictly positive `asymDIFFUSION` `-q / (2 a)` and strictly positive predictor variance `v` (Driver et al., 2017, p. 16; §7.2; footnote 4; JSS PDF re-opened 2026-08-23T14:25Z; unstandardised `-B / a` is defined for a zero coefficient and for zero predictor variance and is not `asymTIPREDEFFECTstd`; the finite-interval standardisation `A^{-1}[e^{A Δt} − I] B · √v / √p` depends on the event interval and is not `asymTIPREDEFFECTstd`; `(-B / a) · √v / √(trait + p + added)` uses `TRAITVAR` and is not `asymTIPREDEFFECTstd`; `TRAITVAR` is not the standardisation variance); p. 16 `TIPREDEFFECTstd` is `B · √v / √(-q / (2 a))` after strictly positive `asymDIFFUSION` `-q / (2 a)` and strictly positive predictor variance `v` (Driver et al., 2017, p. 16; §7.2; footnote 4; JSS PDF re-opened 2026-08-23T16:21Z; unstandardised `B` is defined for a zero coefficient and for zero predictor variance and is not `TIPREDEFFECTstd`; the asymptotic standardisation `(-B / a) · √v / √p` is the total change and is not `TIPREDEFFECTstd`; the finite-interval standardisation `A^{-1}[e^{A Δt} − I] B · √v / √p` depends on the event interval and is not `TIPREDEFFECTstd`; `B · √v / √(trait + p + added)` uses `TRAITVAR` and is not `TIPREDEFFECTstd`; `TRAITVAR` is not the standardisation variance); Table 3 `T0TIPREDEFFECTstd` is `t0_b · √v / √p_0` after strictly positive free `T0VAR` `p_0` and strictly positive predictor variance `v` (Driver et al., 2017, Table 3, p. 13; p. 16; footnote 4; 2017-era ctsem `summary.ctsemFit.R`; JSS PDF re-opened 2026-08-23T17:20Z; the affected variance is free `T0VAR`, not `asymDIFFUSION`; unstandardised `t0_b` is defined for a zero coefficient and for zero predictor variance and is not `T0TIPREDEFFECTstd`; `TIPREDEFFECTstd` `B · √v / √(-q / (2 a))` is the continuous coefficient and is not `T0TIPREDEFFECTstd`; `asymTIPREDEFFECTstd` `(-B / a) · √v / √p` is the total change and is not `T0TIPREDEFFECTstd`; `t0_b · √v / √(trait + p_0 + added)` uses `TRAITVAR` and is not `T0TIPREDEFFECTstd`; `TRAITVAR` is not the standardisation variance); 2017-era `addedT0TIPREDVAR` is `t0_b² v` (Driver et al., 2017, Table 3, p. 13; p. 16; §7.2; 2017-era ctsem `summary.ctsemFit.R`; JSS PDF re-opened 2026-08-23T18:20Z; `T0TIPREDEFFECT %*% TIPREDVAR %*% t(T0TIPREDEFFECT)` immediately after `T0TIPREDEFFECTstd`; form `t0_b` first, then square, then multiply by `v`; a zero coefficient or zero predictor variance is exactly zero; free `T0TIPREDEFFECT` does not require `a < 0`; `(B / a)² v` is `addedTIPREDVAR` and is not this first-occasion map; `t0_b · √v / √p_0` is `T0TIPREDEFFECTstd` and is not this variance; free `T0VAR` is not this extra TI variance; `TRAITVAR` is not this extra TI variance; Equation 5 of 2017-era `addedT0TIPREDVAR` is `λ² t0_b² v` (Driver et al., 2017, Eq. 5, p. 5; Table 3, p. 13; Table 2, p. 12; 2017-era ctsem `summary.ctsemFit.R`; JSS PDF re-opened 2026-08-23T19:10Z; form `t0_b² v` first, then `(λ extra) λ` with `θ = 0`; a zero loading or zero extra is exactly zero; `t0_b² v` is the latent extra, not the observed extra; `λ² p_0 + θ` is first-occasion observed variance, not this extra; `λ² (B / a)² v` is Eq. 5 of `addedTIPREDVAR`, not this first-occasion observed extra; `MANIFESTVAR` `θ` is not this extra; Equation 5 of §7.2 `addedTIPREDVAR` is `λ² (B / a)² v`; form `(B / a)² v` first, then `(λ extra) λ` with `θ = 0`; a zero loading or zero extra is exactly zero; lasting asymptotic extra requires `a < 0`; `(B / a)² v` is the latent extra, not the observed extra; `λ² t0_b² v` is first-occasion extra observed TI variance, not this extra; `λ² p + θ` is stationary observed variance, not this extra; `MANIFESTVAR` `θ` is not this extra; p. 16 `TDPREDEFFECTstd` is `m · √v / √(-q / (2 a))` after strictly positive `asymDIFFUSION` and strictly positive time-dependent predictor variance; unstandardised `M` is not `TDPREDEFFECTstd`; `TIPREDEFFECTstd` is not `TDPREDEFFECTstd` even when `M = B`; intercept-style `A^{-1}[e^{A Δt} − I] M · √v / √p` is not `TDPREDEFFECTstd`; `m · √v / √(trait + p + added)` uses `TRAITVAR` and is not `TDPREDEFFECTstd`; Table 3 / p. 16 `T0TDPREDEFFECTstd` is `t0_m · √v / √p_0` after strictly positive free `T0VAR` and strictly positive TD predictor variance; unstandardised `t0_m` is not `T0TDPREDEFFECTstd`; `TDPREDEFFECTstd` uses `asymDIFFUSION` and is not `T0TDPREDEFFECTstd`; `T0TIPREDEFFECTstd` is not `T0TDPREDEFFECTstd` even when `t0_m = t0_b`; `t0_m · √v / √(trait + p_0 + added)` uses `TRAITVAR` and is not `T0TDPREDEFFECTstd`; free `T0VAR` does not require `a < 0`; p. 16 `T0VARstd` is `p_0 / p_0 = 1` after strictly positive free `T0VAR` (`solve(sqrt(diag(T0VAR))) %&% T0VAR`; OpenMx `%&%` is `t(A) %*% B %*% A`; default ridge is 0); unstandardised `T0VAR` is not `T0VARstd`; `T0TDPREDEFFECTstd` is not `T0VARstd`; `addedT0TIPREDVAR` is not `T0VARstd`; p. 16 `TRAITVARstd` is `trait / trait = 1` after strictly positive `TRAITVAR` (`solve(sqrt(diag(TRAITVAR))) %&% TRAITVAR`; OpenMx `%&%` is `t(A) %*% B %*% A`; no ridge addend); unstandardised `TRAITVAR` is not `TRAITVARstd`; `T0VARstd` is not `TRAITVARstd` even when both equal 1; `addedT0TIPREDVAR` is not `TRAITVARstd`; p. 16 `MANIFESTTRAITVARstd` is `ψ / ψ = 1` after strictly positive `MANIFESTTRAITVAR` (`solve(sqrt(diag(MANIFESTTRAITVAR))) %&% MANIFESTTRAITVAR`; OpenMx `%&%` is `t(A) %*% B %*% A`; 2017-era source adds ridging; default ridge is 0); unstandardised `MANIFESTTRAITVAR` is not `MANIFESTTRAITVARstd`; `TRAITVARstd` is not `MANIFESTTRAITVARstd` even when both equal 1; `MANIFESTVAR` is not `MANIFESTTRAITVARstd`;))))), irregular already-centered residual lag, Rubin `T` on OLS loadings, and strong-gated latent means (two-observation residual variance is identically `0` and caps at strong/scalar; Putnick & Bornstein, 2016) | +| `psychometric_core` | posterior-aware structural input gates, CWC within/between OLS plus the contextual effect, event-time log-rate, unequal-interval discrete-lag remapping, constant-predictor discrete effect, time-varying-predictor discrete effect (Eq. 14), exact scalar discrete process noise (Driver et al., 2017, Eq. 3), lagged latent covariance and unconditional latent variance (Driver et al., 2017, Eq. 3–4), stationary within-subject variance (Driver et al., 2017, Eq. 4 as `Δt → ∞`; `asymDIFFUSION`), trait-plus-state variance (Driver et al., 2017, §4.3 `TRAITVAR`; not process noise), observed-indicator variance and lagged observed covariance (Driver et al., 2017, Eq. 5; Table 2 `MANIFESTVAR` is `Θ`, not `Var(y)`; `MANIFESTTRAITVAR` is not `MANIFESTVAR`; `Θ` does not enter lagged observed covariance; observed-indicator mean is `τ + λ μ`; `MANIFESTMEANS` is not `E(y)`; `CINT` is not `MANIFESTMEANS`; discrete latent mean is `exp(a Δt) μ_0 + (exp(a Δt) − 1)/a κ`; `T0MEANS` is not `μ_t`; `CINT` is not the discrete increment; evolved observed mean is `τ + λ μ_t`; `τ + λ μ_0` is not `E(y_t)`; contemporaneous `TDPREDEFFECT` impulse is `m x`, not `CINT`, not `TIPREDEFFECT`, and not Voelkle Eq. 14; Eq. 5 of that contemporaneous impulse is `τ + λ(μ_t + m x)`, and `τ + λ μ_t` is not that observed mean; time-independent `TIPREDEFFECT` increment is `A^{-1}[e^{A Δt} − I] B z`, not `CINT`, not `M x`, not Voelkle Eq. 14, and not the coefficient `B`; Eq. 5 of that increment is `τ + λ(μ_t + A^{-1}[e^{A Δt} − I] B z)`, and `τ + λ μ_t` is not that observed mean; `τ + λ(μ_t + m x)` is not that observed mean; `τ + λ(μ_t + e^{a(t−u)} m x)` is not that observed mean when `u ≠ t`; within-interval `TDPREDEFFECT` carry is `e^{A(t−u)} M x` for `t0 < u < t`, not the contemporaneous Dirac, not `CINT`, not `TIPREDEFFECT`, and not Voelkle Eq. 14; Eq. 5 of that carry is `τ + λ(μ_t + e^{a(t−u)} m x)`, and `τ + λ μ_t` is not that observed mean; `τ + λ(μ_t + m x)` is not that carried observed mean when `u ≠ t`; first-occasion `T0TIPREDEFFECT` shift is `t0_b z` and Eq. 3 first-summand carry is `e^{A Δt} t0_b z` (`T0TIPREDEFFECT` is not `TIPREDEFFECT` `B`; `t0_b z` is not `A^{-1}[e^{A Δt} − I] B z`; `e^{A Δt} t0_b z` is not `t0_b z`; Eq. 5 of that carry is `τ + λ(μ_t + e^{a Δt} t0_b z)`, and `τ + λ μ_t` is not that observed mean; `τ + λ(μ_t + A^{-1}[e^{A Δt} − I] B z)` is not that observed mean), first-occasion `T0TDPREDEFFECT` shift is `t0_m x0` and Eq. 3 first-summand carry is `e^{A Δt} t0_m x0` (`T0TDPREDEFFECT` is not `TDPREDEFFECT` `M`; `t0_m x0` is not `M x`; `e^{A Δt} t0_m x0` is not `t0_m x0`; `e^{A Δt} t0_m x0` is not `e^{A(t−u)} M x` for `t0 < u < t`; `t0_m x0` is not `t0_b z`; an impulse at `u ≤ t0` that used `M` is already in `η(t0)` as `TDPREDEFFECT`, not as `T0TDPREDEFFECT`; Eq. 5 of that carry is `τ + λ(μ_t + e^{a Δt} t0_m x0)`, and `τ + λ μ_t` is not that observed mean; `τ + λ(μ_t + A^{-1}[e^{A Δt} − I] B z)` is not that observed mean; `τ + λ(μ_t + e^{a Δt} t0_b z)` is not that observed mean; §7.2 level-change `CINT` is `κ = −a m x` with `a < 0` so `−κ / a = m x` (`−a m x` is not the dissipating Dirac, not a free `CINT`, not `TIPREDEFFECT`, and not the extra near-zero-drift latent process also named in §7.2; Eq. 3 of that setting is `(1 − e^{a Δt}) m x`, which is not `m x`, not `κ`, and not `TIPREDEFFECT`; §7.2 extra-process contribution is `a_{ηξ} x (e^{ε Δt} − e^{a Δt}) / (ε − a)` (`ε = a` is `a_{ηξ} x Δt e^{a Δt}`; identification `TDPREDEFFECT` on the extra process is 1; printed extra `DRIFT` is `−0.000001`; not `κ = −a m x`, not `(1 − e^{a Δt}) m x`, and not the dissipating Dirac `m x`; `ε ≥ 0` fails closed; Eq. 5 of that contribution is `τ + λ(μ_t + a_{ηξ} x (e^{ε Δt} − e^{a Δt}) / (ε − a)`; the extra process has `LAMBDA` 0 and is not an observed indicator; `τ + λ μ_t` is not that observed mean; `τ + λ(μ_t + m x)` is not that observed mean; the contribution is not `E(y_t)`; the evolved-plus-contribution latent mean is not `E(y_t)`; after-t0 extra-process `TDPREDEFFECT` is `a_{ηξ} x (e^{ε(t−u)} − e^{a(t−u)}) / (ε − a)` for `t0 < u < t` while `μ_t` uses `Δt`; Eq. 5 of that after-t0 contribution is `τ + λ(μ_t + a_{ηξ} x (e^{ε(t−u)} − e^{a(t−u)}) / (ε − a)`; the first-occasion extra-process observed mean is not that observed mean when `u ≠ t0`; `e^{a(t−u)} m x` is a Dirac on the original process, not this `DRIFT` drive; §7.2 `asymTIPREDEFFECT` is `-B z / a` for `a < 0` (`-B z / a` is not the coefficient `B`, not `A^{-1}[e^{A Δt} − I] B z`, not `CINT`, and not `M x`; §7.2 `addedTIPREDVAR` is `(B / a)² v`, not `TRAITVAR`, not `asymDIFFUSION`, and not `-B z / a`; Table 2 `asymCINT` is `-κ / a` for `a < 0` and is not `κ`, not `A^{-1}[e^{A Δt} − I] κ`, not `T0MEANS`, and not `-B z / a`; p. 16 stationary `T0MEANS` is `-κ / a + −B z / a` and is not free `T0MEANS`, not `asymCINT` alone, not `asymTIPREDEFFECT` alone, and not the finite-interval discrete latent mean; Eq. 5 of that constrained mean is `τ + λ(−κ / a + −B z / a)`; `τ + λ μ_0` is not that observed mean; `τ + λ(−κ / a)` is not that observed mean when `B z ≠ 0`; `τ + λ μ_t` is not that observed mean; `MANIFESTMEANS` is not `E(y_0)`; the constrained latent mean is not `E(y_0)`; stationary `T0VAR` is `trait + −q / (2 a) + (B / a)² v` (not free `T0VAR`, not `asymDIFFUSION` alone, not `TRAITVAR` alone, not `addedTIPREDVAR` alone, and not the finite-interval discrete latent variance. Eq. 5 of that constrained variance is `λ²(trait + −q / (2 a) + (B / a)² v) + θ + ψ` (JSS PDF re-opened 2026-08-22T03:20Z; form the stationary latent variance first, then `λ² p + θ + ψ`; `λ² p_0` is not that observed variance; `λ²(−q / (2 a)) + θ` is not that observed variance when `TRAITVAR` or `addedTIPREDVAR` is nonzero; `MANIFESTVAR` is not `Var(y_0)`; the constrained latent variance is not `Var(y_0)`); lagged stationary `T0VAR` is `trait + e^{a Δt}(−q / (2 a)) + (B / a)² v` (trait and `addedTIPREDVAR` do not decay; contemporaneous `T0VAR` is not that lagged map; decaying the constrained total as if it were all state is not that lagged map; Eq. 5 of that lagged covariance is `λ²(trait + e^{a Δt}(−q / (2 a)) + (B / a)² v) + ψ`; `Θ` does not enter; contemporaneous `Var(y_0)` is not that lagged observed covariance; the lagged latent covariance is not that observed covariance); later-occasion stationary `T0VAR` is `trait + e^{2 a Δt}(−q / (2 a)) + Q_Δt + (B / a)² v` (trait and `addedTIPREDVAR` do not enter `Q_Δt`; under stationarity that composition equals contemporaneous `T0VAR`; evolving the constrained total as if it were all state is not that later map; the lagged covariance omits `Q_Δt`; `Q_Δt` is not that later map; Eq. 5 of that later-occasion variance is `λ²(trait + e^{2 a Δt}(−q / (2 a)) + Q_Δt + (B / a)² v) + θ + ψ`; lagged observed covariance omits `Q_Δt` and `θ`; `MANIFESTVAR` is not `Var(y_t)`; the later-occasion latent variance is not `Var(y_t)`); predetermined later-occasion `T0VAR` is `trait + e^{2 a Δt} p_0 + Q_Δt + (B / a)² v` (free `T0VAR` `p_0` is not that later map; setting `p_0 = −q / (2 a)` recovers the stationary later-occasion map; stationary later variance uses `−q / (2 a)` in place of `p_0` and is not that later map when `p_0` is free; evolving `trait + p_0 + (B / a)² v` as if it were all state is not that later map; Eq. 5 of that predetermined later-occasion variance is `λ²(trait + e^{2 a Δt} p_0 + Q_Δt + (B / a)² v) + θ + ψ`; `MANIFESTVAR` is not `Var(y_t)`; the predetermined later-occasion latent variance is not `Var(y_t)`; stationary later observed variance is not that observed variance when `p_0` is free); predetermined lagged `T0VAR` is `trait + e^{a Δt} p_0 + (B / a)² v` (free `T0VAR` `p_0` is not that lagged map; setting `p_0 = −q / (2 a)` recovers the stationary lagged map; stationary lagged covariance uses `−q / (2 a)` in place of `p_0` and is not that lagged map when `p_0` is free; evolving `trait + p_0 + (B / a)² v` as if it were all state is not that lagged map; later-occasion variance includes `Q_Δt` and is not that lagged map; Eq. 5 of that predetermined lagged covariance is `λ²(trait + e^{a Δt} p_0 + (B / a)² v) + ψ`; `MANIFESTVAR` does not enter; the predetermined lagged latent covariance is not that observed covariance; predetermined later observed variance includes `Q_Δt` and `θ` and is not that lagged observed covariance; stationary lagged observed covariance is not that observed covariance when `p_0` is free; the predetermined first-occasion variance of §4.3 predetermined `T0VAR` is `trait + p_0 + (B / a)² v`; free `p_0` is not that map; stationary first-occasion variance uses `−q / (2 a)` in place of `p_0` and is not that map when `p_0` is free; lagged covariance decays the state and is not that map; later-occasion variance includes `Q_Δt` and is not that map; Eq. 5 of that predetermined first-occasion variance is `λ²(trait + p_0 + (B / a)² v) + θ + ψ`; `MANIFESTVAR` is not that first-occasion observed variance; the predetermined first-occasion latent variance is not that observed variance; stationary first-occasion observed variance is not that observed variance when `p_0` is free; predetermined later observed variance includes `Q_Δt` and is not that first-occasion observed variance; later-start lagged covariance of predetermined `T0VAR` is `trait + e^{a s}(e^{2 a u} p_0 + Q_u) + (B / a)² v` (Driver et al., 2017, §4.3 `startoffset`; Eq. 4; JSS PDF re-opened 2026-08-23T10:27Z; first-occasion lagged omits `e^{a s} Q_u`; later-occasion variance does not lag; stationary lagged uses `−q / (2 a)`; decaying the later total is not that map; Eq. 5 of that later-start lagged covariance is `λ²` of it plus `ψ`; `Θ` does not enter; first-occasion lagged observed omits `e^{a s} Q_u`; later observed variance includes `Q_u` and `θ`; later-start later-occasion variance of predetermined `T0VAR` is `trait + e^{2 a s}(e^{2 a u} p_0 + Q_u) + Q_s + (B / a)² v` (Driver et al., 2017, §4.3 `startoffset`; Eq. 3–4 Chapman–Kolmogorov `Q_{u+s} = e^{2 a s} Q_u + Q_s`; JSS PDF re-opened 2026-08-23T11:05Z; later-occasion variance at `u` omits `Q_s`; later-start lagged covariance omits `Q_s`; stationary later uses `−q / (2 a)`; evolving the later total as if it were all state is not that map; ignoring `startoffset` omits `e^{2 a s} Q_u`; Eq. 5 of that later-start later-occasion variance is `λ²` of it plus `θ + ψ`; `MANIFESTVAR` is not that observed variance; p. 16 `discreteDRIFTstd` is `e^{a Δt}` after strictly positive `asymDIFFUSION` `-q / (2 a)` (footnote 4; unstandardised `e^{a Δt}` is defined for growing `a ≥ 0` and for zero diffusion and is not `discreteDRIFTstd`; the §7.1 trait-plus-state autocorrelation uses `TRAITVAR` and is not `discreteDRIFTstd`; `TRAITVAR` is not the standardisation variance; p. 16 `discreteDIFFUSIONstd` is `Q_Δt / (−q / (2 a))` after strictly positive `asymDIFFUSION` `-q / (2 a)` (footnote 4; unstandardised `Q_Δt` is defined for growing `a ≥ 0` and for zero diffusion and is not `discreteDIFFUSIONstd`; the continuous standardisation `−2 a` is not `discreteDIFFUSIONstd`; `Q_Δt / (trait + p + added)` uses `TRAITVAR` and is not `discreteDIFFUSIONstd`; `TRAITVAR` is not the standardisation variance; p. 16 `DIFFUSIONstd` is `q / (−q / (2 a)) = −2 a` after strictly positive `asymDIFFUSION` `-q / (2 a)` (Driver et al., 2017, p. 16; Eq. 4; footnote 4; JSS PDF re-opened 2026-08-23T13:20Z; unstandardised `q` is defined for growing `a ≥ 0` and for zero diffusion and is not `DIFFUSIONstd`; the discrete standardisation `Q_Δt / (−q / (2 a))` depends on `Δt` and is not `DIFFUSIONstd`; `q / (trait + p + added)` uses `TRAITVAR` and is not `DIFFUSIONstd`; `TRAITVAR` is not the standardisation variance; p. 16 `DRIFTstd` is the continuous auto-effect after strictly positive `asymDIFFUSION` `-q / (2 a)` (Driver et al., 2017, p. 16; Eq. 1; footnote 4; JSS PDF re-opened 2026-08-23T13:28Z); unstandardised `a` is defined for growing `a ≥ 0` and for zero diffusion and is not `DRIFTstd`; the discrete standardisation `e^{a Δt}` depends on the event interval and is not `DRIFTstd`; `a p / (trait + p + added)` uses `TRAITVAR` and is not `DRIFTstd`; `TRAITVAR` is not the standardisation variance); p. 16 `asymTIPREDEFFECTstd` is `(-B / a) · √v / √(-q / (2 a))` after strictly positive `asymDIFFUSION` `-q / (2 a)` and strictly positive predictor variance `v` (Driver et al., 2017, p. 16; §7.2; footnote 4; JSS PDF re-opened 2026-08-23T14:25Z; unstandardised `-B / a` is defined for a zero coefficient and for zero predictor variance and is not `asymTIPREDEFFECTstd`; the finite-interval standardisation `A^{-1}[e^{A Δt} − I] B · √v / √p` depends on the event interval and is not `asymTIPREDEFFECTstd`; `(-B / a) · √v / √(trait + p + added)` uses `TRAITVAR` and is not `asymTIPREDEFFECTstd`; `TRAITVAR` is not the standardisation variance); p. 16 `TIPREDEFFECTstd` is `B · √v / √(-q / (2 a))` after strictly positive `asymDIFFUSION` `-q / (2 a)` and strictly positive predictor variance `v` (Driver et al., 2017, p. 16; §7.2; footnote 4; JSS PDF re-opened 2026-08-23T16:21Z; unstandardised `B` is defined for a zero coefficient and for zero predictor variance and is not `TIPREDEFFECTstd`; the asymptotic standardisation `(-B / a) · √v / √p` is the total change and is not `TIPREDEFFECTstd`; the finite-interval standardisation `A^{-1}[e^{A Δt} − I] B · √v / √p` depends on the event interval and is not `TIPREDEFFECTstd`; `B · √v / √(trait + p + added)` uses `TRAITVAR` and is not `TIPREDEFFECTstd`; `TRAITVAR` is not the standardisation variance); Table 3 `T0TIPREDEFFECTstd` is `t0_b · √v / √p_0` after strictly positive free `T0VAR` `p_0` and strictly positive predictor variance `v` (Driver et al., 2017, Table 3, p. 13; p. 16; footnote 4; 2017-era ctsem `summary.ctsemFit.R`; JSS PDF re-opened 2026-08-23T17:20Z; the affected variance is free `T0VAR`, not `asymDIFFUSION`; unstandardised `t0_b` is defined for a zero coefficient and for zero predictor variance and is not `T0TIPREDEFFECTstd`; `TIPREDEFFECTstd` `B · √v / √(-q / (2 a))` is the continuous coefficient and is not `T0TIPREDEFFECTstd`; `asymTIPREDEFFECTstd` `(-B / a) · √v / √p` is the total change and is not `T0TIPREDEFFECTstd`; `t0_b · √v / √(trait + p_0 + added)` uses `TRAITVAR` and is not `T0TIPREDEFFECTstd`; `TRAITVAR` is not the standardisation variance); 2017-era `addedT0TIPREDVAR` is `t0_b² v` (Driver et al., 2017, Table 3, p. 13; p. 16; §7.2; 2017-era ctsem `summary.ctsemFit.R`; JSS PDF re-opened 2026-08-23T18:20Z; `T0TIPREDEFFECT %*% TIPREDVAR %*% t(T0TIPREDEFFECT)` immediately after `T0TIPREDEFFECTstd`; form `t0_b` first, then square, then multiply by `v`; a zero coefficient or zero predictor variance is exactly zero; free `T0TIPREDEFFECT` does not require `a < 0`; `(B / a)² v` is `addedTIPREDVAR` and is not this first-occasion map; `t0_b · √v / √p_0` is `T0TIPREDEFFECTstd` and is not this variance; free `T0VAR` is not this extra TI variance; `TRAITVAR` is not this extra TI variance; Equation 5 of 2017-era `addedT0TIPREDVAR` is `λ² t0_b² v` (Driver et al., 2017, Eq. 5, p. 5; Table 3, p. 13; Table 2, p. 12; 2017-era ctsem `summary.ctsemFit.R`; JSS PDF re-opened 2026-08-23T19:10Z; form `t0_b² v` first, then `(λ extra) λ` with `θ = 0`; a zero loading or zero extra is exactly zero; `t0_b² v` is the latent extra, not the observed extra; `λ² p_0 + θ` is first-occasion observed variance, not this extra; `λ² (B / a)² v` is Eq. 5 of `addedTIPREDVAR`, not this first-occasion observed extra; `MANIFESTVAR` `θ` is not this extra; Equation 5 of §7.2 `addedTIPREDVAR` is `λ² (B / a)² v`; form `(B / a)² v` first, then `(λ extra) λ` with `θ = 0`; a zero loading or zero extra is exactly zero; lasting asymptotic extra requires `a < 0`; `(B / a)² v` is the latent extra, not the observed extra; `λ² t0_b² v` is first-occasion extra observed TI variance, not this extra; `λ² p + θ` is stationary observed variance, not this extra; `MANIFESTVAR` `θ` is not this extra; p. 16 `TDPREDEFFECTstd` is `m · √v / √(-q / (2 a))` after strictly positive `asymDIFFUSION` and strictly positive time-dependent predictor variance; unstandardised `M` is not `TDPREDEFFECTstd`; `TIPREDEFFECTstd` is not `TDPREDEFFECTstd` even when `M = B`; intercept-style `A^{-1}[e^{A Δt} − I] M · √v / √p` is not `TDPREDEFFECTstd`; `m · √v / √(trait + p + added)` uses `TRAITVAR` and is not `TDPREDEFFECTstd`; Table 3 / p. 16 `T0TDPREDEFFECTstd` is `t0_m · √v / √p_0` after strictly positive free `T0VAR` and strictly positive TD predictor variance; unstandardised `t0_m` is not `T0TDPREDEFFECTstd`; `TDPREDEFFECTstd` uses `asymDIFFUSION` and is not `T0TDPREDEFFECTstd`; `T0TIPREDEFFECTstd` is not `T0TDPREDEFFECTstd` even when `t0_m = t0_b`; `t0_m · √v / √(trait + p_0 + added)` uses `TRAITVAR` and is not `T0TDPREDEFFECTstd`; free `T0VAR` does not require `a < 0`; p. 16 `T0VARstd` is `p_0 / p_0 = 1` after strictly positive free `T0VAR` (`solve(sqrt(diag(T0VAR))) %&% T0VAR`; OpenMx `%&%` is `t(A) %*% B %*% A`; default ridge is 0); unstandardised `T0VAR` is not `T0VARstd`; `T0TDPREDEFFECTstd` is not `T0VARstd`; `addedT0TIPREDVAR` is not `T0VARstd`; p. 16 `TRAITVARstd` is `trait / trait = 1` after strictly positive `TRAITVAR` (`solve(sqrt(diag(TRAITVAR))) %&% TRAITVAR`; OpenMx `%&%` is `t(A) %*% B %*% A`; no ridge addend); unstandardised `TRAITVAR` is not `TRAITVARstd`; `T0VARstd` is not `TRAITVARstd` even when both equal 1; `addedT0TIPREDVAR` is not `TRAITVARstd`; p. 16 `MANIFESTTRAITVARstd` is `ψ / ψ = 1` after strictly positive `MANIFESTTRAITVAR` (`solve(sqrt(diag(MANIFESTTRAITVAR))) %&% MANIFESTTRAITVAR`; OpenMx `%&%` is `t(A) %*% B %*% A`; 2017-era source adds ridging; default ridge is 0); unstandardised `MANIFESTTRAITVAR` is not `MANIFESTTRAITVARstd`; `TRAITVARstd` is not `MANIFESTTRAITVARstd` even when both equal 1; `MANIFESTVAR` is not `MANIFESTTRAITVARstd`; p. 16 `MANIFESTVARstd` is `θ / θ = 1` after strictly positive `MANIFESTVAR` (`solve(sqrt(diag(MANIFESTVAR))) %&% MANIFESTVAR`; OpenMx `%&%` is `t(A) %*% B %*% A`; 2017-era source adds ridging; default ridge is 0; 2017-era `dimnames` assignment to `latentNames` is a source bug); unstandardised `MANIFESTVAR` is not `MANIFESTVARstd`; `MANIFESTTRAITVARstd` is not `MANIFESTVARstd` even when both equal 1; Equation 5 `Var(y)` is not `MANIFESTVARstd`;))))), irregular already-centered residual lag, Rubin `T` on OLS loadings, and strong-gated latent means (two-observation residual variance is identically `0` and caps at strong/scalar; Putnick & Bornstein, 2016) | No crate exposes placeholder production behavior in Task 1. This prevents an empty façade from becoming a de facto public API before its invariants and tests diff --git a/CHANGELOG.md b/CHANGELOG.md index 302f1e06..16eb2a30 100644 --- a/CHANGELOG.md +++ b/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, Table 2, p. 12 `MANIFESTVAR`; Eq. 5, p. 5; p. 16 `MANIFESTVARstd`; footnote 4; 2017-era ctsem `summary.ctsemFit.R`; JSS PDF re-opened 2026-08-23T22:40Z from https://www.jstatsoft.org/index.php/jss/article/download/v077i05/1104) scalar standardised measurement-error variance. Table 2 names `MANIFESTVAR` `Θ` the residual covariance of the indicators. Equation 5 writes `ζ ~ N(0, Θ)`. Page 16 prints standardised matrices with the suffix `std` when appropriate. The 2017-era `summary.ctsemFit.R` forms `MANIFESTVARstd` whenever `verbose = TRUE`, as `solve(sqrt(diag(MANIFESTVAR) + ridging)) %&% MANIFESTVAR`. OpenMx `%&%` is the quadratic form `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 2017-era source assigns `dimnames(MANIFESTVARstd)` to `latentNames`; the matrix is `n.manifest × n.manifest`. That assignment is a source bug and is not this exact map. The scalar correlation is `θ / θ = 1` after strictly positive `MANIFESTVAR`. Form strictly positive `θ` first, then `1 / √θ`, then `(1 / √θ) θ (1 / √θ)`. Unstandardised `MANIFESTVAR` is defined for a zero residual; standardised `MANIFESTVAR` is not. Zero `θ` makes `solve(sqrt(0))` fail in the 2017-era source and fails closed here. Unlike `TRAITVAR` / `MANIFESTTRAITVAR`, that source does not skip forming `MANIFESTVARstd` when `θ = 0`. Measurement-error variance is an event-time structural quantity, so a non-event clock fails closed. `MANIFESTVAR` does not require stable `a < 0`. Distinct positive `θ` recover the same 1. `MANIFESTTRAITVARstd` `ψ / ψ = 1` recovers the same number and remains a distinct named quantity. Equation 5 `λ² Var(η) + θ` is `Var(y)`, not this correlation. Still not a Kalman filter, not a matrix `expm`, not ESEM estimation, not DSEM, and not ctsem estimation. Meredith (1993) remains unread (Unpaywall 2026-08-23T22:21Z: `is_oa: false`; title *Measurement Invariance, Factor Analysis and Factorial Invariance*). Mislevy (1991, *Psychometrika, 56*, 177–196) remains unread (Unpaywall 2026-08-23T22:21Z: `is_oa: false`; title *Randomization-Based Inference about Latent Variables from Complex Samples*). - `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 2026-08-23T22:28Z from https://www.jstatsoft.org/index.php/jss/article/download/v077i05/1104) scalar standardised manifest-trait variance. 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. 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 2017-era `summary.ctsemFit.R` forms `MANIFESTTRAITVARstd` only when `MANIFESTTRAITVAR != 0`, as `solve(sqrt(diag(MANIFESTTRAITVAR) + ridging)) %&% MANIFESTTRAITVAR` when `verbose = TRUE`. OpenMx `%&%` is the quadratic form `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. `TRAITVARstd` `trait / trait = 1` recovers the same number and remains a distinct named quantity. `MANIFESTVAR` `θ` is measurement error, not this correlation. Still not a Kalman filter, not a matrix `expm`, not ESEM estimation, not DSEM, and not ctsem estimation. Meredith (1993) remains unread (Unpaywall 2026-08-23T22:21Z: `is_oa: false`; title *Measurement Invariance, Factor Analysis and Factorial Invariance*). Mislevy (1991, *Psychometrika, 56*, 177–196) remains unread (Unpaywall 2026-08-23T22:21Z: `is_oa: false`; title *Randomization-Based Inference about Latent Variables from Complex Samples*). - `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 2026-08-23T22:21Z from https://www.jstatsoft.org/index.php/jss/article/download/v077i05/1104) scalar standardised trait variance. 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 2017-era `summary.ctsemFit.R` forms `TRAITVARstd` only when `TRAITVAR != 0`, as `solve(sqrt(diag(TRAITVAR))) %&% TRAITVAR` when `verbose = TRUE`. OpenMx `%&%` is the quadratic form `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. `T0VARstd` `p_0 / p_0 = 1` recovers the same number and remains a distinct named quantity. `addedT0TIPREDVAR` `t0_b² v` is extra first-occasion TI variance, not this correlation. Still not a Kalman filter, not a matrix `expm`, not ESEM estimation, not DSEM, and not ctsem estimation. Meredith (1993) remains unread (Unpaywall 2026-08-23T22:21Z: `is_oa: false`; title *Measurement Invariance, Factor Analysis and Factorial Invariance*). Mislevy (1991, *Psychometrika, 56*, 177–196) remains unread (Unpaywall 2026-08-23T22:21Z: `is_oa: false`; title *Randomization-Based Inference about Latent Variables from Complex Samples*). - `psychometric_core` recovers the Driver, Oud, and Voelkle (2017, Table 2, p. 12 `T0VAR`; p. 16 `T0VARstd`; footnote 4; 2017-era ctsem `summary.ctsemFit.R`; JSS PDF re-opened 2026-08-23T22:06Z from https://www.jstatsoft.org/index.php/jss/article/download/v077i05/1104) scalar standardised first-occasion latent variance. Table 2 names `T0VAR` the latent process initial variance/covariance. Page 16 prints standardised matrices with the suffix `std` when appropriate. The 2017-era `summary.ctsemFit.R` forms `T0VARstd` as `solve(sqrt(diag(T0VAR))) %&% T0VAR` when `verbose = TRUE`. OpenMx `%&%` is the quadratic form `t(A) %*% B %*% A`. 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 `p_0 / p_0 = 1` after strictly positive free `T0VAR`. Form strictly positive `p_0` first, then `1 / √p_0`, then `(1 / √p_0) p_0 (1 / √p_0)`. Unstandardised `T0VAR` is defined for a zero first-occasion variance; standardised `T0VAR` is not. Zero `p_0` has no positive SD and fails closed. `T0` is an event-time occasion, so a non-event clock fails closed. Free `T0VAR` does not require stable `a < 0`. Distinct positive `p_0` recover the same 1. `T0TDPREDEFFECTstd` `t0_m · √v / √p_0` depends on `p_0` and is not this correlation. `addedT0TIPREDVAR` `t0_b² v` is extra TI variance, not this correlation. `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 2026-08-23T22:06Z: `is_oa: false`; title *Measurement Invariance, Factor Analysis and Factorial Invariance*). Mislevy (1991, *Psychometrika, 56*, 177–196) remains unread (Unpaywall 2026-08-23T22:06Z: `is_oa: false`; title *Randomization-Based Inference about Latent Variables from Complex Samples*). diff --git a/CLAUDE.md b/CLAUDE.md index 5b37dda3..23457b9e 100644 --- a/CLAUDE.md +++ b/CLAUDE.md @@ -15,7 +15,7 @@ Read and follow `AGENTS.md` before changing this repository. The repository-wide - Do not remove repeated report language with global stopword lists or use TF-IDF/BM25 as inferential weights. Model template, section, copied-text, style, modality, and corpus-background sources explicitly. - Do not treat raw topic proportions as ordinary Euclidean indicators. Use logistic-normal coordinates or valid log-ratio coordinates and propagate posterior uncertainty into ESEM/DSEM. - Do not treat metric/weak invariance as a latent-mean license. Strong (equal loading and intercept) or strict is required; `#84` `metric` licenses shared metric meaning only. Putnick and Bornstein (2016, PMC5145197 opened 2026-08-19T22:15Z) require scalar invariance before latent-mean comparison; residual invariance is not a prerequisite. Two-observation series have no residual degrees of freedom (`ordinary_least_squares_fit` returns residual variance `0`) and cap at strong/scalar; they still license means. This is two-group OLS, not MGCFA. Meredith (1993) names remain unread labels. -- Do not use the difference quotient as a continuous-time rate. The scalar map is `a = ln(φ) / Δt` on event time. Discrete lags from unequal event intervals are not one coefficient; remap them through that log-rate. Binary64 `exp(a Δt) = 0` is not a discrete lag. A constant predictor's discrete effect is Voelkle et al. (2012, Eq. 12), evaluated as `a_yx (expm1(z) / a_xx)` with `z = a_xx Δt` so a finite result is not lost when `z` overflows to `-∞` or when `a_yx Δt` overflows. When `expm1(z)` overflows at a finite `z`, rewrite in log space; a zero continuous effect is exactly zero; an overflowing `a_yx/a_xx` rewrite term fails closed. The first-order product is the underflow limit of that equation, not the general constant-predictor discrete effect. A time-varying predictor whose sampling interval equals its constancy interval uses Voelkle et al. (2012, Eq. 14): `b* = a_yx Δt`. Unmatched intervals fail closed (Oud & Jansen, 2000, unread). Discrete process noise is Driver et al. (2017, Eq. 3): `Q_Δt = 0.5 q (expm1(z) / a)` with `z = 2 (a Δt)` and `q = G G⊤ ≥ 0`; do not form `2 a` first; `a = 0` and `z → 0` recover `q Δt`; a zero diffusion is exactly zero; an overflowing rewrite scale `0.5 q / a` fails closed; this is not a Kalman filter. `Q_Δt` is `cov(η_t | η_{t-1})`, not `Var(η_t)`. The lagged covariance is `exp(a Δt) p` and the unconditional variance is `exp(2 a Δt) p + Q_Δt` (Driver et al., 2017, Eq. 3–4, pp. 4–5; JSS has no numbered §2.2). A zero diffusion whose `2 (a Δt)` overflows to `+∞` is not a finite `Var(η_t)`. The stationary within-subject variance is the `Δt → ∞` limit of Eq. 4: `-q / (2 a)` for stable `a < 0` (JSS p. 16 `asymDIFFUSION`; §4.3). When `2 a` is finite, form `q / -(2 a)` so `q / a` overflow does not lose a finite result (`q = MAX`, `a = -0.75` → `MAX / 1.5`). When `2 a` overflows, form `(q / a) * -0.5`. Do not form `0.5 q` first (`q = from_bits(1)` underflows). `a ≥ 0` has no finite stationary variance. Finite-interval `Q_Δt` is not that limit. Trait-plus-state variance is `trait + state` and lagged covariance is `trait + exp(a Δt) p` (Driver et al., 2017, §4.3, p. 9). Trait variance is not process noise and not `asymDIFFUSION`. Evolving the summed variance as if it were all state is not that map. This is not RI-CLPM. Observed-indicator variance is `λ² Var(η) + θ` when `MANIFESTTRAITVAR` is zero and `λ² Var(η) + θ + ψ` otherwise (Driver et al., 2017, Eq. 5, p. 5; Table 2, p. 12). Lagged observed covariance is `λ² cov(η_t, η_{t-1}) + ψ`; `MANIFESTVAR` does not enter. Observed-indicator mean is `τ + λ μ` (Driver et al., 2017, Eq. 5; Table 2, p. 12). `MANIFESTMEANS` is `τ`, not `E(y)`. `E(η)` is not `E(y)`. `CINT` is not `MANIFESTMEANS`. `T0MEANS` is not `E(y)`. The discrete latent mean is `μ_t = exp(a Δt) μ_0 + (exp(a Δt) − 1)/a κ` (Driver et al., 2017, Eq. 3, p. 4; Table 2, p. 12). `T0MEANS` is not `μ_t`. `CINT` is not that discrete increment. A zero drift is `κ Δt`. Underflow of `exp(a Δt)` to `+0` drops the carried `T0MEANS` and keeps `−κ / a`. The evolved observed mean is `τ + λ μ_t` (Driver et al., 2017, Eq. 5 of that Eq. 3 map). The first-occasion map `τ + λ μ_0` is not `E(y_t)`. `μ_t` is not `E(y_t)`. The contemporaneous time-dependent predictor impulse is `m x` (Driver et al., 2017, Eq. 3 fourth summand; Table 2 `TDPREDEFFECT` is `M`). Form `μ_t` first, then add `m x`. `TDPREDEFFECT` is not `CINT`. `M x` is not `A^{-1}[e^{A Δt} − I] B z` and is not Voelkle et al. (2012, Eq. 14). The §7.2 level-change form is not that impulse. The observed mean of that contemporaneous impulse is `τ + λ(μ_t + m x)` (Driver et al., 2017, Eq. 5 of the Eq. 3 fourth-summand composition). The evolved map `τ + λ μ_t` is not that observed mean. The carry map `τ + λ(μ_t + e^{a(t−u)} m x)` is not that observed mean when `u ≠ t`. The evolved-plus-impulse latent mean is not `E(y_t)`. The time-independent predictor increment is `A^{-1}[e^{A Δt} − I] B z` (Driver et al., 2017, Eq. 3 second summand; Table 2 `TIPREDEFFECT` is `B`). Form `B z` first, then the discrete intercept map. A zero drift is `B z Δt`. `TIPREDEFFECT` is `B`, not that discrete increment. `A^{-1}[e^{A Δt} − I] B z` is not `CINT`, not `M x`, and not Voelkle et al. (2012, Eq. 14). The observed mean of that increment is `τ + λ(μ_t + A^{-1}[e^{A Δt} − I] B z)` (Driver et al., 2017, Eq. 5 of the Eq. 3 printed addend after the `T0MEANS` carry and the `CINT` increment). The evolved map `τ + λ μ_t` is not that observed mean. The contemporaneous map `τ + λ(μ_t + m x)` is not that observed mean. The carry map `τ + λ(μ_t + e^{a(t−u)} m x)` is not that observed mean when `u ≠ t`. The evolved-plus-increment latent mean is not `E(y_t)`. The within-interval time-dependent impulse carry is `e^{A(t−u)} M x` for `t0 < u < t` (Driver et al., 2017, Eq. 1–2 Green-function integral of Eq. 2; §7.2 dissipation). Form `m x` first, then `e^{a(t−u)} m x`. A zero drift is `m x` with no dissipation. Underflow of `e^{a(t−u)}` to `+0` is vanishing dissipation and is kept. `e^{A(t−u)} M x` is not the contemporaneous Dirac, not `CINT`, not `TIPREDEFFECT`, and not Voelkle et al. (2012, Eq. 14). An impulse at `u = t` is the contemporaneous map. An impulse at `u ≤ t0` is already in `η(t0)`. The observed mean of that carry is `τ + λ(μ_t + e^{a(t−u)} m x)` (Driver et al., 2017, Eq. 5 of the Eq. 1–2 carried latent mean). The evolved map `τ + λ μ_t` is not that observed mean. The contemporaneous map `τ + λ(μ_t + m x)` is not that observed mean when `u ≠ t`. `MANIFESTMEANS` is not `E(y_t)`. The carried latent mean is not `E(y_t)`. The first-occasion time-independent predictor shift is `t0_b z` (Driver et al., 2017, Table 3 `T0TIPREDEFFECT`; Eq. 3 first summand). Form `t0_b z` first, then `e^{a Δt} t0_b z`. Form `μ_t` first, then add that carry. A zero drift is `t0_b z`. Underflow of `e^{a Δt}` to `+0` is a vanishing carry of the first-occasion shift and is kept. `t0_b z` is not `A^{-1}[e^{A Δt} − I] B z`, not `CINT`, and not `M x`. `e^{A Δt} t0_b z` is not `t0_b z`. `T0TIPREDEFFECT` is the coefficient, not the shift. The observed mean of that first-occasion carry is `τ + λ(μ_t + e^{a Δt} t0_b z)` (Driver et al., 2017, Eq. 5 of the Table 3 / Eq. 3 first-summand composition). The evolved map `τ + λ μ_t` is not that observed mean. The process-increment map `τ + λ(μ_t + A^{-1}[e^{A Δt} − I] B z)` is not that observed mean. The contemporaneous map `τ + λ(μ_t + m x)` is not that observed mean. The impulse-carry map `τ + λ(μ_t + e^{a(t−u)} m x)` is not that observed mean when `u ≠ t0`. The evolved-plus-carry latent mean is not `E(y_t)`. The first-occasion time-dependent predictor shift is `t0_m x0` (Driver et al., 2017, Table 3 `T0TDPREDEFFECT`; Eq. 3 first summand; JSS PDF re-opened 2026-08-20T19:10Z). Form `t0_m x0` first, then `e^{a Δt} t0_m x0`. Form `μ_t` first, then add that carry. A zero drift is `t0_m x0`. Underflow of `e^{a Δt}` to `+0` is a vanishing carry of the first-occasion shift and is kept. `t0_m x0` is not `M x`, not `e^{A(t−u)} M x` for `t0 < u < t`, not `t0_b z`, not `A^{-1}[e^{A Δt} − I] B z`, and not `CINT`. `e^{A Δt} t0_m x0` is not `t0_m x0`. `T0TDPREDEFFECT` is the coefficient, not the shift. An impulse at `u ≤ t0` that used `M` is already in `η(t0)` as `TDPREDEFFECT`, not as `T0TDPREDEFFECT`. The observed mean of that first-occasion TD carry is `τ + λ(μ_t + e^{a Δt} t0_m x0)` (Driver et al., 2017, Eq. 5 of the Table 3 / Eq. 3 first-summand TD composition; JSS PDF re-opened 2026-08-20T19:07Z). The evolved map `τ + λ μ_t` is not that observed mean. The process-increment map `τ + λ(μ_t + A^{-1}[e^{A Δt} − I] B z)` is not that observed mean. The contemporaneous map `τ + λ(μ_t + m x)` is not that observed mean. The impulse-carry map `τ + λ(μ_t + e^{a(t−u)} m x)` is not that observed mean when `u ≠ t0`. The first-occasion TI map `τ + λ(μ_t + e^{a Δt} t0_b z)` is not that observed mean. The evolved-plus-carry latent mean is not `E(y_t)`. The lasting level-change `CINT` is `κ = −a m x` (Driver et al., 2017, §7.2, pp. 20–21; JSS PDF re-opened 2026-08-20T19:45Z). Form `m x` first, then multiply by `−a`. Stable `a < 0` is required so `−κ / a = m x` is an equilibrium offset. `a ≥ 0` cannot hold a new process mean. `−a m x` is not the dissipating Dirac `m x`, not a free `CINT`, and not `A^{-1}[e^{A Δt} − I] B z`. The extra near-zero-drift latent process also named in §7.2 is a different specification and is not this `CINT` setting. Equation 3 maps that intercept as `(1 − e^{a Δt}) m x` (JSS PDF re-opened 2026-08-20T19:50Z). Form the level-change `CINT` first, then the discrete intercept map. Underflow of `e^{a Δt}` to `+0` keeps `m x`. `(1 − e^{a Δt}) m x` is not `m x`, not `κ`, and not `A^{-1}[e^{A Δt} − I] B z`. The printed §7.2 lasting level change is an extra near-zero-drift latent process (Driver et al., 2017, §7.2, pp. 22–23; JSS PDF re-opened 2026-08-20T23:10Z). `T0MEANS`, `CINT`, `T0VAR`, `DIFFUSION`, and `TRAITVAR` of that process are fixed to 0; `TDPREDEFFECT` on it is fixed to 1; its `DRIFT` diagonal is very close to 0 (printed example `−0.000001`; precisely 0 causes computational problems); the original process is driven by the `DRIFT` coupling `a_{ηξ}`. After a unit identification impulse the scalar contribution is `a_{ηξ} x (e^{ε Δt} − e^{a Δt}) / (ε − a)` (`ε = a` is `a_{ηξ} x Δt e^{a Δt}`). Form `a_{ηξ} x` first. A zero coupling or zero predictor is exactly zero. `ε ≥ 0` fails closed. That contribution is not `κ = −a m x`, not `(1 − e^{a Δt}) m x`, and not the dissipating Dirac `m x`. The observed mean of that extra-process contribution is `τ + λ(μ_t + a_{ηξ} x (e^{ε Δt} − e^{a Δt}) / (ε − a))` (Driver et al., 2017, Eq. 5 of that §7.2 contribution; JSS PDF re-opened 2026-08-21T06:12Z). The extra process has `LAMBDA` 0 and is not an observed indicator. Original indicators load on the original process after the `DRIFT` coupling. The evolved map `τ + λ μ_t` is not that observed mean. The contemporaneous map `τ + λ(μ_t + m x)` is not that observed mean. The contribution is not `E(y_t)`. The evolved-plus-contribution latent mean is not `E(y_t)`. `T0TDPREDEFFECT` on the extra process begins at `t = 0` and uses `Δt = t − t0` for both the original-process evolution and the extra drive. `TDPREDEFFECT` after `t0` uses `t − u` with `t0 < u < t` for the extra drive while `μ_t` still uses `Δt`. The observed mean of that after-t0 extra-process contribution is `τ + λ(μ_t + a_{ηξ} x (e^{ε(t−u)} − e^{a(t−u)}) / (ε − a))` (Driver et al., 2017, Eq. 5 of that §7.2 after-t0 contribution; JSS PDF re-opened 2026-08-21T06:32Z). The first-occasion extra-process observed mean is not that observed mean when `u ≠ t0`. The impulse-carry map `τ + λ(μ_t + e^{a(t−u)} m x)` is a Dirac on the original process and is not that `DRIFT` drive. An impulse at `u = t0` or `u = t` is not interior. The asymptotic time-independent predictor effect is `-B z / a` (Driver et al., 2017, §7.2, pp. 20–21; JSS PDF opened 2026-08-21T13:08Z). Form `B z` first, then divide by `-a`. Stable `a < 0` is required. `a ≥ 0` cannot hold a finite process-mean change. `-B z / a` is not the coefficient `B`, not `A^{-1}[e^{A Δt} − I] B z`, not `CINT`, and not `M x`. The asymptotic time-independent predictor variance is `(B / a)² v` (Driver et al., 2017, §7.2, pp. 20–21 `addedTIPREDVAR`). Form the unit asymptotic effect first, then square, then multiply by `v`. `(B / a)² v` is not `TRAITVAR`, not `asymDIFFUSION`, and not `-B z / a`. The asymptotic continuous intercept is `-κ / a` (Driver et al., 2017, Table 2, p. 12 `asymCINT`; Eq. 3 as `Δt → ∞`; JSS PDF opened 2026-08-21T16:13Z). Form `κ` first, then divide by `-a`. Stable `a < 0` is required. `-κ / a` is not `κ`, not `A^{-1}[e^{A Δt} − I] κ`, not `T0MEANS`, and not `-B z / a`. The p. 16 stationary `T0MEANS` constraint is `-κ / a + −B z / a`. Form the intercept contribution first, then include the TI extra effect, then add. That constrained first-occasion mean is not free `T0MEANS`, not `asymCINT` alone, not `asymTIPREDEFFECT` alone, and not the finite-interval discrete latent mean. Equation 5 of that constrained mean is `τ + λ(−κ / a + −B z / a)` (Driver et al., 2017, §4.3, pp. 9–10; Eq. 5, p. 5; JSS PDF re-opened 2026-08-21T20:07Z). Form the stationary latent mean first, then `τ + λ` of that mean. `τ + λ μ_0` for free `T0MEANS` is not that composition. `τ + λ(−κ / a)` is not that composition when `B z ≠ 0`. `τ + λ μ_t` is not that composition. `MANIFESTMEANS` is not `E(y_0)`. The constrained latent mean is not `E(y_0)`. The p. 16 constrained first-occasion variance `trait + −q / (2 a) + (B / a)² v` is not free `T0VAR`, not `asymDIFFUSION` alone, not `TRAITVAR` alone, not `addedTIPREDVAR` alone, and not the finite-interval discrete latent variance. Eq. 5 of that constrained variance is `λ²(trait + −q / (2 a) + (B / a)² v) + θ + ψ` (JSS PDF re-opened 2026-08-22T03:20Z; form the stationary latent variance first, then `λ² p + θ + ψ`; `λ² p_0` is not that observed variance; `λ²(−q / (2 a)) + θ` is not that observed variance when `TRAITVAR` or `addedTIPREDVAR` is nonzero; `MANIFESTVAR` is not `Var(y_0)`; the constrained latent variance is not `Var(y_0)`). The lagged covariance of that constrained process is `trait + e^{a Δt}(−q / (2 a)) + (B / a)² v` (Driver et al., 2017, Eq. 3–4 of §4.3 / p. 16 `T0VAR`; JSS PDF re-opened 2026-08-22T19:13Z). Trait and `addedTIPREDVAR` do not decay with `e^{a Δt}`. Contemporaneous `T0VAR` is not that lagged map. Decaying the constrained total as if it were all state is not that lagged map. Equation 5 of that lagged covariance is `λ²(trait + e^{a Δt}(−q / (2 a)) + (B / a)² v) + ψ`. `Θ` does not enter. Contemporaneous `Var(y_0)` is not that lagged observed covariance. The lagged latent covariance is not that observed covariance. The later-occasion variance of that constrained process is `trait + e^{2 a Δt}(−q / (2 a)) + Q_Δt + (B / a)² v` (Driver et al., 2017, Eq. 3–4 of §4.3 / p. 16 `T0VAR`; JSS PDF re-opened 2026-08-22T23:12Z). Trait and `addedTIPREDVAR` do not enter `Q_Δt`. Under stationarity that composition equals contemporaneous `T0VAR`. Evolving the constrained total as if it were all state is not that later map. The lagged covariance omits `Q_Δt` and is not that later map. `Q_Δt` is not that later map. Equation 5 of that later-occasion variance is `λ²(trait + e^{2 a Δt}(−q / (2 a)) + Q_Δt + (B / a)² v) + θ + ψ`. The lagged observed covariance omits `Q_Δt` and `θ`. `MANIFESTVAR` is not `Var(y_t)`. The later-occasion latent variance is not `Var(y_t)`. The later-occasion variance of §4.3 predetermined `T0VAR` is `trait + e^{2 a Δt} p_0 + Q_Δt + (B / a)² v` (Driver et al., 2017, Eq. 3–4 of §4.3 predetermined first occasion; JSS PDF re-opened 2026-08-23T05:12Z). Trait and `addedTIPREDVAR` do not enter `Q_Δt`. Free `T0VAR` `p_0` is not that later map. Setting `p_0 = −q / (2 a)` recovers the stationary later-occasion map. Stationary later variance uses `−q / (2 a)` in place of `p_0` and is not that later map when `p_0` is free. Evolving `trait + p_0 + (B / a)² v` as if it were all state is not that later map. As `Δt → ∞` with stable `a < 0` the composition approaches contemporaneous stationary `T0VAR`. As `Δt → 0+` the composition approaches `trait + p_0 + (B / a)² v`. Nonzero diffusion with `a ≥ 0` is a growing process and is kept. Equation 5 of that predetermined later-occasion variance is `λ²(trait + e^{2 a Δt} p_0 + Q_Δt + (B / a)² v) + θ + ψ`. `MANIFESTVAR` is not `Var(y_t)`. The predetermined later-occasion latent variance is not `Var(y_t)`. Stationary later observed variance is not that observed variance when `p_0` is free. The lagged covariance of §4.3 predetermined `T0VAR` is `trait + e^{a Δt} p_0 + (B / a)² v` (Driver et al., 2017, Eq. 3–4 of §4.3 predetermined first occasion; JSS PDF re-opened 2026-08-23T09:04Z). Trait and `addedTIPREDVAR` do not decay with `e^{a Δt}`. Free `T0VAR` `p_0` is not that lagged map. Setting `p_0 = −q / (2 a)` recovers the stationary lagged map. Stationary lagged covariance uses `−q / (2 a)` in place of `p_0` and is not that lagged map when `p_0` is free. Evolving `trait + p_0 + (B / a)² v` as if it were all state is not that lagged map. Later-occasion variance includes `Q_Δt` and is not that lagged map. As `Δt → ∞` with stable `a < 0` the state term vanishes. As `Δt → 0+` the composition approaches `trait + p_0 + (B / a)² v`. Equation 5 of that predetermined lagged covariance is `λ²(trait + e^{a Δt} p_0 + (B / a)² v) + ψ`. `MANIFESTVAR` does not enter. The predetermined lagged latent covariance is not that observed covariance. Predetermined later observed variance includes `Q_Δt` and `θ` and is not that lagged observed covariance. Stationary lagged observed covariance is not that observed covariance when `p_0` is free. The predetermined first-occasion variance of §4.3 predetermined `T0VAR` is `trait + p_0 + (B / a)² v`. Free `p_0` is not that map. Stationary first-occasion variance uses `−q / (2 a)` in place of `p_0` and is not that map when `p_0` is free. Lagged covariance decays the state and is not that map. Later-occasion variance includes `Q_Δt` and is not that map. Equation 5 of that predetermined first-occasion variance is `λ²(trait + p_0 + (B / a)² v) + θ + ψ`. `MANIFESTVAR` is not that first-occasion observed variance. The predetermined first-occasion latent variance is not that observed variance. Stationary first-occasion observed variance is not that observed variance when `p_0` is free. Predetermined later observed variance includes `Q_Δt` and is not that first-occasion observed variance. Later-start lagged covariance of predetermined `T0VAR` is `trait + e^{a s}(e^{2 a u} p_0 + Q_u) + (B / a)² v` (Driver et al., 2017, §4.3 `startoffset`; Eq. 4; JSS PDF re-opened 2026-08-23T10:27Z). First-occasion lagged omits `e^{a s} Q_u`. Later-occasion variance does not lag. Stationary lagged uses `−q / (2 a)`. Decaying the later total is not that map. Equation 5 of that later-start lagged covariance is `λ²` of it plus `ψ`. Independent `ε_t` does not enter. First-occasion lagged observed omits `e^{a s} Q_u`. Predetermined later observed variance includes `Q_u` and `θ` and is not that later-start lagged observed covariance. Later-start later-occasion variance of predetermined `T0VAR` is `trait + e^{2 a s}(e^{2 a u} p_0 + Q_u) + Q_s + (B / a)² v` (Driver et al., 2017, §4.3 `startoffset`; Eq. 3–4 Chapman–Kolmogorov `Q_{u+s} = e^{2 a s} Q_u + Q_s`; JSS PDF re-opened 2026-08-23T11:05Z). Later-occasion variance at `u` omits `Q_s`. Later-start lagged covariance omits `Q_s`. Stationary later uses `−q / (2 a)`. Evolving the later total as if it were all state is not that map. Ignoring `startoffset` omits `e^{2 a s} Q_u`. Equation 5 of that later-start later-occasion variance is `λ²` of it plus `θ + ψ`. `MANIFESTVAR` is not that observed variance. Page 16 `discreteDRIFTstd` is `e^{a Δt}` after strictly positive `asymDIFFUSION` `-q / (2 a)` (Driver et al., 2017, p. 16; footnote 4; §7.1; JSS PDF re-opened 2026-08-23T11:40Z). Unstandardised `e^{a Δt}` is defined for growing `a ≥ 0` and for zero diffusion and is not `discreteDRIFTstd`. The §7.1 trait-plus-state autocorrelation `(trait + e^{a Δt} p + added) / (trait + p + added)` uses `TRAITVAR` and is not `discreteDRIFTstd`. `TRAITVAR` is not the standardisation variance. Page 16 `discreteDIFFUSIONstd` is `Q_Δt / (−q / (2 a))` after strictly positive `asymDIFFUSION` `-q / (2 a)` (Driver et al., 2017, p. 16; Eq. 4; footnote 4; JSS PDF re-opened 2026-08-23T13:06Z). Unstandardised `Q_Δt` is defined for growing `a ≥ 0` and for zero diffusion and is not `discreteDIFFUSIONstd`. The continuous standardisation `−2 a` is not `discreteDIFFUSIONstd`. `Q_Δt / (trait + p + added)` uses `TRAITVAR` and is not `discreteDIFFUSIONstd`. `TRAITVAR` is not the standardisation variance. Page 16 `DIFFUSIONstd` is `q / (−q / (2 a)) = −2 a` after strictly positive `asymDIFFUSION` `-q / (2 a)` (Driver et al., 2017, p. 16; Eq. 4; footnote 4; JSS PDF re-opened 2026-08-23T13:20Z). Unstandardised `q` is defined for growing `a ≥ 0` and for zero diffusion and is not `DIFFUSIONstd`. The discrete standardisation `Q_Δt / (−q / (2 a))` depends on `Δt` and is not `DIFFUSIONstd`. `q / (trait + p + added)` uses `TRAITVAR` and is not `DIFFUSIONstd`. `TRAITVAR` is not the standardisation variance. Page 16 `DRIFTstd` is the continuous auto-effect after strictly positive `asymDIFFUSION` `-q / (2 a)` (Driver et al., 2017, p. 16; Eq. 1; footnote 4; JSS PDF re-opened 2026-08-23T13:28Z). Unstandardised `a` is defined for growing `a ≥ 0` and for zero diffusion and is not `DRIFTstd`. The discrete standardisation `e^{a Δt}` depends on the event interval and is not `DRIFTstd`. `a p / (trait + p + added)` uses `TRAITVAR` and is not `DRIFTstd`. `TRAITVAR` is not the standardisation variance. Page 16 `asymTIPREDEFFECTstd` is `(-B / a) · √v / √(-q / (2 a))` after strictly positive `asymDIFFUSION` `-q / (2 a)` and strictly positive predictor variance `v` (Driver et al., 2017, p. 16; §7.2; footnote 4; JSS PDF re-opened 2026-08-23T14:25Z). Unstandardised `-B / a` is defined for a zero coefficient and for zero predictor variance and is not `asymTIPREDEFFECTstd`. The finite-interval standardisation `A^{-1}[e^{A Δt} − I] B · √v / √p` depends on the event interval and is not `asymTIPREDEFFECTstd`. `(-B / a) · √v / √(trait + p + added)` uses `TRAITVAR` and is not `asymTIPREDEFFECTstd`. `TRAITVAR` is not the standardisation variance. Page 16 `TIPREDEFFECTstd` is `B · √v / √(-q / (2 a))` after strictly positive `asymDIFFUSION` `-q / (2 a)` and strictly positive predictor variance `v` (Driver et al., 2017, p. 16; §7.2; footnote 4; JSS PDF re-opened 2026-08-23T16:21Z). Unstandardised `B` is defined for a zero coefficient and for zero predictor variance and is not `TIPREDEFFECTstd`. The asymptotic standardisation `(-B / a) · √v / √p` is the total change and is not `TIPREDEFFECTstd`. The finite-interval standardisation `A^{-1}[e^{A Δt} − I] B · √v / √p` depends on the event interval and is not `TIPREDEFFECTstd`. `B · √v / √(trait + p + added)` uses `TRAITVAR` and is not `TIPREDEFFECTstd`. `TRAITVAR` is not the standardisation variance. Page 16 / Table 3 `T0TIPREDEFFECTstd` is `t0_b · √v / √p_0` after strictly positive free `T0VAR` `p_0` and strictly positive predictor variance `v` (Driver et al., 2017, Table 3, p. 13; p. 16; footnote 4; 2017-era ctsem `summary.ctsemFit.R`; JSS PDF re-opened 2026-08-23T17:20Z). The affected variance is free first-occasion `T0VAR`, not `asymDIFFUSION`. Unstandardised `t0_b` is defined for a zero coefficient and for zero predictor variance and is not `T0TIPREDEFFECTstd`. `TIPREDEFFECTstd` `B · √v / √(-q / (2 a))` is the continuous coefficient and is not `T0TIPREDEFFECTstd`. `asymTIPREDEFFECTstd` `(-B / a) · √v / √p` is the total change and is not `T0TIPREDEFFECTstd`. `t0_b · √v / √(trait + p_0 + added)` uses `TRAITVAR` and is not `T0TIPREDEFFECTstd`. `TRAITVAR` is not the standardisation variance. 2017-era `addedT0TIPREDVAR` is `t0_b² v` after a first-occasion time-independent predictor (Driver et al., 2017, Table 3, p. 13; p. 16; §7.2; 2017-era ctsem `summary.ctsemFit.R`; JSS PDF re-opened 2026-08-23T18:20Z). Form `t0_b` first, then square, then multiply by `v`. A zero coefficient or zero predictor variance is exactly zero. Free `T0TIPREDEFFECT` does not require `a < 0`. `(B / a)² v` is `addedTIPREDVAR` and is not this first-occasion map. `t0_b · √v / √p_0` is `T0TIPREDEFFECTstd` and is not this variance. Free `T0VAR` is not this extra TI variance. `TRAITVAR` is not this extra TI variance. Equation 5 of 2017-era `addedT0TIPREDVAR` is `λ² t0_b² v` (Driver et al., 2017, Eq. 5, p. 5; Table 3, p. 13; Table 2, p. 12; 2017-era ctsem `summary.ctsemFit.R`; JSS PDF re-opened 2026-08-23T19:10Z). Form `t0_b² v` first, then `(λ extra) λ` with `θ = 0`. A zero loading or zero extra is exactly zero. `t0_b² v` is the latent extra, not the observed extra. `λ² p_0 + θ` is first-occasion observed variance, not this extra. `λ² (B / a)² v` is Eq. 5 of `addedTIPREDVAR`, not this first-occasion observed extra. `MANIFESTVAR` `θ` is not this extra. Equation 5 of §7.2 `addedTIPREDVAR` is `λ² (B / a)² v` (Driver et al., 2017, Eq. 5, p. 5; Table 2, p. 12; §7.2, pp. 20–21; 2017-era ctsem `summary.ctsemFit.R`; JSS PDF re-opened 2026-08-23T19:23Z). Form `(B / a)² v` first, then `(λ extra) λ` with `θ = 0`. A zero loading or zero extra is exactly zero. Lasting asymptotic extra requires `a < 0`. `(B / a)² v` is the latent extra, not the observed extra. `λ² t0_b² v` is first-occasion extra observed TI variance, not this extra. `λ² p + θ` is stationary observed variance, not this extra. `MANIFESTVAR` `θ` is not this extra. Page 16 `TDPREDEFFECTstd` is `m · √v / √(-q / (2 a))` after strictly positive `asymDIFFUSION` and strictly positive time-dependent predictor variance. Unstandardised `M` is not `TDPREDEFFECTstd`. `TIPREDEFFECTstd` is not `TDPREDEFFECTstd` even when `M = B`. intercept-style `A^{-1}[e^{A Δt} − I] M · √v / √p` is not `TDPREDEFFECTstd`. `m · √v / √(trait + p + added)` uses `TRAITVAR` and is not `TDPREDEFFECTstd`. Table 3 / p. 16 `T0TDPREDEFFECTstd` is `t0_m · √v / √p_0` after strictly positive free `T0VAR` and strictly positive TD predictor variance. Unstandardised `t0_m` is not `T0TDPREDEFFECTstd`. `TDPREDEFFECTstd` uses `asymDIFFUSION` and is not `T0TDPREDEFFECTstd`. `T0TIPREDEFFECTstd` is not `T0TDPREDEFFECTstd` even when `t0_m = t0_b`. `t0_m · √v / √(trait + p_0 + added)` uses `TRAITVAR` and is not `T0TDPREDEFFECTstd`. Free `T0VAR` does not require `a < 0`. Page 16 `T0VARstd` is `p_0 / p_0 = 1` after strictly positive free `T0VAR` (`solve(sqrt(diag(T0VAR))) %&% T0VAR`; OpenMx `%&%` is `t(A) %*% B %*% A`; the default ridge is 0). Unstandardised `T0VAR` is not `T0VARstd`. `T0TDPREDEFFECTstd` is not `T0VARstd`. `addedT0TIPREDVAR` is not `T0VARstd`. Page 16 `TRAITVARstd` is `trait / trait = 1` after strictly positive `TRAITVAR` (`solve(sqrt(diag(TRAITVAR))) %&% TRAITVAR`; OpenMx `%&%` is `t(A) %*% B %*% A`; no ridge addend). Unstandardised `TRAITVAR` is not `TRAITVARstd`. `T0VARstd` is not `TRAITVARstd` even when both equal 1. `addedT0TIPREDVAR` is not `TRAITVARstd`. Page 16 `MANIFESTTRAITVARstd` is `ψ / ψ = 1` after strictly positive `MANIFESTTRAITVAR` (`solve(sqrt(diag(MANIFESTTRAITVAR))) %&% MANIFESTTRAITVAR`; OpenMx `%&%` is `t(A) %*% B %*% A`; 2017-era source adds ridging; default ridge is 0). Unstandardised `MANIFESTTRAITVAR` is not `MANIFESTTRAITVARstd`. `TRAITVARstd` is not `MANIFESTTRAITVARstd` even when both equal 1. `MANIFESTVAR` is not `MANIFESTTRAITVARstd`. Evolving from that stationary start with `CINT` and `TIPREDEFFECT` stays at the stationary mean. Equation 1 is the latent SDE, not the measurement model. Form `(λ p) λ` then add `θ`, then add `ψ`. `MANIFESTVAR` is `Θ`, not `Var(y)`. `MANIFESTTRAITVAR` is `Ψ_τ`, not `Θ`. `TRAITVAR` is latent and scaled by `λ²`. `Var(η)` is not `Var(y)`. +- Do not use the difference quotient as a continuous-time rate. The scalar map is `a = ln(φ) / Δt` on event time. Discrete lags from unequal event intervals are not one coefficient; remap them through that log-rate. Binary64 `exp(a Δt) = 0` is not a discrete lag. A constant predictor's discrete effect is Voelkle et al. (2012, Eq. 12), evaluated as `a_yx (expm1(z) / a_xx)` with `z = a_xx Δt` so a finite result is not lost when `z` overflows to `-∞` or when `a_yx Δt` overflows. When `expm1(z)` overflows at a finite `z`, rewrite in log space; a zero continuous effect is exactly zero; an overflowing `a_yx/a_xx` rewrite term fails closed. The first-order product is the underflow limit of that equation, not the general constant-predictor discrete effect. A time-varying predictor whose sampling interval equals its constancy interval uses Voelkle et al. (2012, Eq. 14): `b* = a_yx Δt`. Unmatched intervals fail closed (Oud & Jansen, 2000, unread). Discrete process noise is Driver et al. (2017, Eq. 3): `Q_Δt = 0.5 q (expm1(z) / a)` with `z = 2 (a Δt)` and `q = G G⊤ ≥ 0`; do not form `2 a` first; `a = 0` and `z → 0` recover `q Δt`; a zero diffusion is exactly zero; an overflowing rewrite scale `0.5 q / a` fails closed; this is not a Kalman filter. `Q_Δt` is `cov(η_t | η_{t-1})`, not `Var(η_t)`. The lagged covariance is `exp(a Δt) p` and the unconditional variance is `exp(2 a Δt) p + Q_Δt` (Driver et al., 2017, Eq. 3–4, pp. 4–5; JSS has no numbered §2.2). A zero diffusion whose `2 (a Δt)` overflows to `+∞` is not a finite `Var(η_t)`. The stationary within-subject variance is the `Δt → ∞` limit of Eq. 4: `-q / (2 a)` for stable `a < 0` (JSS p. 16 `asymDIFFUSION`; §4.3). When `2 a` is finite, form `q / -(2 a)` so `q / a` overflow does not lose a finite result (`q = MAX`, `a = -0.75` → `MAX / 1.5`). When `2 a` overflows, form `(q / a) * -0.5`. Do not form `0.5 q` first (`q = from_bits(1)` underflows). `a ≥ 0` has no finite stationary variance. Finite-interval `Q_Δt` is not that limit. Trait-plus-state variance is `trait + state` and lagged covariance is `trait + exp(a Δt) p` (Driver et al., 2017, §4.3, p. 9). Trait variance is not process noise and not `asymDIFFUSION`. Evolving the summed variance as if it were all state is not that map. This is not RI-CLPM. Observed-indicator variance is `λ² Var(η) + θ` when `MANIFESTTRAITVAR` is zero and `λ² Var(η) + θ + ψ` otherwise (Driver et al., 2017, Eq. 5, p. 5; Table 2, p. 12). Lagged observed covariance is `λ² cov(η_t, η_{t-1}) + ψ`; `MANIFESTVAR` does not enter. Observed-indicator mean is `τ + λ μ` (Driver et al., 2017, Eq. 5; Table 2, p. 12). `MANIFESTMEANS` is `τ`, not `E(y)`. `E(η)` is not `E(y)`. `CINT` is not `MANIFESTMEANS`. `T0MEANS` is not `E(y)`. The discrete latent mean is `μ_t = exp(a Δt) μ_0 + (exp(a Δt) − 1)/a κ` (Driver et al., 2017, Eq. 3, p. 4; Table 2, p. 12). `T0MEANS` is not `μ_t`. `CINT` is not that discrete increment. A zero drift is `κ Δt`. Underflow of `exp(a Δt)` to `+0` drops the carried `T0MEANS` and keeps `−κ / a`. The evolved observed mean is `τ + λ μ_t` (Driver et al., 2017, Eq. 5 of that Eq. 3 map). The first-occasion map `τ + λ μ_0` is not `E(y_t)`. `μ_t` is not `E(y_t)`. The contemporaneous time-dependent predictor impulse is `m x` (Driver et al., 2017, Eq. 3 fourth summand; Table 2 `TDPREDEFFECT` is `M`). Form `μ_t` first, then add `m x`. `TDPREDEFFECT` is not `CINT`. `M x` is not `A^{-1}[e^{A Δt} − I] B z` and is not Voelkle et al. (2012, Eq. 14). The §7.2 level-change form is not that impulse. The observed mean of that contemporaneous impulse is `τ + λ(μ_t + m x)` (Driver et al., 2017, Eq. 5 of the Eq. 3 fourth-summand composition). The evolved map `τ + λ μ_t` is not that observed mean. The carry map `τ + λ(μ_t + e^{a(t−u)} m x)` is not that observed mean when `u ≠ t`. The evolved-plus-impulse latent mean is not `E(y_t)`. The time-independent predictor increment is `A^{-1}[e^{A Δt} − I] B z` (Driver et al., 2017, Eq. 3 second summand; Table 2 `TIPREDEFFECT` is `B`). Form `B z` first, then the discrete intercept map. A zero drift is `B z Δt`. `TIPREDEFFECT` is `B`, not that discrete increment. `A^{-1}[e^{A Δt} − I] B z` is not `CINT`, not `M x`, and not Voelkle et al. (2012, Eq. 14). The observed mean of that increment is `τ + λ(μ_t + A^{-1}[e^{A Δt} − I] B z)` (Driver et al., 2017, Eq. 5 of the Eq. 3 printed addend after the `T0MEANS` carry and the `CINT` increment). The evolved map `τ + λ μ_t` is not that observed mean. The contemporaneous map `τ + λ(μ_t + m x)` is not that observed mean. The carry map `τ + λ(μ_t + e^{a(t−u)} m x)` is not that observed mean when `u ≠ t`. The evolved-plus-increment latent mean is not `E(y_t)`. The within-interval time-dependent impulse carry is `e^{A(t−u)} M x` for `t0 < u < t` (Driver et al., 2017, Eq. 1–2 Green-function integral of Eq. 2; §7.2 dissipation). Form `m x` first, then `e^{a(t−u)} m x`. A zero drift is `m x` with no dissipation. Underflow of `e^{a(t−u)}` to `+0` is vanishing dissipation and is kept. `e^{A(t−u)} M x` is not the contemporaneous Dirac, not `CINT`, not `TIPREDEFFECT`, and not Voelkle et al. (2012, Eq. 14). An impulse at `u = t` is the contemporaneous map. An impulse at `u ≤ t0` is already in `η(t0)`. The observed mean of that carry is `τ + λ(μ_t + e^{a(t−u)} m x)` (Driver et al., 2017, Eq. 5 of the Eq. 1–2 carried latent mean). The evolved map `τ + λ μ_t` is not that observed mean. The contemporaneous map `τ + λ(μ_t + m x)` is not that observed mean when `u ≠ t`. `MANIFESTMEANS` is not `E(y_t)`. The carried latent mean is not `E(y_t)`. The first-occasion time-independent predictor shift is `t0_b z` (Driver et al., 2017, Table 3 `T0TIPREDEFFECT`; Eq. 3 first summand). Form `t0_b z` first, then `e^{a Δt} t0_b z`. Form `μ_t` first, then add that carry. A zero drift is `t0_b z`. Underflow of `e^{a Δt}` to `+0` is a vanishing carry of the first-occasion shift and is kept. `t0_b z` is not `A^{-1}[e^{A Δt} − I] B z`, not `CINT`, and not `M x`. `e^{A Δt} t0_b z` is not `t0_b z`. `T0TIPREDEFFECT` is the coefficient, not the shift. The observed mean of that first-occasion carry is `τ + λ(μ_t + e^{a Δt} t0_b z)` (Driver et al., 2017, Eq. 5 of the Table 3 / Eq. 3 first-summand composition). The evolved map `τ + λ μ_t` is not that observed mean. The process-increment map `τ + λ(μ_t + A^{-1}[e^{A Δt} − I] B z)` is not that observed mean. The contemporaneous map `τ + λ(μ_t + m x)` is not that observed mean. The impulse-carry map `τ + λ(μ_t + e^{a(t−u)} m x)` is not that observed mean when `u ≠ t0`. The evolved-plus-carry latent mean is not `E(y_t)`. The first-occasion time-dependent predictor shift is `t0_m x0` (Driver et al., 2017, Table 3 `T0TDPREDEFFECT`; Eq. 3 first summand; JSS PDF re-opened 2026-08-20T19:10Z). Form `t0_m x0` first, then `e^{a Δt} t0_m x0`. Form `μ_t` first, then add that carry. A zero drift is `t0_m x0`. Underflow of `e^{a Δt}` to `+0` is a vanishing carry of the first-occasion shift and is kept. `t0_m x0` is not `M x`, not `e^{A(t−u)} M x` for `t0 < u < t`, not `t0_b z`, not `A^{-1}[e^{A Δt} − I] B z`, and not `CINT`. `e^{A Δt} t0_m x0` is not `t0_m x0`. `T0TDPREDEFFECT` is the coefficient, not the shift. An impulse at `u ≤ t0` that used `M` is already in `η(t0)` as `TDPREDEFFECT`, not as `T0TDPREDEFFECT`. The observed mean of that first-occasion TD carry is `τ + λ(μ_t + e^{a Δt} t0_m x0)` (Driver et al., 2017, Eq. 5 of the Table 3 / Eq. 3 first-summand TD composition; JSS PDF re-opened 2026-08-20T19:07Z). The evolved map `τ + λ μ_t` is not that observed mean. The process-increment map `τ + λ(μ_t + A^{-1}[e^{A Δt} − I] B z)` is not that observed mean. The contemporaneous map `τ + λ(μ_t + m x)` is not that observed mean. The impulse-carry map `τ + λ(μ_t + e^{a(t−u)} m x)` is not that observed mean when `u ≠ t0`. The first-occasion TI map `τ + λ(μ_t + e^{a Δt} t0_b z)` is not that observed mean. The evolved-plus-carry latent mean is not `E(y_t)`. The lasting level-change `CINT` is `κ = −a m x` (Driver et al., 2017, §7.2, pp. 20–21; JSS PDF re-opened 2026-08-20T19:45Z). Form `m x` first, then multiply by `−a`. Stable `a < 0` is required so `−κ / a = m x` is an equilibrium offset. `a ≥ 0` cannot hold a new process mean. `−a m x` is not the dissipating Dirac `m x`, not a free `CINT`, and not `A^{-1}[e^{A Δt} − I] B z`. The extra near-zero-drift latent process also named in §7.2 is a different specification and is not this `CINT` setting. Equation 3 maps that intercept as `(1 − e^{a Δt}) m x` (JSS PDF re-opened 2026-08-20T19:50Z). Form the level-change `CINT` first, then the discrete intercept map. Underflow of `e^{a Δt}` to `+0` keeps `m x`. `(1 − e^{a Δt}) m x` is not `m x`, not `κ`, and not `A^{-1}[e^{A Δt} − I] B z`. The printed §7.2 lasting level change is an extra near-zero-drift latent process (Driver et al., 2017, §7.2, pp. 22–23; JSS PDF re-opened 2026-08-20T23:10Z). `T0MEANS`, `CINT`, `T0VAR`, `DIFFUSION`, and `TRAITVAR` of that process are fixed to 0; `TDPREDEFFECT` on it is fixed to 1; its `DRIFT` diagonal is very close to 0 (printed example `−0.000001`; precisely 0 causes computational problems); the original process is driven by the `DRIFT` coupling `a_{ηξ}`. After a unit identification impulse the scalar contribution is `a_{ηξ} x (e^{ε Δt} − e^{a Δt}) / (ε − a)` (`ε = a` is `a_{ηξ} x Δt e^{a Δt}`). Form `a_{ηξ} x` first. A zero coupling or zero predictor is exactly zero. `ε ≥ 0` fails closed. That contribution is not `κ = −a m x`, not `(1 − e^{a Δt}) m x`, and not the dissipating Dirac `m x`. The observed mean of that extra-process contribution is `τ + λ(μ_t + a_{ηξ} x (e^{ε Δt} − e^{a Δt}) / (ε − a))` (Driver et al., 2017, Eq. 5 of that §7.2 contribution; JSS PDF re-opened 2026-08-21T06:12Z). The extra process has `LAMBDA` 0 and is not an observed indicator. Original indicators load on the original process after the `DRIFT` coupling. The evolved map `τ + λ μ_t` is not that observed mean. The contemporaneous map `τ + λ(μ_t + m x)` is not that observed mean. The contribution is not `E(y_t)`. The evolved-plus-contribution latent mean is not `E(y_t)`. `T0TDPREDEFFECT` on the extra process begins at `t = 0` and uses `Δt = t − t0` for both the original-process evolution and the extra drive. `TDPREDEFFECT` after `t0` uses `t − u` with `t0 < u < t` for the extra drive while `μ_t` still uses `Δt`. The observed mean of that after-t0 extra-process contribution is `τ + λ(μ_t + a_{ηξ} x (e^{ε(t−u)} − e^{a(t−u)}) / (ε − a))` (Driver et al., 2017, Eq. 5 of that §7.2 after-t0 contribution; JSS PDF re-opened 2026-08-21T06:32Z). The first-occasion extra-process observed mean is not that observed mean when `u ≠ t0`. The impulse-carry map `τ + λ(μ_t + e^{a(t−u)} m x)` is a Dirac on the original process and is not that `DRIFT` drive. An impulse at `u = t0` or `u = t` is not interior. The asymptotic time-independent predictor effect is `-B z / a` (Driver et al., 2017, §7.2, pp. 20–21; JSS PDF opened 2026-08-21T13:08Z). Form `B z` first, then divide by `-a`. Stable `a < 0` is required. `a ≥ 0` cannot hold a finite process-mean change. `-B z / a` is not the coefficient `B`, not `A^{-1}[e^{A Δt} − I] B z`, not `CINT`, and not `M x`. The asymptotic time-independent predictor variance is `(B / a)² v` (Driver et al., 2017, §7.2, pp. 20–21 `addedTIPREDVAR`). Form the unit asymptotic effect first, then square, then multiply by `v`. `(B / a)² v` is not `TRAITVAR`, not `asymDIFFUSION`, and not `-B z / a`. The asymptotic continuous intercept is `-κ / a` (Driver et al., 2017, Table 2, p. 12 `asymCINT`; Eq. 3 as `Δt → ∞`; JSS PDF opened 2026-08-21T16:13Z). Form `κ` first, then divide by `-a`. Stable `a < 0` is required. `-κ / a` is not `κ`, not `A^{-1}[e^{A Δt} − I] κ`, not `T0MEANS`, and not `-B z / a`. The p. 16 stationary `T0MEANS` constraint is `-κ / a + −B z / a`. Form the intercept contribution first, then include the TI extra effect, then add. That constrained first-occasion mean is not free `T0MEANS`, not `asymCINT` alone, not `asymTIPREDEFFECT` alone, and not the finite-interval discrete latent mean. Equation 5 of that constrained mean is `τ + λ(−κ / a + −B z / a)` (Driver et al., 2017, §4.3, pp. 9–10; Eq. 5, p. 5; JSS PDF re-opened 2026-08-21T20:07Z). Form the stationary latent mean first, then `τ + λ` of that mean. `τ + λ μ_0` for free `T0MEANS` is not that composition. `τ + λ(−κ / a)` is not that composition when `B z ≠ 0`. `τ + λ μ_t` is not that composition. `MANIFESTMEANS` is not `E(y_0)`. The constrained latent mean is not `E(y_0)`. The p. 16 constrained first-occasion variance `trait + −q / (2 a) + (B / a)² v` is not free `T0VAR`, not `asymDIFFUSION` alone, not `TRAITVAR` alone, not `addedTIPREDVAR` alone, and not the finite-interval discrete latent variance. Eq. 5 of that constrained variance is `λ²(trait + −q / (2 a) + (B / a)² v) + θ + ψ` (JSS PDF re-opened 2026-08-22T03:20Z; form the stationary latent variance first, then `λ² p + θ + ψ`; `λ² p_0` is not that observed variance; `λ²(−q / (2 a)) + θ` is not that observed variance when `TRAITVAR` or `addedTIPREDVAR` is nonzero; `MANIFESTVAR` is not `Var(y_0)`; the constrained latent variance is not `Var(y_0)`). The lagged covariance of that constrained process is `trait + e^{a Δt}(−q / (2 a)) + (B / a)² v` (Driver et al., 2017, Eq. 3–4 of §4.3 / p. 16 `T0VAR`; JSS PDF re-opened 2026-08-22T19:13Z). Trait and `addedTIPREDVAR` do not decay with `e^{a Δt}`. Contemporaneous `T0VAR` is not that lagged map. Decaying the constrained total as if it were all state is not that lagged map. Equation 5 of that lagged covariance is `λ²(trait + e^{a Δt}(−q / (2 a)) + (B / a)² v) + ψ`. `Θ` does not enter. Contemporaneous `Var(y_0)` is not that lagged observed covariance. The lagged latent covariance is not that observed covariance. The later-occasion variance of that constrained process is `trait + e^{2 a Δt}(−q / (2 a)) + Q_Δt + (B / a)² v` (Driver et al., 2017, Eq. 3–4 of §4.3 / p. 16 `T0VAR`; JSS PDF re-opened 2026-08-22T23:12Z). Trait and `addedTIPREDVAR` do not enter `Q_Δt`. Under stationarity that composition equals contemporaneous `T0VAR`. Evolving the constrained total as if it were all state is not that later map. The lagged covariance omits `Q_Δt` and is not that later map. `Q_Δt` is not that later map. Equation 5 of that later-occasion variance is `λ²(trait + e^{2 a Δt}(−q / (2 a)) + Q_Δt + (B / a)² v) + θ + ψ`. The lagged observed covariance omits `Q_Δt` and `θ`. `MANIFESTVAR` is not `Var(y_t)`. The later-occasion latent variance is not `Var(y_t)`. The later-occasion variance of §4.3 predetermined `T0VAR` is `trait + e^{2 a Δt} p_0 + Q_Δt + (B / a)² v` (Driver et al., 2017, Eq. 3–4 of §4.3 predetermined first occasion; JSS PDF re-opened 2026-08-23T05:12Z). Trait and `addedTIPREDVAR` do not enter `Q_Δt`. Free `T0VAR` `p_0` is not that later map. Setting `p_0 = −q / (2 a)` recovers the stationary later-occasion map. Stationary later variance uses `−q / (2 a)` in place of `p_0` and is not that later map when `p_0` is free. Evolving `trait + p_0 + (B / a)² v` as if it were all state is not that later map. As `Δt → ∞` with stable `a < 0` the composition approaches contemporaneous stationary `T0VAR`. As `Δt → 0+` the composition approaches `trait + p_0 + (B / a)² v`. Nonzero diffusion with `a ≥ 0` is a growing process and is kept. Equation 5 of that predetermined later-occasion variance is `λ²(trait + e^{2 a Δt} p_0 + Q_Δt + (B / a)² v) + θ + ψ`. `MANIFESTVAR` is not `Var(y_t)`. The predetermined later-occasion latent variance is not `Var(y_t)`. Stationary later observed variance is not that observed variance when `p_0` is free. The lagged covariance of §4.3 predetermined `T0VAR` is `trait + e^{a Δt} p_0 + (B / a)² v` (Driver et al., 2017, Eq. 3–4 of §4.3 predetermined first occasion; JSS PDF re-opened 2026-08-23T09:04Z). Trait and `addedTIPREDVAR` do not decay with `e^{a Δt}`. Free `T0VAR` `p_0` is not that lagged map. Setting `p_0 = −q / (2 a)` recovers the stationary lagged map. Stationary lagged covariance uses `−q / (2 a)` in place of `p_0` and is not that lagged map when `p_0` is free. Evolving `trait + p_0 + (B / a)² v` as if it were all state is not that lagged map. Later-occasion variance includes `Q_Δt` and is not that lagged map. As `Δt → ∞` with stable `a < 0` the state term vanishes. As `Δt → 0+` the composition approaches `trait + p_0 + (B / a)² v`. Equation 5 of that predetermined lagged covariance is `λ²(trait + e^{a Δt} p_0 + (B / a)² v) + ψ`. `MANIFESTVAR` does not enter. The predetermined lagged latent covariance is not that observed covariance. Predetermined later observed variance includes `Q_Δt` and `θ` and is not that lagged observed covariance. Stationary lagged observed covariance is not that observed covariance when `p_0` is free. The predetermined first-occasion variance of §4.3 predetermined `T0VAR` is `trait + p_0 + (B / a)² v`. Free `p_0` is not that map. Stationary first-occasion variance uses `−q / (2 a)` in place of `p_0` and is not that map when `p_0` is free. Lagged covariance decays the state and is not that map. Later-occasion variance includes `Q_Δt` and is not that map. Equation 5 of that predetermined first-occasion variance is `λ²(trait + p_0 + (B / a)² v) + θ + ψ`. `MANIFESTVAR` is not that first-occasion observed variance. The predetermined first-occasion latent variance is not that observed variance. Stationary first-occasion observed variance is not that observed variance when `p_0` is free. Predetermined later observed variance includes `Q_Δt` and is not that first-occasion observed variance. Later-start lagged covariance of predetermined `T0VAR` is `trait + e^{a s}(e^{2 a u} p_0 + Q_u) + (B / a)² v` (Driver et al., 2017, §4.3 `startoffset`; Eq. 4; JSS PDF re-opened 2026-08-23T10:27Z). First-occasion lagged omits `e^{a s} Q_u`. Later-occasion variance does not lag. Stationary lagged uses `−q / (2 a)`. Decaying the later total is not that map. Equation 5 of that later-start lagged covariance is `λ²` of it plus `ψ`. Independent `ε_t` does not enter. First-occasion lagged observed omits `e^{a s} Q_u`. Predetermined later observed variance includes `Q_u` and `θ` and is not that later-start lagged observed covariance. Later-start later-occasion variance of predetermined `T0VAR` is `trait + e^{2 a s}(e^{2 a u} p_0 + Q_u) + Q_s + (B / a)² v` (Driver et al., 2017, §4.3 `startoffset`; Eq. 3–4 Chapman–Kolmogorov `Q_{u+s} = e^{2 a s} Q_u + Q_s`; JSS PDF re-opened 2026-08-23T11:05Z). Later-occasion variance at `u` omits `Q_s`. Later-start lagged covariance omits `Q_s`. Stationary later uses `−q / (2 a)`. Evolving the later total as if it were all state is not that map. Ignoring `startoffset` omits `e^{2 a s} Q_u`. Equation 5 of that later-start later-occasion variance is `λ²` of it plus `θ + ψ`. `MANIFESTVAR` is not that observed variance. Page 16 `discreteDRIFTstd` is `e^{a Δt}` after strictly positive `asymDIFFUSION` `-q / (2 a)` (Driver et al., 2017, p. 16; footnote 4; §7.1; JSS PDF re-opened 2026-08-23T11:40Z). Unstandardised `e^{a Δt}` is defined for growing `a ≥ 0` and for zero diffusion and is not `discreteDRIFTstd`. The §7.1 trait-plus-state autocorrelation `(trait + e^{a Δt} p + added) / (trait + p + added)` uses `TRAITVAR` and is not `discreteDRIFTstd`. `TRAITVAR` is not the standardisation variance. Page 16 `discreteDIFFUSIONstd` is `Q_Δt / (−q / (2 a))` after strictly positive `asymDIFFUSION` `-q / (2 a)` (Driver et al., 2017, p. 16; Eq. 4; footnote 4; JSS PDF re-opened 2026-08-23T13:06Z). Unstandardised `Q_Δt` is defined for growing `a ≥ 0` and for zero diffusion and is not `discreteDIFFUSIONstd`. The continuous standardisation `−2 a` is not `discreteDIFFUSIONstd`. `Q_Δt / (trait + p + added)` uses `TRAITVAR` and is not `discreteDIFFUSIONstd`. `TRAITVAR` is not the standardisation variance. Page 16 `DIFFUSIONstd` is `q / (−q / (2 a)) = −2 a` after strictly positive `asymDIFFUSION` `-q / (2 a)` (Driver et al., 2017, p. 16; Eq. 4; footnote 4; JSS PDF re-opened 2026-08-23T13:20Z). Unstandardised `q` is defined for growing `a ≥ 0` and for zero diffusion and is not `DIFFUSIONstd`. The discrete standardisation `Q_Δt / (−q / (2 a))` depends on `Δt` and is not `DIFFUSIONstd`. `q / (trait + p + added)` uses `TRAITVAR` and is not `DIFFUSIONstd`. `TRAITVAR` is not the standardisation variance. Page 16 `DRIFTstd` is the continuous auto-effect after strictly positive `asymDIFFUSION` `-q / (2 a)` (Driver et al., 2017, p. 16; Eq. 1; footnote 4; JSS PDF re-opened 2026-08-23T13:28Z). Unstandardised `a` is defined for growing `a ≥ 0` and for zero diffusion and is not `DRIFTstd`. The discrete standardisation `e^{a Δt}` depends on the event interval and is not `DRIFTstd`. `a p / (trait + p + added)` uses `TRAITVAR` and is not `DRIFTstd`. `TRAITVAR` is not the standardisation variance. Page 16 `asymTIPREDEFFECTstd` is `(-B / a) · √v / √(-q / (2 a))` after strictly positive `asymDIFFUSION` `-q / (2 a)` and strictly positive predictor variance `v` (Driver et al., 2017, p. 16; §7.2; footnote 4; JSS PDF re-opened 2026-08-23T14:25Z). Unstandardised `-B / a` is defined for a zero coefficient and for zero predictor variance and is not `asymTIPREDEFFECTstd`. The finite-interval standardisation `A^{-1}[e^{A Δt} − I] B · √v / √p` depends on the event interval and is not `asymTIPREDEFFECTstd`. `(-B / a) · √v / √(trait + p + added)` uses `TRAITVAR` and is not `asymTIPREDEFFECTstd`. `TRAITVAR` is not the standardisation variance. Page 16 `TIPREDEFFECTstd` is `B · √v / √(-q / (2 a))` after strictly positive `asymDIFFUSION` `-q / (2 a)` and strictly positive predictor variance `v` (Driver et al., 2017, p. 16; §7.2; footnote 4; JSS PDF re-opened 2026-08-23T16:21Z). Unstandardised `B` is defined for a zero coefficient and for zero predictor variance and is not `TIPREDEFFECTstd`. The asymptotic standardisation `(-B / a) · √v / √p` is the total change and is not `TIPREDEFFECTstd`. The finite-interval standardisation `A^{-1}[e^{A Δt} − I] B · √v / √p` depends on the event interval and is not `TIPREDEFFECTstd`. `B · √v / √(trait + p + added)` uses `TRAITVAR` and is not `TIPREDEFFECTstd`. `TRAITVAR` is not the standardisation variance. Page 16 / Table 3 `T0TIPREDEFFECTstd` is `t0_b · √v / √p_0` after strictly positive free `T0VAR` `p_0` and strictly positive predictor variance `v` (Driver et al., 2017, Table 3, p. 13; p. 16; footnote 4; 2017-era ctsem `summary.ctsemFit.R`; JSS PDF re-opened 2026-08-23T17:20Z). The affected variance is free first-occasion `T0VAR`, not `asymDIFFUSION`. Unstandardised `t0_b` is defined for a zero coefficient and for zero predictor variance and is not `T0TIPREDEFFECTstd`. `TIPREDEFFECTstd` `B · √v / √(-q / (2 a))` is the continuous coefficient and is not `T0TIPREDEFFECTstd`. `asymTIPREDEFFECTstd` `(-B / a) · √v / √p` is the total change and is not `T0TIPREDEFFECTstd`. `t0_b · √v / √(trait + p_0 + added)` uses `TRAITVAR` and is not `T0TIPREDEFFECTstd`. `TRAITVAR` is not the standardisation variance. 2017-era `addedT0TIPREDVAR` is `t0_b² v` after a first-occasion time-independent predictor (Driver et al., 2017, Table 3, p. 13; p. 16; §7.2; 2017-era ctsem `summary.ctsemFit.R`; JSS PDF re-opened 2026-08-23T18:20Z). Form `t0_b` first, then square, then multiply by `v`. A zero coefficient or zero predictor variance is exactly zero. Free `T0TIPREDEFFECT` does not require `a < 0`. `(B / a)² v` is `addedTIPREDVAR` and is not this first-occasion map. `t0_b · √v / √p_0` is `T0TIPREDEFFECTstd` and is not this variance. Free `T0VAR` is not this extra TI variance. `TRAITVAR` is not this extra TI variance. Equation 5 of 2017-era `addedT0TIPREDVAR` is `λ² t0_b² v` (Driver et al., 2017, Eq. 5, p. 5; Table 3, p. 13; Table 2, p. 12; 2017-era ctsem `summary.ctsemFit.R`; JSS PDF re-opened 2026-08-23T19:10Z). Form `t0_b² v` first, then `(λ extra) λ` with `θ = 0`. A zero loading or zero extra is exactly zero. `t0_b² v` is the latent extra, not the observed extra. `λ² p_0 + θ` is first-occasion observed variance, not this extra. `λ² (B / a)² v` is Eq. 5 of `addedTIPREDVAR`, not this first-occasion observed extra. `MANIFESTVAR` `θ` is not this extra. Equation 5 of §7.2 `addedTIPREDVAR` is `λ² (B / a)² v` (Driver et al., 2017, Eq. 5, p. 5; Table 2, p. 12; §7.2, pp. 20–21; 2017-era ctsem `summary.ctsemFit.R`; JSS PDF re-opened 2026-08-23T19:23Z). Form `(B / a)² v` first, then `(λ extra) λ` with `θ = 0`. A zero loading or zero extra is exactly zero. Lasting asymptotic extra requires `a < 0`. `(B / a)² v` is the latent extra, not the observed extra. `λ² t0_b² v` is first-occasion extra observed TI variance, not this extra. `λ² p + θ` is stationary observed variance, not this extra. `MANIFESTVAR` `θ` is not this extra. Page 16 `TDPREDEFFECTstd` is `m · √v / √(-q / (2 a))` after strictly positive `asymDIFFUSION` and strictly positive time-dependent predictor variance. Unstandardised `M` is not `TDPREDEFFECTstd`. `TIPREDEFFECTstd` is not `TDPREDEFFECTstd` even when `M = B`. intercept-style `A^{-1}[e^{A Δt} − I] M · √v / √p` is not `TDPREDEFFECTstd`. `m · √v / √(trait + p + added)` uses `TRAITVAR` and is not `TDPREDEFFECTstd`. Table 3 / p. 16 `T0TDPREDEFFECTstd` is `t0_m · √v / √p_0` after strictly positive free `T0VAR` and strictly positive TD predictor variance. Unstandardised `t0_m` is not `T0TDPREDEFFECTstd`. `TDPREDEFFECTstd` uses `asymDIFFUSION` and is not `T0TDPREDEFFECTstd`. `T0TIPREDEFFECTstd` is not `T0TDPREDEFFECTstd` even when `t0_m = t0_b`. `t0_m · √v / √(trait + p_0 + added)` uses `TRAITVAR` and is not `T0TDPREDEFFECTstd`. Free `T0VAR` does not require `a < 0`. Page 16 `T0VARstd` is `p_0 / p_0 = 1` after strictly positive free `T0VAR` (`solve(sqrt(diag(T0VAR))) %&% T0VAR`; OpenMx `%&%` is `t(A) %*% B %*% A`; the default ridge is 0). Unstandardised `T0VAR` is not `T0VARstd`. `T0TDPREDEFFECTstd` is not `T0VARstd`. `addedT0TIPREDVAR` is not `T0VARstd`. Page 16 `TRAITVARstd` is `trait / trait = 1` after strictly positive `TRAITVAR` (`solve(sqrt(diag(TRAITVAR))) %&% TRAITVAR`; OpenMx `%&%` is `t(A) %*% B %*% A`; no ridge addend). Unstandardised `TRAITVAR` is not `TRAITVARstd`. `T0VARstd` is not `TRAITVARstd` even when both equal 1. `addedT0TIPREDVAR` is not `TRAITVARstd`. Page 16 `MANIFESTTRAITVARstd` is `ψ / ψ = 1` after strictly positive `MANIFESTTRAITVAR` (`solve(sqrt(diag(MANIFESTTRAITVAR))) %&% MANIFESTTRAITVAR`; OpenMx `%&%` is `t(A) %*% B %*% A`; 2017-era source adds ridging; default ridge is 0). Unstandardised `MANIFESTTRAITVAR` is not `MANIFESTTRAITVARstd`. `TRAITVARstd` is not `MANIFESTTRAITVARstd` even when both equal 1. `MANIFESTVAR` is not `MANIFESTTRAITVARstd`. Page 16 `MANIFESTVARstd` is `θ / θ = 1` after strictly positive `MANIFESTVAR` (`solve(sqrt(diag(MANIFESTVAR))) %&% MANIFESTVAR`; OpenMx `%&%` is `t(A) %*% B %*% A`; 2017-era source adds ridging; default ridge is 0; 2017-era `dimnames` assignment to `latentNames` is a source bug). Unstandardised `MANIFESTVAR` is not `MANIFESTVARstd`. `MANIFESTTRAITVARstd` is not `MANIFESTVARstd` even when both equal 1. Equation 5 `Var(y)` is not `MANIFESTVARstd`. Evolving from that stationary start with `CINT` and `TIPREDEFFECT` stays at the stationary mean. Equation 1 is the latent SDE, not the measurement model. Form `(λ p) λ` then add `θ`, then add `ψ`. `MANIFESTVAR` is `Θ`, not `Var(y)`. `MANIFESTTRAITVAR` is `Ψ_τ`, not `Θ`. `TRAITVAR` is latent and scaled by `λ²`. `Var(η)` is not `Var(y)`. - Separate cluster means before within-unit lag. CWC plus an event-time lag is not DSEM. Subtracting the person-specific mean from a raw autoregressive series does not isolate the lagged within-person effect (Curran & Bauer, 2011, pp. 607–608); already-centered residuals with irregular event intervals use the exact scalar map. - Do not treat the CWC cluster-mean coefficient as the between-cluster effect. It is the contextual effect `between − within` (Enders & Tofighi, 2007, Table 2, pp. 124–127). - Never use future-available evidence in historical model fits. diff --git a/crates/psychometric_core/src/error.rs b/crates/psychometric_core/src/error.rs index 4bc7a379..be7cbbf7 100644 --- a/crates/psychometric_core/src/error.rs +++ b/crates/psychometric_core/src/error.rs @@ -982,6 +982,26 @@ pub enum PsychometricError { /// `MANIFESTTRAITVARstd`. Measurement error is not the /// correlation form of indicator-level trait variance. MeasurementErrorIsNotStandardisedManifestTraitVariance, + /// Driver p. 16 `MANIFESTVARstd` was requested with a + /// non-positive measurement-error variance. The 2017-era + /// correlation form requires strictly positive `MANIFESTVAR`. + /// Zero `θ` makes `solve(sqrt(0))` fail in that source. + StandardisedManifestVarianceRequiresPositiveManifestVariance, + /// Driver Table 2 unstandardised `MANIFESTVAR` `Θ` was treated + /// as p. 16 `MANIFESTVARstd`. Unstandardised measurement error + /// is defined for a zero residual; standardised `MANIFESTVAR` + /// is not. + UnstandardisedManifestVarianceIsNotStandardisedManifestVariance, + /// Driver p. 16 `MANIFESTTRAITVARstd` was treated as p. 16 + /// `MANIFESTVARstd`. Equal numbers when both correlations equal + /// 1 are still distinct named quantities. `MANIFESTTRAITVAR` is + /// indicator-level trait variance; `MANIFESTVAR` is + /// contemporaneous measurement error. + StandardisedManifestTraitVarianceIsNotStandardisedManifestVariance, + /// Driver Eq. 5 observed-indicator variance was treated as p. 16 + /// `MANIFESTVARstd`. `λ² Var(η) + θ` is `Var(y)`, not the + /// correlation form of `Θ`. + ObservedVarianceIsNotStandardisedManifestVariance, } impl fmt::Display for PsychometricError { @@ -1705,6 +1725,18 @@ impl fmt::Display for PsychometricError { Self::MeasurementErrorIsNotStandardisedManifestTraitVariance => { "measurement error is not standardised manifest-trait variance" } + Self::StandardisedManifestVarianceRequiresPositiveManifestVariance => { + "standardised measurement-error variance requires strictly positive measurement-error variance" + } + Self::UnstandardisedManifestVarianceIsNotStandardisedManifestVariance => { + "unstandardised measurement-error variance is not standardised measurement-error variance" + } + Self::StandardisedManifestTraitVarianceIsNotStandardisedManifestVariance => { + "standardised manifest-trait variance is not standardised measurement-error variance" + } + Self::ObservedVarianceIsNotStandardisedManifestVariance => { + "observed-indicator variance is not standardised measurement-error variance" + } }; formatter.write_str(message) } @@ -2941,4 +2973,27 @@ mod tests { "measurement error is not standardised manifest-trait variance" ); } + + #[test] + fn standardised_manifest_variance_boundary_messages_are_stable() { + assert_eq!( + PsychometricError::StandardisedManifestVarianceRequiresPositiveManifestVariance + .to_string(), + "standardised measurement-error variance requires strictly positive measurement-error variance" + ); + assert_eq!( + PsychometricError::UnstandardisedManifestVarianceIsNotStandardisedManifestVariance + .to_string(), + "unstandardised measurement-error variance is not standardised measurement-error variance" + ); + assert_eq!( + PsychometricError::StandardisedManifestTraitVarianceIsNotStandardisedManifestVariance + .to_string(), + "standardised manifest-trait variance is not standardised measurement-error variance" + ); + assert_eq!( + PsychometricError::ObservedVarianceIsNotStandardisedManifestVariance.to_string(), + "observed-indicator variance is not standardised measurement-error variance" + ); + } } diff --git a/crates/psychometric_core/src/event_time.rs b/crates/psychometric_core/src/event_time.rs index 3b06936d..5310995b 100644 --- a/crates/psychometric_core/src/event_time.rs +++ b/crates/psychometric_core/src/event_time.rs @@ -310,7 +310,16 @@ //! re-opened 2026-08-23T22:28Z). The scalar map is `ψ / ψ = 1`. //! Unstandardised `MANIFESTTRAITVAR` is not `MANIFESTTRAITVARstd`. //! `TRAITVARstd` is not `MANIFESTTRAITVARstd` even when both equal 1. -//! `MANIFESTVAR` is not `MANIFESTTRAITVARstd`. The JSS article +//! `MANIFESTVAR` is not `MANIFESTTRAITVARstd`. Page 16 +//! `MANIFESTVARstd` is the correlation form +//! `solve(sqrt(diag(MANIFESTVAR) + ridging)) %&% MANIFESTVAR` after +//! strictly positive `MANIFESTVAR` (2017-era `summary.ctsemFit.R` +//! forms it whenever `verbose = TRUE`; unlike `TRAITVARstd` the +//! 2017-era source adds ridging; default ridge is 0; JSS PDF +//! re-opened 2026-08-23T22:40Z). The scalar map is `θ / θ = 1`. +//! Unstandardised `MANIFESTVAR` is not `MANIFESTVARstd`. +//! `MANIFESTTRAITVARstd` is not `MANIFESTVARstd` even when both +//! equal 1. `Var(y)` is not `MANIFESTVARstd`. The JSS article //! has no numbered §2.2 (2.1 is Continuous time and SEM; §3 follows). //! The difference quotient `(x(t+Δt) − x(t)) / Δt` (their //! Eqs. 3–4) is refused. This is not DSEM and not a matrix `expm`. @@ -2098,6 +2107,136 @@ pub fn refuse_measurement_error_as_standardised_manifest_trait_variance( Err(PsychometricError::MeasurementErrorIsNotStandardisedManifestTraitVariance) } +/// Exact scalar p. 16 `MANIFESTVARstd` after strictly positive +/// `MANIFESTVAR`. +/// +/// Driver, Oud, and Voelkle (2017, Table 2, p. 12; Eq. 5, p. 5; +/// p. 16; footnote 4; 2017-era ctsem `summary.ctsemFit.R`; JSS PDF +/// re-opened 2026-08-23T22:40Z from +/// ) +/// name `MANIFESTVAR` `Θ` the residual covariance of the +/// indicators. Equation 5 writes `ζ ~ N(0, Θ)`. Page 16 prints +/// standardised matrices with the suffix `std` when appropriate. +/// The 2017-era `summary.ctsemFit.R` forms `MANIFESTVARstd` whenever +/// `verbose = TRUE`, as +/// `solve(sqrt(diag(MANIFESTVAR) + ridging)) %&% MANIFESTVAR`. +/// `OpenMx` `%&%` is the quadratic form `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 2017-era source assigns +/// `dimnames(MANIFESTVARstd)` to `latentNames`; the matrix is +/// `n.manifest × n.manifest`. That assignment is a source bug and +/// is not this exact map. The scalar correlation is `θ / θ = 1` +/// after strictly positive `MANIFESTVAR`. Form strictly positive +/// `θ` first, then `1 / √θ`, then `(1 / √θ) θ (1 / √θ)`. +/// Unstandardised `MANIFESTVAR` is defined for a zero residual; +/// standardised `MANIFESTVAR` is not. Zero `θ` makes +/// `solve(sqrt(0))` fail in the 2017-era source and fails closed +/// here. Unlike `TRAITVAR` / `MANIFESTTRAITVAR`, that source does +/// not skip forming `MANIFESTVARstd` when `θ = 0`; the quadratic +/// still fails. Measurement-error variance is an event-time +/// structural quantity, so a non-event clock fails closed. +/// `MANIFESTVAR` does not require stable `a < 0`. Distinct +/// positive `θ` recover the same 1. `MANIFESTTRAITVARstd` +/// `ψ / ψ = 1` recovers the same number and remains a distinct +/// named quantity. Equation 5 `λ² Var(η) + θ` is `Var(y)`, not +/// this correlation. This is not a Kalman filter, not a matrix +/// `expm`, not DSEM, and not ctsem estimation. +/// +/// # Errors +/// +/// Returns [`PsychometricError::EventTimeRequired`] for any +/// non-event clock, +/// [`PsychometricError::StandardisedManifestVarianceRequiresPositiveManifestVariance`] +/// when `MANIFESTVAR` is zero, and +/// [`PsychometricError::InvalidNumericInput`] when the variance is +/// non-finite, negative, or the quadratic form overflows. +pub fn recover_standardised_manifest_variance( + measurement_error_variance: f64, + clock: LagClock, +) -> Result { + if !clock.admits_structural_lag() { + return Err(PsychometricError::EventTimeRequired); + } + if !measurement_error_variance.is_finite() || measurement_error_variance < 0.0 { + return Err(PsychometricError::InvalidNumericInput); + } + if measurement_error_variance == 0.0 { + return Err( + PsychometricError::StandardisedManifestVarianceRequiresPositiveManifestVariance, + ); + } + let process_sd = measurement_error_variance.sqrt(); + let inverse_sd = require_finite(1.0 / process_sd)?; + let scaled = require_finite(inverse_sd * measurement_error_variance)?; + require_finite(scaled * inverse_sd) +} + +/// Refuse treating unstandardised `MANIFESTVAR` as p. 16 +/// `MANIFESTVARstd`. +/// +/// Unstandardised `Θ` is defined for a zero residual. Footnote 4 +/// `MANIFESTVARstd` requires strictly positive `MANIFESTVAR`. Equal +/// numbers when `θ = 1` are still distinct named quantities. +/// +/// # Errors +/// +/// Always returns +/// [`PsychometricError::UnstandardisedManifestVarianceIsNotStandardisedManifestVariance`]. +pub fn refuse_unstandardised_manifest_variance_as_standardised_manifest_variance( + unstandardised_manifest_variance: f64, + standardised_manifest_variance: f64, +) -> Result { + let _ = ( + unstandardised_manifest_variance, + standardised_manifest_variance, + ); + Err(PsychometricError::UnstandardisedManifestVarianceIsNotStandardisedManifestVariance) +} + +/// Refuse treating p. 16 `MANIFESTTRAITVARstd` as p. 16 +/// `MANIFESTVARstd`. +/// +/// Both scalar correlations equal 1 after strictly positive +/// variances. `MANIFESTTRAITVARstd` standardises indicator-level +/// trait variance `Ψ_τ`. `MANIFESTVARstd` standardises +/// contemporaneous measurement error `Θ`. Equal numbers remain +/// distinct named quantities. +/// +/// # Errors +/// +/// Always returns +/// [`PsychometricError::StandardisedManifestTraitVarianceIsNotStandardisedManifestVariance`]. +pub fn refuse_standardised_manifest_trait_variance_as_standardised_manifest_variance( + standardised_manifest_trait_variance: f64, + standardised_manifest_variance: f64, +) -> Result { + let _ = ( + standardised_manifest_trait_variance, + standardised_manifest_variance, + ); + Err(PsychometricError::StandardisedManifestTraitVarianceIsNotStandardisedManifestVariance) +} + +/// Refuse treating Driver Eq. 5 `Var(y)` as p. 16 `MANIFESTVARstd`. +/// +/// `λ² Var(η) + θ` is the observed-indicator variance. Table 2 +/// names `MANIFESTVAR` `Θ`, not `Var(y)`. The correlation form of +/// `Θ` is not that observed variance. +/// +/// # Errors +/// +/// Always returns +/// [`PsychometricError::ObservedVarianceIsNotStandardisedManifestVariance`]. +pub fn refuse_observed_variance_as_standardised_manifest_variance( + observed_indicator_variance: f64, + standardised_manifest_variance: f64, +) -> Result { + let _ = (observed_indicator_variance, standardised_manifest_variance); + Err(PsychometricError::ObservedVarianceIsNotStandardisedManifestVariance) +} + /// Exact scalar 2017-era `addedT0TIPREDVAR` after a first-occasion /// time-independent predictor. /// @@ -9351,14 +9490,15 @@ mod tests { recover_standardised_initial_latent_variance, recover_standardised_initial_time_dependent_predictor_effect, recover_standardised_initial_time_independent_predictor_effect, - recover_standardised_manifest_trait_variance, recover_standardised_trait_variance, - recover_stationary_initial_latent_mean, recover_stationary_initial_latent_variance, - recover_stationary_initial_observed_mean, recover_stationary_initial_observed_variance, - recover_stationary_lagged_latent_covariance, recover_stationary_lagged_observed_covariance, - recover_stationary_latent_variance, recover_stationary_later_latent_variance, - recover_stationary_later_observed_variance, recover_time_dependent_predictor_impulse, - recover_time_dependent_predictor_impulse_carry, recover_trait_plus_state_lagged_covariance, - recover_trait_plus_state_latent_variance, recover_within_residual_event_time_log_rate, + recover_standardised_manifest_trait_variance, recover_standardised_manifest_variance, + recover_standardised_trait_variance, recover_stationary_initial_latent_mean, + recover_stationary_initial_latent_variance, recover_stationary_initial_observed_mean, + recover_stationary_initial_observed_variance, recover_stationary_lagged_latent_covariance, + recover_stationary_lagged_observed_covariance, recover_stationary_latent_variance, + recover_stationary_later_latent_variance, recover_stationary_later_observed_variance, + recover_time_dependent_predictor_impulse, recover_time_dependent_predictor_impulse_carry, + recover_trait_plus_state_lagged_covariance, recover_trait_plus_state_latent_variance, + recover_within_residual_event_time_log_rate, refuse_after_extra_process_contribution_as_observed_mean, refuse_after_extra_process_latent_mean_as_observed_mean, refuse_asymptotic_continuous_intercept_as_asymptotic_time_independent_effect, @@ -9450,6 +9590,7 @@ mod tests { refuse_measurement_error_as_standardised_manifest_trait_variance, refuse_measurement_error_as_stationary_lagged_observed_covariance, refuse_measurement_error_as_stationary_later_observed_variance, + refuse_observed_variance_as_standardised_manifest_variance, refuse_pooled_discrete_lag_across_unequal_intervals, refuse_predetermined_initial_latent_variance_as_initial_latent_variance, refuse_predetermined_initial_latent_variance_as_lagged_latent_covariance, @@ -9497,6 +9638,7 @@ mod tests { refuse_standardised_initial_latent_variance_as_standardised_trait_variance, refuse_standardised_initial_time_dependent_effect_as_standardised_initial_latent_variance, refuse_standardised_initial_time_independent_effect_as_standardised_initial_time_dependent_effect, + refuse_standardised_manifest_trait_variance_as_standardised_manifest_variance, refuse_standardised_trait_variance_as_standardised_manifest_trait_variance, refuse_stationary_initial_latent_mean_as_asymptotic_continuous_intercept, refuse_stationary_initial_latent_mean_as_asymptotic_time_independent_effect, @@ -9563,6 +9705,7 @@ mod tests { refuse_unstandardised_initial_time_dependent_effect_as_standardised_initial_time_dependent_effect, refuse_unstandardised_initial_time_independent_effect_as_standardised_initial_time_independent_effect, refuse_unstandardised_manifest_trait_variance_as_standardised_manifest_trait_variance, + refuse_unstandardised_manifest_variance_as_standardised_manifest_variance, refuse_unstandardised_trait_variance_as_standardised_trait_variance, ClusteredEventScore, EventOccasion, LagClock, LaggedWithinResidual, }; @@ -22334,6 +22477,72 @@ mod tests { ); } + #[test] + fn standardised_manifest_variance_recovers_driver_table_two_after_positive_theta() { + // Driver et al. (2017, Table 2 MANIFESTVAR Θ; Eq. 5 p.5; + // p. 16 MANIFESTVARstd; 2017-era summary.ctsemFit.R): + // form strictly positive Θ, then (1/√θ) θ (1/√θ) = 1. + // Default ridging = FALSE adds 0. + let measurement_error = 0.4_f64; + let recovered = + recover_standardised_manifest_variance(measurement_error, LagClock::EventTime) + .expect("MANIFESTVARstd"); + assert!((recovered - 1.0).abs() < 1e-15); + let larger_theta = recover_standardised_manifest_variance(1.6, LagClock::EventTime) + .expect("MANIFESTVARstd θ=1.6"); + assert_eq!(larger_theta.to_bits(), recovered.to_bits()); + let manifest_trait_std = + recover_standardised_manifest_trait_variance(measurement_error, LagClock::EventTime) + .expect("MANIFESTTRAITVARstd"); + assert_eq!(manifest_trait_std.to_bits(), recovered.to_bits()); + let observed = + recover_manifest_observed_variance(2.0, 0.4, measurement_error).expect("Var(y)"); + assert_eq!( + refuse_unstandardised_manifest_variance_as_standardised_manifest_variance( + measurement_error, + recovered + ), + Err(PsychometricError::UnstandardisedManifestVarianceIsNotStandardisedManifestVariance) + ); + assert_eq!( + refuse_standardised_manifest_trait_variance_as_standardised_manifest_variance( + manifest_trait_std, + recovered + ), + Err( + PsychometricError::StandardisedManifestTraitVarianceIsNotStandardisedManifestVariance + ) + ); + assert_eq!( + refuse_observed_variance_as_standardised_manifest_variance(observed, recovered), + Err(PsychometricError::ObservedVarianceIsNotStandardisedManifestVariance) + ); + } + + #[test] + fn standardised_manifest_variance_fails_closed_when_unstandardised_is_defined() { + assert_eq!( + recover_standardised_manifest_variance(0.0, LagClock::EventTime), + Err(PsychometricError::StandardisedManifestVarianceRequiresPositiveManifestVariance) + ); + assert_eq!( + recover_standardised_manifest_variance(0.4, LagClock::SystemTime), + Err(PsychometricError::EventTimeRequired) + ); + assert_eq!( + recover_standardised_manifest_variance(-0.4, LagClock::EventTime), + Err(PsychometricError::InvalidNumericInput) + ); + assert_eq!( + recover_standardised_manifest_variance(f64::NAN, LagClock::EventTime), + Err(PsychometricError::InvalidNumericInput) + ); + assert_eq!( + recover_standardised_manifest_variance(f64::INFINITY, LagClock::EventTime), + Err(PsychometricError::InvalidNumericInput) + ); + } + #[test] fn standardised_initial_time_independent_effect_recovers_driver_table_three_after_positive_variances( ) { diff --git a/crates/psychometric_core/src/lib.rs b/crates/psychometric_core/src/lib.rs index 6a311ca2..27efb11b 100644 --- a/crates/psychometric_core/src/lib.rs +++ b/crates/psychometric_core/src/lib.rs @@ -502,6 +502,23 @@ //! distinct positive `ψ` recover the same 1; `TRAITVARstd` //! recovers the same number and remains a distinct named quantity; //! `θ` is not `MANIFESTTRAITVARstd`), +//! recovers the Driver p. 16 `MANIFESTVARstd` as the +//! correlation form `solve(sqrt(diag(MANIFESTVAR))) %&% +//! MANIFESTVAR` after forming strictly positive `MANIFESTVAR` +//! (JSS PDF re-opened 2026-08-23T22:40Z; Table 2, p. 12; Eq. 5, +//! p. 5; 2017-era `summary.ctsemFit.R` forms that quadratic whenever +//! `verbose = TRUE`; `OpenMx` `%&%` is `t(A) %*% B %*% A`; unlike +//! `TRAITVARstd` the 2017-era source adds +//! `diag(c(ridging), n.manifest)`; the default ridge is 0 and is +//! not this exact map; the 2017-era `dimnames` assignment to +//! `latentNames` is a source bug and is not this exact map; the +//! scalar map is `θ / θ = 1`; unstandardised `Θ` is defined for a +//! zero residual and is not `MANIFESTVARstd`; zero `MANIFESTVAR` +//! makes `solve(sqrt(0))` fail and fails closed; a non-event clock +//! fails closed; `MANIFESTVAR` does not require stable `a < 0`; +//! distinct positive `θ` recover the same 1; `MANIFESTTRAITVARstd` +//! recovers the same number and remains a distinct named quantity; +//! `λ² Var(η) + θ` is not `MANIFESTVARstd`), //! and refuses //! latent-mean comparison below strong invariance. @@ -686,6 +703,8 @@ pub use event_time::recover_standardised_initial_time_dependent_predictor_effect pub use event_time::recover_standardised_initial_time_independent_predictor_effect; /// Exact scalar p. 16 `MANIFESTTRAITVARstd` `ψ / ψ = 1` after strictly positive `MANIFESTTRAITVAR`. pub use event_time::recover_standardised_manifest_trait_variance; +/// Exact scalar p. 16 `MANIFESTVARstd` `θ / θ = 1` after strictly positive `MANIFESTVAR`. +pub use event_time::recover_standardised_manifest_variance; /// Exact scalar p. 16 `TRAITVARstd` `trait / trait = 1` after strictly positive `TRAITVAR`. pub use event_time::recover_standardised_trait_variance; /// Exact scalar p. 16 stationary `T0MEANS` `-κ / a + −B z / a`. @@ -906,6 +925,8 @@ pub use event_time::refuse_measurement_error_as_standardised_manifest_trait_vari pub use event_time::refuse_measurement_error_as_stationary_lagged_observed_covariance; /// Refuse treating `MANIFESTVAR` as Eq. 5 of later-occasion §4.3 stationary `T0VAR`. pub use event_time::refuse_measurement_error_as_stationary_later_observed_variance; +/// Refuse treating Driver Eq. 5 `Var(y)` as p. 16 `MANIFESTVARstd`. +pub use event_time::refuse_observed_variance_as_standardised_manifest_variance; /// Refuse pooling discrete lags from unequal event intervals. pub use event_time::refuse_pooled_discrete_lag_across_unequal_intervals; /// Refuse treating predetermined first-occasion variance as free first-occasion `T0VAR`. @@ -1000,6 +1021,8 @@ pub use event_time::refuse_standardised_initial_latent_variance_as_standardised_ pub use event_time::refuse_standardised_initial_time_dependent_effect_as_standardised_initial_latent_variance; /// Refuse treating Table 3 / p. 16 `T0TIPREDEFFECTstd` as Table 3 / p. 16 `T0TDPREDEFFECTstd`. pub use event_time::refuse_standardised_initial_time_independent_effect_as_standardised_initial_time_dependent_effect; +/// Refuse treating p. 16 `MANIFESTTRAITVARstd` as p. 16 `MANIFESTVARstd`. +pub use event_time::refuse_standardised_manifest_trait_variance_as_standardised_manifest_variance; /// Refuse treating p. 16 `TRAITVARstd` as p. 16 `MANIFESTTRAITVARstd`. pub use event_time::refuse_standardised_trait_variance_as_standardised_manifest_trait_variance; /// Refuse treating p. 16 stationary `T0MEANS` as `asymCINT`. @@ -1134,6 +1157,8 @@ pub use event_time::refuse_unstandardised_initial_time_dependent_effect_as_stand pub use event_time::refuse_unstandardised_initial_time_independent_effect_as_standardised_initial_time_independent_effect; /// Refuse treating unstandardised `MANIFESTTRAITVAR` as p. 16 `MANIFESTTRAITVARstd`. pub use event_time::refuse_unstandardised_manifest_trait_variance_as_standardised_manifest_trait_variance; +/// Refuse treating unstandardised `MANIFESTVAR` as p. 16 `MANIFESTVARstd`. +pub use event_time::refuse_unstandardised_manifest_variance_as_standardised_manifest_variance; /// Refuse treating unstandardised `TRAITVAR` as p. 16 `TRAITVARstd`. pub use event_time::refuse_unstandardised_trait_variance_as_standardised_trait_variance; /// Indicator coordinate kind. diff --git a/crates/psychometric_core/tests/multilevel_event_time_recovery_contract.rs b/crates/psychometric_core/tests/multilevel_event_time_recovery_contract.rs index d1f4fc65..c2351743 100644 --- a/crates/psychometric_core/tests/multilevel_event_time_recovery_contract.rs +++ b/crates/psychometric_core/tests/multilevel_event_time_recovery_contract.rs @@ -51,14 +51,15 @@ use psychometric_core::{ recover_standardised_initial_latent_variance, recover_standardised_initial_time_dependent_predictor_effect, recover_standardised_initial_time_independent_predictor_effect, - recover_standardised_manifest_trait_variance, recover_standardised_trait_variance, - recover_stationary_initial_latent_mean, recover_stationary_initial_latent_variance, - recover_stationary_initial_observed_mean, recover_stationary_initial_observed_variance, - recover_stationary_lagged_latent_covariance, recover_stationary_lagged_observed_covariance, - recover_stationary_latent_variance, recover_stationary_later_latent_variance, - recover_stationary_later_observed_variance, recover_time_dependent_predictor_impulse, - recover_time_dependent_predictor_impulse_carry, recover_trait_plus_state_lagged_covariance, - recover_trait_plus_state_latent_variance, recover_within_residual_event_time_log_rate, + recover_standardised_manifest_trait_variance, recover_standardised_manifest_variance, + recover_standardised_trait_variance, recover_stationary_initial_latent_mean, + recover_stationary_initial_latent_variance, recover_stationary_initial_observed_mean, + recover_stationary_initial_observed_variance, recover_stationary_lagged_latent_covariance, + recover_stationary_lagged_observed_covariance, recover_stationary_latent_variance, + recover_stationary_later_latent_variance, recover_stationary_later_observed_variance, + recover_time_dependent_predictor_impulse, recover_time_dependent_predictor_impulse_carry, + recover_trait_plus_state_lagged_covariance, recover_trait_plus_state_latent_variance, + recover_within_residual_event_time_log_rate, refuse_after_extra_process_contribution_as_observed_mean, refuse_after_extra_process_latent_mean_as_observed_mean, refuse_asymptotic_continuous_intercept_as_asymptotic_time_independent_effect, @@ -147,6 +148,7 @@ use psychometric_core::{ refuse_measurement_error_as_standardised_manifest_trait_variance, refuse_measurement_error_as_stationary_lagged_observed_covariance, refuse_measurement_error_as_stationary_later_observed_variance, + refuse_observed_variance_as_standardised_manifest_variance, refuse_pooled_discrete_lag_across_unequal_intervals, refuse_predetermined_initial_latent_variance_as_initial_latent_variance, refuse_predetermined_initial_latent_variance_as_lagged_latent_covariance, @@ -194,6 +196,7 @@ use psychometric_core::{ refuse_standardised_initial_latent_variance_as_standardised_trait_variance, refuse_standardised_initial_time_dependent_effect_as_standardised_initial_latent_variance, refuse_standardised_initial_time_independent_effect_as_standardised_initial_time_dependent_effect, + refuse_standardised_manifest_trait_variance_as_standardised_manifest_variance, refuse_standardised_trait_variance_as_standardised_manifest_trait_variance, refuse_stationary_initial_latent_mean_as_asymptotic_continuous_intercept, refuse_stationary_initial_latent_mean_as_asymptotic_time_independent_effect, @@ -260,6 +263,7 @@ use psychometric_core::{ refuse_unstandardised_initial_time_dependent_effect_as_standardised_initial_time_dependent_effect, refuse_unstandardised_initial_time_independent_effect_as_standardised_initial_time_independent_effect, refuse_unstandardised_manifest_trait_variance_as_standardised_manifest_trait_variance, + refuse_unstandardised_manifest_variance_as_standardised_manifest_variance, refuse_unstandardised_trait_variance_as_standardised_trait_variance, ClusteredEventScore, ClusteredScore, EventOccasion, IndicatorKind, LagClock, LaggedWithinResidual, PsychometricError, @@ -9221,6 +9225,63 @@ fn standardised_manifest_trait_variance_refuses_non_event_clocks_and_does_not_ke ); } +#[test] +fn standardised_manifest_variance_recovers_driver_table_two_correlation() { + let measurement_error = 0.4_f64; + let recovered = recover_standardised_manifest_variance(measurement_error, LagClock::EventTime) + .expect("MANIFESTVARstd"); + assert!((recovered - 1.0).abs() < 1e-15); + let larger_theta = recover_standardised_manifest_variance(1.6, LagClock::EventTime) + .expect("MANIFESTVARstd θ=1.6"); + assert_eq!(larger_theta.to_bits(), recovered.to_bits()); + let manifest_trait_std = + recover_standardised_manifest_trait_variance(measurement_error, LagClock::EventTime) + .expect("MANIFESTTRAITVARstd"); + assert_eq!(manifest_trait_std.to_bits(), recovered.to_bits()); + let recovered_error = (recovered - 1.0).abs(); + let unstandardised_error = (measurement_error - 1.0).abs(); + assert!( + recovered_error < unstandardised_error, + "Driver et al. (2017, Table 2): unstandardised MANIFESTVAR RMSE {unstandardised_error} must exceed MANIFESTVARstd RMSE {recovered_error}" + ); + let observed = recover_manifest_observed_variance(2.0, 0.4, measurement_error).expect("Var(y)"); + let observed_rmse = (observed - 1.0).abs(); + assert!( + recovered_error < observed_rmse, + "Driver et al. (2017, Eq. 5): Var(y) RMSE {observed_rmse} must exceed MANIFESTVARstd RMSE {recovered_error}" + ); + assert_eq!( + refuse_unstandardised_manifest_variance_as_standardised_manifest_variance( + measurement_error, + recovered + ), + Err(PsychometricError::UnstandardisedManifestVarianceIsNotStandardisedManifestVariance) + ); + assert_eq!( + refuse_standardised_manifest_trait_variance_as_standardised_manifest_variance( + manifest_trait_std, + recovered + ), + Err(PsychometricError::StandardisedManifestTraitVarianceIsNotStandardisedManifestVariance) + ); + assert_eq!( + refuse_observed_variance_as_standardised_manifest_variance(observed, recovered), + Err(PsychometricError::ObservedVarianceIsNotStandardisedManifestVariance) + ); +} + +#[test] +fn standardised_manifest_variance_refuses_non_event_clocks_and_does_not_keep_zero_variance() { + assert_eq!( + recover_standardised_manifest_variance(0.4, LagClock::SystemTime), + Err(PsychometricError::EventTimeRequired) + ); + assert_eq!( + recover_standardised_manifest_variance(0.0, LagClock::EventTime), + Err(PsychometricError::StandardisedManifestVarianceRequiresPositiveManifestVariance) + ); +} + #[test] fn standardised_initial_time_independent_effect_recovers_driver_table_three_footnote_four() { let initial_variance = 1.6_f64; diff --git a/crates/psychometric_core/tests/scientific_claim_boundary_contract.rs b/crates/psychometric_core/tests/scientific_claim_boundary_contract.rs index 590d8998..04528406 100644 --- a/crates/psychometric_core/tests/scientific_claim_boundary_contract.rs +++ b/crates/psychometric_core/tests/scientific_claim_boundary_contract.rs @@ -48,14 +48,15 @@ use psychometric_core::{ recover_standardised_initial_latent_variance, recover_standardised_initial_time_dependent_predictor_effect, recover_standardised_initial_time_independent_predictor_effect, - recover_standardised_manifest_trait_variance, recover_standardised_trait_variance, - recover_stationary_initial_latent_mean, recover_stationary_initial_latent_variance, - recover_stationary_initial_observed_mean, recover_stationary_initial_observed_variance, - recover_stationary_lagged_latent_covariance, recover_stationary_lagged_observed_covariance, - recover_stationary_latent_variance, recover_stationary_later_latent_variance, - recover_stationary_later_observed_variance, recover_time_dependent_predictor_impulse, - recover_time_dependent_predictor_impulse_carry, recover_trait_plus_state_lagged_covariance, - recover_trait_plus_state_latent_variance, recover_within_residual_event_time_log_rate, + recover_standardised_manifest_trait_variance, recover_standardised_manifest_variance, + recover_standardised_trait_variance, recover_stationary_initial_latent_mean, + recover_stationary_initial_latent_variance, recover_stationary_initial_observed_mean, + recover_stationary_initial_observed_variance, recover_stationary_lagged_latent_covariance, + recover_stationary_lagged_observed_covariance, recover_stationary_latent_variance, + recover_stationary_later_latent_variance, recover_stationary_later_observed_variance, + recover_time_dependent_predictor_impulse, recover_time_dependent_predictor_impulse_carry, + recover_trait_plus_state_lagged_covariance, recover_trait_plus_state_latent_variance, + recover_within_residual_event_time_log_rate, refuse_after_extra_process_contribution_as_observed_mean, refuse_after_extra_process_latent_mean_as_observed_mean, refuse_asymptotic_continuous_intercept_as_asymptotic_time_independent_effect, @@ -144,6 +145,7 @@ use psychometric_core::{ refuse_measurement_error_as_standardised_manifest_trait_variance, refuse_measurement_error_as_stationary_lagged_observed_covariance, refuse_measurement_error_as_stationary_later_observed_variance, + refuse_observed_variance_as_standardised_manifest_variance, refuse_predetermined_initial_latent_variance_as_initial_latent_variance, refuse_predetermined_initial_latent_variance_as_lagged_latent_covariance, refuse_predetermined_initial_latent_variance_as_later_latent_variance, @@ -190,6 +192,7 @@ use psychometric_core::{ refuse_standardised_initial_latent_variance_as_standardised_trait_variance, refuse_standardised_initial_time_dependent_effect_as_standardised_initial_latent_variance, refuse_standardised_initial_time_independent_effect_as_standardised_initial_time_dependent_effect, + refuse_standardised_manifest_trait_variance_as_standardised_manifest_variance, refuse_standardised_trait_variance_as_standardised_manifest_trait_variance, refuse_stationary_initial_latent_mean_as_asymptotic_continuous_intercept, refuse_stationary_initial_latent_mean_as_asymptotic_time_independent_effect, @@ -255,6 +258,7 @@ use psychometric_core::{ refuse_unstandardised_initial_time_dependent_effect_as_standardised_initial_time_dependent_effect, refuse_unstandardised_initial_time_independent_effect_as_standardised_initial_time_independent_effect, refuse_unstandardised_manifest_trait_variance_as_standardised_manifest_trait_variance, + refuse_unstandardised_manifest_variance_as_standardised_manifest_variance, refuse_unstandardised_trait_variance_as_standardised_trait_variance, ClusteredEventScore, ClusteredScore, IndicatorKind, LagClock, LaggedWithinResidual, }; @@ -5220,6 +5224,70 @@ fn standardised_manifest_trait_variance_is_not_unstandardised_or_traitvarstd() { ); } +#[allow(clippy::too_many_lines)] +#[test] +fn standardised_manifest_variance_is_not_unstandardised_or_manifesttraitvarstd() { + let measurement_error = 0.4_f64; + let recovered = recover_standardised_manifest_variance(measurement_error, LagClock::EventTime) + .expect("MANIFESTVARstd"); + assert!( + (recovered - 1.0).abs() < 1e-15, + "Driver et al. (2017, p. 16 / 2017-era summary.ctsemFit.R): MANIFESTVARstd is θ/θ = 1" + ); + let larger_theta = recover_standardised_manifest_variance(1.6, LagClock::EventTime) + .expect("MANIFESTVARstd θ=1.6"); + assert_eq!( + larger_theta.to_bits(), + recovered.to_bits(), + "Driver et al. (2017, p. 16): distinct positive MANIFESTVAR recover the same MANIFESTVARstd" + ); + let manifest_trait_std = + recover_standardised_manifest_trait_variance(measurement_error, LagClock::EventTime) + .expect("MANIFESTTRAITVARstd"); + assert_eq!( + manifest_trait_std.to_bits(), + recovered.to_bits(), + "Driver et al. (2017, p. 16): MANIFESTTRAITVARstd and MANIFESTVARstd equal 1 and remain distinct named quantities" + ); + assert!((measurement_error - recovered).abs() > 1e-3); + let observed = recover_manifest_observed_variance(2.0, 0.4, measurement_error).expect("Var(y)"); + assert!((observed - recovered).abs() > 1e-3); + assert_eq!( + recover_standardised_manifest_variance(0.0, LagClock::EventTime), + Err( + psychometric_core::PsychometricError::StandardisedManifestVarianceRequiresPositiveManifestVariance + ) + ); + assert_eq!( + recover_standardised_manifest_variance(measurement_error, LagClock::SystemTime), + Err(psychometric_core::PsychometricError::EventTimeRequired) + ); + assert_eq!( + refuse_unstandardised_manifest_variance_as_standardised_manifest_variance( + measurement_error, + recovered + ), + Err( + psychometric_core::PsychometricError::UnstandardisedManifestVarianceIsNotStandardisedManifestVariance + ) + ); + assert_eq!( + refuse_standardised_manifest_trait_variance_as_standardised_manifest_variance( + manifest_trait_std, + recovered + ), + Err( + psychometric_core::PsychometricError::StandardisedManifestTraitVarianceIsNotStandardisedManifestVariance + ) + ); + assert_eq!( + refuse_observed_variance_as_standardised_manifest_variance(observed, recovered), + Err( + psychometric_core::PsychometricError::ObservedVarianceIsNotStandardisedManifestVariance + ) + ); +} + #[allow(clippy::too_many_lines)] #[test] fn standardised_initial_time_independent_effect_is_not_unstandardised_or_trait_contaminated() { diff --git a/docs/TRACEABILITY.md b/docs/TRACEABILITY.md index f87e80d5..7341551a 100644 --- a/docs/TRACEABILITY.md +++ b/docs/TRACEABILITY.md @@ -28,7 +28,7 @@ The full APA 7th standards/literature register remains `docs/research/standards- | report template/section/copied/style/modality method effects | ADR 0004/0012; PRD/TRD | simulation truth factors implemented; estimator-side method model remains future | partial | | candidate K statistical/Pareto gates + blinded LLM review | ADR 0012; research | future `model_selection` | accepted-target | | compositional topic correlation / stable clustering | ADR 0005/0012; research | future `network_analysis` | accepted-target | -| posterior ESEM / longitudinal invariance / DSEM | ADR 0005 | `psychometric_core` construct/input gates, true-loading OLS recovery, posterior-draw point-estimate averaging, Rubin `T` on draw-level OLS loadings, CWC within/between OLS plus the contextual effect, event-time log-rate, constant- and time-varying-predictor discrete effects (Voelkle Eqs. 12 and 14), exact scalar discrete process noise (Driver et al., 2017, Eq. 3), lagged latent covariance and unconditional latent variance (Driver et al., 2017, Eq. 3–4), stationary within-subject variance (Driver et al., 2017, Eq. 4 as `Δt → ∞`; `asymDIFFUSION`), trait-plus-state variance (Driver et al., 2017, §4.3 `TRAITVAR`; not process noise), observed-indicator variance and lagged observed covariance (Driver et al., 2017, Eq. 5; Table 2 `MANIFESTVAR` is `Θ`, not `Var(y)`; `MANIFESTTRAITVAR` is not `MANIFESTVAR`; `Θ` does not enter lagged observed covariance; observed-indicator mean is `τ + λ μ`; `MANIFESTMEANS` is not `E(y)`; `CINT` is not `MANIFESTMEANS`; discrete latent mean is `exp(a Δt) μ_0 + (exp(a Δt) − 1)/a κ`; `T0MEANS` is not `μ_t`; evolved observed mean is `τ + λ μ_t`; `τ + λ μ_0` is not `E(y_t)`; contemporaneous `TDPREDEFFECT` impulse is `m x`, not `CINT`, not `TIPREDEFFECT`, and not Voelkle Eq. 14; Eq. 5 of that contemporaneous impulse is `τ + λ(μ_t + m x)`, and `τ + λ μ_t` is not that observed mean; time-independent `TIPREDEFFECT` increment is `A^{-1}[e^{A Δt} − I] B z`, not `CINT`, not `M x`, not Voelkle Eq. 14, and not the coefficient `B`; Eq. 5 of that increment is `τ + λ(μ_t + A^{-1}[e^{A Δt} − I] B z)`, and `τ + λ μ_t` is not that observed mean; `τ + λ(μ_t + m x)` is not that observed mean; `τ + λ(μ_t + e^{a(t−u)} m x)` is not that observed mean when `u ≠ t`; within-interval `TDPREDEFFECT` carry is `e^{A(t−u)} M x` for `t0 < u < t`, not the contemporaneous Dirac, not `CINT`, not `TIPREDEFFECT`, and not Voelkle Eq. 14; Eq. 5 of that carry is `τ + λ(μ_t + e^{a(t−u)} m x)`, and `τ + λ μ_t` is not that observed mean; `τ + λ(μ_t + m x)` is not that carried observed mean when `u ≠ t`; §7.2 level-change `CINT` is `κ = −a m x` (`a < 0`; not the dissipating Dirac, not a free `CINT`, not `TIPREDEFFECT`; Eq. 3 of that setting is `(1 − e^{a Δt}) m x`); §7.2 extra-process contribution is `a_{ηξ} x (e^{ε Δt} − e^{a Δt}) / (ε − a)` (not `κ = −a m x`, not `(1 − e^{a Δt}) m x`, not the dissipating Dirac; `ε ≥ 0` fails closed; Eq. 5 of that contribution is `τ + λ(μ_t + a_{ηξ} x (e^{ε Δt} − e^{a Δt}) / (ε − a)`; extra `LAMBDA` is 0; `τ + λ μ_t` is not that observed mean; after-t0 extra-process `TDPREDEFFECT` uses `t − u` with `t0 < u < t` while `μ_t` uses `Δt`; that after-t0 observed mean is not the first-occasion extra-process observed mean; §7.2 `asymTIPREDEFFECT` is `-B z / a` for `a < 0` and is not `B`, not `A^{-1}[e^{A Δt} − I] B z`, not `CINT`, and not `M x`; §7.2 `addedTIPREDVAR` is `(B / a)² v` and is not `TRAITVAR`, not `asymDIFFUSION`, and not `-B z / a`; Table 2 `asymCINT` is `-κ / a` for `a < 0` and is not `κ`, not `A^{-1}[e^{A Δt} − I] κ`, not `T0MEANS`, and not `-B z / a`; p. 16 stationary `T0MEANS` is `-κ / a + −B z / a` and is not free `T0MEANS`, not `asymCINT` alone, not `asymTIPREDEFFECT` alone, and not the finite-interval discrete latent mean; Eq. 5 of that constrained mean is `τ + λ(−κ / a + −B z / a)`; `τ + λ μ_0` is not that observed mean; `MANIFESTMEANS` is not `E(y_0)`; the constrained latent mean is not `E(y_0)`; stationary `T0VAR` is `trait + −q / (2 a) + (B / a)² v` (not free `T0VAR`, not `asymDIFFUSION` alone, not `TRAITVAR` alone, not `addedTIPREDVAR` alone, and not the finite-interval discrete latent variance. Eq. 5 of that constrained variance is `λ²(trait + −q / (2 a) + (B / a)² v) + θ + ψ` (JSS PDF re-opened 2026-08-22T03:20Z; form the stationary latent variance first, then `λ² p + θ + ψ`; `λ² p_0` is not that observed variance; `λ²(−q / (2 a)) + θ` is not that observed variance when `TRAITVAR` or `addedTIPREDVAR` is nonzero; `MANIFESTVAR` is not `Var(y_0)`; the constrained latent variance is not `Var(y_0)`)); lagged stationary `T0VAR` is `trait + e^{a Δt}(−q / (2 a)) + (B / a)² v` (trait and `addedTIPREDVAR` do not decay; contemporaneous `T0VAR` is not that lagged map; decaying the constrained total as if it were all state is not that lagged map; Eq. 5 of that lagged covariance is `λ²(trait + e^{a Δt}(−q / (2 a)) + (B / a)² v) + ψ`; `Θ` does not enter; contemporaneous `Var(y_0)` is not that lagged observed covariance; the lagged latent covariance is not that observed covariance); later-occasion stationary `T0VAR` is `trait + e^{2 a Δt}(−q / (2 a)) + Q_Δt + (B / a)² v` (trait and `addedTIPREDVAR` do not enter `Q_Δt`; under stationarity that composition equals contemporaneous `T0VAR`; evolving the constrained total as if it were all state is not that later map; the lagged covariance omits `Q_Δt`; `Q_Δt` is not that later map; Eq. 5 of that later-occasion variance is `λ²(trait + e^{2 a Δt}(−q / (2 a)) + Q_Δt + (B / a)² v) + θ + ψ`; lagged observed covariance omits `Q_Δt` and `θ`; `MANIFESTVAR` is not `Var(y_t)`; the later-occasion latent variance is not `Var(y_t)`); predetermined later-occasion `T0VAR` is `trait + e^{2 a Δt} p_0 + Q_Δt + (B / a)² v` (free `T0VAR` `p_0` is not that later map; setting `p_0 = −q / (2 a)` recovers the stationary later-occasion map; stationary later variance uses `−q / (2 a)` in place of `p_0` and is not that later map when `p_0` is free; evolving `trait + p_0 + (B / a)² v` as if it were all state is not that later map; Eq. 5 of that predetermined later-occasion variance is `λ²(trait + e^{2 a Δt} p_0 + Q_Δt + (B / a)² v) + θ + ψ`; `MANIFESTVAR` is not `Var(y_t)`; the predetermined later-occasion latent variance is not `Var(y_t)`; stationary later observed variance is not that observed variance when `p_0` is free); predetermined lagged `T0VAR` is `trait + e^{a Δt} p_0 + (B / a)² v` (free `T0VAR` `p_0` is not that lagged map; setting `p_0 = −q / (2 a)` recovers the stationary lagged map; stationary lagged covariance uses `−q / (2 a)` in place of `p_0` and is not that lagged map when `p_0` is free; evolving `trait + p_0 + (B / a)² v` as if it were all state is not that lagged map; later-occasion variance includes `Q_Δt` and is not that lagged map; Eq. 5 of that predetermined lagged covariance is `λ²(trait + e^{a Δt} p_0 + (B / a)² v) + ψ`; `MANIFESTVAR` does not enter; the predetermined lagged latent covariance is not that observed covariance; predetermined later observed variance includes `Q_Δt` and `θ` and is not that lagged observed covariance; stationary lagged observed covariance is not that observed covariance when `p_0` is free; the predetermined first-occasion variance of §4.3 predetermined `T0VAR` is `trait + p_0 + (B / a)² v`; free `p_0` is not that map; stationary first-occasion variance uses `−q / (2 a)` in place of `p_0` and is not that map when `p_0` is free; lagged covariance decays the state and is not that map; later-occasion variance includes `Q_Δt` and is not that map; Eq. 5 of that predetermined first-occasion variance is `λ²(trait + p_0 + (B / a)² v) + θ + ψ`; `MANIFESTVAR` is not that first-occasion observed variance; the predetermined first-occasion latent variance is not that observed variance; stationary first-occasion observed variance is not that observed variance when `p_0` is free; predetermined later observed variance includes `Q_Δt` and is not that first-occasion observed variance; later-start lagged covariance of predetermined `T0VAR` is `trait + e^{a s}(e^{2 a u} p_0 + Q_u) + (B / a)² v` (Driver et al., 2017, §4.3 `startoffset`; Eq. 4; JSS PDF re-opened 2026-08-23T10:27Z; first-occasion lagged omits `e^{a s} Q_u`; later-occasion variance does not lag; stationary lagged uses `−q / (2 a)`; decaying the later total is not that map; Eq. 5 of that later-start lagged covariance is `λ²` of it plus `ψ`; `Θ` does not enter; first-occasion lagged observed omits `e^{a s} Q_u`; later observed variance includes `Q_u` and `θ`; later-start later-occasion variance of predetermined `T0VAR` is `trait + e^{2 a s}(e^{2 a u} p_0 + Q_u) + Q_s + (B / a)² v` (Driver et al., 2017, §4.3 `startoffset`; Eq. 3–4 Chapman–Kolmogorov `Q_{u+s} = e^{2 a s} Q_u + Q_s`; JSS PDF re-opened 2026-08-23T11:05Z; later-occasion variance at `u` omits `Q_s`; later-start lagged covariance omits `Q_s`; stationary later uses `−q / (2 a)`; evolving the later total as if it were all state is not that map; ignoring `startoffset` omits `e^{2 a s} Q_u`; Eq. 5 of that later-start later-occasion variance is `λ²` of it plus `θ + ψ`; `MANIFESTVAR` is not that observed variance; p. 16 `discreteDRIFTstd` is `e^{a Δt}` after strictly positive `asymDIFFUSION` `-q / (2 a)` (footnote 4; unstandardised `e^{a Δt}` is defined for growing `a ≥ 0` and for zero diffusion and is not `discreteDRIFTstd`; the §7.1 trait-plus-state autocorrelation uses `TRAITVAR` and is not `discreteDRIFTstd`; p. 16 `discreteDIFFUSIONstd` is `Q_Δt / (−q / (2 a))` after strictly positive `asymDIFFUSION` `-q / (2 a)` (footnote 4; unstandardised `Q_Δt` is defined for growing `a ≥ 0` and for zero diffusion and is not `discreteDIFFUSIONstd`; the continuous standardisation `−2 a` is not `discreteDIFFUSIONstd`; `Q_Δt / (trait + p + added)` uses `TRAITVAR` and is not `discreteDIFFUSIONstd`; `TRAITVAR` is not the standardisation variance; p. 16 `DIFFUSIONstd` is `q / (−q / (2 a)) = −2 a` after strictly positive `asymDIFFUSION` `-q / (2 a)` (Driver et al., 2017, p. 16; Eq. 4; footnote 4; JSS PDF re-opened 2026-08-23T13:20Z; unstandardised `q` is defined for growing `a ≥ 0` and for zero diffusion and is not `DIFFUSIONstd`; the discrete standardisation `Q_Δt / (−q / (2 a))` depends on `Δt` and is not `DIFFUSIONstd`; `q / (trait + p + added)` uses `TRAITVAR` and is not `DIFFUSIONstd`; `TRAITVAR` is not the standardisation variance; p. 16 `DRIFTstd` is the continuous auto-effect after strictly positive `asymDIFFUSION` `-q / (2 a)` (Driver et al., 2017, p. 16; Eq. 1; footnote 4; JSS PDF re-opened 2026-08-23T13:28Z); unstandardised `a` is defined for growing `a ≥ 0` and for zero diffusion and is not `DRIFTstd`; the discrete standardisation `e^{a Δt}` depends on the event interval and is not `DRIFTstd`; `a p / (trait + p + added)` uses `TRAITVAR` and is not `DRIFTstd`; `TRAITVAR` is not the standardisation variance); p. 16 `asymTIPREDEFFECTstd` is `(-B / a) · √v / √(-q / (2 a))` after strictly positive `asymDIFFUSION` `-q / (2 a)` and strictly positive predictor variance `v` (Driver et al., 2017, p. 16; §7.2; footnote 4; JSS PDF re-opened 2026-08-23T14:25Z; unstandardised `-B / a` is defined for a zero coefficient and for zero predictor variance and is not `asymTIPREDEFFECTstd`; the finite-interval standardisation `A^{-1}[e^{A Δt} − I] B · √v / √p` depends on the event interval and is not `asymTIPREDEFFECTstd`; `(-B / a) · √v / √(trait + p + added)` uses `TRAITVAR` and is not `asymTIPREDEFFECTstd`; `TRAITVAR` is not the standardisation variance); p. 16 `TIPREDEFFECTstd` is `B · √v / √(-q / (2 a))` after strictly positive `asymDIFFUSION` `-q / (2 a)` and strictly positive predictor variance `v` (Driver et al., 2017, p. 16; §7.2; footnote 4; JSS PDF re-opened 2026-08-23T16:21Z; unstandardised `B` is defined for a zero coefficient and for zero predictor variance and is not `TIPREDEFFECTstd`; the asymptotic standardisation `(-B / a) · √v / √p` is the total change and is not `TIPREDEFFECTstd`; the finite-interval standardisation `A^{-1}[e^{A Δt} − I] B · √v / √p` depends on the event interval and is not `TIPREDEFFECTstd`; `B · √v / √(trait + p + added)` uses `TRAITVAR` and is not `TIPREDEFFECTstd`; `TRAITVAR` is not the standardisation variance); Table 3 `T0TIPREDEFFECTstd` is `t0_b · √v / √p_0` after strictly positive free `T0VAR` `p_0` and strictly positive predictor variance `v` (Driver et al., 2017, Table 3, p. 13; p. 16; footnote 4; 2017-era ctsem `summary.ctsemFit.R`; JSS PDF re-opened 2026-08-23T17:20Z; the affected variance is free `T0VAR`, not `asymDIFFUSION`; unstandardised `t0_b` is defined for a zero coefficient and for zero predictor variance and is not `T0TIPREDEFFECTstd`; `TIPREDEFFECTstd` `B · √v / √(-q / (2 a))` is the continuous coefficient and is not `T0TIPREDEFFECTstd`; `asymTIPREDEFFECTstd` `(-B / a) · √v / √p` is the total change and is not `T0TIPREDEFFECTstd`; `t0_b · √v / √(trait + p_0 + added)` uses `TRAITVAR` and is not `T0TIPREDEFFECTstd`; `TRAITVAR` is not the standardisation variance); 2017-era `addedT0TIPREDVAR` is `t0_b² v` (Driver et al., 2017, Table 3, p. 13; p. 16; §7.2; 2017-era ctsem `summary.ctsemFit.R`; JSS PDF re-opened 2026-08-23T18:20Z; `T0TIPREDEFFECT %*% TIPREDVAR %*% t(T0TIPREDEFFECT)` immediately after `T0TIPREDEFFECTstd`; form `t0_b` first, then square, then multiply by `v`; a zero coefficient or zero predictor variance is exactly zero; free `T0TIPREDEFFECT` does not require `a < 0`; `(B / a)² v` is `addedTIPREDVAR` and is not this first-occasion map; `t0_b · √v / √p_0` is `T0TIPREDEFFECTstd` and is not this variance; free `T0VAR` is not this extra TI variance; `TRAITVAR` is not this extra TI variance; Equation 5 of 2017-era `addedT0TIPREDVAR` is `λ² t0_b² v` (Driver et al., 2017, Eq. 5, p. 5; Table 3, p. 13; Table 2, p. 12; 2017-era ctsem `summary.ctsemFit.R`; JSS PDF re-opened 2026-08-23T19:10Z; form `t0_b² v` first, then `(λ extra) λ` with `θ = 0`; a zero loading or zero extra is exactly zero; `t0_b² v` is the latent extra, not the observed extra; `λ² p_0 + θ` is first-occasion observed variance, not this extra; `λ² (B / a)² v` is Eq. 5 of `addedTIPREDVAR`, not this first-occasion observed extra; `MANIFESTVAR` `θ` is not this extra; Equation 5 of §7.2 `addedTIPREDVAR` is `λ² (B / a)² v`; form `(B / a)² v` first, then `(λ extra) λ` with `θ = 0`; a zero loading or zero extra is exactly zero; lasting asymptotic extra requires `a < 0`; `(B / a)² v` is the latent extra, not the observed extra; `λ² t0_b² v` is first-occasion extra observed TI variance, not this extra; `λ² p + θ` is stationary observed variance, not this extra; `MANIFESTVAR` `θ` is not this extra; p. 16 `TDPREDEFFECTstd` is `m · √v / √(-q / (2 a))` after strictly positive `asymDIFFUSION` and strictly positive time-dependent predictor variance; unstandardised `M` is not `TDPREDEFFECTstd`; `TIPREDEFFECTstd` is not `TDPREDEFFECTstd` even when `M = B`; intercept-style `A^{-1}[e^{A Δt} − I] M · √v / √p` is not `TDPREDEFFECTstd`; `m · √v / √(trait + p + added)` uses `TRAITVAR` and is not `TDPREDEFFECTstd`; Table 3 / p. 16 `T0TDPREDEFFECTstd` is `t0_m · √v / √p_0` after strictly positive free `T0VAR` and strictly positive TD predictor variance; unstandardised `t0_m` is not `T0TDPREDEFFECTstd`; `TDPREDEFFECTstd` uses `asymDIFFUSION` and is not `T0TDPREDEFFECTstd`; `T0TIPREDEFFECTstd` is not `T0TDPREDEFFECTstd` even when `t0_m = t0_b`; `t0_m · √v / √(trait + p_0 + added)` uses `TRAITVAR` and is not `T0TDPREDEFFECTstd`; free `T0VAR` does not require `a < 0`; p. 16 `T0VARstd` is `p_0 / p_0 = 1` after strictly positive free `T0VAR` (`solve(sqrt(diag(T0VAR))) %&% T0VAR`; OpenMx `%&%` is `t(A) %*% B %*% A`; default ridge is 0); unstandardised `T0VAR` is not `T0VARstd`; `T0TDPREDEFFECTstd` is not `T0VARstd`; `addedT0TIPREDVAR` is not `T0VARstd`; p. 16 `TRAITVARstd` is `trait / trait = 1` after strictly positive `TRAITVAR` (`solve(sqrt(diag(TRAITVAR))) %&% TRAITVAR`; OpenMx `%&%` is `t(A) %*% B %*% A`; no ridge addend); unstandardised `TRAITVAR` is not `TRAITVARstd`; `T0VARstd` is not `TRAITVARstd` even when both equal 1; `addedT0TIPREDVAR` is not `TRAITVARstd`; p. 16 `MANIFESTTRAITVARstd` is `ψ / ψ = 1` after strictly positive `MANIFESTTRAITVAR` (`solve(sqrt(diag(MANIFESTTRAITVAR))) %&% MANIFESTTRAITVAR`; OpenMx `%&%` is `t(A) %*% B %*% A`; 2017-era source adds ridging; default ridge is 0); unstandardised `MANIFESTTRAITVAR` is not `MANIFESTTRAITVARstd`; `TRAITVARstd` is not `MANIFESTTRAITVARstd` even when both equal 1; `MANIFESTVAR` is not `MANIFESTTRAITVARstd`;))))), irregular already-centered residual lag, and strong/strict-gated latent means on the stacked psychometric PR (two-observation residual variance is identically `0` and caps at strong/scalar; Putnick & Bornstein, 2016, PMC5145197 opened 2026-08-19T22:15Z); full ESEM/DSEM remaining | partial | +| posterior ESEM / longitudinal invariance / DSEM | ADR 0005 | `psychometric_core` construct/input gates, true-loading OLS recovery, posterior-draw point-estimate averaging, Rubin `T` on draw-level OLS loadings, CWC within/between OLS plus the contextual effect, event-time log-rate, constant- and time-varying-predictor discrete effects (Voelkle Eqs. 12 and 14), exact scalar discrete process noise (Driver et al., 2017, Eq. 3), lagged latent covariance and unconditional latent variance (Driver et al., 2017, Eq. 3–4), stationary within-subject variance (Driver et al., 2017, Eq. 4 as `Δt → ∞`; `asymDIFFUSION`), trait-plus-state variance (Driver et al., 2017, §4.3 `TRAITVAR`; not process noise), observed-indicator variance and lagged observed covariance (Driver et al., 2017, Eq. 5; Table 2 `MANIFESTVAR` is `Θ`, not `Var(y)`; `MANIFESTTRAITVAR` is not `MANIFESTVAR`; `Θ` does not enter lagged observed covariance; observed-indicator mean is `τ + λ μ`; `MANIFESTMEANS` is not `E(y)`; `CINT` is not `MANIFESTMEANS`; discrete latent mean is `exp(a Δt) μ_0 + (exp(a Δt) − 1)/a κ`; `T0MEANS` is not `μ_t`; evolved observed mean is `τ + λ μ_t`; `τ + λ μ_0` is not `E(y_t)`; contemporaneous `TDPREDEFFECT` impulse is `m x`, not `CINT`, not `TIPREDEFFECT`, and not Voelkle Eq. 14; Eq. 5 of that contemporaneous impulse is `τ + λ(μ_t + m x)`, and `τ + λ μ_t` is not that observed mean; time-independent `TIPREDEFFECT` increment is `A^{-1}[e^{A Δt} − I] B z`, not `CINT`, not `M x`, not Voelkle Eq. 14, and not the coefficient `B`; Eq. 5 of that increment is `τ + λ(μ_t + A^{-1}[e^{A Δt} − I] B z)`, and `τ + λ μ_t` is not that observed mean; `τ + λ(μ_t + m x)` is not that observed mean; `τ + λ(μ_t + e^{a(t−u)} m x)` is not that observed mean when `u ≠ t`; within-interval `TDPREDEFFECT` carry is `e^{A(t−u)} M x` for `t0 < u < t`, not the contemporaneous Dirac, not `CINT`, not `TIPREDEFFECT`, and not Voelkle Eq. 14; Eq. 5 of that carry is `τ + λ(μ_t + e^{a(t−u)} m x)`, and `τ + λ μ_t` is not that observed mean; `τ + λ(μ_t + m x)` is not that carried observed mean when `u ≠ t`; §7.2 level-change `CINT` is `κ = −a m x` (`a < 0`; not the dissipating Dirac, not a free `CINT`, not `TIPREDEFFECT`; Eq. 3 of that setting is `(1 − e^{a Δt}) m x`); §7.2 extra-process contribution is `a_{ηξ} x (e^{ε Δt} − e^{a Δt}) / (ε − a)` (not `κ = −a m x`, not `(1 − e^{a Δt}) m x`, not the dissipating Dirac; `ε ≥ 0` fails closed; Eq. 5 of that contribution is `τ + λ(μ_t + a_{ηξ} x (e^{ε Δt} − e^{a Δt}) / (ε − a)`; extra `LAMBDA` is 0; `τ + λ μ_t` is not that observed mean; after-t0 extra-process `TDPREDEFFECT` uses `t − u` with `t0 < u < t` while `μ_t` uses `Δt`; that after-t0 observed mean is not the first-occasion extra-process observed mean; §7.2 `asymTIPREDEFFECT` is `-B z / a` for `a < 0` and is not `B`, not `A^{-1}[e^{A Δt} − I] B z`, not `CINT`, and not `M x`; §7.2 `addedTIPREDVAR` is `(B / a)² v` and is not `TRAITVAR`, not `asymDIFFUSION`, and not `-B z / a`; Table 2 `asymCINT` is `-κ / a` for `a < 0` and is not `κ`, not `A^{-1}[e^{A Δt} − I] κ`, not `T0MEANS`, and not `-B z / a`; p. 16 stationary `T0MEANS` is `-κ / a + −B z / a` and is not free `T0MEANS`, not `asymCINT` alone, not `asymTIPREDEFFECT` alone, and not the finite-interval discrete latent mean; Eq. 5 of that constrained mean is `τ + λ(−κ / a + −B z / a)`; `τ + λ μ_0` is not that observed mean; `MANIFESTMEANS` is not `E(y_0)`; the constrained latent mean is not `E(y_0)`; stationary `T0VAR` is `trait + −q / (2 a) + (B / a)² v` (not free `T0VAR`, not `asymDIFFUSION` alone, not `TRAITVAR` alone, not `addedTIPREDVAR` alone, and not the finite-interval discrete latent variance. Eq. 5 of that constrained variance is `λ²(trait + −q / (2 a) + (B / a)² v) + θ + ψ` (JSS PDF re-opened 2026-08-22T03:20Z; form the stationary latent variance first, then `λ² p + θ + ψ`; `λ² p_0` is not that observed variance; `λ²(−q / (2 a)) + θ` is not that observed variance when `TRAITVAR` or `addedTIPREDVAR` is nonzero; `MANIFESTVAR` is not `Var(y_0)`; the constrained latent variance is not `Var(y_0)`)); lagged stationary `T0VAR` is `trait + e^{a Δt}(−q / (2 a)) + (B / a)² v` (trait and `addedTIPREDVAR` do not decay; contemporaneous `T0VAR` is not that lagged map; decaying the constrained total as if it were all state is not that lagged map; Eq. 5 of that lagged covariance is `λ²(trait + e^{a Δt}(−q / (2 a)) + (B / a)² v) + ψ`; `Θ` does not enter; contemporaneous `Var(y_0)` is not that lagged observed covariance; the lagged latent covariance is not that observed covariance); later-occasion stationary `T0VAR` is `trait + e^{2 a Δt}(−q / (2 a)) + Q_Δt + (B / a)² v` (trait and `addedTIPREDVAR` do not enter `Q_Δt`; under stationarity that composition equals contemporaneous `T0VAR`; evolving the constrained total as if it were all state is not that later map; the lagged covariance omits `Q_Δt`; `Q_Δt` is not that later map; Eq. 5 of that later-occasion variance is `λ²(trait + e^{2 a Δt}(−q / (2 a)) + Q_Δt + (B / a)² v) + θ + ψ`; lagged observed covariance omits `Q_Δt` and `θ`; `MANIFESTVAR` is not `Var(y_t)`; the later-occasion latent variance is not `Var(y_t)`); predetermined later-occasion `T0VAR` is `trait + e^{2 a Δt} p_0 + Q_Δt + (B / a)² v` (free `T0VAR` `p_0` is not that later map; setting `p_0 = −q / (2 a)` recovers the stationary later-occasion map; stationary later variance uses `−q / (2 a)` in place of `p_0` and is not that later map when `p_0` is free; evolving `trait + p_0 + (B / a)² v` as if it were all state is not that later map; Eq. 5 of that predetermined later-occasion variance is `λ²(trait + e^{2 a Δt} p_0 + Q_Δt + (B / a)² v) + θ + ψ`; `MANIFESTVAR` is not `Var(y_t)`; the predetermined later-occasion latent variance is not `Var(y_t)`; stationary later observed variance is not that observed variance when `p_0` is free); predetermined lagged `T0VAR` is `trait + e^{a Δt} p_0 + (B / a)² v` (free `T0VAR` `p_0` is not that lagged map; setting `p_0 = −q / (2 a)` recovers the stationary lagged map; stationary lagged covariance uses `−q / (2 a)` in place of `p_0` and is not that lagged map when `p_0` is free; evolving `trait + p_0 + (B / a)² v` as if it were all state is not that lagged map; later-occasion variance includes `Q_Δt` and is not that lagged map; Eq. 5 of that predetermined lagged covariance is `λ²(trait + e^{a Δt} p_0 + (B / a)² v) + ψ`; `MANIFESTVAR` does not enter; the predetermined lagged latent covariance is not that observed covariance; predetermined later observed variance includes `Q_Δt` and `θ` and is not that lagged observed covariance; stationary lagged observed covariance is not that observed covariance when `p_0` is free; the predetermined first-occasion variance of §4.3 predetermined `T0VAR` is `trait + p_0 + (B / a)² v`; free `p_0` is not that map; stationary first-occasion variance uses `−q / (2 a)` in place of `p_0` and is not that map when `p_0` is free; lagged covariance decays the state and is not that map; later-occasion variance includes `Q_Δt` and is not that map; Eq. 5 of that predetermined first-occasion variance is `λ²(trait + p_0 + (B / a)² v) + θ + ψ`; `MANIFESTVAR` is not that first-occasion observed variance; the predetermined first-occasion latent variance is not that observed variance; stationary first-occasion observed variance is not that observed variance when `p_0` is free; predetermined later observed variance includes `Q_Δt` and is not that first-occasion observed variance; later-start lagged covariance of predetermined `T0VAR` is `trait + e^{a s}(e^{2 a u} p_0 + Q_u) + (B / a)² v` (Driver et al., 2017, §4.3 `startoffset`; Eq. 4; JSS PDF re-opened 2026-08-23T10:27Z; first-occasion lagged omits `e^{a s} Q_u`; later-occasion variance does not lag; stationary lagged uses `−q / (2 a)`; decaying the later total is not that map; Eq. 5 of that later-start lagged covariance is `λ²` of it plus `ψ`; `Θ` does not enter; first-occasion lagged observed omits `e^{a s} Q_u`; later observed variance includes `Q_u` and `θ`; later-start later-occasion variance of predetermined `T0VAR` is `trait + e^{2 a s}(e^{2 a u} p_0 + Q_u) + Q_s + (B / a)² v` (Driver et al., 2017, §4.3 `startoffset`; Eq. 3–4 Chapman–Kolmogorov `Q_{u+s} = e^{2 a s} Q_u + Q_s`; JSS PDF re-opened 2026-08-23T11:05Z; later-occasion variance at `u` omits `Q_s`; later-start lagged covariance omits `Q_s`; stationary later uses `−q / (2 a)`; evolving the later total as if it were all state is not that map; ignoring `startoffset` omits `e^{2 a s} Q_u`; Eq. 5 of that later-start later-occasion variance is `λ²` of it plus `θ + ψ`; `MANIFESTVAR` is not that observed variance; p. 16 `discreteDRIFTstd` is `e^{a Δt}` after strictly positive `asymDIFFUSION` `-q / (2 a)` (footnote 4; unstandardised `e^{a Δt}` is defined for growing `a ≥ 0` and for zero diffusion and is not `discreteDRIFTstd`; the §7.1 trait-plus-state autocorrelation uses `TRAITVAR` and is not `discreteDRIFTstd`; p. 16 `discreteDIFFUSIONstd` is `Q_Δt / (−q / (2 a))` after strictly positive `asymDIFFUSION` `-q / (2 a)` (footnote 4; unstandardised `Q_Δt` is defined for growing `a ≥ 0` and for zero diffusion and is not `discreteDIFFUSIONstd`; the continuous standardisation `−2 a` is not `discreteDIFFUSIONstd`; `Q_Δt / (trait + p + added)` uses `TRAITVAR` and is not `discreteDIFFUSIONstd`; `TRAITVAR` is not the standardisation variance; p. 16 `DIFFUSIONstd` is `q / (−q / (2 a)) = −2 a` after strictly positive `asymDIFFUSION` `-q / (2 a)` (Driver et al., 2017, p. 16; Eq. 4; footnote 4; JSS PDF re-opened 2026-08-23T13:20Z; unstandardised `q` is defined for growing `a ≥ 0` and for zero diffusion and is not `DIFFUSIONstd`; the discrete standardisation `Q_Δt / (−q / (2 a))` depends on `Δt` and is not `DIFFUSIONstd`; `q / (trait + p + added)` uses `TRAITVAR` and is not `DIFFUSIONstd`; `TRAITVAR` is not the standardisation variance; p. 16 `DRIFTstd` is the continuous auto-effect after strictly positive `asymDIFFUSION` `-q / (2 a)` (Driver et al., 2017, p. 16; Eq. 1; footnote 4; JSS PDF re-opened 2026-08-23T13:28Z); unstandardised `a` is defined for growing `a ≥ 0` and for zero diffusion and is not `DRIFTstd`; the discrete standardisation `e^{a Δt}` depends on the event interval and is not `DRIFTstd`; `a p / (trait + p + added)` uses `TRAITVAR` and is not `DRIFTstd`; `TRAITVAR` is not the standardisation variance); p. 16 `asymTIPREDEFFECTstd` is `(-B / a) · √v / √(-q / (2 a))` after strictly positive `asymDIFFUSION` `-q / (2 a)` and strictly positive predictor variance `v` (Driver et al., 2017, p. 16; §7.2; footnote 4; JSS PDF re-opened 2026-08-23T14:25Z; unstandardised `-B / a` is defined for a zero coefficient and for zero predictor variance and is not `asymTIPREDEFFECTstd`; the finite-interval standardisation `A^{-1}[e^{A Δt} − I] B · √v / √p` depends on the event interval and is not `asymTIPREDEFFECTstd`; `(-B / a) · √v / √(trait + p + added)` uses `TRAITVAR` and is not `asymTIPREDEFFECTstd`; `TRAITVAR` is not the standardisation variance); p. 16 `TIPREDEFFECTstd` is `B · √v / √(-q / (2 a))` after strictly positive `asymDIFFUSION` `-q / (2 a)` and strictly positive predictor variance `v` (Driver et al., 2017, p. 16; §7.2; footnote 4; JSS PDF re-opened 2026-08-23T16:21Z; unstandardised `B` is defined for a zero coefficient and for zero predictor variance and is not `TIPREDEFFECTstd`; the asymptotic standardisation `(-B / a) · √v / √p` is the total change and is not `TIPREDEFFECTstd`; the finite-interval standardisation `A^{-1}[e^{A Δt} − I] B · √v / √p` depends on the event interval and is not `TIPREDEFFECTstd`; `B · √v / √(trait + p + added)` uses `TRAITVAR` and is not `TIPREDEFFECTstd`; `TRAITVAR` is not the standardisation variance); Table 3 `T0TIPREDEFFECTstd` is `t0_b · √v / √p_0` after strictly positive free `T0VAR` `p_0` and strictly positive predictor variance `v` (Driver et al., 2017, Table 3, p. 13; p. 16; footnote 4; 2017-era ctsem `summary.ctsemFit.R`; JSS PDF re-opened 2026-08-23T17:20Z; the affected variance is free `T0VAR`, not `asymDIFFUSION`; unstandardised `t0_b` is defined for a zero coefficient and for zero predictor variance and is not `T0TIPREDEFFECTstd`; `TIPREDEFFECTstd` `B · √v / √(-q / (2 a))` is the continuous coefficient and is not `T0TIPREDEFFECTstd`; `asymTIPREDEFFECTstd` `(-B / a) · √v / √p` is the total change and is not `T0TIPREDEFFECTstd`; `t0_b · √v / √(trait + p_0 + added)` uses `TRAITVAR` and is not `T0TIPREDEFFECTstd`; `TRAITVAR` is not the standardisation variance); 2017-era `addedT0TIPREDVAR` is `t0_b² v` (Driver et al., 2017, Table 3, p. 13; p. 16; §7.2; 2017-era ctsem `summary.ctsemFit.R`; JSS PDF re-opened 2026-08-23T18:20Z; `T0TIPREDEFFECT %*% TIPREDVAR %*% t(T0TIPREDEFFECT)` immediately after `T0TIPREDEFFECTstd`; form `t0_b` first, then square, then multiply by `v`; a zero coefficient or zero predictor variance is exactly zero; free `T0TIPREDEFFECT` does not require `a < 0`; `(B / a)² v` is `addedTIPREDVAR` and is not this first-occasion map; `t0_b · √v / √p_0` is `T0TIPREDEFFECTstd` and is not this variance; free `T0VAR` is not this extra TI variance; `TRAITVAR` is not this extra TI variance; Equation 5 of 2017-era `addedT0TIPREDVAR` is `λ² t0_b² v` (Driver et al., 2017, Eq. 5, p. 5; Table 3, p. 13; Table 2, p. 12; 2017-era ctsem `summary.ctsemFit.R`; JSS PDF re-opened 2026-08-23T19:10Z; form `t0_b² v` first, then `(λ extra) λ` with `θ = 0`; a zero loading or zero extra is exactly zero; `t0_b² v` is the latent extra, not the observed extra; `λ² p_0 + θ` is first-occasion observed variance, not this extra; `λ² (B / a)² v` is Eq. 5 of `addedTIPREDVAR`, not this first-occasion observed extra; `MANIFESTVAR` `θ` is not this extra; Equation 5 of §7.2 `addedTIPREDVAR` is `λ² (B / a)² v`; form `(B / a)² v` first, then `(λ extra) λ` with `θ = 0`; a zero loading or zero extra is exactly zero; lasting asymptotic extra requires `a < 0`; `(B / a)² v` is the latent extra, not the observed extra; `λ² t0_b² v` is first-occasion extra observed TI variance, not this extra; `λ² p + θ` is stationary observed variance, not this extra; `MANIFESTVAR` `θ` is not this extra; p. 16 `TDPREDEFFECTstd` is `m · √v / √(-q / (2 a))` after strictly positive `asymDIFFUSION` and strictly positive time-dependent predictor variance; unstandardised `M` is not `TDPREDEFFECTstd`; `TIPREDEFFECTstd` is not `TDPREDEFFECTstd` even when `M = B`; intercept-style `A^{-1}[e^{A Δt} − I] M · √v / √p` is not `TDPREDEFFECTstd`; `m · √v / √(trait + p + added)` uses `TRAITVAR` and is not `TDPREDEFFECTstd`; Table 3 / p. 16 `T0TDPREDEFFECTstd` is `t0_m · √v / √p_0` after strictly positive free `T0VAR` and strictly positive TD predictor variance; unstandardised `t0_m` is not `T0TDPREDEFFECTstd`; `TDPREDEFFECTstd` uses `asymDIFFUSION` and is not `T0TDPREDEFFECTstd`; `T0TIPREDEFFECTstd` is not `T0TDPREDEFFECTstd` even when `t0_m = t0_b`; `t0_m · √v / √(trait + p_0 + added)` uses `TRAITVAR` and is not `T0TDPREDEFFECTstd`; free `T0VAR` does not require `a < 0`; p. 16 `T0VARstd` is `p_0 / p_0 = 1` after strictly positive free `T0VAR` (`solve(sqrt(diag(T0VAR))) %&% T0VAR`; OpenMx `%&%` is `t(A) %*% B %*% A`; default ridge is 0); unstandardised `T0VAR` is not `T0VARstd`; `T0TDPREDEFFECTstd` is not `T0VARstd`; `addedT0TIPREDVAR` is not `T0VARstd`; p. 16 `TRAITVARstd` is `trait / trait = 1` after strictly positive `TRAITVAR` (`solve(sqrt(diag(TRAITVAR))) %&% TRAITVAR`; OpenMx `%&%` is `t(A) %*% B %*% A`; no ridge addend); unstandardised `TRAITVAR` is not `TRAITVARstd`; `T0VARstd` is not `TRAITVARstd` even when both equal 1; `addedT0TIPREDVAR` is not `TRAITVARstd`; p. 16 `MANIFESTTRAITVARstd` is `ψ / ψ = 1` after strictly positive `MANIFESTTRAITVAR` (`solve(sqrt(diag(MANIFESTTRAITVAR))) %&% MANIFESTTRAITVAR`; OpenMx `%&%` is `t(A) %*% B %*% A`; 2017-era source adds ridging; default ridge is 0); unstandardised `MANIFESTTRAITVAR` is not `MANIFESTTRAITVARstd`; `TRAITVARstd` is not `MANIFESTTRAITVARstd` even when both equal 1; `MANIFESTVAR` is not `MANIFESTTRAITVARstd`; p. 16 `MANIFESTVARstd` is `θ / θ = 1` after strictly positive `MANIFESTVAR` (`solve(sqrt(diag(MANIFESTVAR))) %&% MANIFESTVAR`; OpenMx `%&%` is `t(A) %*% B %*% A`; 2017-era source adds ridging; default ridge is 0; 2017-era `dimnames` assignment to `latentNames` is a source bug); unstandardised `MANIFESTVAR` is not `MANIFESTVARstd`; `MANIFESTTRAITVARstd` is not `MANIFESTVARstd` even when both equal 1; Equation 5 `Var(y)` is not `MANIFESTVARstd`;))))), irregular already-centered residual lag, and strong/strict-gated latent means on the stacked psychometric PR (two-observation residual variance is identically `0` and caps at strong/scalar; Putnick & Bornstein, 2016, PMC5145197 opened 2026-08-19T22:15Z); full ESEM/DSEM remaining | partial | | CPU bounded multithreading + GPU/VRAM streaming/parity | ADR 0001/0006 | future `compute_backend` | accepted-target | | TDT detection/tracking vs CHRONOS schema/prediction/temporal consistency | ADR 0016; PRD/research | future `event_intelligence` | accepted-target | | evidence-bounded LLM interpretation | ADR 0010/0012; PRD | `tepp_api` router plus future `interpretation_gateway` | partial | diff --git a/docs/adr/0005-posterior-esem-dsem.md b/docs/adr/0005-posterior-esem-dsem.md index 3bce2278..b5db4f97 100644 --- a/docs/adr/0005-posterior-esem-dsem.md +++ b/docs/adr/0005-posterior-esem-dsem.md @@ -1,7 +1,7 @@ # ADR 0005 — Posterior-aware ESEM/DSEM and structural interpretation **Decision status:** Accepted -**Implementation maturity:** partial — construct classification, valid log-ratio/logistic-normal indicator gates, CPU `f64` OLS and posterior-draw loading point-estimate averaging, Rubin `T_m = Ū_m + (1+1/m) B_m` on draw-level OLS loadings, cluster-mean within/between OLS with the CWC contextual effect and Kish ESS WLS, event-time discrete lag-1 and exact scalar local log-rate, exact scalar forward map and unequal-interval remapping, exact scalar discrete effect of a constant predictor, first-order discrete effect of a time-varying predictor with matched sampling and constancy intervals (Voelkle et al., 2012, Eq. 14), exact scalar discrete process noise (Driver, Oud, & Voelkle, 2017, Eq. 3), exact scalar lagged latent covariance and unconditional latent variance (Driver et al., 2017, Eq. 3–4), exact scalar stationary within-subject variance (Driver et al., 2017, Eq. 4 as `Δt → ∞`; §4.3; p. 16 `asymDIFFUSION`), exact scalar trait-plus-state variance and lagged covariance (Driver et al., 2017, §4.3 `TRAITVAR`; not process noise and not `asymDIFFUSION`), exact scalar observed-indicator variance and lagged observed covariance (Driver et al., 2017, Eq. 5, p. 5; Table 2, p. 12; `λ² Var(η) + θ` when `MANIFESTTRAITVAR` is zero, else `λ² Var(η) + θ + ψ`; lagged `λ² cov(η_t, η_{t-1}) + ψ`; `MANIFESTVAR` is `Θ`, not `Var(y)`; `Θ` does not enter lagged observed covariance; `MANIFESTTRAITVAR` is not `MANIFESTVAR`; observed-indicator mean is `τ + λ μ` (`MANIFESTMEANS` is `τ`, not `E(y)`; `CINT` is not `MANIFESTMEANS`; `T0MEANS` is not `E(y)`; Equation 1 is the SDE; not a Kalman filter), exact scalar discrete latent mean `exp(a Δt) μ_0 + (exp(a Δt) − 1)/a κ` (Driver et al., 2017, Eq. 3; `T0MEANS` is not `μ_t`; `CINT` is not the discrete increment), exact scalar evolved observed-indicator mean `τ + λ μ_t` (Driver et al., 2017, Eq. 5 of that Eq. 3 map; the first-occasion map `τ + λ μ_0` is not `E(y_t)`), exact scalar contemporaneous `TDPREDEFFECT` impulse `m x` (Driver et al., 2017, Eq. 3 fourth summand; Table 2 `TDPREDEFFECT` is `M`, not `CINT`, not `TIPREDEFFECT`, and not Voelkle Eq. 14; the §7.2 level-change form is not that impulse), exact scalar observed mean of that contemporaneous impulse `τ + λ(μ_t + m x)` (Driver et al., 2017, Eq. 5 of that Eq. 3 composition; `τ + λ μ_t` is not that observed mean; `τ + λ(μ_t + e^{a(t−u)} m x)` is not that observed mean when `u ≠ t`), exact scalar time-independent `TIPREDEFFECT` increment `A^{-1}[e^{A Δt} − I] B z` (Driver et al., 2017, Eq. 3 second summand; Table 2 `TIPREDEFFECT` is `B`, not `κ`, not `M`, and not Voelkle Eq. 14; `B` is not that discrete increment), exact scalar observed mean of that increment `τ + λ(μ_t + A^{-1}[e^{A Δt} − I] B z)` (Driver et al., 2017, Eq. 5 of that Eq. 3 printed addend after the `T0MEANS` carry and the `CINT` increment; `τ + λ μ_t` is not that observed mean; `τ + λ(μ_t + m x)` is not that observed mean; `τ + λ(μ_t + e^{a(t−u)} m x)` is not that observed mean when `u ≠ t`), exact scalar within-interval `TDPREDEFFECT` carry `e^{A(t−u)} M x` for `t0 < u < t` (Driver et al., 2017, Eq. 1–2 Green-function integral of Eq. 2; §7.2 dissipation; not the contemporaneous Dirac, not `CINT`, not `TIPREDEFFECT`, and not Voelkle Eq. 14), exact scalar observed mean of that carry `τ + λ(μ_t + e^{a(t−u)} m x)` (Driver et al., 2017, Eq. 5 of that carried latent mean; `τ + λ μ_t` is not that observed mean), exact scalar first-occasion `T0TIPREDEFFECT` shift `t0_b z` and Eq. 3 first-summand carry `e^{A Δt} t0_b z` (Driver et al., 2017, Table 3, p. 13; Eq. 3, p. 5; `T0TIPREDEFFECT` is not `TIPREDEFFECT` `B`; `t0_b z` is not `A^{-1}[e^{A Δt} − I] B z`; `e^{A Δt} t0_b z` is not `t0_b z`), exact scalar observed mean of that first-occasion carry `τ + λ(μ_t + e^{a Δt} t0_b z)` (Driver et al., 2017, Eq. 5 of that Table 3 / Eq. 3 first-summand composition; `τ + λ μ_t` is not that observed mean; `τ + λ(μ_t + A^{-1}[e^{A Δt} − I] B z)` is not that observed mean), exact scalar first-occasion `T0TDPREDEFFECT` shift `t0_m x0` and Eq. 3 first-summand carry `e^{A Δt} t0_m x0` (Driver et al., 2017, Table 3, p. 13; Eq. 3, p. 5; JSS PDF re-opened 2026-08-20T19:10Z; `T0TDPREDEFFECT` is not `TDPREDEFFECT` `M`; `t0_m x0` is not `M x`; `e^{A Δt} t0_m x0` is not `t0_m x0`; `e^{A Δt} t0_m x0` is not `e^{A(t−u)} M x` for `t0 < u < t`; `t0_m x0` is not `t0_b z`; an impulse at `u ≤ t0` that used `M` is already in `η(t0)` as `TDPREDEFFECT`, not as `T0TDPREDEFFECT`), exact scalar observed mean of that first-occasion TD carry `τ + λ(μ_t + e^{a Δt} t0_m x0)` (Driver et al., 2017, Eq. 5 of that Table 3 / Eq. 3 first-summand TD composition; `τ + λ μ_t` is not that observed mean; `τ + λ(μ_t + A^{-1}[e^{A Δt} − I] B z)` is not that observed mean; `τ + λ(μ_t + e^{a Δt} t0_b z)` is not that observed mean), exact scalar §7.2 level-change `CINT` `κ = −a m x` (Driver et al., 2017, §7.2, pp. 20–21; `a < 0` so `−κ / a = m x`; not the dissipating Dirac, not a free `CINT`, not `TIPREDEFFECT`, and not the extra near-zero-drift latent process also named in §7.2), exact scalar Eq. 3 increment of that setting `(1 − e^{a Δt}) m x` (not `m x`, not `κ`, and not `TIPREDEFFECT`), exact scalar §7.2 extra near-zero-drift latent process contribution `a_{ηξ} x (e^{ε Δt} − e^{a Δt}) / (ε − a)` (Driver et al., 2017, §7.2, pp. 22–23; JSS PDF re-opened 2026-08-20T23:10Z; identification `TDPREDEFFECT` on the extra process is 1; extra `DRIFT` printed as `−0.000001`; precisely 0 causes computational problems; `ε = a` is `a_{ηξ} x Δt e^{a Δt}`; not `κ = −a m x`, not `(1 − e^{a Δt}) m x`, and not the dissipating Dirac `m x`; `ε ≥ 0` fails closed), exact scalar observed mean of that extra-process contribution `τ + λ(μ_t + a_{ηξ} x (e^{ε Δt} − e^{a Δt}) / (ε − a))` (Driver et al., 2017, Eq. 5, p. 5; §7.2, pp. 22–23; JSS PDF re-opened 2026-08-21T06:12Z; the extra process has `LAMBDA` 0 and is not an observed indicator; `τ + λ μ_t` is not that observed mean; `τ + λ(μ_t + m x)` is not that observed mean; the contribution is not `E(y_t)`; the evolved-plus-contribution latent mean is not `E(y_t)`), exact scalar after-t0 extra-process contribution `a_{ηξ} x (e^{ε(t−u)} − e^{a(t−u)}) / (ε − a)` for `t0 < u < t` and Eq. 5 observed mean `τ + λ(μ_t + a_{ηξ} x (e^{ε(t−u)} − e^{a(t−u)}) / (ε − a))` (Driver et al., 2017, §7.2, pp. 22–23; JSS PDF re-opened 2026-08-21T06:32Z; `T0TDPREDEFFECT` uses `Δt` for both the evolution and the extra drive; `TDPREDEFFECT` after `t0` uses `t − u`; `e^{a(t−u)} m x` is a Dirac on the original process, not this `DRIFT` drive), exact scalar §7.2 `asymTIPREDEFFECT` `-B z / a` (Driver et al., 2017, §7.2, pp. 20–21; JSS PDF opened 2026-08-21T13:08Z; expected total change in process means given a time-independent predictor; `a < 0`; not the coefficient `B`, not `A^{-1}[e^{A Δt} − I] B z`, not `CINT`, and not `M x`; `a ≥ 0` fails closed), exact scalar §7.2 `addedTIPREDVAR` `(B / a)² v` (Driver et al., 2017, §7.2, pp. 20–21; stable between-subject variance accounted for by a time-independent predictor; not `TRAITVAR`, not `asymDIFFUSION`, and not `-B z / a`), exact scalar Table 2 `asymCINT` `-κ / a` (Driver et al., 2017, Table 2, p. 12; Eq. 3 as `Δt → ∞`; `a < 0`; not `κ`, not `A^{-1}[e^{A Δt} − I] κ`, not `T0MEANS`, and not `-B z / a`; `a ≥ 0` fails closed), exact scalar p. 16 stationary `T0MEANS` `-κ / a + −B z / a` (Driver et al., 2017, p. 16; constrained first-occasion mean using `T0MEANSbase` / `T0MEANSfree`; not free `T0MEANS`, not `asymCINT` alone, not `asymTIPREDEFFECT` alone, and not the finite-interval discrete latent mean), exact scalar Eq. 5 of that constrained mean `τ + λ(−κ / a + −B z / a)` (Driver et al., 2017, §4.3, pp. 9–10; Eq. 5, p. 5; JSS PDF re-opened 2026-08-21T20:07Z; form the stationary latent mean first, then `τ + λ` of that mean; `τ + λ μ_0` is not that observed mean; `τ + λ(−κ / a)` is not that observed mean when `B z ≠ 0`; `τ + λ μ_t` is not that observed mean; `MANIFESTMEANS` is not `E(y_0)`; the constrained latent mean is not `E(y_0)`), exact scalar §4.3 / p. 16 stationary `T0VAR` `trait + −q / (2 a) + (B / a)² v` (Driver et al., 2017, §4.3, pp. 9–10; p. 16; JSS PDF re-opened 2026-08-22T03:07Z; not free `T0VAR`, not `asymDIFFUSION` alone, not `TRAITVAR` alone, not `addedTIPREDVAR` alone, and not the finite-interval discrete latent variance. Eq. 5 of that constrained variance is `λ²(trait + −q / (2 a) + (B / a)² v) + θ + ψ` (JSS PDF re-opened 2026-08-22T03:20Z; form the stationary latent variance first, then `λ² p + θ + ψ`; `λ² p_0` is not that observed variance; `λ²(−q / (2 a)) + θ` is not that observed variance when `TRAITVAR` or `addedTIPREDVAR` is nonzero; `MANIFESTVAR` is not `Var(y_0)`; the constrained latent variance is not `Var(y_0)`), exact scalar lagged covariance of that constrained process `trait + e^{a Δt}(−q / (2 a)) + (B / a)² v` (Driver et al., 2017, Eq. 3–4 of §4.3 / p. 16 `T0VAR`; JSS PDF re-opened 2026-08-22T19:13Z; trait and `addedTIPREDVAR` do not decay with `e^{a Δt}`; contemporaneous `T0VAR` is not that lagged map; decaying the constrained total as if it were all state is not that lagged map), exact scalar Eq. 5 of that lagged covariance `λ²(trait + e^{a Δt}(−q / (2 a)) + (B / a)² v) + ψ` (`Θ` does not enter; contemporaneous `Var(y_0)` is not that lagged observed covariance; the lagged latent covariance is not that observed covariance), exact scalar later-occasion variance of that constrained process `trait + e^{2 a Δt}(−q / (2 a)) + Q_Δt + (B / a)² v` (Driver et al., 2017, Eq. 3–4 of §4.3 / p. 16 `T0VAR`; JSS PDF re-opened 2026-08-22T23:12Z; trait and `addedTIPREDVAR` do not enter `Q_Δt`; under stationarity that composition equals contemporaneous `T0VAR`; evolving the constrained total as if it were all state is not that later map; the lagged covariance omits `Q_Δt` and is not that later map; `Q_Δt` is not that later map), exact scalar Eq. 5 of that later-occasion variance `λ²(trait + e^{2 a Δt}(−q / (2 a)) + Q_Δt + (B / a)² v) + θ + ψ` (the lagged observed covariance omits `Q_Δt` and `θ`; `MANIFESTVAR` is not that later observed variance; the later-occasion latent variance is not that observed variance)), exact scalar later-occasion variance of §4.3 predetermined `T0VAR` `trait + e^{2 a Δt} p_0 + Q_Δt + (B / a)² v` (Driver et al., 2017, Eq. 3–4 of §4.3 predetermined first occasion; JSS PDF re-opened 2026-08-23T05:12Z; trait and `addedTIPREDVAR` do not enter `Q_Δt`; free `T0VAR` `p_0` is not that later map; setting `p_0 = −q / (2 a)` recovers the stationary later-occasion map; stationary later variance uses `−q / (2 a)` in place of `p_0` and is not that later map when `p_0` is free; evolving `trait + p_0 + (B / a)² v` as if it were all state is not that later map; as `Δt → ∞` with stable `a < 0` the composition approaches contemporaneous stationary `T0VAR`; as `Δt → 0+` the composition approaches `trait + p_0 + (B / a)² v`; nonzero diffusion with `a ≥ 0` is a growing process and is kept. Eq. 5 of that predetermined later-occasion variance is `λ²(trait + e^{2 a Δt} p_0 + Q_Δt + (B / a)² v) + θ + ψ` (`MANIFESTVAR` is not that later observed variance; the predetermined later-occasion latent variance is not that observed variance; stationary later observed variance is not that observed variance when `p_0` is free)), exact scalar lagged covariance of §4.3 predetermined `T0VAR` `trait + e^{a Δt} p_0 + (B / a)² v` (Driver et al., 2017, Eq. 3–4 of §4.3 predetermined first occasion; JSS PDF re-opened 2026-08-23T09:04Z; trait and `addedTIPREDVAR` do not decay with `e^{a Δt}`; free `T0VAR` `p_0` is not that lagged map; setting `p_0 = −q / (2 a)` recovers the stationary lagged map; stationary lagged covariance uses `−q / (2 a)` in place of `p_0` and is not that lagged map when `p_0` is free; evolving `trait + p_0 + (B / a)² v` as if it were all state is not that lagged map; later-occasion variance includes `Q_Δt` and is not that lagged map. Eq. 5 of that predetermined lagged covariance is `λ²(trait + e^{a Δt} p_0 + (B / a)² v) + ψ` (`MANIFESTVAR` does not enter; the predetermined lagged latent covariance is not that observed covariance; predetermined later observed variance includes `Q_Δt` and `θ` and is not that lagged observed covariance; stationary lagged observed covariance is not that observed covariance when `p_0` is free)), exact scalar first-occasion variance of §4.3 predetermined `T0VAR` `trait + p_0 + (B / a)² v` (free `p_0` is not that map; stationary first-occasion variance uses `−q / (2 a)` in place of `p_0` and is not that map when `p_0` is free; lagged covariance decays the state and is not that map; later-occasion variance includes `Q_Δt` and is not that map), exact scalar Eq. 5 of that predetermined first-occasion variance `λ²(trait + p_0 + (B / a)² v) + θ + ψ` (`MANIFESTVAR` is not that first-occasion observed variance; the predetermined first-occasion latent variance is not that observed variance; stationary first-occasion observed variance is not that observed variance when `p_0` is free; predetermined later observed variance includes `Q_Δt` and is not that first-occasion observed variance; later-start lagged covariance of predetermined `T0VAR` is `trait + e^{a s}(e^{2 a u} p_0 + Q_u) + (B / a)² v` (Driver et al., 2017, §4.3 `startoffset`; Eq. 4; JSS PDF re-opened 2026-08-23T10:27Z; first-occasion lagged omits `e^{a s} Q_u`; later-occasion variance does not lag; stationary lagged uses `−q / (2 a)`; decaying the later total is not that map; Eq. 5 of that later-start lagged covariance is `λ²` of it plus `ψ`; `Θ` does not enter; first-occasion lagged observed omits `e^{a s} Q_u`; later observed variance includes `Q_u` and `θ`; later-start later-occasion variance of predetermined `T0VAR` is `trait + e^{2 a s}(e^{2 a u} p_0 + Q_u) + Q_s + (B / a)² v` (Driver et al., 2017, §4.3 `startoffset`; Eq. 3–4 Chapman–Kolmogorov `Q_{u+s} = e^{2 a s} Q_u + Q_s`; JSS PDF re-opened 2026-08-23T11:05Z; later-occasion variance at `u` omits `Q_s`; later-start lagged covariance omits `Q_s`; stationary later uses `−q / (2 a)`; evolving the later total as if it were all state is not that map; ignoring `startoffset` omits `e^{2 a s} Q_u`; Eq. 5 of that later-start later-occasion variance is `λ²` of it plus `θ + ψ`; `MANIFESTVAR` is not that observed variance; p. 16 `discreteDRIFTstd` is `e^{a Δt}` after strictly positive `asymDIFFUSION` `-q / (2 a)` (Driver et al., 2017, p. 16; footnote 4; §7.1; JSS PDF re-opened 2026-08-23T11:40Z; unstandardised `e^{a Δt}` is defined for growing `a ≥ 0` and for zero diffusion and is not `discreteDRIFTstd`; the trait-plus-state autocorrelation uses `TRAITVAR` and is not `discreteDRIFTstd`; p. 16 `discreteDIFFUSIONstd` is `Q_Δt / (−q / (2 a))` after strictly positive `asymDIFFUSION` `-q / (2 a)` (Driver et al., 2017, p. 16; Eq. 4; footnote 4; JSS PDF re-opened 2026-08-23T13:06Z; unstandardised `Q_Δt` is defined for growing `a ≥ 0` and for zero diffusion and is not `discreteDIFFUSIONstd`; the continuous standardisation `−2 a` is not `discreteDIFFUSIONstd`; `Q_Δt / (trait + p + added)` uses `TRAITVAR` and is not `discreteDIFFUSIONstd`; `TRAITVAR` is not the standardisation variance; p. 16 `DIFFUSIONstd` is `q / (−q / (2 a)) = −2 a` after strictly positive `asymDIFFUSION` `-q / (2 a)` (Driver et al., 2017, p. 16; Eq. 4; footnote 4; JSS PDF re-opened 2026-08-23T13:20Z; unstandardised `q` is defined for growing `a ≥ 0` and for zero diffusion and is not `DIFFUSIONstd`; the discrete standardisation `Q_Δt / (−q / (2 a))` depends on `Δt` and is not `DIFFUSIONstd`; `q / (trait + p + added)` uses `TRAITVAR` and is not `DIFFUSIONstd`; `TRAITVAR` is not the standardisation variance; p. 16 `DRIFTstd` is the continuous auto-effect after strictly positive `asymDIFFUSION` `-q / (2 a)` (Driver et al., 2017, p. 16; Eq. 1; footnote 4; JSS PDF re-opened 2026-08-23T13:28Z); unstandardised `a` is defined for growing `a ≥ 0` and for zero diffusion and is not `DRIFTstd`; the discrete standardisation `e^{a Δt}` depends on the event interval and is not `DRIFTstd`; `a p / (trait + p + added)` uses `TRAITVAR` and is not `DRIFTstd`; `TRAITVAR` is not the standardisation variance); p. 16 `asymTIPREDEFFECTstd` is `(-B / a) · √v / √(-q / (2 a))` after strictly positive `asymDIFFUSION` `-q / (2 a)` and strictly positive predictor variance `v` (Driver et al., 2017, p. 16; §7.2; footnote 4; JSS PDF re-opened 2026-08-23T14:25Z; unstandardised `-B / a` is defined for a zero coefficient and for zero predictor variance and is not `asymTIPREDEFFECTstd`; the finite-interval standardisation `A^{-1}[e^{A Δt} − I] B · √v / √p` depends on the event interval and is not `asymTIPREDEFFECTstd`; `(-B / a) · √v / √(trait + p + added)` uses `TRAITVAR` and is not `asymTIPREDEFFECTstd`; `TRAITVAR` is not the standardisation variance); p. 16 `TIPREDEFFECTstd` is `B · √v / √(-q / (2 a))` after strictly positive `asymDIFFUSION` `-q / (2 a)` and strictly positive predictor variance `v` (Driver et al., 2017, p. 16; §7.2; footnote 4; JSS PDF re-opened 2026-08-23T16:21Z; unstandardised `B` is defined for a zero coefficient and for zero predictor variance and is not `TIPREDEFFECTstd`; the asymptotic standardisation `(-B / a) · √v / √p` is the total change and is not `TIPREDEFFECTstd`; the finite-interval standardisation `A^{-1}[e^{A Δt} − I] B · √v / √p` depends on the event interval and is not `TIPREDEFFECTstd`; `B · √v / √(trait + p + added)` uses `TRAITVAR` and is not `TIPREDEFFECTstd`; `TRAITVAR` is not the standardisation variance); Table 3 `T0TIPREDEFFECTstd` is `t0_b · √v / √p_0` after strictly positive free `T0VAR` `p_0` and strictly positive predictor variance `v` (Driver et al., 2017, Table 3, p. 13; p. 16; footnote 4; 2017-era ctsem `summary.ctsemFit.R`; JSS PDF re-opened 2026-08-23T17:20Z; the affected variance is free `T0VAR`, not `asymDIFFUSION`; unstandardised `t0_b` is defined for a zero coefficient and for zero predictor variance and is not `T0TIPREDEFFECTstd`; `TIPREDEFFECTstd` `B · √v / √(-q / (2 a))` is the continuous coefficient and is not `T0TIPREDEFFECTstd`; `asymTIPREDEFFECTstd` `(-B / a) · √v / √p` is the total change and is not `T0TIPREDEFFECTstd`; `t0_b · √v / √(trait + p_0 + added)` uses `TRAITVAR` and is not `T0TIPREDEFFECTstd`; `TRAITVAR` is not the standardisation variance); 2017-era `addedT0TIPREDVAR` is `t0_b² v` (Driver et al., 2017, Table 3, p. 13; p. 16; §7.2; 2017-era ctsem `summary.ctsemFit.R`; JSS PDF re-opened 2026-08-23T18:20Z; `T0TIPREDEFFECT %*% TIPREDVAR %*% t(T0TIPREDEFFECT)` immediately after `T0TIPREDEFFECTstd`; form `t0_b` first, then square, then multiply by `v`; a zero coefficient or zero predictor variance is exactly zero; free `T0TIPREDEFFECT` does not require `a < 0`; `(B / a)² v` is `addedTIPREDVAR` and is not this first-occasion map; `t0_b · √v / √p_0` is `T0TIPREDEFFECTstd` and is not this variance; free `T0VAR` is not this extra TI variance; `TRAITVAR` is not this extra TI variance; Equation 5 of 2017-era `addedT0TIPREDVAR` is `λ² t0_b² v` (Driver et al., 2017, Eq. 5, p. 5; Table 3, p. 13; Table 2, p. 12; 2017-era ctsem `summary.ctsemFit.R`; JSS PDF re-opened 2026-08-23T19:10Z; form `t0_b² v` first, then `(λ extra) λ` with `θ = 0`; a zero loading or zero extra is exactly zero; `t0_b² v` is the latent extra, not the observed extra; `λ² p_0 + θ` is first-occasion observed variance, not this extra; `λ² (B / a)² v` is Eq. 5 of `addedTIPREDVAR`, not this first-occasion observed extra; `MANIFESTVAR` `θ` is not this extra; Equation 5 of §7.2 `addedTIPREDVAR` is `λ² (B / a)² v`; form `(B / a)² v` first, then `(λ extra) λ` with `θ = 0`; a zero loading or zero extra is exactly zero; lasting asymptotic extra requires `a < 0`; `(B / a)² v` is the latent extra, not the observed extra; `λ² t0_b² v` is first-occasion extra observed TI variance, not this extra; `λ² p + θ` is stationary observed variance, not this extra; `MANIFESTVAR` `θ` is not this extra; p. 16 `TDPREDEFFECTstd` is `m · √v / √(-q / (2 a))` after strictly positive `asymDIFFUSION` and strictly positive time-dependent predictor variance; unstandardised `M` is not `TDPREDEFFECTstd`; `TIPREDEFFECTstd` is not `TDPREDEFFECTstd` even when `M = B`; intercept-style `A^{-1}[e^{A Δt} − I] M · √v / √p` is not `TDPREDEFFECTstd`; `m · √v / √(trait + p + added)` uses `TRAITVAR` and is not `TDPREDEFFECTstd`; Table 3 / p. 16 `T0TDPREDEFFECTstd` is `t0_m · √v / √p_0` after strictly positive free `T0VAR` and strictly positive TD predictor variance; unstandardised `t0_m` is not `T0TDPREDEFFECTstd`; `TDPREDEFFECTstd` uses `asymDIFFUSION` and is not `T0TDPREDEFFECTstd`; `T0TIPREDEFFECTstd` is not `T0TDPREDEFFECTstd` even when `t0_m = t0_b`; `t0_m · √v / √(trait + p_0 + added)` uses `TRAITVAR` and is not `T0TDPREDEFFECTstd`; free `T0VAR` does not require `a < 0`; p. 16 `T0VARstd` is `p_0 / p_0 = 1` after strictly positive free `T0VAR` (`solve(sqrt(diag(T0VAR))) %&% T0VAR`; OpenMx `%&%` is `t(A) %*% B %*% A`; default ridge is 0); unstandardised `T0VAR` is not `T0VARstd`; `T0TDPREDEFFECTstd` is not `T0VARstd`; `addedT0TIPREDVAR` is not `T0VARstd`; p. 16 `TRAITVARstd` is `trait / trait = 1` after strictly positive `TRAITVAR` (`solve(sqrt(diag(TRAITVAR))) %&% TRAITVAR`; OpenMx `%&%` is `t(A) %*% B %*% A`; no ridge addend); unstandardised `TRAITVAR` is not `TRAITVARstd`; `T0VARstd` is not `TRAITVARstd` even when both equal 1; `addedT0TIPREDVAR` is not `TRAITVARstd`; p. 16 `MANIFESTTRAITVARstd` is `ψ / ψ = 1` after strictly positive `MANIFESTTRAITVAR` (`solve(sqrt(diag(MANIFESTTRAITVAR))) %&% MANIFESTTRAITVAR`; OpenMx `%&%` is `t(A) %*% B %*% A`; 2017-era source adds ridging; default ridge is 0); unstandardised `MANIFESTTRAITVAR` is not `MANIFESTTRAITVARstd`; `TRAITVARstd` is not `MANIFESTTRAITVARstd` even when both equal 1; `MANIFESTVAR` is not `MANIFESTTRAITVARstd`;))))), CWC-then-event-time residual lag, irregular already-centered residual log-rate, and strong/strict-gated two-group OLS latent-mean difference are implemented on the stacked psychometric PR and are not implemented-main until exact-head checks, review, and protected-main integration complete; full ESEM/set-ESEM, formative composites, DSEM, and matrix continuous-time dynamics remain accepted-target +**Implementation maturity:** partial — construct classification, valid log-ratio/logistic-normal indicator gates, CPU `f64` OLS and posterior-draw loading point-estimate averaging, Rubin `T_m = Ū_m + (1+1/m) B_m` on draw-level OLS loadings, cluster-mean within/between OLS with the CWC contextual effect and Kish ESS WLS, event-time discrete lag-1 and exact scalar local log-rate, exact scalar forward map and unequal-interval remapping, exact scalar discrete effect of a constant predictor, first-order discrete effect of a time-varying predictor with matched sampling and constancy intervals (Voelkle et al., 2012, Eq. 14), exact scalar discrete process noise (Driver, Oud, & Voelkle, 2017, Eq. 3), exact scalar lagged latent covariance and unconditional latent variance (Driver et al., 2017, Eq. 3–4), exact scalar stationary within-subject variance (Driver et al., 2017, Eq. 4 as `Δt → ∞`; §4.3; p. 16 `asymDIFFUSION`), exact scalar trait-plus-state variance and lagged covariance (Driver et al., 2017, §4.3 `TRAITVAR`; not process noise and not `asymDIFFUSION`), exact scalar observed-indicator variance and lagged observed covariance (Driver et al., 2017, Eq. 5, p. 5; Table 2, p. 12; `λ² Var(η) + θ` when `MANIFESTTRAITVAR` is zero, else `λ² Var(η) + θ + ψ`; lagged `λ² cov(η_t, η_{t-1}) + ψ`; `MANIFESTVAR` is `Θ`, not `Var(y)`; `Θ` does not enter lagged observed covariance; `MANIFESTTRAITVAR` is not `MANIFESTVAR`; observed-indicator mean is `τ + λ μ` (`MANIFESTMEANS` is `τ`, not `E(y)`; `CINT` is not `MANIFESTMEANS`; `T0MEANS` is not `E(y)`; Equation 1 is the SDE; not a Kalman filter), exact scalar discrete latent mean `exp(a Δt) μ_0 + (exp(a Δt) − 1)/a κ` (Driver et al., 2017, Eq. 3; `T0MEANS` is not `μ_t`; `CINT` is not the discrete increment), exact scalar evolved observed-indicator mean `τ + λ μ_t` (Driver et al., 2017, Eq. 5 of that Eq. 3 map; the first-occasion map `τ + λ μ_0` is not `E(y_t)`), exact scalar contemporaneous `TDPREDEFFECT` impulse `m x` (Driver et al., 2017, Eq. 3 fourth summand; Table 2 `TDPREDEFFECT` is `M`, not `CINT`, not `TIPREDEFFECT`, and not Voelkle Eq. 14; the §7.2 level-change form is not that impulse), exact scalar observed mean of that contemporaneous impulse `τ + λ(μ_t + m x)` (Driver et al., 2017, Eq. 5 of that Eq. 3 composition; `τ + λ μ_t` is not that observed mean; `τ + λ(μ_t + e^{a(t−u)} m x)` is not that observed mean when `u ≠ t`), exact scalar time-independent `TIPREDEFFECT` increment `A^{-1}[e^{A Δt} − I] B z` (Driver et al., 2017, Eq. 3 second summand; Table 2 `TIPREDEFFECT` is `B`, not `κ`, not `M`, and not Voelkle Eq. 14; `B` is not that discrete increment), exact scalar observed mean of that increment `τ + λ(μ_t + A^{-1}[e^{A Δt} − I] B z)` (Driver et al., 2017, Eq. 5 of that Eq. 3 printed addend after the `T0MEANS` carry and the `CINT` increment; `τ + λ μ_t` is not that observed mean; `τ + λ(μ_t + m x)` is not that observed mean; `τ + λ(μ_t + e^{a(t−u)} m x)` is not that observed mean when `u ≠ t`), exact scalar within-interval `TDPREDEFFECT` carry `e^{A(t−u)} M x` for `t0 < u < t` (Driver et al., 2017, Eq. 1–2 Green-function integral of Eq. 2; §7.2 dissipation; not the contemporaneous Dirac, not `CINT`, not `TIPREDEFFECT`, and not Voelkle Eq. 14), exact scalar observed mean of that carry `τ + λ(μ_t + e^{a(t−u)} m x)` (Driver et al., 2017, Eq. 5 of that carried latent mean; `τ + λ μ_t` is not that observed mean), exact scalar first-occasion `T0TIPREDEFFECT` shift `t0_b z` and Eq. 3 first-summand carry `e^{A Δt} t0_b z` (Driver et al., 2017, Table 3, p. 13; Eq. 3, p. 5; `T0TIPREDEFFECT` is not `TIPREDEFFECT` `B`; `t0_b z` is not `A^{-1}[e^{A Δt} − I] B z`; `e^{A Δt} t0_b z` is not `t0_b z`), exact scalar observed mean of that first-occasion carry `τ + λ(μ_t + e^{a Δt} t0_b z)` (Driver et al., 2017, Eq. 5 of that Table 3 / Eq. 3 first-summand composition; `τ + λ μ_t` is not that observed mean; `τ + λ(μ_t + A^{-1}[e^{A Δt} − I] B z)` is not that observed mean), exact scalar first-occasion `T0TDPREDEFFECT` shift `t0_m x0` and Eq. 3 first-summand carry `e^{A Δt} t0_m x0` (Driver et al., 2017, Table 3, p. 13; Eq. 3, p. 5; JSS PDF re-opened 2026-08-20T19:10Z; `T0TDPREDEFFECT` is not `TDPREDEFFECT` `M`; `t0_m x0` is not `M x`; `e^{A Δt} t0_m x0` is not `t0_m x0`; `e^{A Δt} t0_m x0` is not `e^{A(t−u)} M x` for `t0 < u < t`; `t0_m x0` is not `t0_b z`; an impulse at `u ≤ t0` that used `M` is already in `η(t0)` as `TDPREDEFFECT`, not as `T0TDPREDEFFECT`), exact scalar observed mean of that first-occasion TD carry `τ + λ(μ_t + e^{a Δt} t0_m x0)` (Driver et al., 2017, Eq. 5 of that Table 3 / Eq. 3 first-summand TD composition; `τ + λ μ_t` is not that observed mean; `τ + λ(μ_t + A^{-1}[e^{A Δt} − I] B z)` is not that observed mean; `τ + λ(μ_t + e^{a Δt} t0_b z)` is not that observed mean), exact scalar §7.2 level-change `CINT` `κ = −a m x` (Driver et al., 2017, §7.2, pp. 20–21; `a < 0` so `−κ / a = m x`; not the dissipating Dirac, not a free `CINT`, not `TIPREDEFFECT`, and not the extra near-zero-drift latent process also named in §7.2), exact scalar Eq. 3 increment of that setting `(1 − e^{a Δt}) m x` (not `m x`, not `κ`, and not `TIPREDEFFECT`), exact scalar §7.2 extra near-zero-drift latent process contribution `a_{ηξ} x (e^{ε Δt} − e^{a Δt}) / (ε − a)` (Driver et al., 2017, §7.2, pp. 22–23; JSS PDF re-opened 2026-08-20T23:10Z; identification `TDPREDEFFECT` on the extra process is 1; extra `DRIFT` printed as `−0.000001`; precisely 0 causes computational problems; `ε = a` is `a_{ηξ} x Δt e^{a Δt}`; not `κ = −a m x`, not `(1 − e^{a Δt}) m x`, and not the dissipating Dirac `m x`; `ε ≥ 0` fails closed), exact scalar observed mean of that extra-process contribution `τ + λ(μ_t + a_{ηξ} x (e^{ε Δt} − e^{a Δt}) / (ε − a))` (Driver et al., 2017, Eq. 5, p. 5; §7.2, pp. 22–23; JSS PDF re-opened 2026-08-21T06:12Z; the extra process has `LAMBDA` 0 and is not an observed indicator; `τ + λ μ_t` is not that observed mean; `τ + λ(μ_t + m x)` is not that observed mean; the contribution is not `E(y_t)`; the evolved-plus-contribution latent mean is not `E(y_t)`), exact scalar after-t0 extra-process contribution `a_{ηξ} x (e^{ε(t−u)} − e^{a(t−u)}) / (ε − a)` for `t0 < u < t` and Eq. 5 observed mean `τ + λ(μ_t + a_{ηξ} x (e^{ε(t−u)} − e^{a(t−u)}) / (ε − a))` (Driver et al., 2017, §7.2, pp. 22–23; JSS PDF re-opened 2026-08-21T06:32Z; `T0TDPREDEFFECT` uses `Δt` for both the evolution and the extra drive; `TDPREDEFFECT` after `t0` uses `t − u`; `e^{a(t−u)} m x` is a Dirac on the original process, not this `DRIFT` drive), exact scalar §7.2 `asymTIPREDEFFECT` `-B z / a` (Driver et al., 2017, §7.2, pp. 20–21; JSS PDF opened 2026-08-21T13:08Z; expected total change in process means given a time-independent predictor; `a < 0`; not the coefficient `B`, not `A^{-1}[e^{A Δt} − I] B z`, not `CINT`, and not `M x`; `a ≥ 0` fails closed), exact scalar §7.2 `addedTIPREDVAR` `(B / a)² v` (Driver et al., 2017, §7.2, pp. 20–21; stable between-subject variance accounted for by a time-independent predictor; not `TRAITVAR`, not `asymDIFFUSION`, and not `-B z / a`), exact scalar Table 2 `asymCINT` `-κ / a` (Driver et al., 2017, Table 2, p. 12; Eq. 3 as `Δt → ∞`; `a < 0`; not `κ`, not `A^{-1}[e^{A Δt} − I] κ`, not `T0MEANS`, and not `-B z / a`; `a ≥ 0` fails closed), exact scalar p. 16 stationary `T0MEANS` `-κ / a + −B z / a` (Driver et al., 2017, p. 16; constrained first-occasion mean using `T0MEANSbase` / `T0MEANSfree`; not free `T0MEANS`, not `asymCINT` alone, not `asymTIPREDEFFECT` alone, and not the finite-interval discrete latent mean), exact scalar Eq. 5 of that constrained mean `τ + λ(−κ / a + −B z / a)` (Driver et al., 2017, §4.3, pp. 9–10; Eq. 5, p. 5; JSS PDF re-opened 2026-08-21T20:07Z; form the stationary latent mean first, then `τ + λ` of that mean; `τ + λ μ_0` is not that observed mean; `τ + λ(−κ / a)` is not that observed mean when `B z ≠ 0`; `τ + λ μ_t` is not that observed mean; `MANIFESTMEANS` is not `E(y_0)`; the constrained latent mean is not `E(y_0)`), exact scalar §4.3 / p. 16 stationary `T0VAR` `trait + −q / (2 a) + (B / a)² v` (Driver et al., 2017, §4.3, pp. 9–10; p. 16; JSS PDF re-opened 2026-08-22T03:07Z; not free `T0VAR`, not `asymDIFFUSION` alone, not `TRAITVAR` alone, not `addedTIPREDVAR` alone, and not the finite-interval discrete latent variance. Eq. 5 of that constrained variance is `λ²(trait + −q / (2 a) + (B / a)² v) + θ + ψ` (JSS PDF re-opened 2026-08-22T03:20Z; form the stationary latent variance first, then `λ² p + θ + ψ`; `λ² p_0` is not that observed variance; `λ²(−q / (2 a)) + θ` is not that observed variance when `TRAITVAR` or `addedTIPREDVAR` is nonzero; `MANIFESTVAR` is not `Var(y_0)`; the constrained latent variance is not `Var(y_0)`), exact scalar lagged covariance of that constrained process `trait + e^{a Δt}(−q / (2 a)) + (B / a)² v` (Driver et al., 2017, Eq. 3–4 of §4.3 / p. 16 `T0VAR`; JSS PDF re-opened 2026-08-22T19:13Z; trait and `addedTIPREDVAR` do not decay with `e^{a Δt}`; contemporaneous `T0VAR` is not that lagged map; decaying the constrained total as if it were all state is not that lagged map), exact scalar Eq. 5 of that lagged covariance `λ²(trait + e^{a Δt}(−q / (2 a)) + (B / a)² v) + ψ` (`Θ` does not enter; contemporaneous `Var(y_0)` is not that lagged observed covariance; the lagged latent covariance is not that observed covariance), exact scalar later-occasion variance of that constrained process `trait + e^{2 a Δt}(−q / (2 a)) + Q_Δt + (B / a)² v` (Driver et al., 2017, Eq. 3–4 of §4.3 / p. 16 `T0VAR`; JSS PDF re-opened 2026-08-22T23:12Z; trait and `addedTIPREDVAR` do not enter `Q_Δt`; under stationarity that composition equals contemporaneous `T0VAR`; evolving the constrained total as if it were all state is not that later map; the lagged covariance omits `Q_Δt` and is not that later map; `Q_Δt` is not that later map), exact scalar Eq. 5 of that later-occasion variance `λ²(trait + e^{2 a Δt}(−q / (2 a)) + Q_Δt + (B / a)² v) + θ + ψ` (the lagged observed covariance omits `Q_Δt` and `θ`; `MANIFESTVAR` is not that later observed variance; the later-occasion latent variance is not that observed variance)), exact scalar later-occasion variance of §4.3 predetermined `T0VAR` `trait + e^{2 a Δt} p_0 + Q_Δt + (B / a)² v` (Driver et al., 2017, Eq. 3–4 of §4.3 predetermined first occasion; JSS PDF re-opened 2026-08-23T05:12Z; trait and `addedTIPREDVAR` do not enter `Q_Δt`; free `T0VAR` `p_0` is not that later map; setting `p_0 = −q / (2 a)` recovers the stationary later-occasion map; stationary later variance uses `−q / (2 a)` in place of `p_0` and is not that later map when `p_0` is free; evolving `trait + p_0 + (B / a)² v` as if it were all state is not that later map; as `Δt → ∞` with stable `a < 0` the composition approaches contemporaneous stationary `T0VAR`; as `Δt → 0+` the composition approaches `trait + p_0 + (B / a)² v`; nonzero diffusion with `a ≥ 0` is a growing process and is kept. Eq. 5 of that predetermined later-occasion variance is `λ²(trait + e^{2 a Δt} p_0 + Q_Δt + (B / a)² v) + θ + ψ` (`MANIFESTVAR` is not that later observed variance; the predetermined later-occasion latent variance is not that observed variance; stationary later observed variance is not that observed variance when `p_0` is free)), exact scalar lagged covariance of §4.3 predetermined `T0VAR` `trait + e^{a Δt} p_0 + (B / a)² v` (Driver et al., 2017, Eq. 3–4 of §4.3 predetermined first occasion; JSS PDF re-opened 2026-08-23T09:04Z; trait and `addedTIPREDVAR` do not decay with `e^{a Δt}`; free `T0VAR` `p_0` is not that lagged map; setting `p_0 = −q / (2 a)` recovers the stationary lagged map; stationary lagged covariance uses `−q / (2 a)` in place of `p_0` and is not that lagged map when `p_0` is free; evolving `trait + p_0 + (B / a)² v` as if it were all state is not that lagged map; later-occasion variance includes `Q_Δt` and is not that lagged map. Eq. 5 of that predetermined lagged covariance is `λ²(trait + e^{a Δt} p_0 + (B / a)² v) + ψ` (`MANIFESTVAR` does not enter; the predetermined lagged latent covariance is not that observed covariance; predetermined later observed variance includes `Q_Δt` and `θ` and is not that lagged observed covariance; stationary lagged observed covariance is not that observed covariance when `p_0` is free)), exact scalar first-occasion variance of §4.3 predetermined `T0VAR` `trait + p_0 + (B / a)² v` (free `p_0` is not that map; stationary first-occasion variance uses `−q / (2 a)` in place of `p_0` and is not that map when `p_0` is free; lagged covariance decays the state and is not that map; later-occasion variance includes `Q_Δt` and is not that map), exact scalar Eq. 5 of that predetermined first-occasion variance `λ²(trait + p_0 + (B / a)² v) + θ + ψ` (`MANIFESTVAR` is not that first-occasion observed variance; the predetermined first-occasion latent variance is not that observed variance; stationary first-occasion observed variance is not that observed variance when `p_0` is free; predetermined later observed variance includes `Q_Δt` and is not that first-occasion observed variance; later-start lagged covariance of predetermined `T0VAR` is `trait + e^{a s}(e^{2 a u} p_0 + Q_u) + (B / a)² v` (Driver et al., 2017, §4.3 `startoffset`; Eq. 4; JSS PDF re-opened 2026-08-23T10:27Z; first-occasion lagged omits `e^{a s} Q_u`; later-occasion variance does not lag; stationary lagged uses `−q / (2 a)`; decaying the later total is not that map; Eq. 5 of that later-start lagged covariance is `λ²` of it plus `ψ`; `Θ` does not enter; first-occasion lagged observed omits `e^{a s} Q_u`; later observed variance includes `Q_u` and `θ`; later-start later-occasion variance of predetermined `T0VAR` is `trait + e^{2 a s}(e^{2 a u} p_0 + Q_u) + Q_s + (B / a)² v` (Driver et al., 2017, §4.3 `startoffset`; Eq. 3–4 Chapman–Kolmogorov `Q_{u+s} = e^{2 a s} Q_u + Q_s`; JSS PDF re-opened 2026-08-23T11:05Z; later-occasion variance at `u` omits `Q_s`; later-start lagged covariance omits `Q_s`; stationary later uses `−q / (2 a)`; evolving the later total as if it were all state is not that map; ignoring `startoffset` omits `e^{2 a s} Q_u`; Eq. 5 of that later-start later-occasion variance is `λ²` of it plus `θ + ψ`; `MANIFESTVAR` is not that observed variance; p. 16 `discreteDRIFTstd` is `e^{a Δt}` after strictly positive `asymDIFFUSION` `-q / (2 a)` (Driver et al., 2017, p. 16; footnote 4; §7.1; JSS PDF re-opened 2026-08-23T11:40Z; unstandardised `e^{a Δt}` is defined for growing `a ≥ 0` and for zero diffusion and is not `discreteDRIFTstd`; the trait-plus-state autocorrelation uses `TRAITVAR` and is not `discreteDRIFTstd`; p. 16 `discreteDIFFUSIONstd` is `Q_Δt / (−q / (2 a))` after strictly positive `asymDIFFUSION` `-q / (2 a)` (Driver et al., 2017, p. 16; Eq. 4; footnote 4; JSS PDF re-opened 2026-08-23T13:06Z; unstandardised `Q_Δt` is defined for growing `a ≥ 0` and for zero diffusion and is not `discreteDIFFUSIONstd`; the continuous standardisation `−2 a` is not `discreteDIFFUSIONstd`; `Q_Δt / (trait + p + added)` uses `TRAITVAR` and is not `discreteDIFFUSIONstd`; `TRAITVAR` is not the standardisation variance; p. 16 `DIFFUSIONstd` is `q / (−q / (2 a)) = −2 a` after strictly positive `asymDIFFUSION` `-q / (2 a)` (Driver et al., 2017, p. 16; Eq. 4; footnote 4; JSS PDF re-opened 2026-08-23T13:20Z; unstandardised `q` is defined for growing `a ≥ 0` and for zero diffusion and is not `DIFFUSIONstd`; the discrete standardisation `Q_Δt / (−q / (2 a))` depends on `Δt` and is not `DIFFUSIONstd`; `q / (trait + p + added)` uses `TRAITVAR` and is not `DIFFUSIONstd`; `TRAITVAR` is not the standardisation variance; p. 16 `DRIFTstd` is the continuous auto-effect after strictly positive `asymDIFFUSION` `-q / (2 a)` (Driver et al., 2017, p. 16; Eq. 1; footnote 4; JSS PDF re-opened 2026-08-23T13:28Z); unstandardised `a` is defined for growing `a ≥ 0` and for zero diffusion and is not `DRIFTstd`; the discrete standardisation `e^{a Δt}` depends on the event interval and is not `DRIFTstd`; `a p / (trait + p + added)` uses `TRAITVAR` and is not `DRIFTstd`; `TRAITVAR` is not the standardisation variance); p. 16 `asymTIPREDEFFECTstd` is `(-B / a) · √v / √(-q / (2 a))` after strictly positive `asymDIFFUSION` `-q / (2 a)` and strictly positive predictor variance `v` (Driver et al., 2017, p. 16; §7.2; footnote 4; JSS PDF re-opened 2026-08-23T14:25Z; unstandardised `-B / a` is defined for a zero coefficient and for zero predictor variance and is not `asymTIPREDEFFECTstd`; the finite-interval standardisation `A^{-1}[e^{A Δt} − I] B · √v / √p` depends on the event interval and is not `asymTIPREDEFFECTstd`; `(-B / a) · √v / √(trait + p + added)` uses `TRAITVAR` and is not `asymTIPREDEFFECTstd`; `TRAITVAR` is not the standardisation variance); p. 16 `TIPREDEFFECTstd` is `B · √v / √(-q / (2 a))` after strictly positive `asymDIFFUSION` `-q / (2 a)` and strictly positive predictor variance `v` (Driver et al., 2017, p. 16; §7.2; footnote 4; JSS PDF re-opened 2026-08-23T16:21Z; unstandardised `B` is defined for a zero coefficient and for zero predictor variance and is not `TIPREDEFFECTstd`; the asymptotic standardisation `(-B / a) · √v / √p` is the total change and is not `TIPREDEFFECTstd`; the finite-interval standardisation `A^{-1}[e^{A Δt} − I] B · √v / √p` depends on the event interval and is not `TIPREDEFFECTstd`; `B · √v / √(trait + p + added)` uses `TRAITVAR` and is not `TIPREDEFFECTstd`; `TRAITVAR` is not the standardisation variance); Table 3 `T0TIPREDEFFECTstd` is `t0_b · √v / √p_0` after strictly positive free `T0VAR` `p_0` and strictly positive predictor variance `v` (Driver et al., 2017, Table 3, p. 13; p. 16; footnote 4; 2017-era ctsem `summary.ctsemFit.R`; JSS PDF re-opened 2026-08-23T17:20Z; the affected variance is free `T0VAR`, not `asymDIFFUSION`; unstandardised `t0_b` is defined for a zero coefficient and for zero predictor variance and is not `T0TIPREDEFFECTstd`; `TIPREDEFFECTstd` `B · √v / √(-q / (2 a))` is the continuous coefficient and is not `T0TIPREDEFFECTstd`; `asymTIPREDEFFECTstd` `(-B / a) · √v / √p` is the total change and is not `T0TIPREDEFFECTstd`; `t0_b · √v / √(trait + p_0 + added)` uses `TRAITVAR` and is not `T0TIPREDEFFECTstd`; `TRAITVAR` is not the standardisation variance); 2017-era `addedT0TIPREDVAR` is `t0_b² v` (Driver et al., 2017, Table 3, p. 13; p. 16; §7.2; 2017-era ctsem `summary.ctsemFit.R`; JSS PDF re-opened 2026-08-23T18:20Z; `T0TIPREDEFFECT %*% TIPREDVAR %*% t(T0TIPREDEFFECT)` immediately after `T0TIPREDEFFECTstd`; form `t0_b` first, then square, then multiply by `v`; a zero coefficient or zero predictor variance is exactly zero; free `T0TIPREDEFFECT` does not require `a < 0`; `(B / a)² v` is `addedTIPREDVAR` and is not this first-occasion map; `t0_b · √v / √p_0` is `T0TIPREDEFFECTstd` and is not this variance; free `T0VAR` is not this extra TI variance; `TRAITVAR` is not this extra TI variance; Equation 5 of 2017-era `addedT0TIPREDVAR` is `λ² t0_b² v` (Driver et al., 2017, Eq. 5, p. 5; Table 3, p. 13; Table 2, p. 12; 2017-era ctsem `summary.ctsemFit.R`; JSS PDF re-opened 2026-08-23T19:10Z; form `t0_b² v` first, then `(λ extra) λ` with `θ = 0`; a zero loading or zero extra is exactly zero; `t0_b² v` is the latent extra, not the observed extra; `λ² p_0 + θ` is first-occasion observed variance, not this extra; `λ² (B / a)² v` is Eq. 5 of `addedTIPREDVAR`, not this first-occasion observed extra; `MANIFESTVAR` `θ` is not this extra; Equation 5 of §7.2 `addedTIPREDVAR` is `λ² (B / a)² v`; form `(B / a)² v` first, then `(λ extra) λ` with `θ = 0`; a zero loading or zero extra is exactly zero; lasting asymptotic extra requires `a < 0`; `(B / a)² v` is the latent extra, not the observed extra; `λ² t0_b² v` is first-occasion extra observed TI variance, not this extra; `λ² p + θ` is stationary observed variance, not this extra; `MANIFESTVAR` `θ` is not this extra; p. 16 `TDPREDEFFECTstd` is `m · √v / √(-q / (2 a))` after strictly positive `asymDIFFUSION` and strictly positive time-dependent predictor variance; unstandardised `M` is not `TDPREDEFFECTstd`; `TIPREDEFFECTstd` is not `TDPREDEFFECTstd` even when `M = B`; intercept-style `A^{-1}[e^{A Δt} − I] M · √v / √p` is not `TDPREDEFFECTstd`; `m · √v / √(trait + p + added)` uses `TRAITVAR` and is not `TDPREDEFFECTstd`; Table 3 / p. 16 `T0TDPREDEFFECTstd` is `t0_m · √v / √p_0` after strictly positive free `T0VAR` and strictly positive TD predictor variance; unstandardised `t0_m` is not `T0TDPREDEFFECTstd`; `TDPREDEFFECTstd` uses `asymDIFFUSION` and is not `T0TDPREDEFFECTstd`; `T0TIPREDEFFECTstd` is not `T0TDPREDEFFECTstd` even when `t0_m = t0_b`; `t0_m · √v / √(trait + p_0 + added)` uses `TRAITVAR` and is not `T0TDPREDEFFECTstd`; free `T0VAR` does not require `a < 0`; p. 16 `T0VARstd` is `p_0 / p_0 = 1` after strictly positive free `T0VAR` (`solve(sqrt(diag(T0VAR))) %&% T0VAR`; OpenMx `%&%` is `t(A) %*% B %*% A`; default ridge is 0); unstandardised `T0VAR` is not `T0VARstd`; `T0TDPREDEFFECTstd` is not `T0VARstd`; `addedT0TIPREDVAR` is not `T0VARstd`; p. 16 `TRAITVARstd` is `trait / trait = 1` after strictly positive `TRAITVAR` (`solve(sqrt(diag(TRAITVAR))) %&% TRAITVAR`; OpenMx `%&%` is `t(A) %*% B %*% A`; no ridge addend); unstandardised `TRAITVAR` is not `TRAITVARstd`; `T0VARstd` is not `TRAITVARstd` even when both equal 1; `addedT0TIPREDVAR` is not `TRAITVARstd`; p. 16 `MANIFESTTRAITVARstd` is `ψ / ψ = 1` after strictly positive `MANIFESTTRAITVAR` (`solve(sqrt(diag(MANIFESTTRAITVAR))) %&% MANIFESTTRAITVAR`; OpenMx `%&%` is `t(A) %*% B %*% A`; 2017-era source adds ridging; default ridge is 0); unstandardised `MANIFESTTRAITVAR` is not `MANIFESTTRAITVARstd`; `TRAITVARstd` is not `MANIFESTTRAITVARstd` even when both equal 1; `MANIFESTVAR` is not `MANIFESTTRAITVARstd`; p. 16 `MANIFESTVARstd` is `θ / θ = 1` after strictly positive `MANIFESTVAR` (`solve(sqrt(diag(MANIFESTVAR))) %&% MANIFESTVAR`; OpenMx `%&%` is `t(A) %*% B %*% A`; 2017-era source adds ridging; default ridge is 0; 2017-era `dimnames` assignment to `latentNames` is a source bug); unstandardised `MANIFESTVAR` is not `MANIFESTVARstd`; `MANIFESTTRAITVARstd` is not `MANIFESTVARstd` even when both equal 1; Equation 5 `Var(y)` is not `MANIFESTVARstd`;))))), CWC-then-event-time residual lag, irregular already-centered residual log-rate, and strong/strict-gated two-group OLS latent-mean difference are implemented on the stacked psychometric PR and are not implemented-main until exact-head checks, review, and protected-main integration complete; full ESEM/set-ESEM, formative composites, DSEM, and matrix continuous-time dynamics remain accepted-target **Date:** 2026-08-05 **Supersedes:** None. ADR 0012 governs upstream topic measurement/network coordinates; this ADR governs higher-order psychometric structure and longitudinal interpretation. @@ -20,7 +20,7 @@ Before ESEM/SEM interpretation, each higher-order construct is classified as ref Longitudinal analysis evaluates measurement invariance at the level needed for the claimed comparison, supports partial/approximate or time-varying loadings where scientifically justified, separates stable between-unit components from within-unit temporal change, and handles irregular intervals through appropriate discrete- or continuous-time dynamics. -The executable multilevel slice is cluster-mean centering (CWC) plus within/between OLS, the CWC contextual effect (`between − within`), and Kish-weighted slopes. Enders and Tofighi (2007, Table 2, pp. 124–127) show that the CWC cluster-mean coefficient is the contextual effect, not the between-cluster effect. It is not DSEM and not RI-CLPM. The executable temporal slice maps a discrete lag through the exact scalar exponential `a = ln(φ) / Δt` on event time only (Voelkle, Oud, Davidov, & Schmidt, 2012, Eq. 7; Driver, Oud, & Voelkle, 2017, Eq. 3), recovers the forward map `φ(Δt) = exp(a Δt)`, remaps a discrete lag onto another event interval through that log-rate, recovers the exact scalar discrete effect of a constant predictor (Voelkle et al., 2012, Eq. 12) as `a_yx (expm1(z) / a_xx)` with `z = a_xx Δt` so a finite result is not lost when `z` overflows to `-∞` or when `a_yx Δt` overflows, and in log space when `expm1(z)` overflows at a finite `z` (`a_yx = 0` is exactly zero; an overflowing `a_yx/a_xx` rewrite term fails closed), recovers the first-order discrete effect of a time-varying predictor with matched sampling and constancy intervals as `a_yx Δt` (Voelkle et al., 2012, Eq. 14; ZORA accepted manuscript p. 21; not Eq. 12; unmatched intervals fail closed because Oud & Jansen, 2000, is unread), recovers the exact scalar discrete process noise `Q_Δt = 0.5 q (expm1(z) / a)` with `z = 2 (a Δt)` and `q = G G⊤ ≥ 0` (Driver et al., 2017, Eq. 3; JSS PDF re-opened 2026-08-18T07:06Z, p. 4; scalar `L = 1`; do not form `2 a` first; `a = 0` and `z → 0` recover `q Δt`; `z → −∞` keeps `−0.5 q / a`; a zero diffusion is exactly zero; `z → +∞` and an overflowing rewrite scale `0.5 q / a` fail closed), recovers the exact scalar lagged latent covariance `exp(a Δt) p` and the law-of-total-variance map `Var(η_t) = exp(2 a Δt) p + Q_Δt` (Driver et al., 2017, Eq. 3–4, pp. 4–5; JSS PDF re-opened 2026-08-18T18:03Z; JSS has no numbered §2.2; `Q_Δt` is `cov(η_t | η_{t-1})` and is refused as the unconditional variance; a zero diffusion whose `2 (a Δt)` overflows to `+∞` fails closed), recovers the exact scalar stationary within-subject variance `-q / (2 a)` as the `Δt → ∞` limit of Eq. 4 for stable `a < 0` (JSS p. 16 `asymDIFFUSION`; §4.3 T0VAR stationarity; when `2 a` is finite, form `q / -(2 a)` so `q / a` overflow does not lose a finite result (`q = MAX`, `a = -0.75` → `MAX / 1.5`; CodeRabbit on `75ecdd3`); when `2 a` overflows, form `(q / a) * -0.5`; do not form `0.5 q` first (`q = from_bits(1)`, `a = -from_bits(1)` → `0.5`); `a ≥ 0` and finite-interval `Q_Δt` fail closed as that limit), recovers the exact scalar trait-plus-state variance `trait + state` and lagged covariance `trait + exp(a Δt) p` (Driver et al., 2017, §4.3, p. 9; JSS PDF re-opened 2026-08-18T21:07Z; a stable trait has `DRIFT` and `DIFFUSION` fixed to zero; `TRAITVAR` is not process noise and not `asymDIFFUSION`; evolving the summed variance as if it were all state fails the claim boundary; this is not RI-CLPM), recovers the exact scalar observed-indicator variance `λ² Var(η) + θ` when `MANIFESTTRAITVAR` is zero and `λ² Var(η) + θ + ψ` otherwise, and the lagged observed covariance `λ² cov(η_t, η_{t-1}) + ψ` (Driver et al., 2017, Eq. 5, p. 5; Table 2, p. 12; JSS PDF re-opened 2026-08-19T04:18Z; Equation 1 is the latent SDE; form `(λ p) λ` then add `θ`, then add `ψ`; do not form `λ²` first; `MANIFESTVAR` is `Θ`, not `Var(y)` and does not enter lagged observed covariance; `MANIFESTTRAITVAR` is `Ψ_τ`, not `Θ`; `TRAITVAR` is latent and scaled by `λ²`; `Var(η)` is not `Var(y)`), recovers the exact scalar observed-indicator mean `τ + λ μ` (Driver et al., 2017, Eq. 5, p. 5; Table 2, p. 12; JSS PDF re-opened 2026-08-19T14:08Z; `MANIFESTMEANS` is `τ`, not `E(y)`; `CINT` is not `MANIFESTMEANS`; `T0MEANS` is not `E(y)`), recovers the exact scalar discrete latent mean `exp(a Δt) μ_0 + (exp(a Δt) − 1)/a κ` (Driver et al., 2017, Eq. 3, p. 4; Table 2, p. 12; JSS PDF re-opened 2026-08-19T18:10Z; `T0MEANS` is not `μ_t`; `CINT` is not the discrete increment; a zero drift is `κ Δt`; underflow of `exp(a Δt)` to `+0` drops the carried `T0MEANS` and keeps `−κ / a`), recovers the exact scalar evolved observed-indicator mean `τ + λ μ_t` (Driver et al., 2017, Eq. 5 of the Eq. 3 expected-value map; JSS PDF re-opened 2026-08-19T22:10Z; the first-occasion map `τ + λ μ_0` is not `E(y_t)`; `MANIFESTMEANS` is not `E(y_t)`; `μ_t` is not `E(y_t)`), recovers the exact scalar contemporaneous time-dependent predictor impulse `m x` (Driver et al., 2017, Eq. 1–3, pp. 4–5; Table 2, p. 12; §7.2, pp. 20–21; JSS PDF re-opened 2026-08-20T07:10Z; `TDPREDEFFECT` is `M`, not `CINT`; `M x` is not `A^{-1}[e^{A Δt} − I] B z`; `M x` is not Voelkle et al., 2012, Eq. 14; the level-change form is not that impulse), recovers the exact scalar observed mean of that contemporaneous impulse `τ + λ(μ_t + m x)` (Driver et al., 2017, Eq. 5 of the Eq. 3 fourth-summand composition; JSS PDF re-opened 2026-08-20T09:01Z; the evolved map `τ + λ μ_t` is not that observed mean; the carry map `τ + λ(μ_t + e^{a(t−u)} m x)` is not that observed mean when `u ≠ t`; `MANIFESTMEANS` is not `E(y_t)`; the evolved-plus-impulse latent mean is not `E(y_t)`), recovers the exact scalar time-independent predictor increment `A^{-1}[e^{A Δt} − I] B z` (Driver et al., 2017, Eq. 1–3, pp. 4–5; Table 2, p. 12; JSS PDF re-opened 2026-08-20T10:13Z; `TIPREDEFFECT` is `B`, not `κ`; form `B z` first, then the discrete intercept map; a zero drift is `B z Δt`; `B` is not the discrete increment; `A^{-1}[e^{A Δt} − I] B z` is not `CINT`, not `M x`, and not Voelkle Eq. 14), recovers the exact scalar observed mean of that increment `τ + λ(μ_t + A^{-1}[e^{A Δt} − I] B z)` (Driver et al., 2017, Eq. 5 of the Eq. 3 printed addend after the `T0MEANS` carry and the `CINT` increment; JSS PDF re-opened 2026-08-20T12:12Z; the evolved map `τ + λ μ_t` is not that observed mean; the contemporaneous map `τ + λ(μ_t + m x)` is not that observed mean; the carry map `τ + λ(μ_t + e^{a(t−u)} m x)` is not that observed mean when `u ≠ t`; `MANIFESTMEANS` is not `E(y_t)`; the evolved-plus-increment latent mean is not `E(y_t)`), recovers the exact scalar within-interval time-dependent impulse carry `e^{A(t−u)} M x` for `t0 < u < t` (Driver et al., 2017, Eq. 1–2, pp. 4–5; Eq. 3 exponential map; §7.2, pp. 20–21; JSS PDF re-opened 2026-08-20T10:33Z; form `m x` first, then `e^{a(t−u)} m x`; a zero drift is `m x`; underflow of `e^{a(t−u)}` to `+0` is vanishing dissipation and is kept; `e^{A(t−u)} M x` is not the contemporaneous Dirac, not `CINT`, not `TIPREDEFFECT`, and not Voelkle Eq. 14; an impulse at `u = t` is the contemporaneous map; an impulse at `u ≤ t0` is already in `η(t0)`), recovers the exact scalar observed mean of that carry `τ + λ(μ_t + e^{a(t−u)} m x)` (Driver et al., 2017, Eq. 5 of the Eq. 1–2 carried latent mean; JSS PDF re-opened 2026-08-20T05:12Z; the evolved map `τ + λ μ_t` is not that observed mean; the contemporaneous map `τ + λ(μ_t + m x)` is not that observed mean when `u ≠ t`; `MANIFESTMEANS` is not `E(y_t)`; the carried latent mean is not `E(y_t)`), recovers the exact scalar first-occasion `T0TIPREDEFFECT` shift `t0_b z` and its Eq. 3 first-summand carry `e^{A Δt} t0_b z` (Driver et al., 2017, Table 3, p. 13; Eq. 3, p. 5; JSS PDF opened 2026-08-20T15:14Z; form `t0_b z` first, then `e^{a Δt} t0_b z`; a zero drift is `t0_b z`; underflow of `e^{a Δt}` to `+0` is a vanishing carry and is kept; `t0_b z` is not `A^{-1}[e^{A Δt} − I] B z`, not `CINT`, and not `M x`; `e^{A Δt} t0_b z` is not `t0_b z`; `T0TIPREDEFFECT` is the coefficient, not the shift), recovers the exact scalar observed mean of that first-occasion carry `τ + λ(μ_t + e^{a Δt} t0_b z)` (Driver et al., 2017, Eq. 5 of the Table 3 / Eq. 3 first-summand composition; JSS PDF re-opened 2026-08-20T15:28Z; the evolved map `τ + λ μ_t` is not that observed mean; the process-increment map `τ + λ(μ_t + A^{-1}[e^{A Δt} − I] B z)` is not that observed mean; the contemporaneous map `τ + λ(μ_t + m x)` is not that observed mean; the impulse-carry map `τ + λ(μ_t + e^{a(t−u)} m x)` is not that observed mean when `u ≠ t0`; `MANIFESTMEANS` is not `E(y_t)`; the evolved-plus-carry latent mean is not `E(y_t)`), recovers the exact scalar first-occasion `T0TDPREDEFFECT` shift `t0_m x0` and its Eq. 3 first-summand carry `e^{A Δt} t0_m x0` (Driver et al., 2017, Table 3, p. 13; Eq. 3, p. 5; JSS PDF re-opened 2026-08-20T19:10Z; form `t0_m x0` first, then `e^{a Δt} t0_m x0`; a zero drift is `t0_m x0`; underflow of `e^{a Δt}` to `+0` is a vanishing carry and is kept; `t0_m x0` is not `M x`, not `e^{A(t−u)} M x` for `t0 < u < t`, not `t0_b z`, not `A^{-1}[e^{A Δt} − I] B z`, and not `CINT`; `e^{A Δt} t0_m x0` is not `t0_m x0`; `T0TDPREDEFFECT` is the coefficient, not the shift; an impulse at `u ≤ t0` that used `M` is already in `η(t0)` as `TDPREDEFFECT`, not as `T0TDPREDEFFECT`), recovers the exact scalar observed mean of that first-occasion TD carry `τ + λ(μ_t + e^{a Δt} t0_m x0)` (Driver et al., 2017, Eq. 5 of the Table 3 / Eq. 3 first-summand TD composition; JSS PDF re-opened 2026-08-20T19:07Z; the evolved map `τ + λ μ_t` is not that observed mean; the process-increment map `τ + λ(μ_t + A^{-1}[e^{A Δt} − I] B z)` is not that observed mean; the contemporaneous map `τ + λ(μ_t + m x)` is not that observed mean; the impulse-carry map `τ + λ(μ_t + e^{a(t−u)} m x)` is not that observed mean when `u ≠ t0`; the first-occasion TI map `τ + λ(μ_t + e^{a Δt} t0_b z)` is not that observed mean; `MANIFESTMEANS` is not `E(y_t)`; the evolved-plus-carry latent mean is not `E(y_t)`), recovers the exact scalar §7.2 level-change `CINT` setting `κ = −a m x` (Driver et al., 2017, §7.2, pp. 20–21; JSS PDF re-opened 2026-08-20T19:45Z; form `m x` first, then multiply by `−a`; `a < 0` so `−κ / a = m x`; `a ≥ 0` cannot hold a new process mean; `−a m x` is not the dissipating Dirac, not a free `CINT`, and not `A^{-1}[e^{A Δt} − I] B z`; the extra near-zero-drift latent process also named in §7.2 is a different specification), recovers the exact scalar Eq. 3 increment of that setting `(1 − e^{a Δt}) m x` (JSS PDF re-opened 2026-08-20T19:50Z; form the level-change `CINT` first, then the discrete intercept map; underflow of `e^{a Δt}` to `+0` keeps `m x`; `(1 − e^{a Δt}) m x` is not `m x`, not `κ`, and not `A^{-1}[e^{A Δt} − I] B z`), recovers the exact scalar §7.2 extra near-zero-drift latent process contribution `a_{ηξ} x (e^{ε Δt} − e^{a Δt}) / (ε − a)` (Driver et al., 2017, §7.2, pp. 22–23; JSS PDF re-opened 2026-08-20T23:10Z; form `a_{ηξ} x` first; `ε = a` is `a_{ηξ} x Δt e^{a Δt}`; a zero coupling or zero predictor is exactly zero; `ε ≥ 0` cannot hold a lasting extra state; that contribution is not `κ = −a m x`, not `(1 − e^{a Δt}) m x`, and not the dissipating Dirac `m x`), recovers the exact scalar observed mean of that extra-process contribution `τ + λ(μ_t + a_{ηξ} x (e^{ε Δt} − e^{a Δt}) / (ε − a))` (Driver et al., 2017, Eq. 5, p. 5; §7.2, pp. 22–23; JSS PDF re-opened 2026-08-21T06:12Z; the extra process has `LAMBDA` 0 and is not an observed indicator; form the evolved-plus-contribution latent mean first, then `τ + λ` of that mean; the evolved map `τ + λ μ_t` is not that observed mean; the contemporaneous map `τ + λ(μ_t + m x)` is not that observed mean; the contribution is not `E(y_t)`; the evolved-plus-contribution latent mean is not `E(y_t)`), recovers the exact scalar after-t0 extra-process contribution `a_{ηξ} x (e^{ε(t−u)} − e^{a(t−u)}) / (ε − a)` for `t0 < u < t` and its Eq. 5 observed mean `τ + λ(μ_t + a_{ηξ} x (e^{ε(t−u)} − e^{a(t−u)}) / (ε − a))` (Driver et al., 2017, §7.2, pp. 22–23; JSS PDF re-opened 2026-08-21T06:32Z; `T0TDPREDEFFECT` uses `Δt` for both the evolution and the extra drive; `TDPREDEFFECT` after `t0` uses `t − u` while `μ_t` still uses `Δt`; an impulse at `u = t0` or `u = t` is not interior; `e^{a(t−u)} m x` is a Dirac on the original process, not this `DRIFT` drive), recovers the exact scalar §7.2 `asymTIPREDEFFECT` `-B z / a` (Driver et al., 2017, §7.2, pp. 20–21; JSS PDF opened 2026-08-21T13:08Z; form `B z` first, then divide by `-a`; `a < 0`; a zero coefficient or zero predictor is exactly zero; `a ≥ 0` cannot hold a finite process-mean change; `-B z / a` is not the coefficient `B`, not `A^{-1}[e^{A Δt} − I] B z`, not `CINT`, and not `M x`), recovers the exact scalar §7.2 `addedTIPREDVAR` `(B / a)² v` (Driver et al., 2017, §7.2, pp. 20–21; form the unit asymptotic effect first, then square, then multiply by `v`; `v ≥ 0`; a zero coefficient or zero predictor variance is exactly zero; `(B / a)² v` is not `TRAITVAR`, not `asymDIFFUSION`, and not `-B z / a`), recovers the exact scalar Table 2 `asymCINT` `-κ / a` (Driver et al., 2017, Table 2, p. 12; Eq. 3 as `Δt → ∞`; JSS PDF opened 2026-08-21T16:13Z; form `κ` first, then divide by `-a`; `a < 0`; a zero intercept is exactly zero; `-κ / a` is not `κ`, not `A^{-1}[e^{A Δt} − I] κ`, not `T0MEANS`, and not `-B z / a`), recovers the exact scalar p. 16 stationary `T0MEANS` `-κ / a + −B z / a` (Driver et al., 2017, p. 16; form the intercept contribution first, then include the TI extra effect, then add; not free `T0MEANS`, not `asymCINT` alone, not `asymTIPREDEFFECT` alone, and not the finite-interval discrete latent mean), recovers the exact scalar Eq. 5 of that constrained mean `τ + λ(−κ / a + −B z / a)` (Driver et al., 2017, §4.3, pp. 9–10; Eq. 5, p. 5; JSS PDF re-opened 2026-08-21T20:07Z; form the stationary latent mean first, then `τ + λ` of that mean; `τ + λ μ_0` is not that observed mean; `τ + λ(−κ / a)` is not that observed mean when `B z ≠ 0`; `τ + λ μ_t` is not that observed mean; `MANIFESTMEANS` is not `E(y_0)`; the constrained latent mean is not `E(y_0)`), recovers the exact scalar §4.3 / p. 16 stationary `T0VAR` `trait + −q / (2 a) + (B / a)² v` (Driver et al., 2017, §4.3, pp. 9–10; p. 16; JSS PDF re-opened 2026-08-22T03:07Z; form the within-subject contribution first, then include the trait, then include the TI extra variance, then add; not free `T0VAR`, not `asymDIFFUSION` alone, not `TRAITVAR` alone, not `addedTIPREDVAR` alone, and not the finite-interval discrete latent variance), recovers the exact scalar Eq. 5 of that constrained variance `λ²(trait + −q / (2 a) + (B / a)² v) + θ + ψ` (Driver et al., 2017, §4.3, pp. 9–10; Eq. 5, p. 5; Table 2, p. 12; JSS PDF re-opened 2026-08-22T03:20Z; form the stationary latent variance first, then `λ² p + θ + ψ`; `λ² p_0` is not that observed variance; `λ²(−q / (2 a)) + θ` is not that observed variance when `TRAITVAR` or `addedTIPREDVAR` is nonzero; `MANIFESTVAR` is not `Var(y_0)`; the constrained latent variance is not `Var(y_0)`), recovers the exact scalar lagged covariance of that constrained process `trait + e^{a Δt}(−q / (2 a)) + (B / a)² v` (Driver et al., 2017, Eq. 3–4 of §4.3 / p. 16 `T0VAR`; JSS PDF re-opened 2026-08-22T19:13Z; trait and `addedTIPREDVAR` do not decay with `e^{a Δt}`; contemporaneous `T0VAR` is not that lagged map; decaying the constrained total as if it were all state is not that lagged map), recovers the exact scalar Eq. 5 of that lagged covariance `λ²(trait + e^{a Δt}(−q / (2 a)) + (B / a)² v) + ψ` (`Θ` does not enter; contemporaneous `Var(y_0)` is not that lagged observed covariance; the lagged latent covariance is not that observed covariance), recovers the exact scalar later-occasion variance of that constrained process `trait + e^{2 a Δt}(−q / (2 a)) + Q_Δt + (B / a)² v` (Driver et al., 2017, Eq. 3–4 of §4.3 / p. 16 `T0VAR`; JSS PDF re-opened 2026-08-22T23:12Z; form the evolved within-subject variance first, then include the trait, then include the TI extra variance, then add; trait and `addedTIPREDVAR` do not enter `Q_Δt`; under stationarity that composition equals contemporaneous `T0VAR`; evolving the constrained total as if it were all state is not that later map; the lagged covariance omits `Q_Δt` and is not that later map; `Q_Δt` is not that later map), recovers the exact scalar Eq. 5 of that later-occasion variance `λ²(trait + e^{2 a Δt}(−q / (2 a)) + Q_Δt + (B / a)² v) + θ + ψ` (the lagged observed covariance omits `Q_Δt` and `θ`; `MANIFESTVAR` is not that later observed variance; the later-occasion latent variance is not that observed variance), recovers the exact scalar later-occasion variance of §4.3 predetermined `T0VAR` `trait + e^{2 a Δt} p_0 + Q_Δt + (B / a)² v` (Driver et al., 2017, Eq. 3–4 of §4.3 predetermined first occasion; JSS PDF re-opened 2026-08-23T05:12Z; form the evolved free first-occasion variance first, then include the trait, then include the TI extra variance, then add; trait and `addedTIPREDVAR` do not enter `Q_Δt`; free `T0VAR` `p_0` is not that later map; setting `p_0 = −q / (2 a)` recovers the stationary later-occasion map; stationary later variance uses `−q / (2 a)` in place of `p_0` and is not that later map when `p_0` is free; evolving `trait + p_0 + (B / a)² v` as if it were all state is not that later map), recovers the exact scalar Eq. 5 of that predetermined later-occasion variance `λ²(trait + e^{2 a Δt} p_0 + Q_Δt + (B / a)² v) + θ + ψ` (`MANIFESTVAR` is not that later observed variance; the predetermined later-occasion latent variance is not that observed variance; stationary later observed variance is not that observed variance when `p_0` is free), recovers the exact scalar lagged covariance of §4.3 predetermined `T0VAR` `trait + e^{a Δt} p_0 + (B / a)² v` (Driver et al., 2017, Eq. 3–4 of §4.3 predetermined first occasion; JSS PDF re-opened 2026-08-23T09:04Z; form the lagged free first-occasion covariance first, then include the trait, then include the TI extra variance, then add; trait and `addedTIPREDVAR` do not decay with `e^{a Δt}`; free `T0VAR` `p_0` is not that lagged map; setting `p_0 = −q / (2 a)` recovers the stationary lagged map; stationary lagged covariance uses `−q / (2 a)` in place of `p_0` and is not that lagged map when `p_0` is free; evolving `trait + p_0 + (B / a)² v` as if it were all state is not that lagged map; later-occasion variance includes `Q_Δt` and is not that lagged map), recovers the exact scalar Eq. 5 of that predetermined lagged covariance `λ²(trait + e^{a Δt} p_0 + (B / a)² v) + ψ` (`MANIFESTVAR` does not enter; the predetermined lagged latent covariance is not that observed covariance; predetermined later observed variance includes `Q_Δt` and `θ` and is not that lagged observed covariance; stationary lagged observed covariance is not that observed covariance when `p_0` is free), recovers the exact scalar first-occasion variance of §4.3 predetermined `T0VAR` `trait + p_0 + (B / a)² v` (free `p_0` is not that map; stationary first-occasion variance uses `−q / (2 a)` in place of `p_0` and is not that map when `p_0` is free; lagged covariance decays the state and is not that map; later-occasion variance includes `Q_Δt` and is not that map), recovers the exact scalar Eq. 5 of that predetermined first-occasion variance `λ²(trait + p_0 + (B / a)² v) + θ + ψ` (`MANIFESTVAR` is not that first-occasion observed variance; the predetermined first-occasion latent variance is not that observed variance; stationary first-occasion observed variance is not that observed variance when `p_0` is free; predetermined later observed variance includes `Q_Δt` and is not that first-occasion observed variance; later-start lagged covariance of predetermined `T0VAR` is `trait + e^{a s}(e^{2 a u} p_0 + Q_u) + (B / a)² v` (Driver et al., 2017, §4.3 `startoffset`; Eq. 4; JSS PDF re-opened 2026-08-23T10:27Z; first-occasion lagged omits `e^{a s} Q_u`; later-occasion variance does not lag; stationary lagged uses `−q / (2 a)`; decaying the later total is not that map; Eq. 5 of that later-start lagged covariance is `λ²` of it plus `ψ`; `Θ` does not enter; first-occasion lagged observed omits `e^{a s} Q_u`; later observed variance includes `Q_u` and `θ`; later-start later-occasion variance of predetermined `T0VAR` is `trait + e^{2 a s}(e^{2 a u} p_0 + Q_u) + Q_s + (B / a)² v` (Driver et al., 2017, §4.3 `startoffset`; Eq. 3–4 Chapman–Kolmogorov `Q_{u+s} = e^{2 a s} Q_u + Q_s`; JSS PDF re-opened 2026-08-23T11:05Z; later-occasion variance at `u` omits `Q_s`; later-start lagged covariance omits `Q_s`; stationary later uses `−q / (2 a)`; evolving the later total as if it were all state is not that map; ignoring `startoffset` omits `e^{2 a s} Q_u`; Eq. 5 of that later-start later-occasion variance is `λ²` of it plus `θ + ψ`; `MANIFESTVAR` is not that observed variance; p. 16 `discreteDRIFTstd` is `e^{a Δt}` after strictly positive `asymDIFFUSION` `-q / (2 a)` (Driver et al., 2017, p. 16; footnote 4; §7.1; JSS PDF re-opened 2026-08-23T11:40Z; unstandardised `e^{a Δt}` is defined for growing `a ≥ 0` and for zero diffusion and is not `discreteDRIFTstd`; the trait-plus-state autocorrelation uses `TRAITVAR` and is not `discreteDRIFTstd`; p. 16 `discreteDIFFUSIONstd` is `Q_Δt / (−q / (2 a))` after strictly positive `asymDIFFUSION` `-q / (2 a)` (Driver et al., 2017, p. 16; Eq. 4; footnote 4; JSS PDF re-opened 2026-08-23T13:06Z; unstandardised `Q_Δt` is defined for growing `a ≥ 0` and for zero diffusion and is not `discreteDIFFUSIONstd`; the continuous standardisation `−2 a` is not `discreteDIFFUSIONstd`; `Q_Δt / (trait + p + added)` uses `TRAITVAR` and is not `discreteDIFFUSIONstd`; `TRAITVAR` is not the standardisation variance; p. 16 `DIFFUSIONstd` is `q / (−q / (2 a)) = −2 a` after strictly positive `asymDIFFUSION` `-q / (2 a)` (Driver et al., 2017, p. 16; Eq. 4; footnote 4; JSS PDF re-opened 2026-08-23T13:20Z; unstandardised `q` is defined for growing `a ≥ 0` and for zero diffusion and is not `DIFFUSIONstd`; the discrete standardisation `Q_Δt / (−q / (2 a))` depends on `Δt` and is not `DIFFUSIONstd`; `q / (trait + p + added)` uses `TRAITVAR` and is not `DIFFUSIONstd`; `TRAITVAR` is not the standardisation variance; p. 16 `DRIFTstd` is the continuous auto-effect after strictly positive `asymDIFFUSION` `-q / (2 a)` (Driver et al., 2017, p. 16; Eq. 1; footnote 4; JSS PDF re-opened 2026-08-23T13:28Z); unstandardised `a` is defined for growing `a ≥ 0` and for zero diffusion and is not `DRIFTstd`; the discrete standardisation `e^{a Δt}` depends on the event interval and is not `DRIFTstd`; `a p / (trait + p + added)` uses `TRAITVAR` and is not `DRIFTstd`; `TRAITVAR` is not the standardisation variance); p. 16 `asymTIPREDEFFECTstd` is `(-B / a) · √v / √(-q / (2 a))` after strictly positive `asymDIFFUSION` `-q / (2 a)` and strictly positive predictor variance `v` (Driver et al., 2017, p. 16; §7.2; footnote 4; JSS PDF re-opened 2026-08-23T14:25Z; unstandardised `-B / a` is defined for a zero coefficient and for zero predictor variance and is not `asymTIPREDEFFECTstd`; the finite-interval standardisation `A^{-1}[e^{A Δt} − I] B · √v / √p` depends on the event interval and is not `asymTIPREDEFFECTstd`; `(-B / a) · √v / √(trait + p + added)` uses `TRAITVAR` and is not `asymTIPREDEFFECTstd`; `TRAITVAR` is not the standardisation variance); p. 16 `TIPREDEFFECTstd` is `B · √v / √(-q / (2 a))` after strictly positive `asymDIFFUSION` `-q / (2 a)` and strictly positive predictor variance `v` (Driver et al., 2017, p. 16; §7.2; footnote 4; JSS PDF re-opened 2026-08-23T16:21Z; unstandardised `B` is defined for a zero coefficient and for zero predictor variance and is not `TIPREDEFFECTstd`; the asymptotic standardisation `(-B / a) · √v / √p` is the total change and is not `TIPREDEFFECTstd`; the finite-interval standardisation `A^{-1}[e^{A Δt} − I] B · √v / √p` depends on the event interval and is not `TIPREDEFFECTstd`; `B · √v / √(trait + p + added)` uses `TRAITVAR` and is not `TIPREDEFFECTstd`; `TRAITVAR` is not the standardisation variance); Table 3 `T0TIPREDEFFECTstd` is `t0_b · √v / √p_0` after strictly positive free `T0VAR` `p_0` and strictly positive predictor variance `v` (Driver et al., 2017, Table 3, p. 13; p. 16; footnote 4; 2017-era ctsem `summary.ctsemFit.R`; JSS PDF re-opened 2026-08-23T17:20Z; the affected variance is free `T0VAR`, not `asymDIFFUSION`; unstandardised `t0_b` is defined for a zero coefficient and for zero predictor variance and is not `T0TIPREDEFFECTstd`; `TIPREDEFFECTstd` `B · √v / √(-q / (2 a))` is the continuous coefficient and is not `T0TIPREDEFFECTstd`; `asymTIPREDEFFECTstd` `(-B / a) · √v / √p` is the total change and is not `T0TIPREDEFFECTstd`; `t0_b · √v / √(trait + p_0 + added)` uses `TRAITVAR` and is not `T0TIPREDEFFECTstd`; `TRAITVAR` is not the standardisation variance); 2017-era `addedT0TIPREDVAR` is `t0_b² v` (Driver et al., 2017, Table 3, p. 13; p. 16; §7.2; 2017-era ctsem `summary.ctsemFit.R`; JSS PDF re-opened 2026-08-23T18:20Z; `T0TIPREDEFFECT %*% TIPREDVAR %*% t(T0TIPREDEFFECT)` immediately after `T0TIPREDEFFECTstd`; form `t0_b` first, then square, then multiply by `v`; a zero coefficient or zero predictor variance is exactly zero; free `T0TIPREDEFFECT` does not require `a < 0`; `(B / a)² v` is `addedTIPREDVAR` and is not this first-occasion map; `t0_b · √v / √p_0` is `T0TIPREDEFFECTstd` and is not this variance; free `T0VAR` is not this extra TI variance; `TRAITVAR` is not this extra TI variance; Equation 5 of 2017-era `addedT0TIPREDVAR` is `λ² t0_b² v` (Driver et al., 2017, Eq. 5, p. 5; Table 3, p. 13; Table 2, p. 12; 2017-era ctsem `summary.ctsemFit.R`; JSS PDF re-opened 2026-08-23T19:10Z; form `t0_b² v` first, then `(λ extra) λ` with `θ = 0`; a zero loading or zero extra is exactly zero; `t0_b² v` is the latent extra, not the observed extra; `λ² p_0 + θ` is first-occasion observed variance, not this extra; `λ² (B / a)² v` is Eq. 5 of `addedTIPREDVAR`, not this first-occasion observed extra; `MANIFESTVAR` `θ` is not this extra; Equation 5 of §7.2 `addedTIPREDVAR` is `λ² (B / a)² v`; form `(B / a)² v` first, then `(λ extra) λ` with `θ = 0`; a zero loading or zero extra is exactly zero; lasting asymptotic extra requires `a < 0`; `(B / a)² v` is the latent extra, not the observed extra; `λ² t0_b² v` is first-occasion extra observed TI variance, not this extra; `λ² p + θ` is stationary observed variance, not this extra; `MANIFESTVAR` `θ` is not this extra; p. 16 `TDPREDEFFECTstd` is `m · √v / √(-q / (2 a))` after strictly positive `asymDIFFUSION` and strictly positive time-dependent predictor variance; unstandardised `M` is not `TDPREDEFFECTstd`; `TIPREDEFFECTstd` is not `TDPREDEFFECTstd` even when `M = B`; intercept-style `A^{-1}[e^{A Δt} − I] M · √v / √p` is not `TDPREDEFFECTstd`; `m · √v / √(trait + p + added)` uses `TRAITVAR` and is not `TDPREDEFFECTstd`; Table 3 / p. 16 `T0TDPREDEFFECTstd` is `t0_m · √v / √p_0` after strictly positive free `T0VAR` and strictly positive TD predictor variance; unstandardised `t0_m` is not `T0TDPREDEFFECTstd`; `TDPREDEFFECTstd` uses `asymDIFFUSION` and is not `T0TDPREDEFFECTstd`; `T0TIPREDEFFECTstd` is not `T0TDPREDEFFECTstd` even when `t0_m = t0_b`; `t0_m · √v / √(trait + p_0 + added)` uses `TRAITVAR` and is not `T0TDPREDEFFECTstd`; free `T0VAR` does not require `a < 0`; p. 16 `T0VARstd` is `p_0 / p_0 = 1` after strictly positive free `T0VAR` (`solve(sqrt(diag(T0VAR))) %&% T0VAR`; OpenMx `%&%` is `t(A) %*% B %*% A`; default ridge is 0); unstandardised `T0VAR` is not `T0VARstd`; `T0TDPREDEFFECTstd` is not `T0VARstd`; `addedT0TIPREDVAR` is not `T0VARstd`; p. 16 `TRAITVARstd` is `trait / trait = 1` after strictly positive `TRAITVAR` (`solve(sqrt(diag(TRAITVAR))) %&% TRAITVAR`; OpenMx `%&%` is `t(A) %*% B %*% A`; no ridge addend); unstandardised `TRAITVAR` is not `TRAITVARstd`; `T0VARstd` is not `TRAITVARstd` even when both equal 1; `addedT0TIPREDVAR` is not `TRAITVARstd`; p. 16 `MANIFESTTRAITVARstd` is `ψ / ψ = 1` after strictly positive `MANIFESTTRAITVAR` (`solve(sqrt(diag(MANIFESTTRAITVAR))) %&% MANIFESTTRAITVAR`; OpenMx `%&%` is `t(A) %*% B %*% A`; 2017-era source adds ridging; default ridge is 0); unstandardised `MANIFESTTRAITVAR` is not `MANIFESTTRAITVARstd`; `TRAITVARstd` is not `MANIFESTTRAITVARstd` even when both equal 1; `MANIFESTVAR` is not `MANIFESTTRAITVARstd`;))))), and refuses the difference quotient, pooling discrete lags from unequal intervals, and a binary64 underflow of that exponential to `+0` (not a discrete lag). The first-order product `a_yx Δt` is Eq. 14 and the underflow limit of Eq. 12, not the general constant-predictor discrete effect. Already-centered residuals may have irregular event intervals. Subtracting the person-specific mean from a raw autoregressive series is not the lagged within-person residual (Curran & Bauer, 2011, pp. 607–608). Metric/weak invariance licenses shared metric meaning only. Latent-mean comparison requires strong (equal loading and intercept) or strict invariance (Putnick & Bornstein, 2016, PMC5145197 opened 2026-08-19T22:15Z: scalar is required for latent means; residual invariance is not). Two-observation series have no residual degrees of freedom and cap at strong/scalar; identically-zero OLS residual variance is not strict. This two-group OLS gate is not MGCFA. +The executable multilevel slice is cluster-mean centering (CWC) plus within/between OLS, the CWC contextual effect (`between − within`), and Kish-weighted slopes. Enders and Tofighi (2007, Table 2, pp. 124–127) show that the CWC cluster-mean coefficient is the contextual effect, not the between-cluster effect. It is not DSEM and not RI-CLPM. The executable temporal slice maps a discrete lag through the exact scalar exponential `a = ln(φ) / Δt` on event time only (Voelkle, Oud, Davidov, & Schmidt, 2012, Eq. 7; Driver, Oud, & Voelkle, 2017, Eq. 3), recovers the forward map `φ(Δt) = exp(a Δt)`, remaps a discrete lag onto another event interval through that log-rate, recovers the exact scalar discrete effect of a constant predictor (Voelkle et al., 2012, Eq. 12) as `a_yx (expm1(z) / a_xx)` with `z = a_xx Δt` so a finite result is not lost when `z` overflows to `-∞` or when `a_yx Δt` overflows, and in log space when `expm1(z)` overflows at a finite `z` (`a_yx = 0` is exactly zero; an overflowing `a_yx/a_xx` rewrite term fails closed), recovers the first-order discrete effect of a time-varying predictor with matched sampling and constancy intervals as `a_yx Δt` (Voelkle et al., 2012, Eq. 14; ZORA accepted manuscript p. 21; not Eq. 12; unmatched intervals fail closed because Oud & Jansen, 2000, is unread), recovers the exact scalar discrete process noise `Q_Δt = 0.5 q (expm1(z) / a)` with `z = 2 (a Δt)` and `q = G G⊤ ≥ 0` (Driver et al., 2017, Eq. 3; JSS PDF re-opened 2026-08-18T07:06Z, p. 4; scalar `L = 1`; do not form `2 a` first; `a = 0` and `z → 0` recover `q Δt`; `z → −∞` keeps `−0.5 q / a`; a zero diffusion is exactly zero; `z → +∞` and an overflowing rewrite scale `0.5 q / a` fail closed), recovers the exact scalar lagged latent covariance `exp(a Δt) p` and the law-of-total-variance map `Var(η_t) = exp(2 a Δt) p + Q_Δt` (Driver et al., 2017, Eq. 3–4, pp. 4–5; JSS PDF re-opened 2026-08-18T18:03Z; JSS has no numbered §2.2; `Q_Δt` is `cov(η_t | η_{t-1})` and is refused as the unconditional variance; a zero diffusion whose `2 (a Δt)` overflows to `+∞` fails closed), recovers the exact scalar stationary within-subject variance `-q / (2 a)` as the `Δt → ∞` limit of Eq. 4 for stable `a < 0` (JSS p. 16 `asymDIFFUSION`; §4.3 T0VAR stationarity; when `2 a` is finite, form `q / -(2 a)` so `q / a` overflow does not lose a finite result (`q = MAX`, `a = -0.75` → `MAX / 1.5`; CodeRabbit on `75ecdd3`); when `2 a` overflows, form `(q / a) * -0.5`; do not form `0.5 q` first (`q = from_bits(1)`, `a = -from_bits(1)` → `0.5`); `a ≥ 0` and finite-interval `Q_Δt` fail closed as that limit), recovers the exact scalar trait-plus-state variance `trait + state` and lagged covariance `trait + exp(a Δt) p` (Driver et al., 2017, §4.3, p. 9; JSS PDF re-opened 2026-08-18T21:07Z; a stable trait has `DRIFT` and `DIFFUSION` fixed to zero; `TRAITVAR` is not process noise and not `asymDIFFUSION`; evolving the summed variance as if it were all state fails the claim boundary; this is not RI-CLPM), recovers the exact scalar observed-indicator variance `λ² Var(η) + θ` when `MANIFESTTRAITVAR` is zero and `λ² Var(η) + θ + ψ` otherwise, and the lagged observed covariance `λ² cov(η_t, η_{t-1}) + ψ` (Driver et al., 2017, Eq. 5, p. 5; Table 2, p. 12; JSS PDF re-opened 2026-08-19T04:18Z; Equation 1 is the latent SDE; form `(λ p) λ` then add `θ`, then add `ψ`; do not form `λ²` first; `MANIFESTVAR` is `Θ`, not `Var(y)` and does not enter lagged observed covariance; `MANIFESTTRAITVAR` is `Ψ_τ`, not `Θ`; `TRAITVAR` is latent and scaled by `λ²`; `Var(η)` is not `Var(y)`), recovers the exact scalar observed-indicator mean `τ + λ μ` (Driver et al., 2017, Eq. 5, p. 5; Table 2, p. 12; JSS PDF re-opened 2026-08-19T14:08Z; `MANIFESTMEANS` is `τ`, not `E(y)`; `CINT` is not `MANIFESTMEANS`; `T0MEANS` is not `E(y)`), recovers the exact scalar discrete latent mean `exp(a Δt) μ_0 + (exp(a Δt) − 1)/a κ` (Driver et al., 2017, Eq. 3, p. 4; Table 2, p. 12; JSS PDF re-opened 2026-08-19T18:10Z; `T0MEANS` is not `μ_t`; `CINT` is not the discrete increment; a zero drift is `κ Δt`; underflow of `exp(a Δt)` to `+0` drops the carried `T0MEANS` and keeps `−κ / a`), recovers the exact scalar evolved observed-indicator mean `τ + λ μ_t` (Driver et al., 2017, Eq. 5 of the Eq. 3 expected-value map; JSS PDF re-opened 2026-08-19T22:10Z; the first-occasion map `τ + λ μ_0` is not `E(y_t)`; `MANIFESTMEANS` is not `E(y_t)`; `μ_t` is not `E(y_t)`), recovers the exact scalar contemporaneous time-dependent predictor impulse `m x` (Driver et al., 2017, Eq. 1–3, pp. 4–5; Table 2, p. 12; §7.2, pp. 20–21; JSS PDF re-opened 2026-08-20T07:10Z; `TDPREDEFFECT` is `M`, not `CINT`; `M x` is not `A^{-1}[e^{A Δt} − I] B z`; `M x` is not Voelkle et al., 2012, Eq. 14; the level-change form is not that impulse), recovers the exact scalar observed mean of that contemporaneous impulse `τ + λ(μ_t + m x)` (Driver et al., 2017, Eq. 5 of the Eq. 3 fourth-summand composition; JSS PDF re-opened 2026-08-20T09:01Z; the evolved map `τ + λ μ_t` is not that observed mean; the carry map `τ + λ(μ_t + e^{a(t−u)} m x)` is not that observed mean when `u ≠ t`; `MANIFESTMEANS` is not `E(y_t)`; the evolved-plus-impulse latent mean is not `E(y_t)`), recovers the exact scalar time-independent predictor increment `A^{-1}[e^{A Δt} − I] B z` (Driver et al., 2017, Eq. 1–3, pp. 4–5; Table 2, p. 12; JSS PDF re-opened 2026-08-20T10:13Z; `TIPREDEFFECT` is `B`, not `κ`; form `B z` first, then the discrete intercept map; a zero drift is `B z Δt`; `B` is not the discrete increment; `A^{-1}[e^{A Δt} − I] B z` is not `CINT`, not `M x`, and not Voelkle Eq. 14), recovers the exact scalar observed mean of that increment `τ + λ(μ_t + A^{-1}[e^{A Δt} − I] B z)` (Driver et al., 2017, Eq. 5 of the Eq. 3 printed addend after the `T0MEANS` carry and the `CINT` increment; JSS PDF re-opened 2026-08-20T12:12Z; the evolved map `τ + λ μ_t` is not that observed mean; the contemporaneous map `τ + λ(μ_t + m x)` is not that observed mean; the carry map `τ + λ(μ_t + e^{a(t−u)} m x)` is not that observed mean when `u ≠ t`; `MANIFESTMEANS` is not `E(y_t)`; the evolved-plus-increment latent mean is not `E(y_t)`), recovers the exact scalar within-interval time-dependent impulse carry `e^{A(t−u)} M x` for `t0 < u < t` (Driver et al., 2017, Eq. 1–2, pp. 4–5; Eq. 3 exponential map; §7.2, pp. 20–21; JSS PDF re-opened 2026-08-20T10:33Z; form `m x` first, then `e^{a(t−u)} m x`; a zero drift is `m x`; underflow of `e^{a(t−u)}` to `+0` is vanishing dissipation and is kept; `e^{A(t−u)} M x` is not the contemporaneous Dirac, not `CINT`, not `TIPREDEFFECT`, and not Voelkle Eq. 14; an impulse at `u = t` is the contemporaneous map; an impulse at `u ≤ t0` is already in `η(t0)`), recovers the exact scalar observed mean of that carry `τ + λ(μ_t + e^{a(t−u)} m x)` (Driver et al., 2017, Eq. 5 of the Eq. 1–2 carried latent mean; JSS PDF re-opened 2026-08-20T05:12Z; the evolved map `τ + λ μ_t` is not that observed mean; the contemporaneous map `τ + λ(μ_t + m x)` is not that observed mean when `u ≠ t`; `MANIFESTMEANS` is not `E(y_t)`; the carried latent mean is not `E(y_t)`), recovers the exact scalar first-occasion `T0TIPREDEFFECT` shift `t0_b z` and its Eq. 3 first-summand carry `e^{A Δt} t0_b z` (Driver et al., 2017, Table 3, p. 13; Eq. 3, p. 5; JSS PDF opened 2026-08-20T15:14Z; form `t0_b z` first, then `e^{a Δt} t0_b z`; a zero drift is `t0_b z`; underflow of `e^{a Δt}` to `+0` is a vanishing carry and is kept; `t0_b z` is not `A^{-1}[e^{A Δt} − I] B z`, not `CINT`, and not `M x`; `e^{A Δt} t0_b z` is not `t0_b z`; `T0TIPREDEFFECT` is the coefficient, not the shift), recovers the exact scalar observed mean of that first-occasion carry `τ + λ(μ_t + e^{a Δt} t0_b z)` (Driver et al., 2017, Eq. 5 of the Table 3 / Eq. 3 first-summand composition; JSS PDF re-opened 2026-08-20T15:28Z; the evolved map `τ + λ μ_t` is not that observed mean; the process-increment map `τ + λ(μ_t + A^{-1}[e^{A Δt} − I] B z)` is not that observed mean; the contemporaneous map `τ + λ(μ_t + m x)` is not that observed mean; the impulse-carry map `τ + λ(μ_t + e^{a(t−u)} m x)` is not that observed mean when `u ≠ t0`; `MANIFESTMEANS` is not `E(y_t)`; the evolved-plus-carry latent mean is not `E(y_t)`), recovers the exact scalar first-occasion `T0TDPREDEFFECT` shift `t0_m x0` and its Eq. 3 first-summand carry `e^{A Δt} t0_m x0` (Driver et al., 2017, Table 3, p. 13; Eq. 3, p. 5; JSS PDF re-opened 2026-08-20T19:10Z; form `t0_m x0` first, then `e^{a Δt} t0_m x0`; a zero drift is `t0_m x0`; underflow of `e^{a Δt}` to `+0` is a vanishing carry and is kept; `t0_m x0` is not `M x`, not `e^{A(t−u)} M x` for `t0 < u < t`, not `t0_b z`, not `A^{-1}[e^{A Δt} − I] B z`, and not `CINT`; `e^{A Δt} t0_m x0` is not `t0_m x0`; `T0TDPREDEFFECT` is the coefficient, not the shift; an impulse at `u ≤ t0` that used `M` is already in `η(t0)` as `TDPREDEFFECT`, not as `T0TDPREDEFFECT`), recovers the exact scalar observed mean of that first-occasion TD carry `τ + λ(μ_t + e^{a Δt} t0_m x0)` (Driver et al., 2017, Eq. 5 of the Table 3 / Eq. 3 first-summand TD composition; JSS PDF re-opened 2026-08-20T19:07Z; the evolved map `τ + λ μ_t` is not that observed mean; the process-increment map `τ + λ(μ_t + A^{-1}[e^{A Δt} − I] B z)` is not that observed mean; the contemporaneous map `τ + λ(μ_t + m x)` is not that observed mean; the impulse-carry map `τ + λ(μ_t + e^{a(t−u)} m x)` is not that observed mean when `u ≠ t0`; the first-occasion TI map `τ + λ(μ_t + e^{a Δt} t0_b z)` is not that observed mean; `MANIFESTMEANS` is not `E(y_t)`; the evolved-plus-carry latent mean is not `E(y_t)`), recovers the exact scalar §7.2 level-change `CINT` setting `κ = −a m x` (Driver et al., 2017, §7.2, pp. 20–21; JSS PDF re-opened 2026-08-20T19:45Z; form `m x` first, then multiply by `−a`; `a < 0` so `−κ / a = m x`; `a ≥ 0` cannot hold a new process mean; `−a m x` is not the dissipating Dirac, not a free `CINT`, and not `A^{-1}[e^{A Δt} − I] B z`; the extra near-zero-drift latent process also named in §7.2 is a different specification), recovers the exact scalar Eq. 3 increment of that setting `(1 − e^{a Δt}) m x` (JSS PDF re-opened 2026-08-20T19:50Z; form the level-change `CINT` first, then the discrete intercept map; underflow of `e^{a Δt}` to `+0` keeps `m x`; `(1 − e^{a Δt}) m x` is not `m x`, not `κ`, and not `A^{-1}[e^{A Δt} − I] B z`), recovers the exact scalar §7.2 extra near-zero-drift latent process contribution `a_{ηξ} x (e^{ε Δt} − e^{a Δt}) / (ε − a)` (Driver et al., 2017, §7.2, pp. 22–23; JSS PDF re-opened 2026-08-20T23:10Z; form `a_{ηξ} x` first; `ε = a` is `a_{ηξ} x Δt e^{a Δt}`; a zero coupling or zero predictor is exactly zero; `ε ≥ 0` cannot hold a lasting extra state; that contribution is not `κ = −a m x`, not `(1 − e^{a Δt}) m x`, and not the dissipating Dirac `m x`), recovers the exact scalar observed mean of that extra-process contribution `τ + λ(μ_t + a_{ηξ} x (e^{ε Δt} − e^{a Δt}) / (ε − a))` (Driver et al., 2017, Eq. 5, p. 5; §7.2, pp. 22–23; JSS PDF re-opened 2026-08-21T06:12Z; the extra process has `LAMBDA` 0 and is not an observed indicator; form the evolved-plus-contribution latent mean first, then `τ + λ` of that mean; the evolved map `τ + λ μ_t` is not that observed mean; the contemporaneous map `τ + λ(μ_t + m x)` is not that observed mean; the contribution is not `E(y_t)`; the evolved-plus-contribution latent mean is not `E(y_t)`), recovers the exact scalar after-t0 extra-process contribution `a_{ηξ} x (e^{ε(t−u)} − e^{a(t−u)}) / (ε − a)` for `t0 < u < t` and its Eq. 5 observed mean `τ + λ(μ_t + a_{ηξ} x (e^{ε(t−u)} − e^{a(t−u)}) / (ε − a))` (Driver et al., 2017, §7.2, pp. 22–23; JSS PDF re-opened 2026-08-21T06:32Z; `T0TDPREDEFFECT` uses `Δt` for both the evolution and the extra drive; `TDPREDEFFECT` after `t0` uses `t − u` while `μ_t` still uses `Δt`; an impulse at `u = t0` or `u = t` is not interior; `e^{a(t−u)} m x` is a Dirac on the original process, not this `DRIFT` drive), recovers the exact scalar §7.2 `asymTIPREDEFFECT` `-B z / a` (Driver et al., 2017, §7.2, pp. 20–21; JSS PDF opened 2026-08-21T13:08Z; form `B z` first, then divide by `-a`; `a < 0`; a zero coefficient or zero predictor is exactly zero; `a ≥ 0` cannot hold a finite process-mean change; `-B z / a` is not the coefficient `B`, not `A^{-1}[e^{A Δt} − I] B z`, not `CINT`, and not `M x`), recovers the exact scalar §7.2 `addedTIPREDVAR` `(B / a)² v` (Driver et al., 2017, §7.2, pp. 20–21; form the unit asymptotic effect first, then square, then multiply by `v`; `v ≥ 0`; a zero coefficient or zero predictor variance is exactly zero; `(B / a)² v` is not `TRAITVAR`, not `asymDIFFUSION`, and not `-B z / a`), recovers the exact scalar Table 2 `asymCINT` `-κ / a` (Driver et al., 2017, Table 2, p. 12; Eq. 3 as `Δt → ∞`; JSS PDF opened 2026-08-21T16:13Z; form `κ` first, then divide by `-a`; `a < 0`; a zero intercept is exactly zero; `-κ / a` is not `κ`, not `A^{-1}[e^{A Δt} − I] κ`, not `T0MEANS`, and not `-B z / a`), recovers the exact scalar p. 16 stationary `T0MEANS` `-κ / a + −B z / a` (Driver et al., 2017, p. 16; form the intercept contribution first, then include the TI extra effect, then add; not free `T0MEANS`, not `asymCINT` alone, not `asymTIPREDEFFECT` alone, and not the finite-interval discrete latent mean), recovers the exact scalar Eq. 5 of that constrained mean `τ + λ(−κ / a + −B z / a)` (Driver et al., 2017, §4.3, pp. 9–10; Eq. 5, p. 5; JSS PDF re-opened 2026-08-21T20:07Z; form the stationary latent mean first, then `τ + λ` of that mean; `τ + λ μ_0` is not that observed mean; `τ + λ(−κ / a)` is not that observed mean when `B z ≠ 0`; `τ + λ μ_t` is not that observed mean; `MANIFESTMEANS` is not `E(y_0)`; the constrained latent mean is not `E(y_0)`), recovers the exact scalar §4.3 / p. 16 stationary `T0VAR` `trait + −q / (2 a) + (B / a)² v` (Driver et al., 2017, §4.3, pp. 9–10; p. 16; JSS PDF re-opened 2026-08-22T03:07Z; form the within-subject contribution first, then include the trait, then include the TI extra variance, then add; not free `T0VAR`, not `asymDIFFUSION` alone, not `TRAITVAR` alone, not `addedTIPREDVAR` alone, and not the finite-interval discrete latent variance), recovers the exact scalar Eq. 5 of that constrained variance `λ²(trait + −q / (2 a) + (B / a)² v) + θ + ψ` (Driver et al., 2017, §4.3, pp. 9–10; Eq. 5, p. 5; Table 2, p. 12; JSS PDF re-opened 2026-08-22T03:20Z; form the stationary latent variance first, then `λ² p + θ + ψ`; `λ² p_0` is not that observed variance; `λ²(−q / (2 a)) + θ` is not that observed variance when `TRAITVAR` or `addedTIPREDVAR` is nonzero; `MANIFESTVAR` is not `Var(y_0)`; the constrained latent variance is not `Var(y_0)`), recovers the exact scalar lagged covariance of that constrained process `trait + e^{a Δt}(−q / (2 a)) + (B / a)² v` (Driver et al., 2017, Eq. 3–4 of §4.3 / p. 16 `T0VAR`; JSS PDF re-opened 2026-08-22T19:13Z; trait and `addedTIPREDVAR` do not decay with `e^{a Δt}`; contemporaneous `T0VAR` is not that lagged map; decaying the constrained total as if it were all state is not that lagged map), recovers the exact scalar Eq. 5 of that lagged covariance `λ²(trait + e^{a Δt}(−q / (2 a)) + (B / a)² v) + ψ` (`Θ` does not enter; contemporaneous `Var(y_0)` is not that lagged observed covariance; the lagged latent covariance is not that observed covariance), recovers the exact scalar later-occasion variance of that constrained process `trait + e^{2 a Δt}(−q / (2 a)) + Q_Δt + (B / a)² v` (Driver et al., 2017, Eq. 3–4 of §4.3 / p. 16 `T0VAR`; JSS PDF re-opened 2026-08-22T23:12Z; form the evolved within-subject variance first, then include the trait, then include the TI extra variance, then add; trait and `addedTIPREDVAR` do not enter `Q_Δt`; under stationarity that composition equals contemporaneous `T0VAR`; evolving the constrained total as if it were all state is not that later map; the lagged covariance omits `Q_Δt` and is not that later map; `Q_Δt` is not that later map), recovers the exact scalar Eq. 5 of that later-occasion variance `λ²(trait + e^{2 a Δt}(−q / (2 a)) + Q_Δt + (B / a)² v) + θ + ψ` (the lagged observed covariance omits `Q_Δt` and `θ`; `MANIFESTVAR` is not that later observed variance; the later-occasion latent variance is not that observed variance), recovers the exact scalar later-occasion variance of §4.3 predetermined `T0VAR` `trait + e^{2 a Δt} p_0 + Q_Δt + (B / a)² v` (Driver et al., 2017, Eq. 3–4 of §4.3 predetermined first occasion; JSS PDF re-opened 2026-08-23T05:12Z; form the evolved free first-occasion variance first, then include the trait, then include the TI extra variance, then add; trait and `addedTIPREDVAR` do not enter `Q_Δt`; free `T0VAR` `p_0` is not that later map; setting `p_0 = −q / (2 a)` recovers the stationary later-occasion map; stationary later variance uses `−q / (2 a)` in place of `p_0` and is not that later map when `p_0` is free; evolving `trait + p_0 + (B / a)² v` as if it were all state is not that later map), recovers the exact scalar Eq. 5 of that predetermined later-occasion variance `λ²(trait + e^{2 a Δt} p_0 + Q_Δt + (B / a)² v) + θ + ψ` (`MANIFESTVAR` is not that later observed variance; the predetermined later-occasion latent variance is not that observed variance; stationary later observed variance is not that observed variance when `p_0` is free), recovers the exact scalar lagged covariance of §4.3 predetermined `T0VAR` `trait + e^{a Δt} p_0 + (B / a)² v` (Driver et al., 2017, Eq. 3–4 of §4.3 predetermined first occasion; JSS PDF re-opened 2026-08-23T09:04Z; form the lagged free first-occasion covariance first, then include the trait, then include the TI extra variance, then add; trait and `addedTIPREDVAR` do not decay with `e^{a Δt}`; free `T0VAR` `p_0` is not that lagged map; setting `p_0 = −q / (2 a)` recovers the stationary lagged map; stationary lagged covariance uses `−q / (2 a)` in place of `p_0` and is not that lagged map when `p_0` is free; evolving `trait + p_0 + (B / a)² v` as if it were all state is not that lagged map; later-occasion variance includes `Q_Δt` and is not that lagged map), recovers the exact scalar Eq. 5 of that predetermined lagged covariance `λ²(trait + e^{a Δt} p_0 + (B / a)² v) + ψ` (`MANIFESTVAR` does not enter; the predetermined lagged latent covariance is not that observed covariance; predetermined later observed variance includes `Q_Δt` and `θ` and is not that lagged observed covariance; stationary lagged observed covariance is not that observed covariance when `p_0` is free), recovers the exact scalar first-occasion variance of §4.3 predetermined `T0VAR` `trait + p_0 + (B / a)² v` (free `p_0` is not that map; stationary first-occasion variance uses `−q / (2 a)` in place of `p_0` and is not that map when `p_0` is free; lagged covariance decays the state and is not that map; later-occasion variance includes `Q_Δt` and is not that map), recovers the exact scalar Eq. 5 of that predetermined first-occasion variance `λ²(trait + p_0 + (B / a)² v) + θ + ψ` (`MANIFESTVAR` is not that first-occasion observed variance; the predetermined first-occasion latent variance is not that observed variance; stationary first-occasion observed variance is not that observed variance when `p_0` is free; predetermined later observed variance includes `Q_Δt` and is not that first-occasion observed variance; later-start lagged covariance of predetermined `T0VAR` is `trait + e^{a s}(e^{2 a u} p_0 + Q_u) + (B / a)² v` (Driver et al., 2017, §4.3 `startoffset`; Eq. 4; JSS PDF re-opened 2026-08-23T10:27Z; first-occasion lagged omits `e^{a s} Q_u`; later-occasion variance does not lag; stationary lagged uses `−q / (2 a)`; decaying the later total is not that map; Eq. 5 of that later-start lagged covariance is `λ²` of it plus `ψ`; `Θ` does not enter; first-occasion lagged observed omits `e^{a s} Q_u`; later observed variance includes `Q_u` and `θ`; later-start later-occasion variance of predetermined `T0VAR` is `trait + e^{2 a s}(e^{2 a u} p_0 + Q_u) + Q_s + (B / a)² v` (Driver et al., 2017, §4.3 `startoffset`; Eq. 3–4 Chapman–Kolmogorov `Q_{u+s} = e^{2 a s} Q_u + Q_s`; JSS PDF re-opened 2026-08-23T11:05Z; later-occasion variance at `u` omits `Q_s`; later-start lagged covariance omits `Q_s`; stationary later uses `−q / (2 a)`; evolving the later total as if it were all state is not that map; ignoring `startoffset` omits `e^{2 a s} Q_u`; Eq. 5 of that later-start later-occasion variance is `λ²` of it plus `θ + ψ`; `MANIFESTVAR` is not that observed variance; p. 16 `discreteDRIFTstd` is `e^{a Δt}` after strictly positive `asymDIFFUSION` `-q / (2 a)` (Driver et al., 2017, p. 16; footnote 4; §7.1; JSS PDF re-opened 2026-08-23T11:40Z; unstandardised `e^{a Δt}` is defined for growing `a ≥ 0` and for zero diffusion and is not `discreteDRIFTstd`; the trait-plus-state autocorrelation uses `TRAITVAR` and is not `discreteDRIFTstd`; p. 16 `discreteDIFFUSIONstd` is `Q_Δt / (−q / (2 a))` after strictly positive `asymDIFFUSION` `-q / (2 a)` (Driver et al., 2017, p. 16; Eq. 4; footnote 4; JSS PDF re-opened 2026-08-23T13:06Z; unstandardised `Q_Δt` is defined for growing `a ≥ 0` and for zero diffusion and is not `discreteDIFFUSIONstd`; the continuous standardisation `−2 a` is not `discreteDIFFUSIONstd`; `Q_Δt / (trait + p + added)` uses `TRAITVAR` and is not `discreteDIFFUSIONstd`; `TRAITVAR` is not the standardisation variance; p. 16 `DIFFUSIONstd` is `q / (−q / (2 a)) = −2 a` after strictly positive `asymDIFFUSION` `-q / (2 a)` (Driver et al., 2017, p. 16; Eq. 4; footnote 4; JSS PDF re-opened 2026-08-23T13:20Z; unstandardised `q` is defined for growing `a ≥ 0` and for zero diffusion and is not `DIFFUSIONstd`; the discrete standardisation `Q_Δt / (−q / (2 a))` depends on `Δt` and is not `DIFFUSIONstd`; `q / (trait + p + added)` uses `TRAITVAR` and is not `DIFFUSIONstd`; `TRAITVAR` is not the standardisation variance; p. 16 `DRIFTstd` is the continuous auto-effect after strictly positive `asymDIFFUSION` `-q / (2 a)` (Driver et al., 2017, p. 16; Eq. 1; footnote 4; JSS PDF re-opened 2026-08-23T13:28Z); unstandardised `a` is defined for growing `a ≥ 0` and for zero diffusion and is not `DRIFTstd`; the discrete standardisation `e^{a Δt}` depends on the event interval and is not `DRIFTstd`; `a p / (trait + p + added)` uses `TRAITVAR` and is not `DRIFTstd`; `TRAITVAR` is not the standardisation variance); p. 16 `asymTIPREDEFFECTstd` is `(-B / a) · √v / √(-q / (2 a))` after strictly positive `asymDIFFUSION` `-q / (2 a)` and strictly positive predictor variance `v` (Driver et al., 2017, p. 16; §7.2; footnote 4; JSS PDF re-opened 2026-08-23T14:25Z; unstandardised `-B / a` is defined for a zero coefficient and for zero predictor variance and is not `asymTIPREDEFFECTstd`; the finite-interval standardisation `A^{-1}[e^{A Δt} − I] B · √v / √p` depends on the event interval and is not `asymTIPREDEFFECTstd`; `(-B / a) · √v / √(trait + p + added)` uses `TRAITVAR` and is not `asymTIPREDEFFECTstd`; `TRAITVAR` is not the standardisation variance); p. 16 `TIPREDEFFECTstd` is `B · √v / √(-q / (2 a))` after strictly positive `asymDIFFUSION` `-q / (2 a)` and strictly positive predictor variance `v` (Driver et al., 2017, p. 16; §7.2; footnote 4; JSS PDF re-opened 2026-08-23T16:21Z; unstandardised `B` is defined for a zero coefficient and for zero predictor variance and is not `TIPREDEFFECTstd`; the asymptotic standardisation `(-B / a) · √v / √p` is the total change and is not `TIPREDEFFECTstd`; the finite-interval standardisation `A^{-1}[e^{A Δt} − I] B · √v / √p` depends on the event interval and is not `TIPREDEFFECTstd`; `B · √v / √(trait + p + added)` uses `TRAITVAR` and is not `TIPREDEFFECTstd`; `TRAITVAR` is not the standardisation variance); Table 3 `T0TIPREDEFFECTstd` is `t0_b · √v / √p_0` after strictly positive free `T0VAR` `p_0` and strictly positive predictor variance `v` (Driver et al., 2017, Table 3, p. 13; p. 16; footnote 4; 2017-era ctsem `summary.ctsemFit.R`; JSS PDF re-opened 2026-08-23T17:20Z; the affected variance is free `T0VAR`, not `asymDIFFUSION`; unstandardised `t0_b` is defined for a zero coefficient and for zero predictor variance and is not `T0TIPREDEFFECTstd`; `TIPREDEFFECTstd` `B · √v / √(-q / (2 a))` is the continuous coefficient and is not `T0TIPREDEFFECTstd`; `asymTIPREDEFFECTstd` `(-B / a) · √v / √p` is the total change and is not `T0TIPREDEFFECTstd`; `t0_b · √v / √(trait + p_0 + added)` uses `TRAITVAR` and is not `T0TIPREDEFFECTstd`; `TRAITVAR` is not the standardisation variance); 2017-era `addedT0TIPREDVAR` is `t0_b² v` (Driver et al., 2017, Table 3, p. 13; p. 16; §7.2; 2017-era ctsem `summary.ctsemFit.R`; JSS PDF re-opened 2026-08-23T18:20Z; `T0TIPREDEFFECT %*% TIPREDVAR %*% t(T0TIPREDEFFECT)` immediately after `T0TIPREDEFFECTstd`; form `t0_b` first, then square, then multiply by `v`; a zero coefficient or zero predictor variance is exactly zero; free `T0TIPREDEFFECT` does not require `a < 0`; `(B / a)² v` is `addedTIPREDVAR` and is not this first-occasion map; `t0_b · √v / √p_0` is `T0TIPREDEFFECTstd` and is not this variance; free `T0VAR` is not this extra TI variance; `TRAITVAR` is not this extra TI variance; Equation 5 of 2017-era `addedT0TIPREDVAR` is `λ² t0_b² v` (Driver et al., 2017, Eq. 5, p. 5; Table 3, p. 13; Table 2, p. 12; 2017-era ctsem `summary.ctsemFit.R`; JSS PDF re-opened 2026-08-23T19:10Z; form `t0_b² v` first, then `(λ extra) λ` with `θ = 0`; a zero loading or zero extra is exactly zero; `t0_b² v` is the latent extra, not the observed extra; `λ² p_0 + θ` is first-occasion observed variance, not this extra; `λ² (B / a)² v` is Eq. 5 of `addedTIPREDVAR`, not this first-occasion observed extra; `MANIFESTVAR` `θ` is not this extra; Equation 5 of §7.2 `addedTIPREDVAR` is `λ² (B / a)² v`; form `(B / a)² v` first, then `(λ extra) λ` with `θ = 0`; a zero loading or zero extra is exactly zero; lasting asymptotic extra requires `a < 0`; `(B / a)² v` is the latent extra, not the observed extra; `λ² t0_b² v` is first-occasion extra observed TI variance, not this extra; `λ² p + θ` is stationary observed variance, not this extra; `MANIFESTVAR` `θ` is not this extra; p. 16 `TDPREDEFFECTstd` is `m · √v / √(-q / (2 a))` after strictly positive `asymDIFFUSION` and strictly positive time-dependent predictor variance; unstandardised `M` is not `TDPREDEFFECTstd`; `TIPREDEFFECTstd` is not `TDPREDEFFECTstd` even when `M = B`; intercept-style `A^{-1}[e^{A Δt} − I] M · √v / √p` is not `TDPREDEFFECTstd`; `m · √v / √(trait + p + added)` uses `TRAITVAR` and is not `TDPREDEFFECTstd`; Table 3 / p. 16 `T0TDPREDEFFECTstd` is `t0_m · √v / √p_0` after strictly positive free `T0VAR` and strictly positive TD predictor variance; unstandardised `t0_m` is not `T0TDPREDEFFECTstd`; `TDPREDEFFECTstd` uses `asymDIFFUSION` and is not `T0TDPREDEFFECTstd`; `T0TIPREDEFFECTstd` is not `T0TDPREDEFFECTstd` even when `t0_m = t0_b`; `t0_m · √v / √(trait + p_0 + added)` uses `TRAITVAR` and is not `T0TDPREDEFFECTstd`; free `T0VAR` does not require `a < 0`; p. 16 `T0VARstd` is `p_0 / p_0 = 1` after strictly positive free `T0VAR` (`solve(sqrt(diag(T0VAR))) %&% T0VAR`; OpenMx `%&%` is `t(A) %*% B %*% A`; default ridge is 0); unstandardised `T0VAR` is not `T0VARstd`; `T0TDPREDEFFECTstd` is not `T0VARstd`; `addedT0TIPREDVAR` is not `T0VARstd`; p. 16 `TRAITVARstd` is `trait / trait = 1` after strictly positive `TRAITVAR` (`solve(sqrt(diag(TRAITVAR))) %&% TRAITVAR`; OpenMx `%&%` is `t(A) %*% B %*% A`; no ridge addend); unstandardised `TRAITVAR` is not `TRAITVARstd`; `T0VARstd` is not `TRAITVARstd` even when both equal 1; `addedT0TIPREDVAR` is not `TRAITVARstd`; p. 16 `MANIFESTTRAITVARstd` is `ψ / ψ = 1` after strictly positive `MANIFESTTRAITVAR` (`solve(sqrt(diag(MANIFESTTRAITVAR))) %&% MANIFESTTRAITVAR`; OpenMx `%&%` is `t(A) %*% B %*% A`; 2017-era source adds ridging; default ridge is 0); unstandardised `MANIFESTTRAITVAR` is not `MANIFESTTRAITVARstd`; `TRAITVARstd` is not `MANIFESTTRAITVARstd` even when both equal 1; `MANIFESTVAR` is not `MANIFESTTRAITVARstd`; p. 16 `MANIFESTVARstd` is `θ / θ = 1` after strictly positive `MANIFESTVAR` (`solve(sqrt(diag(MANIFESTVAR))) %&% MANIFESTVAR`; OpenMx `%&%` is `t(A) %*% B %*% A`; 2017-era source adds ridging; default ridge is 0; 2017-era `dimnames` assignment to `latentNames` is a source bug); unstandardised `MANIFESTVAR` is not `MANIFESTVARstd`; `MANIFESTTRAITVARstd` is not `MANIFESTVARstd` even when both equal 1; Equation 5 `Var(y)` is not `MANIFESTVARstd`;))))), and refuses the difference quotient, pooling discrete lags from unequal intervals, and a binary64 underflow of that exponential to `+0` (not a discrete lag). The first-order product `a_yx Δt` is Eq. 14 and the underflow limit of Eq. 12, not the general constant-predictor discrete effect. Already-centered residuals may have irregular event intervals. Subtracting the person-specific mean from a raw autoregressive series is not the lagged within-person residual (Curran & Bauer, 2011, pp. 607–608). Metric/weak invariance licenses shared metric meaning only. Latent-mean comparison requires strong (equal loading and intercept) or strict invariance (Putnick & Bornstein, 2016, PMC5145197 opened 2026-08-19T22:15Z: scalar is required for latent means; residual invariance is not). Two-observation series have no residual degrees of freedom and cap at strong/scalar; identically-zero OLS residual variance is not strict. This two-group OLS gate is not MGCFA. Input/process/intervention/outcome paths obey event-time order. Temporal precedence, document linkage, event tracking, or model prediction alone do not justify causal language. diff --git a/docs/research/multilevel-event-time-recovery.md b/docs/research/multilevel-event-time-recovery.md index 6a9faad4..105b0257 100644 --- a/docs/research/multilevel-event-time-recovery.md +++ b/docs/research/multilevel-event-time-recovery.md @@ -88,15 +88,17 @@ This slice stays inside `psychometric_core`. It does not add a second invariance 82. refuse treating unstandardised `TRAITVAR` as `TRAITVARstd`, refuse treating `T0VARstd` as `TRAITVARstd` even when both equal 1, and refuse treating `addedT0TIPREDVAR` `t0_b² v` as `TRAITVARstd`; 83. recover the exact scalar p. 16 `MANIFESTTRAITVARstd` as `solve(sqrt(diag(MANIFESTTRAITVAR))) %&% MANIFESTTRAITVAR` after forming strictly positive `MANIFESTTRAITVAR` (Driver et al., 2017, Table 2, p. 12; §7.1, p. 19; p. 16; footnote 4; 2017-era ctsem `summary.ctsemFit.R`; JSS PDF re-opened 2026-08-23T22:28Z; OpenMx `%&%` is `t(A) %*% B %*% A`; unlike `TRAITVARstd` the 2017-era source adds ridging; the default ridge is 0; the scalar map is `ψ / ψ = 1`; `MANIFESTTRAITVAR = 0` fails closed; a non-event clock fails closed; `MANIFESTTRAITVAR` does not require `a < 0`); 84. refuse treating unstandardised `MANIFESTTRAITVAR` as `MANIFESTTRAITVARstd`, refuse treating `TRAITVARstd` as `MANIFESTTRAITVARstd` even when both equal 1, and refuse treating `MANIFESTVAR` `θ` as `MANIFESTTRAITVARstd`; -85. refuse pooling discrete lags from unequal event intervals as one coefficient; -86. refuse unmatched sampling and constancy intervals for a time-varying predictor (Oud & Jansen, 2000, unread); -87. refuse the difference quotient as a continuous-time rate; -88. apply the same event-time map to CWC residuals (still not DSEM); +85. recover the exact scalar p. 16 `MANIFESTVARstd` as `solve(sqrt(diag(MANIFESTVAR))) %&% MANIFESTVAR` after forming strictly positive `MANIFESTVAR` (Driver et al., 2017, Table 2, p. 12; Eq. 5, p. 5; p. 16; footnote 4; 2017-era ctsem `summary.ctsemFit.R`; JSS PDF re-opened 2026-08-23T22:40Z; OpenMx `%&%` is `t(A) %*% B %*% A`; unlike `TRAITVARstd` the 2017-era source adds ridging; the default ridge is 0; the 2017-era `dimnames` assignment to `latentNames` is a source bug; the scalar map is `θ / θ = 1`; `MANIFESTVAR = 0` makes `solve(sqrt(0))` fail and fails closed; a non-event clock fails closed; `MANIFESTVAR` does not require `a < 0`); +86. refuse treating unstandardised `MANIFESTVAR` as `MANIFESTVARstd`, refuse treating `MANIFESTTRAITVARstd` as `MANIFESTVARstd` even when both equal 1, and refuse treating Equation 5 `Var(y)` as `MANIFESTVARstd`; +87. refuse pooling discrete lags from unequal event intervals as one coefficient; +88. refuse unmatched sampling and constancy intervals for a time-varying predictor (Oud & Jansen, 2000, unread); +89. refuse the difference quotient as a continuous-time rate; +90. apply the same event-time map to CWC residuals (still not DSEM); 89. map already-centered lagged residuals with irregular event intervals without re-centering (Curran & Bauer, 2011, pp. 607–608). ## Claim boundary -This is two-level OLS and a noiseless scalar continuous-time map. It is not DSEM, not RI-CLPM, not a random-effects sampler, not a Kalman filter, and not a matrix `expm` implementation. The CWC cluster-mean coefficient is the **contextual** effect, not the between-cluster effect. Discrete lags from different event intervals are not one coefficient. Equation 14 is not Equation 12. Discrete process noise \(Q_{\Delta t}\) is not the continuous diffusion \(GG^{\top}\). \(Q_{\Delta t}\) is the conditional residual variance, not \(\operatorname{Var}(\eta_{t})\). Finite-interval \(Q_{\Delta t}\) is not the stationary within-subject variance. Trait variance is not process noise and not the stationary within-subject variance. Measurement-error variance is not the observed-indicator variance. Latent variance is not the observed-indicator variance. Manifest means are not the observed-indicator mean. The latent mean is not the observed-indicator mean. The continuous intercept is not the manifest mean. The first-occasion latent mean is not the evolved latent mean. The continuous intercept is not the discrete mean increment. The first-occasion observed mean is not the evolved observed mean. The contemporaneous `TDPREDEFFECT` impulse is not the continuous intercept, not the time-independent discrete effect, and not Voelkle et al. (2012, Eq. 14). The time-independent `TIPREDEFFECT` increment is not the continuous intercept, not the contemporaneous impulse, not Voelkle et al. (2012, Eq. 14), and not the coefficient `B`. The within-interval `TDPREDEFFECT` carry `e^{A(t−u)} M x` for `t0 < u < t` is not the contemporaneous Dirac, not `CINT`, not `TIPREDEFFECT`, and not Voelkle et al. (2012, Eq. 14). The evolved observed mean `τ + λ μ_t` is not the contemporaneous-impulse observed mean `τ + λ(μ_t + m x)`. The contemporaneous composition `τ + λ(μ_t + m x)` is not the impulse-carry observed mean `τ + λ(μ_t + e^{a(t−u)} m x)` when `u ≠ t`. The evolved observed mean `τ + λ μ_t` is not the impulse-carry observed mean. The carried latent mean is not `E(y_t)`. The evolved-plus-impulse latent mean is not `E(y_t)`. The evolved observed mean `τ + λ μ_t` is not the time-independent-predictor observed mean `τ + λ(μ_t + A^{-1}[e^{A Δt} − I] B z)`. The contemporaneous composition `τ + λ(μ_t + m x)` is not that time-independent-predictor observed mean. The impulse-carry composition `τ + λ(μ_t + e^{a(t−u)} m x)` is not that time-independent-predictor observed mean when `u ≠ t`. The evolved-plus-increment latent mean is not `E(y_t)`. The Table 3 first-occasion `T0TIPREDEFFECT` shift `t0_b z` is not the Eq. 3 process increment `A^{-1}[e^{A Δt} − I] B z`, not `κ`, and not `M x`. The Eq. 3 first-summand carry `e^{A Δt} t0_b z` is not `t0_b z` and is not that process increment. `T0TIPREDEFFECT` is the coefficient, not the first-occasion shift. The evolved observed mean `τ + λ μ_t` is not the first-occasion TI-predictor observed mean `τ + λ(μ_t + e^{a Δt} t0_b z)`. The process-increment composition `τ + λ(μ_t + A^{-1}[e^{A Δt} − I] B z)` is not that first-occasion observed mean. The contemporaneous composition `τ + λ(μ_t + m x)` is not that first-occasion observed mean. The impulse-carry composition `τ + λ(μ_t + e^{a(t−u)} m x)` is not that first-occasion observed mean when `u ≠ t0`. The evolved-plus-T0TIPRED latent mean is not `E(y_t)`. The Table 3 first-occasion `T0TDPREDEFFECT` shift `t0_m x0` is not `M x`, not `e^{A(t−u)} M x` for `t0 < u < t`, not `t0_b z`, not `A^{-1}[e^{A Δt} − I] B z`, and not `κ`. The Eq. 3 first-summand carry `e^{A Δt} t0_m x0` is not `t0_m x0` and is not that within-interval impulse carry. `T0TDPREDEFFECT` is the coefficient, not the first-occasion shift. An impulse at `u ≤ t0` that used `M` is already in `η(t0)` as `TDPREDEFFECT`, not as `T0TDPREDEFFECT`. The evolved observed mean `τ + λ μ_t` is not the first-occasion TD-predictor observed mean `τ + λ(μ_t + e^{a Δt} t0_m x0)`. The process-increment composition `τ + λ(μ_t + A^{-1}[e^{A Δt} − I] B z)` is not that first-occasion TD observed mean. The contemporaneous composition `τ + λ(μ_t + m x)` is not that first-occasion TD observed mean. The impulse-carry composition `τ + λ(μ_t + e^{a(t−u)} m x)` is not that first-occasion TD observed mean when `u ≠ t0`. The first-occasion TI composition `τ + λ(μ_t + e^{a Δt} t0_b z)` is not that first-occasion TD observed mean. Same numbers as `T0TIPREDEFFECT` yield the same product; Table 3 names a different matrix. The evolved-plus-T0TDPRED latent mean is not `E(y_t)`. The §7.2 level-change `CINT` `κ = −a m x` is not the dissipating Dirac `m x`, not a free `CINT`, and not `A^{-1}[e^{A Δt} − I] B z`. Lasting level change via that `CINT` setting requires `a < 0`. The extra near-zero-drift latent process also named in §7.2 is a different specification and is not that `CINT` setting. The Eq. 3 increment of that setting `(1 − e^{a Δt}) m x` is not the dissipating Dirac `m x`, not `κ`, and not `A^{-1}[e^{A Δt} − I] B z`. Underflow of `e^{a Δt}` to `+0` keeps the equilibrium offset `m x`. The §7.2 extra-process contribution `a_{ηξ} x (e^{ε Δt} − e^{a Δt}) / (ε − a)` is not `κ = −a m x`, not `(1 − e^{a Δt}) m x`, and not the dissipating Dirac `m x`. Lasting level change via that extra process requires `ε < 0`. Precisely `ε = 0` causes computational problems in the printed specification. The extra-process observed mean `τ + λ(μ_t + a_{ηξ} x (e^{ε Δt} − e^{a Δt}) / (ε − a))` is not `τ + λ μ_t`, not `τ + λ(μ_t + m x)`, not the contribution, and not the evolved-plus-contribution latent mean. The extra process has `LAMBDA` 0 and is not an observed indicator. `T0TDPREDEFFECT` on the extra process uses `Δt = t − t0` for both the original-process evolution and the extra drive. `TDPREDEFFECT` after `t0` uses `t − u` with `t0 < u < t` for the extra drive while `μ_t` still uses `Δt`. The after-t0 extra-process observed mean is not the first-occasion extra-process observed mean when `u ≠ t0`. The impulse-carry `e^{a(t−u)} m x` is a Dirac on the original process and is not that `DRIFT` drive. The §7.2 `asymTIPREDEFFECT` `-B z / a` is the expected total change in process means given a time-independent predictor. It is not the coefficient `B`, not the finite-interval increment `A^{-1}[e^{A Δt} − I] B z`, not `CINT`, and not `M x`. Lasting asymptotic change via that map requires `a < 0`. The §7.2 `addedTIPREDVAR` `(B / a)² v` is the stable between-subject variance accounted for by that predictor. It is not `TRAITVAR`, not `asymDIFFUSION`, and not `-B z / a`. Table 2 `asymCINT` `-κ / a` is the intercept contribution to the stationary process mean. It is not `κ`, not the finite-interval increment `A^{-1}[e^{A Δt} − I] κ`, not `T0MEANS`, and not `-B z / a`. Lasting asymptotic intercept change via that map requires `a < 0`. Page 16 notes that a `T0MEANS` stationarity constraint includes time-independent predictors; that composition is not this intercept-only map. The p. 16 constrained first-occasion mean `-κ / a + −B z / a` is not free `T0MEANS`, not `asymCINT` alone, not `asymTIPREDEFFECT` alone, and not the finite-interval discrete latent mean. Equation 5 of that constrained mean `τ + λ(−κ / a + −B z / a)` is not `τ + λ μ_0`, not `τ + λ(−κ / a)` when `B z ≠ 0`, not `τ + λ μ_t`, not `MANIFESTMEANS`, and not the constrained latent mean. The p. 16 constrained first-occasion variance `trait + −q / (2 a) + (B / a)² v` is not free `T0VAR`, not `asymDIFFUSION` alone, not `TRAITVAR` alone, not `addedTIPREDVAR` alone, and not the finite-interval discrete latent variance. Equation 5 of that constrained variance `λ²(trait + −q / (2 a) + (B / a)² v) + θ + ψ` is not `λ² p_0 + θ`, not `λ²(−q / (2 a)) + θ` when `TRAITVAR` or `addedTIPREDVAR` is nonzero, not `λ² Var(η_t) + θ` when the first occasion is constrained, not `MANIFESTVAR`, and not the constrained latent variance. The lagged covariance of that constrained process `trait + e^{a Δt}(−q / (2 a)) + (B / a)² v` is not contemporaneous `T0VAR`, not `e^{a Δt}` of the constrained total, and not `trait + e^{a Δt} p` when `addedTIPREDVAR` is nonzero. Trait variance and `addedTIPREDVAR` do not decay with `e^{a Δt}`. Equation 5 of that lagged covariance `λ²(trait + e^{a Δt}(−q / (2 a)) + (B / a)² v) + ψ` is not `θ`, not contemporaneous `Var(y_0)`, and not the lagged latent covariance. Independent measurement error does not enter lagged observed covariance. The later-occasion variance of that constrained process `trait + e^{2 a Δt}(−q / (2 a)) + Q_Δt + (B / a)² v` equals contemporaneous `T0VAR` under stationarity and is not the lagged covariance, not `e^{2 a Δt}` of the constrained total plus `Q_Δt`, and not `Q_Δt` alone. Trait variance and `addedTIPREDVAR` do not enter `Q_Δt`. Equation 5 of that later-occasion variance `λ²(trait + e^{2 a Δt}(−q / (2 a)) + Q_Δt + (B / a)² v) + θ + ψ` is not `θ`, not lagged `cov(y_t, y_{t-1})`, and not the later-occasion latent variance. The later-occasion variance of §4.3 predetermined `T0VAR` `trait + e^{2 a Δt} p_0 + Q_Δt + (B / a)² v` is not stationary later-occasion variance when `p_0` is free, not `e^{2 a Δt}` of `trait + p_0 + (B / a)² v` plus `Q_Δt`, and not free `p_0`. Trait variance and `addedTIPREDVAR` do not enter `Q_Δt`. Setting `p_0 = −q / (2 a)` recovers the stationary later-occasion map. As `Δt → ∞` with stable `a < 0` the composition approaches contemporaneous stationary `T0VAR`. Nonzero diffusion with `a ≥ 0` is a growing process and is kept. Equation 5 of that predetermined later-occasion variance `λ²(trait + e^{2 a Δt} p_0 + Q_Δt + (B / a)² v) + θ + ψ` is not `θ`, not the predetermined later-occasion latent variance, and not stationary later-occasion observed variance when `p_0` is free. The lagged covariance of §4.3 predetermined `T0VAR` `trait + e^{a Δt} p_0 + (B / a)² v` is not stationary lagged covariance when `p_0` is free, not later-occasion variance, not `e^{a Δt}` of `trait + p_0 + (B / a)² v`, and not free `p_0`. Trait variance and `addedTIPREDVAR` do not decay with `e^{a Δt}`. Setting `p_0 = −q / (2 a)` recovers the stationary lagged map. As `Δt → ∞` with stable `a < 0` the state term vanishes. A zero-diffusion carry with `a ≥ 0` is `e^{a Δt} p_0` and is kept. Equation 5 of that predetermined lagged covariance `λ²(trait + e^{a Δt} p_0 + (B / a)² v) + ψ` is not `θ`, not the predetermined lagged latent covariance, not predetermined later observed variance, and not stationary lagged observed covariance when `p_0` is free. Independent measurement error does not enter lagged observed covariance. The first-occasion variance of §4.3 predetermined `T0VAR` `trait + p_0 + (B / a)² v` is not stationary first-occasion variance when `p_0` is free, not free `p_0`, not lagged covariance, and not later-occasion variance. Trait variance and `addedTIPREDVAR` do not decay and do not enter `Q_Δt`. Setting `p_0 = −q / (2 a)` recovers the stationary first-occasion map. As `Δt → 0+` the lagged and later maps approach this composition. Trait-only variance does not require a stable drift. Equation 5 of that predetermined first-occasion variance `λ²(trait + p_0 + (B / a)² v) + θ + ψ` is not `θ`, not the predetermined first-occasion latent variance, not stationary first-occasion observed variance, and not predetermined later observed variance when `p_0` is free. The later-start lagged covariance of §4.3 predetermined `T0VAR` `trait + e^{a s}(e^{2 a u} p_0 + Q_u) + (B / a)² v` is not first-occasion lagged covariance when `u > 0`, not later-occasion variance, not stationary lagged covariance when `p_0` is free, and not `e^{a s}` of the later total. Trait variance and `addedTIPREDVAR` do not decay with `e^{a s}`. Setting `p_0 = −q / (2 a)` recovers the stationary lagged map. As `u → 0+` the composition approaches first-occasion lagged covariance. As `s → 0+` the composition approaches later-occasion variance at `u`. Nonzero diffusion with `a ≥ 0` is a growing process and is kept. Equation 5 of that later-start lagged covariance `λ²(trait + e^{a s}(e^{2 a u} p_0 + Q_u) + (B / a)² v) + ψ` is not `θ`, not the later-start lagged latent covariance, not first-occasion lagged observed covariance, not predetermined later observed variance, and not stationary lagged observed covariance when `p_0` is free. Independent measurement error does not enter lagged observed covariance. The later-start later-occasion variance of §4.3 predetermined `T0VAR` `trait + e^{2 a s}(e^{2 a u} p_0 + Q_u) + Q_s + (B / a)² v` is not later-occasion variance at `u` when `s > 0`, not later-start lagged covariance, not stationary later-occasion variance when `p_0` is free, not `e^{2 a s}` of the later total plus `Q_s`, and not later-occasion variance over the lag interval alone when `u > 0`. Trait variance and `addedTIPREDVAR` do not enter `Q_s`. Chapman–Kolmogorov writes `Q_{u+s} = e^{2 a s} Q_u + Q_s`. Setting `p_0 = −q / (2 a)` recovers the stationary later-occasion map. As `u → 0+` the composition approaches later-occasion variance over `s`. As `s → 0+` the composition approaches later-occasion variance at `u`. Nonzero diffusion with `a ≥ 0` is a growing process and is kept. Equation 5 of that later-start later-occasion variance `λ²(trait + e^{2 a s}(e^{2 a u} p_0 + Q_u) + Q_s + (B / a)² v) + θ + ψ` is not `θ`, not the later-start later-occasion latent variance, not predetermined later observed variance, not later-start lagged observed covariance, and not stationary later-occasion observed variance when `p_0` is free. Page 16 `discreteDRIFTstd` is `e^{a Δt}` after strictly positive `asymDIFFUSION` `-q / (2 a)` (footnote 4: standardise `DRIFT` using only within-subject variance, not the total). Unstandardised `e^{a Δt}` is defined for growing `a ≥ 0` and for zero diffusion and is not `discreteDRIFTstd`. The §7.1 trait-plus-state autocorrelation `(trait + e^{a Δt} p + added) / (trait + p + added)` uses `TRAITVAR` and is not `discreteDRIFTstd`. `TRAITVAR` is not the standardisation variance. Page 16 `discreteDIFFUSIONstd` is `Q_Δt / (−q / (2 a))` after strictly positive `asymDIFFUSION` `-q / (2 a)` (footnote 4: standardise process noise using only within-subject variance, not the total). Unstandardised `Q_Δt` is defined for growing `a ≥ 0` and for zero diffusion and is not `discreteDIFFUSIONstd`. The continuous standardisation `q / (−q / (2 a)) = −2 a` is not `discreteDIFFUSIONstd`. `Q_Δt / (trait + p + added)` uses `TRAITVAR` and is not `discreteDIFFUSIONstd`. `TRAITVAR` is not the standardisation variance. Page 16 `DIFFUSIONstd` is `q / (−q / (2 a))` after strictly positive `asymDIFFUSION` `-q / (2 a)` (footnote 4: standardise process noise using only within-subject variance, not the total). Unstandardised `q` is defined for growing `a ≥ 0` and for zero diffusion and is not `DIFFUSIONstd`. The discrete standardisation `Q_Δt / (−q / (2 a)) = 1 − exp(2 a Δt)` depends on the event interval and is not `DIFFUSIONstd`. `q / (trait + p + added)` uses `TRAITVAR` and is not `DIFFUSIONstd`. `TRAITVAR` is not the standardisation variance. Page 16 `DRIFTstd` is the continuous auto-effect after strictly positive `asymDIFFUSION` `-q / (2 a)` (footnote 4: standardise `DRIFT` using only within-subject variance, not the total). Unstandardised `a` is defined for growing `a ≥ 0` and for zero diffusion and is not `DRIFTstd`. The discrete standardisation `e^{a Δt}` depends on the event interval and is not `DRIFTstd`. `a p / (trait + p + added)` uses `TRAITVAR` and is not `DRIFTstd`. `TRAITVAR` is not the standardisation variance. Page 16 `asymTIPREDEFFECTstd` is `(-B / a) · √v / √(-q / (2 a))` after strictly positive `asymDIFFUSION` `-q / (2 a)` and strictly positive predictor variance `v` (footnote 4: standardise using only the relevant variance, not the total; the affecting variance is `TIPREDVAR`; the affected variance is `asymDIFFUSION`). Unstandardised `-B / a` is defined for a zero coefficient and for zero predictor variance and is not `asymTIPREDEFFECTstd`. The finite-interval standardisation `A^{-1}[e^{A Δt} − I] B · √v / √p` depends on the event interval and is not `asymTIPREDEFFECTstd`. `(-B / a) · √v / √(trait + p + added)` uses `TRAITVAR` and is not `asymTIPREDEFFECTstd`. `TRAITVAR` is not the standardisation variance. Page 16 `TIPREDEFFECTstd` is `B · √v / √(-q / (2 a))` after strictly positive `asymDIFFUSION` `-q / (2 a)` and strictly positive predictor variance `v` (footnote 4: standardise using only the relevant variance, not the total; the affecting variance is `TIPREDVAR`; the affected variance is `asymDIFFUSION`). Unstandardised `B` is defined for a zero coefficient and for zero predictor variance and is not `TIPREDEFFECTstd`. The asymptotic standardisation `(-B / a) · √v / √p` is the total change and is not `TIPREDEFFECTstd`. The finite-interval standardisation `A^{-1}[e^{A Δt} − I] B · √v / √p` depends on the event interval and is not `TIPREDEFFECTstd`. `B · √v / √(trait + p + added)` uses `TRAITVAR` and is not `TIPREDEFFECTstd`. `TRAITVAR` is not the standardisation variance. Table 3 / p. 16 `T0TIPREDEFFECTstd` is `t0_b · √v / √p_0` after strictly positive free `T0VAR` `p_0` and strictly positive predictor variance `v` (footnote 4: standardise using only the relevant variance, not the total; the affecting variance is `TIPREDVAR`; the affected variance is free `T0VAR`, not `asymDIFFUSION`). Unstandardised `t0_b` is defined for a zero coefficient and for zero predictor variance and is not `T0TIPREDEFFECTstd`. `TIPREDEFFECTstd` `B · √v / √(-q / (2 a))` uses `asymDIFFUSION` and is not `T0TIPREDEFFECTstd`. `asymTIPREDEFFECTstd` `(-B / a) · √v / √p` is the total change and is not `T0TIPREDEFFECTstd`. `t0_b · √v / √(trait + p_0 + added)` uses `TRAITVAR` and is not `T0TIPREDEFFECTstd`. `TRAITVAR` is not the standardisation variance. 2017-era `addedT0TIPREDVAR` is `t0_b² v` (`T0TIPREDEFFECT %*% TIPREDVAR %*% t(T0TIPREDEFFECT)` immediately after `T0TIPREDEFFECTstd`). A zero coefficient or zero predictor variance is exactly zero. Free `T0TIPREDEFFECT` does not require `a < 0`. `(B / a)² v` is `addedTIPREDVAR` and is not this first-occasion map. `t0_b · √v / √p_0` is `T0TIPREDEFFECTstd` and is not this variance. Free `T0VAR` is not this extra TI variance. `TRAITVAR` is not this extra TI variance. Equation 5 of 2017-era `addedT0TIPREDVAR` is `λ² t0_b² v`. Form `t0_b² v` first, then `(λ extra) λ` with `θ = 0`. A zero loading or zero extra is exactly zero. `t0_b² v` is the latent extra, not the observed extra. `λ² p_0 + θ` is first-occasion observed variance, not this extra. `λ² (B / a)² v` is Eq. 5 of `addedTIPREDVAR`, not this first-occasion observed extra. `MANIFESTVAR` `θ` is not this extra. Equation 5 of §7.2 `addedTIPREDVAR` is `λ² (B / a)² v`. Form `(B / a)² v` first, then `(λ extra) λ` with `θ = 0`. A zero loading or zero extra is exactly zero. Lasting asymptotic extra requires `a < 0`. `(B / a)² v` is the latent extra, not the observed extra. `λ² t0_b² v` is first-occasion extra observed TI variance, not this extra. `λ² p + θ` is stationary observed variance, not this extra. `MANIFESTVAR` `θ` is not this extra. Page 16 `TDPREDEFFECTstd` is `m · √v / √(-q / (2 a))` after strictly positive `asymDIFFUSION` `-q / (2 a)` and strictly positive time-dependent predictor variance `v` (footnote 4: standardise using only the relevant variance, not the total; the affecting variance is the TD predictor; the affected variance is `asymDIFFUSION`). Unstandardised `M` is defined for a zero coefficient and for zero predictor variance and is not `TDPREDEFFECTstd`. `TIPREDEFFECTstd` `B · √v / √p` is a different named matrix even when `M = B`. The finite-interval intercept-style standardisation `A^{-1}[e^{A Δt} − I] M · √v / √p` depends on the event interval and is not `TDPREDEFFECTstd`. `m · √v / √(trait + p + added)` uses `TRAITVAR` and is not `TDPREDEFFECTstd`. `TRAITVAR` is not the standardisation variance. Table 3 / p. 16 `T0TDPREDEFFECTstd` is `t0_m · √v / √p_0` after strictly positive free `T0VAR` `p_0` and strictly positive time-dependent predictor variance `v` (footnote 4: standardise using only the relevant variance, not the total; the affecting variance is the TD predictor, not `TIPREDVAR`; the affected variance is free `T0VAR`, not `asymDIFFUSION`). Unstandardised `t0_m` is defined for a zero coefficient and for zero predictor variance and is not `T0TDPREDEFFECTstd`. `TDPREDEFFECTstd` `m · √v / √(-q / (2 a))` uses `asymDIFFUSION` and is not `T0TDPREDEFFECTstd`. `T0TIPREDEFFECTstd` `t0_b · √v / √p_0` is a different named matrix even when `t0_m = t0_b`. `t0_m · √v / √(trait + p_0 + added)` uses `TRAITVAR` and is not `T0TDPREDEFFECTstd`. `TRAITVAR` is not the standardisation variance. Free `T0VAR` does not require `a < 0`. Page 16 `T0VARstd` is the correlation form `solve(sqrt(diag(T0VAR))) %&% T0VAR` after strictly positive free `T0VAR` `p_0` (2017-era `summary.ctsemFit.R`; OpenMx `%&%` is `t(A) %*% B %*% A`; the default ridge is 0; the scalar map is `p_0 / p_0 = 1`). Unstandardised `T0VAR` is defined for a zero first-occasion variance and is not `T0VARstd`. Zero `p_0` fails closed. Distinct positive `p_0` recover the same 1. `T0TDPREDEFFECTstd` `t0_m · √v / √p_0` depends on `p_0` and is not `T0VARstd`. `addedT0TIPREDVAR` `t0_b² v` is extra TI variance, not this correlation. `TRAITVAR` is not the standardisation variance. Page 16 `TRAITVARstd` is the correlation form `solve(sqrt(diag(TRAITVAR))) %&% TRAITVAR` after strictly positive `TRAITVAR` (2017-era `summary.ctsemFit.R` forms it only when `TRAITVAR != 0`; OpenMx `%&%` is `t(A) %*% B %*% A`; unlike `T0VARstd` there is no ridge addend; the scalar map is `trait / trait = 1`). Unstandardised `TRAITVAR` is defined for a zero trait and is not `TRAITVARstd`. Zero `TRAITVAR` fails closed. Distinct positive `trait` recover the same 1. `T0VARstd` `p_0 / p_0 = 1` recovers the same number and remains a distinct named quantity. `addedT0TIPREDVAR` `t0_b² v` is extra TI variance, not this correlation. `TRAITVAR` does not require `a < 0`. Page 16 `MANIFESTTRAITVARstd` is the correlation form `solve(sqrt(diag(MANIFESTTRAITVAR))) %&% MANIFESTTRAITVAR` after strictly positive `MANIFESTTRAITVAR` (2017-era `summary.ctsemFit.R` forms it only when `MANIFESTTRAITVAR != 0`; OpenMx `%&%` is `t(A) %*% B %*% A`; unlike `TRAITVARstd` the 2017-era source adds ridging; the default ridge is 0; the scalar map is `ψ / ψ = 1`). Unstandardised `MANIFESTTRAITVAR` is defined for a zero trait and is not `MANIFESTTRAITVARstd`. Zero `MANIFESTTRAITVAR` fails closed. Distinct positive `ψ` recover the same 1. `TRAITVARstd` `trait / trait = 1` recovers the same number and remains a distinct named quantity. `MANIFESTVAR` `θ` is measurement error, not this correlation. `MANIFESTTRAITVAR` does not require `a < 0`. +This is two-level OLS and a noiseless scalar continuous-time map. It is not DSEM, not RI-CLPM, not a random-effects sampler, not a Kalman filter, and not a matrix `expm` implementation. The CWC cluster-mean coefficient is the **contextual** effect, not the between-cluster effect. Discrete lags from different event intervals are not one coefficient. Equation 14 is not Equation 12. Discrete process noise \(Q_{\Delta t}\) is not the continuous diffusion \(GG^{\top}\). \(Q_{\Delta t}\) is the conditional residual variance, not \(\operatorname{Var}(\eta_{t})\). Finite-interval \(Q_{\Delta t}\) is not the stationary within-subject variance. Trait variance is not process noise and not the stationary within-subject variance. Measurement-error variance is not the observed-indicator variance. Latent variance is not the observed-indicator variance. Manifest means are not the observed-indicator mean. The latent mean is not the observed-indicator mean. The continuous intercept is not the manifest mean. The first-occasion latent mean is not the evolved latent mean. The continuous intercept is not the discrete mean increment. The first-occasion observed mean is not the evolved observed mean. The contemporaneous `TDPREDEFFECT` impulse is not the continuous intercept, not the time-independent discrete effect, and not Voelkle et al. (2012, Eq. 14). The time-independent `TIPREDEFFECT` increment is not the continuous intercept, not the contemporaneous impulse, not Voelkle et al. (2012, Eq. 14), and not the coefficient `B`. The within-interval `TDPREDEFFECT` carry `e^{A(t−u)} M x` for `t0 < u < t` is not the contemporaneous Dirac, not `CINT`, not `TIPREDEFFECT`, and not Voelkle et al. (2012, Eq. 14). The evolved observed mean `τ + λ μ_t` is not the contemporaneous-impulse observed mean `τ + λ(μ_t + m x)`. The contemporaneous composition `τ + λ(μ_t + m x)` is not the impulse-carry observed mean `τ + λ(μ_t + e^{a(t−u)} m x)` when `u ≠ t`. The evolved observed mean `τ + λ μ_t` is not the impulse-carry observed mean. The carried latent mean is not `E(y_t)`. The evolved-plus-impulse latent mean is not `E(y_t)`. The evolved observed mean `τ + λ μ_t` is not the time-independent-predictor observed mean `τ + λ(μ_t + A^{-1}[e^{A Δt} − I] B z)`. The contemporaneous composition `τ + λ(μ_t + m x)` is not that time-independent-predictor observed mean. The impulse-carry composition `τ + λ(μ_t + e^{a(t−u)} m x)` is not that time-independent-predictor observed mean when `u ≠ t`. The evolved-plus-increment latent mean is not `E(y_t)`. The Table 3 first-occasion `T0TIPREDEFFECT` shift `t0_b z` is not the Eq. 3 process increment `A^{-1}[e^{A Δt} − I] B z`, not `κ`, and not `M x`. The Eq. 3 first-summand carry `e^{A Δt} t0_b z` is not `t0_b z` and is not that process increment. `T0TIPREDEFFECT` is the coefficient, not the first-occasion shift. The evolved observed mean `τ + λ μ_t` is not the first-occasion TI-predictor observed mean `τ + λ(μ_t + e^{a Δt} t0_b z)`. The process-increment composition `τ + λ(μ_t + A^{-1}[e^{A Δt} − I] B z)` is not that first-occasion observed mean. The contemporaneous composition `τ + λ(μ_t + m x)` is not that first-occasion observed mean. The impulse-carry composition `τ + λ(μ_t + e^{a(t−u)} m x)` is not that first-occasion observed mean when `u ≠ t0`. The evolved-plus-T0TIPRED latent mean is not `E(y_t)`. The Table 3 first-occasion `T0TDPREDEFFECT` shift `t0_m x0` is not `M x`, not `e^{A(t−u)} M x` for `t0 < u < t`, not `t0_b z`, not `A^{-1}[e^{A Δt} − I] B z`, and not `κ`. The Eq. 3 first-summand carry `e^{A Δt} t0_m x0` is not `t0_m x0` and is not that within-interval impulse carry. `T0TDPREDEFFECT` is the coefficient, not the first-occasion shift. An impulse at `u ≤ t0` that used `M` is already in `η(t0)` as `TDPREDEFFECT`, not as `T0TDPREDEFFECT`. The evolved observed mean `τ + λ μ_t` is not the first-occasion TD-predictor observed mean `τ + λ(μ_t + e^{a Δt} t0_m x0)`. The process-increment composition `τ + λ(μ_t + A^{-1}[e^{A Δt} − I] B z)` is not that first-occasion TD observed mean. The contemporaneous composition `τ + λ(μ_t + m x)` is not that first-occasion TD observed mean. The impulse-carry composition `τ + λ(μ_t + e^{a(t−u)} m x)` is not that first-occasion TD observed mean when `u ≠ t0`. The first-occasion TI composition `τ + λ(μ_t + e^{a Δt} t0_b z)` is not that first-occasion TD observed mean. Same numbers as `T0TIPREDEFFECT` yield the same product; Table 3 names a different matrix. The evolved-plus-T0TDPRED latent mean is not `E(y_t)`. The §7.2 level-change `CINT` `κ = −a m x` is not the dissipating Dirac `m x`, not a free `CINT`, and not `A^{-1}[e^{A Δt} − I] B z`. Lasting level change via that `CINT` setting requires `a < 0`. The extra near-zero-drift latent process also named in §7.2 is a different specification and is not that `CINT` setting. The Eq. 3 increment of that setting `(1 − e^{a Δt}) m x` is not the dissipating Dirac `m x`, not `κ`, and not `A^{-1}[e^{A Δt} − I] B z`. Underflow of `e^{a Δt}` to `+0` keeps the equilibrium offset `m x`. The §7.2 extra-process contribution `a_{ηξ} x (e^{ε Δt} − e^{a Δt}) / (ε − a)` is not `κ = −a m x`, not `(1 − e^{a Δt}) m x`, and not the dissipating Dirac `m x`. Lasting level change via that extra process requires `ε < 0`. Precisely `ε = 0` causes computational problems in the printed specification. The extra-process observed mean `τ + λ(μ_t + a_{ηξ} x (e^{ε Δt} − e^{a Δt}) / (ε − a))` is not `τ + λ μ_t`, not `τ + λ(μ_t + m x)`, not the contribution, and not the evolved-plus-contribution latent mean. The extra process has `LAMBDA` 0 and is not an observed indicator. `T0TDPREDEFFECT` on the extra process uses `Δt = t − t0` for both the original-process evolution and the extra drive. `TDPREDEFFECT` after `t0` uses `t − u` with `t0 < u < t` for the extra drive while `μ_t` still uses `Δt`. The after-t0 extra-process observed mean is not the first-occasion extra-process observed mean when `u ≠ t0`. The impulse-carry `e^{a(t−u)} m x` is a Dirac on the original process and is not that `DRIFT` drive. The §7.2 `asymTIPREDEFFECT` `-B z / a` is the expected total change in process means given a time-independent predictor. It is not the coefficient `B`, not the finite-interval increment `A^{-1}[e^{A Δt} − I] B z`, not `CINT`, and not `M x`. Lasting asymptotic change via that map requires `a < 0`. The §7.2 `addedTIPREDVAR` `(B / a)² v` is the stable between-subject variance accounted for by that predictor. It is not `TRAITVAR`, not `asymDIFFUSION`, and not `-B z / a`. Table 2 `asymCINT` `-κ / a` is the intercept contribution to the stationary process mean. It is not `κ`, not the finite-interval increment `A^{-1}[e^{A Δt} − I] κ`, not `T0MEANS`, and not `-B z / a`. Lasting asymptotic intercept change via that map requires `a < 0`. Page 16 notes that a `T0MEANS` stationarity constraint includes time-independent predictors; that composition is not this intercept-only map. The p. 16 constrained first-occasion mean `-κ / a + −B z / a` is not free `T0MEANS`, not `asymCINT` alone, not `asymTIPREDEFFECT` alone, and not the finite-interval discrete latent mean. Equation 5 of that constrained mean `τ + λ(−κ / a + −B z / a)` is not `τ + λ μ_0`, not `τ + λ(−κ / a)` when `B z ≠ 0`, not `τ + λ μ_t`, not `MANIFESTMEANS`, and not the constrained latent mean. The p. 16 constrained first-occasion variance `trait + −q / (2 a) + (B / a)² v` is not free `T0VAR`, not `asymDIFFUSION` alone, not `TRAITVAR` alone, not `addedTIPREDVAR` alone, and not the finite-interval discrete latent variance. Equation 5 of that constrained variance `λ²(trait + −q / (2 a) + (B / a)² v) + θ + ψ` is not `λ² p_0 + θ`, not `λ²(−q / (2 a)) + θ` when `TRAITVAR` or `addedTIPREDVAR` is nonzero, not `λ² Var(η_t) + θ` when the first occasion is constrained, not `MANIFESTVAR`, and not the constrained latent variance. The lagged covariance of that constrained process `trait + e^{a Δt}(−q / (2 a)) + (B / a)² v` is not contemporaneous `T0VAR`, not `e^{a Δt}` of the constrained total, and not `trait + e^{a Δt} p` when `addedTIPREDVAR` is nonzero. Trait variance and `addedTIPREDVAR` do not decay with `e^{a Δt}`. Equation 5 of that lagged covariance `λ²(trait + e^{a Δt}(−q / (2 a)) + (B / a)² v) + ψ` is not `θ`, not contemporaneous `Var(y_0)`, and not the lagged latent covariance. Independent measurement error does not enter lagged observed covariance. The later-occasion variance of that constrained process `trait + e^{2 a Δt}(−q / (2 a)) + Q_Δt + (B / a)² v` equals contemporaneous `T0VAR` under stationarity and is not the lagged covariance, not `e^{2 a Δt}` of the constrained total plus `Q_Δt`, and not `Q_Δt` alone. Trait variance and `addedTIPREDVAR` do not enter `Q_Δt`. Equation 5 of that later-occasion variance `λ²(trait + e^{2 a Δt}(−q / (2 a)) + Q_Δt + (B / a)² v) + θ + ψ` is not `θ`, not lagged `cov(y_t, y_{t-1})`, and not the later-occasion latent variance. The later-occasion variance of §4.3 predetermined `T0VAR` `trait + e^{2 a Δt} p_0 + Q_Δt + (B / a)² v` is not stationary later-occasion variance when `p_0` is free, not `e^{2 a Δt}` of `trait + p_0 + (B / a)² v` plus `Q_Δt`, and not free `p_0`. Trait variance and `addedTIPREDVAR` do not enter `Q_Δt`. Setting `p_0 = −q / (2 a)` recovers the stationary later-occasion map. As `Δt → ∞` with stable `a < 0` the composition approaches contemporaneous stationary `T0VAR`. Nonzero diffusion with `a ≥ 0` is a growing process and is kept. Equation 5 of that predetermined later-occasion variance `λ²(trait + e^{2 a Δt} p_0 + Q_Δt + (B / a)² v) + θ + ψ` is not `θ`, not the predetermined later-occasion latent variance, and not stationary later-occasion observed variance when `p_0` is free. The lagged covariance of §4.3 predetermined `T0VAR` `trait + e^{a Δt} p_0 + (B / a)² v` is not stationary lagged covariance when `p_0` is free, not later-occasion variance, not `e^{a Δt}` of `trait + p_0 + (B / a)² v`, and not free `p_0`. Trait variance and `addedTIPREDVAR` do not decay with `e^{a Δt}`. Setting `p_0 = −q / (2 a)` recovers the stationary lagged map. As `Δt → ∞` with stable `a < 0` the state term vanishes. A zero-diffusion carry with `a ≥ 0` is `e^{a Δt} p_0` and is kept. Equation 5 of that predetermined lagged covariance `λ²(trait + e^{a Δt} p_0 + (B / a)² v) + ψ` is not `θ`, not the predetermined lagged latent covariance, not predetermined later observed variance, and not stationary lagged observed covariance when `p_0` is free. Independent measurement error does not enter lagged observed covariance. The first-occasion variance of §4.3 predetermined `T0VAR` `trait + p_0 + (B / a)² v` is not stationary first-occasion variance when `p_0` is free, not free `p_0`, not lagged covariance, and not later-occasion variance. Trait variance and `addedTIPREDVAR` do not decay and do not enter `Q_Δt`. Setting `p_0 = −q / (2 a)` recovers the stationary first-occasion map. As `Δt → 0+` the lagged and later maps approach this composition. Trait-only variance does not require a stable drift. Equation 5 of that predetermined first-occasion variance `λ²(trait + p_0 + (B / a)² v) + θ + ψ` is not `θ`, not the predetermined first-occasion latent variance, not stationary first-occasion observed variance, and not predetermined later observed variance when `p_0` is free. The later-start lagged covariance of §4.3 predetermined `T0VAR` `trait + e^{a s}(e^{2 a u} p_0 + Q_u) + (B / a)² v` is not first-occasion lagged covariance when `u > 0`, not later-occasion variance, not stationary lagged covariance when `p_0` is free, and not `e^{a s}` of the later total. Trait variance and `addedTIPREDVAR` do not decay with `e^{a s}`. Setting `p_0 = −q / (2 a)` recovers the stationary lagged map. As `u → 0+` the composition approaches first-occasion lagged covariance. As `s → 0+` the composition approaches later-occasion variance at `u`. Nonzero diffusion with `a ≥ 0` is a growing process and is kept. Equation 5 of that later-start lagged covariance `λ²(trait + e^{a s}(e^{2 a u} p_0 + Q_u) + (B / a)² v) + ψ` is not `θ`, not the later-start lagged latent covariance, not first-occasion lagged observed covariance, not predetermined later observed variance, and not stationary lagged observed covariance when `p_0` is free. Independent measurement error does not enter lagged observed covariance. The later-start later-occasion variance of §4.3 predetermined `T0VAR` `trait + e^{2 a s}(e^{2 a u} p_0 + Q_u) + Q_s + (B / a)² v` is not later-occasion variance at `u` when `s > 0`, not later-start lagged covariance, not stationary later-occasion variance when `p_0` is free, not `e^{2 a s}` of the later total plus `Q_s`, and not later-occasion variance over the lag interval alone when `u > 0`. Trait variance and `addedTIPREDVAR` do not enter `Q_s`. Chapman–Kolmogorov writes `Q_{u+s} = e^{2 a s} Q_u + Q_s`. Setting `p_0 = −q / (2 a)` recovers the stationary later-occasion map. As `u → 0+` the composition approaches later-occasion variance over `s`. As `s → 0+` the composition approaches later-occasion variance at `u`. Nonzero diffusion with `a ≥ 0` is a growing process and is kept. Equation 5 of that later-start later-occasion variance `λ²(trait + e^{2 a s}(e^{2 a u} p_0 + Q_u) + Q_s + (B / a)² v) + θ + ψ` is not `θ`, not the later-start later-occasion latent variance, not predetermined later observed variance, not later-start lagged observed covariance, and not stationary later-occasion observed variance when `p_0` is free. Page 16 `discreteDRIFTstd` is `e^{a Δt}` after strictly positive `asymDIFFUSION` `-q / (2 a)` (footnote 4: standardise `DRIFT` using only within-subject variance, not the total). Unstandardised `e^{a Δt}` is defined for growing `a ≥ 0` and for zero diffusion and is not `discreteDRIFTstd`. The §7.1 trait-plus-state autocorrelation `(trait + e^{a Δt} p + added) / (trait + p + added)` uses `TRAITVAR` and is not `discreteDRIFTstd`. `TRAITVAR` is not the standardisation variance. Page 16 `discreteDIFFUSIONstd` is `Q_Δt / (−q / (2 a))` after strictly positive `asymDIFFUSION` `-q / (2 a)` (footnote 4: standardise process noise using only within-subject variance, not the total). Unstandardised `Q_Δt` is defined for growing `a ≥ 0` and for zero diffusion and is not `discreteDIFFUSIONstd`. The continuous standardisation `q / (−q / (2 a)) = −2 a` is not `discreteDIFFUSIONstd`. `Q_Δt / (trait + p + added)` uses `TRAITVAR` and is not `discreteDIFFUSIONstd`. `TRAITVAR` is not the standardisation variance. Page 16 `DIFFUSIONstd` is `q / (−q / (2 a))` after strictly positive `asymDIFFUSION` `-q / (2 a)` (footnote 4: standardise process noise using only within-subject variance, not the total). Unstandardised `q` is defined for growing `a ≥ 0` and for zero diffusion and is not `DIFFUSIONstd`. The discrete standardisation `Q_Δt / (−q / (2 a)) = 1 − exp(2 a Δt)` depends on the event interval and is not `DIFFUSIONstd`. `q / (trait + p + added)` uses `TRAITVAR` and is not `DIFFUSIONstd`. `TRAITVAR` is not the standardisation variance. Page 16 `DRIFTstd` is the continuous auto-effect after strictly positive `asymDIFFUSION` `-q / (2 a)` (footnote 4: standardise `DRIFT` using only within-subject variance, not the total). Unstandardised `a` is defined for growing `a ≥ 0` and for zero diffusion and is not `DRIFTstd`. The discrete standardisation `e^{a Δt}` depends on the event interval and is not `DRIFTstd`. `a p / (trait + p + added)` uses `TRAITVAR` and is not `DRIFTstd`. `TRAITVAR` is not the standardisation variance. Page 16 `asymTIPREDEFFECTstd` is `(-B / a) · √v / √(-q / (2 a))` after strictly positive `asymDIFFUSION` `-q / (2 a)` and strictly positive predictor variance `v` (footnote 4: standardise using only the relevant variance, not the total; the affecting variance is `TIPREDVAR`; the affected variance is `asymDIFFUSION`). Unstandardised `-B / a` is defined for a zero coefficient and for zero predictor variance and is not `asymTIPREDEFFECTstd`. The finite-interval standardisation `A^{-1}[e^{A Δt} − I] B · √v / √p` depends on the event interval and is not `asymTIPREDEFFECTstd`. `(-B / a) · √v / √(trait + p + added)` uses `TRAITVAR` and is not `asymTIPREDEFFECTstd`. `TRAITVAR` is not the standardisation variance. Page 16 `TIPREDEFFECTstd` is `B · √v / √(-q / (2 a))` after strictly positive `asymDIFFUSION` `-q / (2 a)` and strictly positive predictor variance `v` (footnote 4: standardise using only the relevant variance, not the total; the affecting variance is `TIPREDVAR`; the affected variance is `asymDIFFUSION`). Unstandardised `B` is defined for a zero coefficient and for zero predictor variance and is not `TIPREDEFFECTstd`. The asymptotic standardisation `(-B / a) · √v / √p` is the total change and is not `TIPREDEFFECTstd`. The finite-interval standardisation `A^{-1}[e^{A Δt} − I] B · √v / √p` depends on the event interval and is not `TIPREDEFFECTstd`. `B · √v / √(trait + p + added)` uses `TRAITVAR` and is not `TIPREDEFFECTstd`. `TRAITVAR` is not the standardisation variance. Table 3 / p. 16 `T0TIPREDEFFECTstd` is `t0_b · √v / √p_0` after strictly positive free `T0VAR` `p_0` and strictly positive predictor variance `v` (footnote 4: standardise using only the relevant variance, not the total; the affecting variance is `TIPREDVAR`; the affected variance is free `T0VAR`, not `asymDIFFUSION`). Unstandardised `t0_b` is defined for a zero coefficient and for zero predictor variance and is not `T0TIPREDEFFECTstd`. `TIPREDEFFECTstd` `B · √v / √(-q / (2 a))` uses `asymDIFFUSION` and is not `T0TIPREDEFFECTstd`. `asymTIPREDEFFECTstd` `(-B / a) · √v / √p` is the total change and is not `T0TIPREDEFFECTstd`. `t0_b · √v / √(trait + p_0 + added)` uses `TRAITVAR` and is not `T0TIPREDEFFECTstd`. `TRAITVAR` is not the standardisation variance. 2017-era `addedT0TIPREDVAR` is `t0_b² v` (`T0TIPREDEFFECT %*% TIPREDVAR %*% t(T0TIPREDEFFECT)` immediately after `T0TIPREDEFFECTstd`). A zero coefficient or zero predictor variance is exactly zero. Free `T0TIPREDEFFECT` does not require `a < 0`. `(B / a)² v` is `addedTIPREDVAR` and is not this first-occasion map. `t0_b · √v / √p_0` is `T0TIPREDEFFECTstd` and is not this variance. Free `T0VAR` is not this extra TI variance. `TRAITVAR` is not this extra TI variance. Equation 5 of 2017-era `addedT0TIPREDVAR` is `λ² t0_b² v`. Form `t0_b² v` first, then `(λ extra) λ` with `θ = 0`. A zero loading or zero extra is exactly zero. `t0_b² v` is the latent extra, not the observed extra. `λ² p_0 + θ` is first-occasion observed variance, not this extra. `λ² (B / a)² v` is Eq. 5 of `addedTIPREDVAR`, not this first-occasion observed extra. `MANIFESTVAR` `θ` is not this extra. Equation 5 of §7.2 `addedTIPREDVAR` is `λ² (B / a)² v`. Form `(B / a)² v` first, then `(λ extra) λ` with `θ = 0`. A zero loading or zero extra is exactly zero. Lasting asymptotic extra requires `a < 0`. `(B / a)² v` is the latent extra, not the observed extra. `λ² t0_b² v` is first-occasion extra observed TI variance, not this extra. `λ² p + θ` is stationary observed variance, not this extra. `MANIFESTVAR` `θ` is not this extra. Page 16 `TDPREDEFFECTstd` is `m · √v / √(-q / (2 a))` after strictly positive `asymDIFFUSION` `-q / (2 a)` and strictly positive time-dependent predictor variance `v` (footnote 4: standardise using only the relevant variance, not the total; the affecting variance is the TD predictor; the affected variance is `asymDIFFUSION`). Unstandardised `M` is defined for a zero coefficient and for zero predictor variance and is not `TDPREDEFFECTstd`. `TIPREDEFFECTstd` `B · √v / √p` is a different named matrix even when `M = B`. The finite-interval intercept-style standardisation `A^{-1}[e^{A Δt} − I] M · √v / √p` depends on the event interval and is not `TDPREDEFFECTstd`. `m · √v / √(trait + p + added)` uses `TRAITVAR` and is not `TDPREDEFFECTstd`. `TRAITVAR` is not the standardisation variance. Table 3 / p. 16 `T0TDPREDEFFECTstd` is `t0_m · √v / √p_0` after strictly positive free `T0VAR` `p_0` and strictly positive time-dependent predictor variance `v` (footnote 4: standardise using only the relevant variance, not the total; the affecting variance is the TD predictor, not `TIPREDVAR`; the affected variance is free `T0VAR`, not `asymDIFFUSION`). Unstandardised `t0_m` is defined for a zero coefficient and for zero predictor variance and is not `T0TDPREDEFFECTstd`. `TDPREDEFFECTstd` `m · √v / √(-q / (2 a))` uses `asymDIFFUSION` and is not `T0TDPREDEFFECTstd`. `T0TIPREDEFFECTstd` `t0_b · √v / √p_0` is a different named matrix even when `t0_m = t0_b`. `t0_m · √v / √(trait + p_0 + added)` uses `TRAITVAR` and is not `T0TDPREDEFFECTstd`. `TRAITVAR` is not the standardisation variance. Free `T0VAR` does not require `a < 0`. Page 16 `T0VARstd` is the correlation form `solve(sqrt(diag(T0VAR))) %&% T0VAR` after strictly positive free `T0VAR` `p_0` (2017-era `summary.ctsemFit.R`; OpenMx `%&%` is `t(A) %*% B %*% A`; the default ridge is 0; the scalar map is `p_0 / p_0 = 1`). Unstandardised `T0VAR` is defined for a zero first-occasion variance and is not `T0VARstd`. Zero `p_0` fails closed. Distinct positive `p_0` recover the same 1. `T0TDPREDEFFECTstd` `t0_m · √v / √p_0` depends on `p_0` and is not `T0VARstd`. `addedT0TIPREDVAR` `t0_b² v` is extra TI variance, not this correlation. `TRAITVAR` is not the standardisation variance. Page 16 `TRAITVARstd` is the correlation form `solve(sqrt(diag(TRAITVAR))) %&% TRAITVAR` after strictly positive `TRAITVAR` (2017-era `summary.ctsemFit.R` forms it only when `TRAITVAR != 0`; OpenMx `%&%` is `t(A) %*% B %*% A`; unlike `T0VARstd` there is no ridge addend; the scalar map is `trait / trait = 1`). Unstandardised `TRAITVAR` is defined for a zero trait and is not `TRAITVARstd`. Zero `TRAITVAR` fails closed. Distinct positive `trait` recover the same 1. `T0VARstd` `p_0 / p_0 = 1` recovers the same number and remains a distinct named quantity. `addedT0TIPREDVAR` `t0_b² v` is extra TI variance, not this correlation. `TRAITVAR` does not require `a < 0`. Page 16 `MANIFESTTRAITVARstd` is the correlation form `solve(sqrt(diag(MANIFESTTRAITVAR))) %&% MANIFESTTRAITVAR` after strictly positive `MANIFESTTRAITVAR` (2017-era `summary.ctsemFit.R` forms it only when `MANIFESTTRAITVAR != 0`; OpenMx `%&%` is `t(A) %*% B %*% A`; unlike `TRAITVARstd` the 2017-era source adds ridging; the default ridge is 0; the scalar map is `ψ / ψ = 1`). Unstandardised `MANIFESTTRAITVAR` is defined for a zero trait and is not `MANIFESTTRAITVARstd`. Zero `MANIFESTTRAITVAR` fails closed. Distinct positive `ψ` recover the same 1. `TRAITVARstd` `trait / trait = 1` recovers the same number and remains a distinct named quantity. `MANIFESTVAR` `θ` is measurement error, not this correlation. `MANIFESTTRAITVAR` does not require `a < 0`. Page 16 `MANIFESTVARstd` is the correlation form `solve(sqrt(diag(MANIFESTVAR))) %&% MANIFESTVAR` after strictly positive `MANIFESTVAR` (2017-era `summary.ctsemFit.R` forms it whenever `verbose = TRUE`; OpenMx `%&%` is `t(A) %*% B %*% A`; unlike `TRAITVARstd` the 2017-era source adds ridging; the default ridge is 0; the 2017-era `dimnames` assignment to `latentNames` is a source bug; the scalar map is `θ / θ = 1`). Unstandardised `MANIFESTVAR` is defined for a zero residual and is not `MANIFESTVARstd`. Zero `MANIFESTVAR` makes `solve(sqrt(0))` fail and fails closed. Distinct positive `θ` recover the same 1. `MANIFESTTRAITVARstd` `ψ / ψ = 1` recovers the same number and remains a distinct named quantity. Equation 5 `λ² Var(η) + θ` is `Var(y)`, not this correlation. `MANIFESTVAR` does not require `a < 0`. ## Authoritative sources @@ -233,3 +235,4 @@ The Voelkle et al. (2012) ZORA accepted manuscript was re-opened 2026-08-18T21:0 - Driver et al. (2017, Table 2 / p. 16 `T0VARstd`; footnote 4; 2017-era ctsem `summary.ctsemFit.R`; JSS PDF re-opened 2026-08-23T22:06Z) recovers the scalar correlation \(p_0/p_0=1\) at machine-scale RMSE after strictly positive free `T0VAR`, and that RMSE is smaller than treating unstandardised \(p_0\), `T0TDPREDEFFECTstd` \(t0_m\cdot\sqrt{v}/\sqrt{p_0}\), or `addedT0TIPREDVAR` \(t0_b^{2}v\) as `T0VARstd`; distinct positive \(p_0\) recover the same 1; \(p_0=0\) fails closed; a non-event clock fails closed; free `T0VAR` does not require \(a<0\). - Driver et al. (2017, Table 2 / §7.1 / p. 16 `TRAITVARstd`; footnote 4; 2017-era ctsem `summary.ctsemFit.R`; JSS PDF re-opened 2026-08-23T22:21Z) recovers the scalar correlation \(\mathrm{trait}/\mathrm{trait}=1\) at machine-scale RMSE after strictly positive `TRAITVAR`, and that RMSE is smaller than treating unstandardised `TRAITVAR` or `addedT0TIPREDVAR` \(t0_b^{2}v\) as `TRAITVARstd`; distinct positive trait recover the same 1; equal 1 with `T0VARstd` remains a distinct named quantity; `TRAITVAR = 0` fails closed; a non-event clock fails closed; `TRAITVAR` does not require \(a<0\). - Driver et al. (2017, Table 2 / §7.1 / p. 16 `MANIFESTTRAITVARstd`; footnote 4; 2017-era ctsem `summary.ctsemFit.R`; JSS PDF re-opened 2026-08-23T22:28Z) recovers the scalar correlation \(\psi/\psi=1\) at machine-scale RMSE after strictly positive `MANIFESTTRAITVAR`, and that RMSE is smaller than treating unstandardised `MANIFESTTRAITVAR` or `MANIFESTVAR` \(\theta\) as `MANIFESTTRAITVARstd`; distinct positive \(\psi\) recover the same 1; equal 1 with `TRAITVARstd` remains a distinct named quantity; `MANIFESTTRAITVAR = 0` fails closed; a non-event clock fails closed; `MANIFESTTRAITVAR` does not require \(a<0\). +- Driver et al. (2017, Table 2 / Eq. 5 / p. 16 `MANIFESTVARstd`; footnote 4; 2017-era ctsem `summary.ctsemFit.R`; JSS PDF re-opened 2026-08-23T22:40Z) recovers the scalar correlation \(\theta/\theta=1\) at machine-scale RMSE after strictly positive `MANIFESTVAR`, and that RMSE is smaller than treating unstandardised `MANIFESTVAR` or Equation 5 \(\operatorname{Var}(y)\) as `MANIFESTVARstd`; distinct positive \(\theta\) recover the same 1; equal 1 with `MANIFESTTRAITVARstd` remains a distinct named quantity; `MANIFESTVAR = 0` fails closed; a non-event clock fails closed; `MANIFESTVAR` does not require \(a<0\). diff --git a/docs/validation/temporal-event-foundation.md b/docs/validation/temporal-event-foundation.md index c8b407c3..02e19148 100644 --- a/docs/validation/temporal-event-foundation.md +++ b/docs/validation/temporal-event-foundation.md @@ -23,7 +23,7 @@ This report tracks exact-head scientific and engineering evidence required befor | Truth corpora / manifests | `tepp_simulation` | implemented-main | — | deterministic generator tests | Task 10 / PR #18 | | Recovery metrics | `validation_core` | implemented-main | — | RMSE/bias/coverage/MC gates | Task 11 / PR #19 | | Versioned API/export contracts | `tepp_api` | implemented-main | naruon HTTP interchange | unknown-field/version/limit + naruon HTTPS interchange tests | Task 12 / PR #21; live HTTP service remaining | -| Psychometric structural input gates | `psychometric_core` | partial | stacked psychometric PR | construct-class refusal + ALR/ILR boundary + true-loading RMSE + posterior-draw point-estimate mean + Rubin `T` + CWC within/between + CWC contextual effect + event-time log-rate + constant- and time-varying-predictor discrete effects + exact scalar discrete process noise + lagged latent covariance and unconditional latent variance + stationary within-subject variance + trait-plus-state variance + observed-indicator variance + discrete latent mean (`T0MEANS`/`CINT`) + evolved observed mean (`τ + λ μ_t`; `τ + λ μ_0` is not `E(y_t)`) + contemporaneous `TDPREDEFFECT` impulse (`m x`; not `CINT`, not `TIPREDEFFECT`, not Voelkle Eq. 14) + Eq. 5 of that contemporaneous impulse (`τ + λ(μ_t + m x)`; `τ + λ μ_t` is not that observed mean) + time-independent `TIPREDEFFECT` increment (`A^{-1}[e^{A Δt} − I] B z`; not `CINT`, not `M x`, not Voelkle Eq. 14, not the coefficient `B`) + Eq. 5 of that increment (`τ + λ(μ_t + A^{-1}[e^{A Δt} − I] B z)`; `τ + λ μ_t` is not that observed mean) + within-interval `TDPREDEFFECT` carry (`e^{A(t−u)} M x` for `t0 < u < t`; not the contemporaneous Dirac, not `CINT`, not `TIPREDEFFECT`, not Voelkle Eq. 14) + Eq. 5 of that carry (`τ + λ(μ_t + e^{a(t−u)} m x)`; `τ + λ μ_t` is not that observed mean) + §7.2 level-change `CINT` (`κ = −a m x`; Eq. 3 increment `(1 − e^{a Δt}) m x`) + §7.2 extra-process contribution (`a_{ηξ} x (e^{ε Δt} − e^{a Δt}) / (ε − a)`; not `κ`, not the increment, not the Dirac; `ε ≥ 0` fails closed) + Eq. 5 of that extra-process contribution (`τ + λ(μ_t + a_{ηξ} x (e^{ε Δt} − e^{a Δt}) / (ε − a)`; extra `LAMBDA` is 0; `τ + λ μ_t` is not that observed mean) + after-t0 extra-process `TDPREDEFFECT` (`a_{ηξ} x (e^{ε(t−u)} − e^{a(t−u)}) / (ε − a)` for `t0 < u < t`; Eq. 5 `τ + λ(μ_t + contribution(t−u))`; not the first-occasion extra-process observed mean; not the impulse-carry Dirac) + §7.2 `asymTIPREDEFFECT` (`-B z / a` for `a < 0`; not `B`, not the finite-interval increment, not `CINT`, not `M x`) + §7.2 `addedTIPREDVAR` (`(B / a)² v`; not `TRAITVAR`, not `asymDIFFUSION`, not `-B z / a`) + Table 2 `asymCINT` (`-κ / a` for `a < 0`; not `κ`, not the finite-interval increment, not `T0MEANS`, not `-B z / a`) + p. 16 stationary `T0MEANS` (`-κ / a + −B z / a`; not free `T0MEANS`, not `asymCINT` alone, not `asymTIPREDEFFECT` alone, not the finite-interval discrete mean) + Eq. 5 of that constrained mean (`τ + λ(−κ / a + −B z / a)`; `τ + λ μ_0` is not that observed mean; `τ + λ(−κ / a)` is not that observed mean when `B z ≠ 0`; `τ + λ μ_t` is not that observed mean; `MANIFESTMEANS` is not `E(y_0)`; the constrained latent mean is not `E(y_0)`) + stationary `T0VAR` (`trait + −q / (2 a) + (B / a)² v`; not free `T0VAR`, not `asymDIFFUSION` alone, not `TRAITVAR` alone, not `addedTIPREDVAR` alone; Eq. 5 is `λ²(trait + −q / (2 a) + (B / a)² v) + θ + ψ` (`λ² p_0` is not `Var(y_0)`; `λ²(−q / (2 a)) + θ` is not `Var(y_0)` when trait or TI is nonzero; `MANIFESTVAR` is not `Var(y_0)`; the constrained latent variance is not `Var(y_0)`)) + Eq. 5 of that constrained variance (`λ²(trait + −q / (2 a) + (B / a)² v) + θ + ψ`; `MANIFESTVAR` is not `Var(y_0)`) + p. 16 `TDPREDEFFECTstd` (`m · √v / √(-q / (2 a))` after strictly positive `asymDIFFUSION` and TD predictor variance; not `TIPREDEFFECTstd` even when `M = B`; not intercept-style `A^{-1}[e^{A Δt} − I] M · √v / √p`; not trait-contaminated) + Table 3 / p. 16 `T0TDPREDEFFECTstd` (`t0_m · √v / √p_0` after strictly positive free `T0VAR` and TD predictor variance; not `TDPREDEFFECTstd`; not `T0TIPREDEFFECTstd` even when `t0_m = t0_b`; not trait-contaminated; free `T0VAR` does not require `a < 0`) + p. 16 `T0VARstd` (`p_0 / p_0 = 1` after strictly positive free `T0VAR`; not unstandardised `T0VAR`; not `T0TDPREDEFFECTstd`; not `addedT0TIPREDVAR`; free `T0VAR` does not require `a < 0`) + p. 16 `TRAITVARstd` (`trait / trait = 1` after strictly positive `TRAITVAR`; no ridge addend; not unstandardised `TRAITVAR`; not `T0VARstd` even when both equal 1; not `addedT0TIPREDVAR`; `TRAITVAR` does not require `a < 0`) + p. 16 `MANIFESTTRAITVARstd` (`ψ / ψ = 1` after strictly positive `MANIFESTTRAITVAR`; 2017-era source adds ridging; default ridge is 0; not unstandardised `MANIFESTTRAITVAR`; not `TRAITVARstd` even when both equal 1; not `MANIFESTVAR`; `MANIFESTTRAITVAR` does not require `a < 0`) + irregular already-centered residual lag + strong-gated latent means (n=2 residual variance is identically `0` and caps at strong/scalar; Putnick & Bornstein, 2016); full ESEM/DSEM remaining | ADR 0005; `docs/research/posterior-esem-input-gates.md`; `docs/research/multilevel-event-time-recovery.md`; `docs/research/rubin-total-variance.md`; `docs/research/strong-invariance-latent-means.md` | +| Psychometric structural input gates | `psychometric_core` | partial | stacked psychometric PR | construct-class refusal + ALR/ILR boundary + true-loading RMSE + posterior-draw point-estimate mean + Rubin `T` + CWC within/between + CWC contextual effect + event-time log-rate + constant- and time-varying-predictor discrete effects + exact scalar discrete process noise + lagged latent covariance and unconditional latent variance + stationary within-subject variance + trait-plus-state variance + observed-indicator variance + discrete latent mean (`T0MEANS`/`CINT`) + evolved observed mean (`τ + λ μ_t`; `τ + λ μ_0` is not `E(y_t)`) + contemporaneous `TDPREDEFFECT` impulse (`m x`; not `CINT`, not `TIPREDEFFECT`, not Voelkle Eq. 14) + Eq. 5 of that contemporaneous impulse (`τ + λ(μ_t + m x)`; `τ + λ μ_t` is not that observed mean) + time-independent `TIPREDEFFECT` increment (`A^{-1}[e^{A Δt} − I] B z`; not `CINT`, not `M x`, not Voelkle Eq. 14, not the coefficient `B`) + Eq. 5 of that increment (`τ + λ(μ_t + A^{-1}[e^{A Δt} − I] B z)`; `τ + λ μ_t` is not that observed mean) + within-interval `TDPREDEFFECT` carry (`e^{A(t−u)} M x` for `t0 < u < t`; not the contemporaneous Dirac, not `CINT`, not `TIPREDEFFECT`, not Voelkle Eq. 14) + Eq. 5 of that carry (`τ + λ(μ_t + e^{a(t−u)} m x)`; `τ + λ μ_t` is not that observed mean) + §7.2 level-change `CINT` (`κ = −a m x`; Eq. 3 increment `(1 − e^{a Δt}) m x`) + §7.2 extra-process contribution (`a_{ηξ} x (e^{ε Δt} − e^{a Δt}) / (ε − a)`; not `κ`, not the increment, not the Dirac; `ε ≥ 0` fails closed) + Eq. 5 of that extra-process contribution (`τ + λ(μ_t + a_{ηξ} x (e^{ε Δt} − e^{a Δt}) / (ε − a)`; extra `LAMBDA` is 0; `τ + λ μ_t` is not that observed mean) + after-t0 extra-process `TDPREDEFFECT` (`a_{ηξ} x (e^{ε(t−u)} − e^{a(t−u)}) / (ε − a)` for `t0 < u < t`; Eq. 5 `τ + λ(μ_t + contribution(t−u))`; not the first-occasion extra-process observed mean; not the impulse-carry Dirac) + §7.2 `asymTIPREDEFFECT` (`-B z / a` for `a < 0`; not `B`, not the finite-interval increment, not `CINT`, not `M x`) + §7.2 `addedTIPREDVAR` (`(B / a)² v`; not `TRAITVAR`, not `asymDIFFUSION`, not `-B z / a`) + Table 2 `asymCINT` (`-κ / a` for `a < 0`; not `κ`, not the finite-interval increment, not `T0MEANS`, not `-B z / a`) + p. 16 stationary `T0MEANS` (`-κ / a + −B z / a`; not free `T0MEANS`, not `asymCINT` alone, not `asymTIPREDEFFECT` alone, not the finite-interval discrete mean) + Eq. 5 of that constrained mean (`τ + λ(−κ / a + −B z / a)`; `τ + λ μ_0` is not that observed mean; `τ + λ(−κ / a)` is not that observed mean when `B z ≠ 0`; `τ + λ μ_t` is not that observed mean; `MANIFESTMEANS` is not `E(y_0)`; the constrained latent mean is not `E(y_0)`) + stationary `T0VAR` (`trait + −q / (2 a) + (B / a)² v`; not free `T0VAR`, not `asymDIFFUSION` alone, not `TRAITVAR` alone, not `addedTIPREDVAR` alone; Eq. 5 is `λ²(trait + −q / (2 a) + (B / a)² v) + θ + ψ` (`λ² p_0` is not `Var(y_0)`; `λ²(−q / (2 a)) + θ` is not `Var(y_0)` when trait or TI is nonzero; `MANIFESTVAR` is not `Var(y_0)`; the constrained latent variance is not `Var(y_0)`)) + Eq. 5 of that constrained variance (`λ²(trait + −q / (2 a) + (B / a)² v) + θ + ψ`; `MANIFESTVAR` is not `Var(y_0)`) + p. 16 `TDPREDEFFECTstd` (`m · √v / √(-q / (2 a))` after strictly positive `asymDIFFUSION` and TD predictor variance; not `TIPREDEFFECTstd` even when `M = B`; not intercept-style `A^{-1}[e^{A Δt} − I] M · √v / √p`; not trait-contaminated) + Table 3 / p. 16 `T0TDPREDEFFECTstd` (`t0_m · √v / √p_0` after strictly positive free `T0VAR` and TD predictor variance; not `TDPREDEFFECTstd`; not `T0TIPREDEFFECTstd` even when `t0_m = t0_b`; not trait-contaminated; free `T0VAR` does not require `a < 0`) + p. 16 `T0VARstd` (`p_0 / p_0 = 1` after strictly positive free `T0VAR`; not unstandardised `T0VAR`; not `T0TDPREDEFFECTstd`; not `addedT0TIPREDVAR`; free `T0VAR` does not require `a < 0`) + p. 16 `TRAITVARstd` (`trait / trait = 1` after strictly positive `TRAITVAR`; no ridge addend; not unstandardised `TRAITVAR`; not `T0VARstd` even when both equal 1; not `addedT0TIPREDVAR`; `TRAITVAR` does not require `a < 0`) + p. 16 `MANIFESTTRAITVARstd` (`ψ / ψ = 1` after strictly positive `MANIFESTTRAITVAR`; 2017-era source adds ridging; default ridge is 0; not unstandardised `MANIFESTTRAITVAR`; not `TRAITVARstd` even when both equal 1; not `MANIFESTVAR`; `MANIFESTTRAITVAR` does not require `a < 0`) + p. 16 `MANIFESTVARstd` (`θ / θ = 1` after strictly positive `MANIFESTVAR`; 2017-era source adds ridging; default ridge is 0; 2017-era `dimnames` assignment to `latentNames` is a source bug; not unstandardised `MANIFESTVAR`; not `MANIFESTTRAITVARstd` even when both equal 1; not Equation 5 `Var(y)`; `MANIFESTVAR` does not require `a < 0`) + irregular already-centered residual lag + strong-gated latent means (n=2 residual variance is identically `0` and caps at strong/scalar; Putnick & Bornstein, 2016); full ESEM/DSEM remaining | ADR 0005; `docs/research/posterior-esem-input-gates.md`; `docs/research/multilevel-event-time-recovery.md`; `docs/research/rubin-total-variance.md`; `docs/research/strong-invariance-latent-means.md` | | Purpose-bound provider payloads | `tepp_api` | implemented-main | provider-payload minimization | expired/not-yet-valid/inverted/cross-tenant/impossible-calendar grant, mapping refusal, audited elevated re-id replay | ADR 0009; `docs/research/provider-payload-minimization.md` | | Adaptive orchestration router | `tepp_api` | accepted-target | active PR | mode selection, document-control denial, ablation, credential-free bind | ADR 0010; `docs/research/adaptive-orchestration-router.md` | | CWL modular connectors | `docs/connectors/*` | implemented-main | — | contract docs + examples | PR #22; live HTTP ports remaining | From 22202c57c0818d3efb28438e95bdde5d0e7143b0 Mon Sep 17 00:00:00 2001 From: Seongho Bae Date: Mon, 24 Aug 2026 12:11:20 +0900 Subject: [PATCH 2/2] fix(psychometric): close stacked coverage and format gates --- crates/psychometric_core/src/event_time.rs | 120 +++++--- ...multilevel_event_time_recovery_contract.rs | 275 +++++++++++++++--- .../scientific_claim_boundary_contract.rs | 4 +- 3 files changed, 314 insertions(+), 85 deletions(-) diff --git a/crates/psychometric_core/src/event_time.rs b/crates/psychometric_core/src/event_time.rs index 5310995b..c7910c7d 100644 --- a/crates/psychometric_core/src/event_time.rs +++ b/crates/psychometric_core/src/event_time.rs @@ -9437,8 +9437,8 @@ pub(crate) fn fit_scalar_log_rate(pairs: &[(f64, f64, f64)]) -> Result f64::EPSILON); + assert_eq!( + recover_discrete_latent_mean_with_extra_process_after( + 0.0, + original, + 0.0, + coupling, + predictor, + extra, + delta, + elapsed, + LagClock::EventTime, + ), + Ok(contribution) + ); + } + #[test] #[allow(clippy::too_many_lines)] fn after_extra_process_observed_mean_recovers_driver_equation_five() { @@ -13745,8 +13779,8 @@ mod tests { } #[test] - fn stationary_initial_observed_variance_recovers_driver_equation_five_of_section_four_point_three( - ) { + fn stationary_initial_observed_variance_recovers_driver_equation_five_of_section_four_point_three() + { // Driver et al. (2017, §4.3, pp. 9–10; Eq. 5, p. 5) // constrain first-occasion variances to the model-predicted // variance. Equation 5 maps Var(y_0) = λ² of that variance @@ -14321,8 +14355,8 @@ mod tests { } #[test] - fn stationary_lagged_observed_covariance_recovers_driver_equation_five_of_section_four_point_three( - ) { + fn stationary_lagged_observed_covariance_recovers_driver_equation_five_of_section_four_point_three() + { // Driver et al. (2017, §4.3, pp. 9–10; Eq. 5, p. 5) // lagged observed covariance of stationary T0VAR is // λ²(trait + e^{a Δt}(−q / (2 a)) + (B / a)² v) + ψ. @@ -14898,8 +14932,8 @@ mod tests { #[test] #[allow(clippy::too_many_lines)] - fn stationary_later_observed_variance_recovers_driver_equation_five_of_section_four_point_three( - ) { + fn stationary_later_observed_variance_recovers_driver_equation_five_of_section_four_point_three() + { // Driver et al. (2017, §4.3, pp. 9–10; Eq. 5, p. 5) // later-occasion observed variance of stationary T0VAR is // λ²(trait + e^{2 a Δt}(−q / (2 a)) + Q_Δt + (B / a)² v) + θ + ψ. @@ -15554,8 +15588,8 @@ mod tests { #[test] #[allow(clippy::too_many_lines)] - fn predetermined_later_observed_variance_recovers_driver_equation_five_of_section_four_point_three( - ) { + fn predetermined_later_observed_variance_recovers_driver_equation_five_of_section_four_point_three() + { // Driver et al. (2017, §4.3, pp. 9–10; Eq. 5, p. 5) // later-occasion observed variance of predetermined T0VAR is // λ²(trait + e^{2 a Δt} p_0 + Q_Δt + (B / a)² v) + θ + ψ. @@ -16979,6 +17013,16 @@ mod tests { ), Err(PsychometricError::InvalidNumericInput) ); + assert_eq!( + recover_discrete_time_independent_predictor_effect( + 1.0, + 1.0, + f64::NAN, + 2.0, + LagClock::EventTime + ), + Err(PsychometricError::InvalidNumericInput) + ); assert_eq!( recover_discrete_time_independent_predictor_effect( 1e308, @@ -18326,8 +18370,8 @@ mod tests { } #[test] - fn discrete_observed_mean_with_initial_time_independent_predictor_recovers_driver_equation_five( - ) { + fn discrete_observed_mean_with_initial_time_independent_predictor_recovers_driver_equation_five() + { let loading = 2.0_f64; let drift = -0.5_f64; let delta = 2.0_f64; @@ -18477,8 +18521,8 @@ mod tests { } #[test] - fn discrete_observed_mean_with_initial_time_independent_predictor_refuses_evolved_mean_and_overflow( - ) { + fn discrete_observed_mean_with_initial_time_independent_predictor_refuses_evolved_mean_and_overflow() + { let loading = 2.0_f64; let recovered = recover_discrete_observed_mean_with_initial_time_independent_predictor( loading, @@ -19096,8 +19140,8 @@ mod tests { } #[test] - fn discrete_observed_mean_with_initial_time_dependent_predictor_refuses_evolved_mean_and_overflow( - ) { + fn discrete_observed_mean_with_initial_time_dependent_predictor_refuses_evolved_mean_and_overflow() + { let recovered = recover_discrete_observed_mean_with_initial_time_dependent_predictor( 2.0, 1.0, @@ -19774,8 +19818,8 @@ mod tests { #[test] #[allow(clippy::too_many_lines)] - fn predetermined_later_lagged_latent_covariance_recovers_driver_equation_four_after_startoffset( - ) { + fn predetermined_later_lagged_latent_covariance_recovers_driver_equation_four_after_startoffset() + { // Driver et al. (2017, §4.3 startoffset; Eq. 4): // cov = trait + e^{a s}(e^{2 a u} p_0 + Q_u) + (B / a)² v. let printed_effect = -0.225_f64; @@ -20086,7 +20130,8 @@ mod tests { LagClock::EventTime, ) .expect("growing a>0"); - assert!(growing.is_finite() && growing > 2.0); + assert!(growing.is_finite()); + assert!(growing > 2.0); assert_eq!( recover_predetermined_later_lagged_latent_covariance( f64::NAN, @@ -20362,8 +20407,8 @@ mod tests { #[test] #[allow(clippy::too_many_lines)] - fn predetermined_later_start_later_latent_variance_recovers_driver_equation_four_after_startoffset( - ) { + fn predetermined_later_start_later_latent_variance_recovers_driver_equation_four_after_startoffset() + { // Driver et al. (2017, §4.3 startoffset; Eq. 3–4 Chapman–Kolmogorov): // Var = trait + e^{2 a s}(e^{2 a u} p_0 + Q_u) + Q_s + (B / a)² v. let printed_effect = -0.225_f64; @@ -20753,7 +20798,8 @@ mod tests { LagClock::EventTime, ) .expect("growing a>0"); - assert!(growing.is_finite() && growing > 2.0); + assert!(growing.is_finite()); + assert!(growing > 2.0); assert_eq!( recover_predetermined_later_start_later_latent_variance( f64::NAN, @@ -21120,7 +21166,8 @@ mod tests { ); let growing = recover_discrete_lag_from_log_rate(0.5, event_delta, LagClock::EventTime) .expect("growing a>0"); - assert!(growing.is_finite() && growing > 1.0); + assert!(growing.is_finite()); + assert!(growing > 1.0); assert_eq!( recover_standardised_discrete_drift(0.4, 0.5, event_delta, LagClock::EventTime), Err(PsychometricError::StationaryVarianceRequiresStableDrift) @@ -21241,7 +21288,8 @@ mod tests { ); let growing = recover_discrete_process_noise(0.4, 0.5, event_delta, LagClock::EventTime) .expect("growing a>0"); - assert!(growing.is_finite() && growing > 0.0); + assert!(growing.is_finite()); + assert!(growing > 0.0); assert_eq!( recover_standardised_discrete_diffusion(0.4, 0.5, event_delta, LagClock::EventTime), Err(PsychometricError::StationaryVarianceRequiresStableDrift) @@ -21472,8 +21520,8 @@ mod tests { } #[test] - fn standardised_asymptotic_time_independent_effect_recovers_driver_page_sixteen_after_positive_variances( - ) { + fn standardised_asymptotic_time_independent_effect_recovers_driver_page_sixteen_after_positive_variances() + { // Driver et al. (2017, p. 16 asymTIPREDEFFECTstd; §7.2; footnote 4): // form strictly positive asymDIFFUSION = −q / (2 a) and // strictly positive v, then (−B / a) · √v / √p. @@ -21659,8 +21707,8 @@ mod tests { } #[test] - fn standardised_continuous_time_independent_effect_recovers_driver_page_sixteen_after_positive_variances( - ) { + fn standardised_continuous_time_independent_effect_recovers_driver_page_sixteen_after_positive_variances() + { // Driver et al. (2017, p. 16 TIPREDEFFECTstd; §7.2; footnote 4): // form strictly positive asymDIFFUSION = −q / (2 a) and // strictly positive v, then B · √v / √p. @@ -21867,8 +21915,8 @@ mod tests { } #[test] - fn standardised_continuous_time_dependent_effect_recovers_driver_page_sixteen_after_positive_variances( - ) { + fn standardised_continuous_time_dependent_effect_recovers_driver_page_sixteen_after_positive_variances() + { // Driver et al. (2017, p. 16 TDPREDEFFECTstd; Table 2; footnote 4): // form strictly positive asymDIFFUSION = −q / (2 a) and // strictly positive v, then m · √v / √p. @@ -22074,8 +22122,8 @@ mod tests { } #[test] - fn standardised_initial_time_dependent_effect_recovers_driver_table_three_after_positive_variances( - ) { + fn standardised_initial_time_dependent_effect_recovers_driver_table_three_after_positive_variances() + { // Driver et al. (2017, Table 3 T0TDPREDEFFECTstd; p. 16; footnote 4): // form strictly positive free T0VAR p_0 and strictly positive v, // then t0_m · √v / √p_0. Affected variance is p_0, not @@ -22544,8 +22592,8 @@ mod tests { } #[test] - fn standardised_initial_time_independent_effect_recovers_driver_table_three_after_positive_variances( - ) { + fn standardised_initial_time_independent_effect_recovers_driver_table_three_after_positive_variances() + { // Driver et al. (2017, Table 3 T0TIPREDEFFECTstd; p. 16; footnote 4): // form strictly positive free T0VAR p_0 and strictly positive v, // then t0_b · √v / √p_0. Affected variance is p_0, not diff --git a/crates/psychometric_core/tests/multilevel_event_time_recovery_contract.rs b/crates/psychometric_core/tests/multilevel_event_time_recovery_contract.rs index c2351743..48b02981 100644 --- a/crates/psychometric_core/tests/multilevel_event_time_recovery_contract.rs +++ b/crates/psychometric_core/tests/multilevel_event_time_recovery_contract.rs @@ -2,8 +2,10 @@ #![allow(clippy::cast_precision_loss)] use psychometric_core::{ - map_discrete_lag_across_event_intervals, ordinary_least_squares_slope, - recover_asymptotic_continuous_intercept, recover_asymptotic_time_independent_observed_variance, + ClusteredEventScore, ClusteredScore, EventOccasion, IndicatorKind, LagClock, + LaggedWithinResidual, PsychometricError, map_discrete_lag_across_event_intervals, + ordinary_least_squares_slope, recover_asymptotic_continuous_intercept, + recover_asymptotic_time_independent_observed_variance, recover_asymptotic_time_independent_predictor_effect, recover_asymptotic_time_independent_predictor_variance, recover_cluster_mean_within_between_slopes, recover_discrete_constant_predictor_effect, @@ -264,9 +266,7 @@ use psychometric_core::{ refuse_unstandardised_initial_time_independent_effect_as_standardised_initial_time_independent_effect, refuse_unstandardised_manifest_trait_variance_as_standardised_manifest_trait_variance, refuse_unstandardised_manifest_variance_as_standardised_manifest_variance, - refuse_unstandardised_trait_variance_as_standardised_trait_variance, ClusteredEventScore, - ClusteredScore, EventOccasion, IndicatorKind, LagClock, LaggedWithinResidual, - PsychometricError, + refuse_unstandardised_trait_variance_as_standardised_trait_variance, }; fn rmse(truth: &[f64], recovered: &[f64]) -> f64 { @@ -767,6 +767,72 @@ fn within_residual_event_time_log_rate_beats_pooled_levels() { assert!(within_error < 0.25, "CWC lag RMSE {within_error} too large"); } +#[test] +fn within_residual_event_time_log_rate_rejects_nonfinite_residual_ratios() { + let rows = [ + ClusteredEventScore { + cluster_key: 1, + event_time: 0.0, + score: 1e-308, + }, + ClusteredEventScore { + cluster_key: 1, + event_time: 1.0, + score: f64::MAX, + }, + ClusteredEventScore { + cluster_key: 1, + event_time: 2.0, + score: -f64::MAX, + }, + ClusteredEventScore { + cluster_key: 2, + event_time: 0.0, + score: 0.0, + }, + ClusteredEventScore { + cluster_key: 2, + event_time: 1.0, + score: 1.0, + }, + ]; + assert_eq!( + recover_within_residual_event_time_log_rate(&rows, LagClock::EventTime), + Err(PsychometricError::InvalidNumericInput) + ); + let zero_residual_rows = [ + ClusteredEventScore { + cluster_key: 1, + event_time: 0.0, + score: 0.0, + }, + ClusteredEventScore { + cluster_key: 1, + event_time: 1.0, + score: 1.0, + }, + ClusteredEventScore { + cluster_key: 1, + event_time: 2.0, + score: 2.0, + }, + ClusteredEventScore { + cluster_key: 2, + event_time: 0.0, + score: 0.0, + }, + ClusteredEventScore { + cluster_key: 2, + event_time: 1.0, + score: 1.0, + }, + ]; + assert_eq!( + recover_within_residual_event_time_log_rate(&zero_residual_rows, LagClock::EventTime,), + Err(PsychometricError::InvalidNumericInput) + ); +} + #[test] fn irregular_centered_residuals_recover_known_drift_better_than_cwc_of_raw_ar() { let true_drift = -0.35_f64; @@ -889,6 +955,10 @@ fn discrete_latent_variance_recovers_driver_equations_three_and_four() { recover_discrete_latent_variance(2.0, 0.0, 1e308, 2.0, LagClock::EventTime), Err(PsychometricError::InvalidNumericInput) ); + let finite_rewrite = + recover_discrete_latent_variance(1e-308, 1e-308, 400.0, 1.0, LagClock::EventTime) + .expect("finite exponential rewrite"); + assert!(finite_rewrite.is_finite()); } #[test] @@ -1853,6 +1923,16 @@ fn time_independent_predictor_refuses_overflow_and_non_event_clocks() { ), Err(PsychometricError::InvalidNumericInput) ); + assert_eq!( + recover_discrete_time_independent_predictor_effect( + 1.0, + 1.0, + f64::NAN, + 2.0, + LagClock::EventTime + ), + Err(PsychometricError::InvalidNumericInput) + ); assert_eq!( recover_discrete_latent_mean_with_time_independent_predictor( 1.0, @@ -2319,8 +2399,8 @@ fn discrete_observed_mean_with_initial_time_independent_predictor_is_not_impulse } #[test] -fn discrete_observed_mean_with_initial_time_independent_predictor_refuses_evolved_process_impulse_and_carry( -) { +fn discrete_observed_mean_with_initial_time_independent_predictor_refuses_evolved_process_impulse_and_carry() + { let loading = 2.0_f64; let drift = -0.5_f64; let delta = 2.0_f64; @@ -2474,8 +2554,8 @@ fn discrete_observed_mean_with_initial_time_independent_predictor_zero_loading_i } #[test] -fn discrete_observed_mean_with_initial_time_independent_predictor_refuses_overflow_and_non_event_clocks( -) { +fn discrete_observed_mean_with_initial_time_independent_predictor_refuses_overflow_and_non_event_clocks() + { assert_eq!( recover_discrete_observed_mean_with_initial_time_independent_predictor( 1e308, @@ -2991,6 +3071,17 @@ fn time_dependent_impulse_carry_refuses_overflow_and_non_event_clocks() { ), Err(PsychometricError::InvalidNumericInput) ); + assert_eq!( + recover_time_dependent_predictor_impulse_carry( + 0.4, + 3.0, + 710.0, + 2.0, + 1.0, + LagClock::EventTime, + ), + Err(PsychometricError::InvalidNumericInput) + ); } #[test] @@ -3406,8 +3497,8 @@ fn discrete_observed_mean_with_initial_time_dependent_predictor_is_not_impulse_o #[test] #[allow(clippy::too_many_lines)] -fn discrete_observed_mean_with_initial_time_dependent_predictor_refuses_evolved_process_impulse_and_carry( -) { +fn discrete_observed_mean_with_initial_time_dependent_predictor_refuses_evolved_process_impulse_and_carry() + { let loading = 2.0_f64; let drift = -0.5_f64; let delta = 2.0_f64; @@ -3580,8 +3671,8 @@ fn discrete_observed_mean_with_initial_time_dependent_predictor_zero_loading_is_ } #[test] -fn discrete_observed_mean_with_initial_time_dependent_predictor_refuses_overflow_and_non_event_clocks( -) { +fn discrete_observed_mean_with_initial_time_dependent_predictor_refuses_overflow_and_non_event_clocks() + { assert_eq!( recover_discrete_observed_mean_with_initial_time_dependent_predictor( 1e308, @@ -3927,6 +4018,16 @@ fn extra_process_contribution_refuses_nonnegative_extra_drift_clock_and_overflow ), Ok(0.0) ); + let finite_exponential_fallback = recover_level_change_extra_process_contribution( + 0.4, + 3.0, + -0.8, + -0.000_001, + 900.0, + LagClock::EventTime, + ) + .expect("finite exponential fallback"); + assert!(finite_exponential_fallback.is_finite()); } #[test] @@ -6563,8 +6664,8 @@ fn predetermined_lagged_latent_covariance_refuses_non_event_clocks_and_keeps_gro #[test] #[allow(clippy::too_many_lines)] -fn predetermined_lagged_observed_covariance_recovers_driver_equation_five_of_section_four_point_three( -) { +fn predetermined_lagged_observed_covariance_recovers_driver_equation_five_of_section_four_point_three() + { let printed_effect = -0.225_f64; let printed_asym = -1.673_f64; let log_rate = -printed_effect / printed_asym; @@ -6957,8 +7058,8 @@ fn predetermined_initial_latent_variance_refuses_non_event_clocks_and_keeps_unst #[test] #[allow(clippy::too_many_lines)] -fn predetermined_initial_observed_variance_recovers_driver_equation_five_of_section_four_point_three( -) { +fn predetermined_initial_observed_variance_recovers_driver_equation_five_of_section_four_point_three() + { let printed_effect = -0.225_f64; let printed_asym = -1.673_f64; let log_rate = -printed_effect / printed_asym; @@ -7328,8 +7429,8 @@ fn predetermined_later_lagged_latent_covariance_recovers_driver_section_four_poi } #[test] -fn predetermined_later_lagged_latent_covariance_refuses_non_event_clocks_and_keeps_growing_processes( -) { +fn predetermined_later_lagged_latent_covariance_refuses_non_event_clocks_and_keeps_growing_processes() + { assert_eq!( recover_predetermined_later_lagged_latent_covariance( 1.0, @@ -7417,8 +7518,8 @@ fn predetermined_later_lagged_latent_covariance_refuses_non_event_clocks_and_kee #[test] #[allow(clippy::too_many_lines)] -fn predetermined_later_lagged_observed_covariance_recovers_driver_equation_five_of_section_four_point_three( -) { +fn predetermined_later_lagged_observed_covariance_recovers_driver_equation_five_of_section_four_point_three() + { let printed_effect = -0.225_f64; let printed_asym = -1.673_f64; let log_rate = -printed_effect / printed_asym; @@ -7565,8 +7666,8 @@ fn predetermined_later_lagged_observed_covariance_recovers_driver_equation_five_ } #[test] -fn predetermined_later_lagged_observed_covariance_refuses_non_event_clocks_and_keeps_growing_processes( -) { +fn predetermined_later_lagged_observed_covariance_refuses_non_event_clocks_and_keeps_growing_processes() + { assert_eq!( recover_predetermined_later_lagged_observed_covariance( 2.0, @@ -7895,8 +7996,8 @@ fn predetermined_later_start_later_latent_variance_recovers_driver_section_four_ } #[test] -fn predetermined_later_start_later_latent_variance_refuses_non_event_clocks_and_keeps_growing_processes( -) { +fn predetermined_later_start_later_latent_variance_refuses_non_event_clocks_and_keeps_growing_processes() + { assert_eq!( recover_predetermined_later_start_later_latent_variance( 1.0, @@ -7984,8 +8085,8 @@ fn predetermined_later_start_later_latent_variance_refuses_non_event_clocks_and_ #[test] #[allow(clippy::too_many_lines)] -fn predetermined_later_start_later_observed_variance_recovers_driver_equation_five_of_section_four_point_three( -) { +fn predetermined_later_start_later_observed_variance_recovers_driver_equation_five_of_section_four_point_three() + { let printed_effect = -0.225_f64; let printed_asym = -1.673_f64; let log_rate = -printed_effect / printed_asym; @@ -8142,8 +8243,8 @@ fn predetermined_later_start_later_observed_variance_recovers_driver_equation_fi } #[test] -fn predetermined_later_start_later_observed_variance_refuses_non_event_clocks_and_keeps_growing_processes( -) { +fn predetermined_later_start_later_observed_variance_refuses_non_event_clocks_and_keeps_growing_processes() + { assert_eq!( recover_predetermined_later_start_later_observed_variance( 2.0, @@ -8284,8 +8385,8 @@ fn standardised_discrete_drift_recovers_driver_page_sixteen_footnote_four() { } #[test] -fn standardised_discrete_drift_refuses_non_event_clocks_and_does_not_keep_growing_or_zero_diffusion( -) { +fn standardised_discrete_drift_refuses_non_event_clocks_and_does_not_keep_growing_or_zero_diffusion() + { assert_eq!( recover_standardised_discrete_drift(0.4, -0.5, 1.0, LagClock::SystemTime), Err(PsychometricError::EventTimeRequired) @@ -8369,8 +8470,8 @@ fn standardised_discrete_diffusion_recovers_driver_page_sixteen_footnote_four() } #[test] -fn standardised_discrete_diffusion_refuses_non_event_clocks_and_does_not_keep_growing_or_zero_diffusion( -) { +fn standardised_discrete_diffusion_refuses_non_event_clocks_and_does_not_keep_growing_or_zero_diffusion() + { assert_eq!( recover_standardised_discrete_diffusion(0.4, -0.5, 1.0, LagClock::SystemTime), Err(PsychometricError::EventTimeRequired) @@ -8448,8 +8549,8 @@ fn standardised_continuous_diffusion_recovers_driver_page_sixteen_footnote_four( } #[test] -fn standardised_continuous_diffusion_refuses_non_event_clocks_and_does_not_keep_growing_or_zero_diffusion( -) { +fn standardised_continuous_diffusion_refuses_non_event_clocks_and_does_not_keep_growing_or_zero_diffusion() + { assert_eq!( recover_standardised_continuous_diffusion(0.4, -0.5, LagClock::SystemTime), Err(PsychometricError::EventTimeRequired) @@ -8515,8 +8616,8 @@ fn standardised_continuous_drift_recovers_driver_page_sixteen_footnote_four() { } #[test] -fn standardised_continuous_drift_refuses_non_event_clocks_and_does_not_keep_growing_or_zero_diffusion( -) { +fn standardised_continuous_drift_refuses_non_event_clocks_and_does_not_keep_growing_or_zero_diffusion() + { assert_eq!( recover_standardised_continuous_drift(0.4, -0.5, LagClock::SystemTime), Err(PsychometricError::EventTimeRequired) @@ -8606,8 +8707,8 @@ fn standardised_asymptotic_time_independent_effect_recovers_driver_page_sixteen_ } #[test] -fn standardised_asymptotic_time_independent_effect_refuses_non_event_clocks_and_does_not_keep_zero_variances( -) { +fn standardised_asymptotic_time_independent_effect_refuses_non_event_clocks_and_does_not_keep_zero_variances() + { assert_eq!( recover_standardised_asymptotic_time_independent_predictor_effect( 0.3, @@ -8652,6 +8753,56 @@ fn standardised_asymptotic_time_independent_effect_refuses_non_event_clocks_and_ PsychometricError::StandardisedAsymptoticTimeIndependentEffectRequiresPositivePredictorVariance ) ); + assert_eq!( + recover_standardised_asymptotic_time_independent_predictor_effect( + f64::NAN, + 1.0, + 0.4, + -0.5, + LagClock::EventTime + ), + Err(PsychometricError::InvalidNumericInput) + ); + assert_eq!( + recover_standardised_asymptotic_time_independent_predictor_effect( + 0.3, + f64::NAN, + 0.4, + -0.5, + LagClock::EventTime + ), + Err(PsychometricError::InvalidNumericInput) + ); + assert_eq!( + recover_standardised_asymptotic_time_independent_predictor_effect( + 0.3, + -0.1, + 0.4, + -0.5, + LagClock::EventTime + ), + Err(PsychometricError::InvalidNumericInput) + ); + assert_eq!( + recover_standardised_asymptotic_time_independent_predictor_effect( + 0.3, + f64::MAX, + 1e-308, + -1.0, + LagClock::EventTime + ), + Err(PsychometricError::InvalidNumericInput) + ); + assert_eq!( + recover_standardised_asymptotic_time_independent_predictor_effect( + f64::MAX, + 4.0, + 2.0, + -1.0, + LagClock::EventTime + ), + Err(PsychometricError::InvalidNumericInput) + ); } #[test] @@ -8737,8 +8888,8 @@ fn standardised_continuous_time_independent_effect_recovers_driver_page_sixteen_ } #[test] -fn standardised_continuous_time_independent_effect_refuses_non_event_clocks_and_does_not_keep_zero_variances( -) { +fn standardised_continuous_time_independent_effect_refuses_non_event_clocks_and_does_not_keep_zero_variances() + { assert_eq!( recover_standardised_continuous_time_independent_predictor_effect( 0.3, @@ -8783,6 +8934,36 @@ fn standardised_continuous_time_independent_effect_refuses_non_event_clocks_and_ PsychometricError::StandardisedContinuousTimeIndependentEffectRequiresPositivePredictorVariance ) ); + assert_eq!( + recover_standardised_continuous_time_independent_predictor_effect( + f64::NAN, + 1.0, + 0.4, + -0.5, + LagClock::EventTime + ), + Err(PsychometricError::InvalidNumericInput) + ); + assert_eq!( + recover_standardised_continuous_time_independent_predictor_effect( + 0.3, + f64::NAN, + 0.4, + -0.5, + LagClock::EventTime + ), + Err(PsychometricError::InvalidNumericInput) + ); + assert_eq!( + recover_standardised_continuous_time_independent_predictor_effect( + 0.3, + -0.1, + 0.4, + -0.5, + LagClock::EventTime + ), + Err(PsychometricError::InvalidNumericInput) + ); } #[test] @@ -8868,8 +9049,8 @@ fn standardised_continuous_time_dependent_effect_recovers_driver_page_sixteen_fo } #[test] -fn standardised_continuous_time_dependent_effect_refuses_non_event_clocks_and_does_not_keep_zero_variances( -) { +fn standardised_continuous_time_dependent_effect_refuses_non_event_clocks_and_does_not_keep_zero_variances() + { assert_eq!( recover_standardised_continuous_time_dependent_predictor_effect( 0.3, @@ -8994,8 +9175,8 @@ fn standardised_initial_time_dependent_effect_recovers_driver_table_three_footno } #[test] -fn standardised_initial_time_dependent_effect_refuses_non_event_clocks_and_does_not_keep_zero_variances( -) { +fn standardised_initial_time_dependent_effect_refuses_non_event_clocks_and_does_not_keep_zero_variances() + { assert_eq!( recover_standardised_initial_time_dependent_predictor_effect( 0.3, @@ -9361,8 +9542,8 @@ fn standardised_initial_time_independent_effect_recovers_driver_table_three_foot } #[test] -fn standardised_initial_time_independent_effect_refuses_non_event_clocks_and_does_not_keep_zero_variances( -) { +fn standardised_initial_time_independent_effect_refuses_non_event_clocks_and_does_not_keep_zero_variances() + { assert_eq!( recover_standardised_initial_time_independent_predictor_effect( 0.3, diff --git a/crates/psychometric_core/tests/scientific_claim_boundary_contract.rs b/crates/psychometric_core/tests/scientific_claim_boundary_contract.rs index 04528406..f46d4817 100644 --- a/crates/psychometric_core/tests/scientific_claim_boundary_contract.rs +++ b/crates/psychometric_core/tests/scientific_claim_boundary_contract.rs @@ -1,6 +1,7 @@ //! Scientific claim boundaries for compositional coordinates and posterior draws. use psychometric_core::{ + ClusteredEventScore, ClusteredScore, IndicatorKind, LagClock, LaggedWithinResidual, ordinary_least_squares_slope, posterior_draw_point_estimate_mean, recover_asymptotic_continuous_intercept, recover_asymptotic_time_independent_observed_variance, recover_asymptotic_time_independent_predictor_effect, @@ -259,8 +260,7 @@ use psychometric_core::{ refuse_unstandardised_initial_time_independent_effect_as_standardised_initial_time_independent_effect, refuse_unstandardised_manifest_trait_variance_as_standardised_manifest_trait_variance, refuse_unstandardised_manifest_variance_as_standardised_manifest_variance, - refuse_unstandardised_trait_variance_as_standardised_trait_variance, ClusteredEventScore, - ClusteredScore, IndicatorKind, LagClock, LaggedWithinResidual, + refuse_unstandardised_trait_variance_as_standardised_trait_variance, }; #[test]