Psychometric Process-Data Models

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The package prepares linked person-item-trial data and delegates mature IRT estimation to optional engines where appropriate.

Response and response-time matrices

y  <- response_matrix(x)
rt <- response_time_matrix(x, log_transform = TRUE)
aligned <- align_response_matrices(y, rt)

Conventional and explanatory IRT

fit_mirt <- fit_irt(x, engine = "mirt", model = 1, itemtype = "2PL")
fit_tam  <- fit_irt(x, engine = "TAM")

fit_explanatory <- fit_explanatory_irt(
  x,
  score ~ dwell_time + first_fixation_latency + pupil_auc,
  engine = "lme4"
)

Accuracy and response time

fit_rt <- fit_accuracy_rt(x, engine = "LNIRT")

LNIRT receives aligned response matrices and log response times. A two-stage fallback is available for transparent exploratory work, but it is not treated as equivalent to a joint latent model.

Process-informed and experimental models

spec <- process_irt_spec(
  response = "score",
  gaze_features = c("dwell_time", "first_fixation_latency"),
  pupil_features = c("pupil_auc"),
  response_time = "response_time"
)

fit <- fit_process_irt(x, spec, engine = "lme4")
process_irt_diagnostics(fit)

shared <- fit_shared_process_factor(
  x,
  features = c("dwell_time", "fixation_count", "pupil_auc")
)

Shared process factors are intentionally neutral labels until construct validity is established. Advanced joint and dynamic functions are marked experimental and require simulation, parameter-recovery, and empirical validation before confirmatory use.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.