fit_process_hmm_irt: Process-state HMM with an IRT response layer

In eyeprocess: Harmonize Eye-Tracking, Pupillometry, Biometrics, and Psychometric Process Data

Process-state HMM with an IRT response layer

Description

Fits a diagonal-Gaussian HMM to standardized process features within each sequence, then uses state occupancy as explicit process evidence in a response model. This two-stage reference engine is deliberately interpretable and should be distinguished from a fully joint HMM-IRT likelihood.

Usage

fit_process_hmm_irt(
  data,
  sequence_id = "trial_id",
  order = "timestamp",
  process_features = c("x", "y"),
  response = "response",
  person = "participant_id",
  item = "item_id",
  n_states = 3L,
  max_iter = 100L,
  tol = 1e-05,
  seed = 1
)

Arguments

data	Input data frame or compatible tabular object.
sequence_id	Sequence identifier.
order	Within-sequence ordering variable.
process_features	Names of process-derived features.
response	Response variable or response-column name.
person	Person or participant identifier column.
item	Item identifier, name, or item column.
n_states	Number of latent process states.
max_iter	Maximum number of iterations.
tol	Numerical convergence tolerance.
seed	Random-number seed.

Value

An object of class "eye_process_hmm_irt", stored as a named list, with components "pi", "transition", "means", "sds", "posterior_state", "state", "row_data", "occupancy", "summary_data", "response_model", "logLik", "logLik_history", and additional components. It contains process-state HMM with an IRT response layer and associated metadata or diagnostics needed to interpret the result.

eyeprocess documentation built on Sept. 28, 2026, 5:08 p.m.