detect_irt_changepoints: Detect IRT/process change points using an SIC-inspired...

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

Detect IRT/process change points using an SIC-inspired multichannel score

Description

This implementation is a transparent package reference inspired by the 2026 SIC-CPA literature. It combines Bernoulli response likelihood with normal log-RT and standardized gaze likelihoods. It is not a line-for-line reproduction of the article's estimator.

Usage

detect_irt_changepoints(
  data,
  person = "participant_id",
  order = "item_order",
  response = "response",
  rt = "rt",
  gaze = NULL,
  min_segment = 5L,
  min_delta_sic = 2,
  max_changes = 2L
)

Arguments

data	Input data frame or compatible tabular object.
person	Person or participant identifier column.
order	Within-sequence ordering variable.
response	Response variable or response-column name.
rt	Response-time variable or column name.
gaze	Gaze/process variable or column name.
min_segment	Minimum segment length.
min_delta_sic	Minimum information-criterion improvement.
max_changes	Maximum number of change points.

Value

An object of class "eye_irt_changepoints", stored as a named list, with components "results", "channels", "method", "min_segment", "min_delta_sic", "max_changes". It contains iRT/process change points using an SIC-inspired multichannel score and associated metadata or diagnostics needed to interpret the result.

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